alpes_valaisannes.ipynb

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    "# -*- coding: utf-8 -*-\n",
    "# This is a report using the data from IQAASL.\n",
    "# IQAASL was a project funded by the Swiss Confederation\n",
    "# It produces a summary of litter survey results for a defined region.\n",
    "# These charts serve as the models for the development of plagespropres.ch\n",
    "# The data is gathered by volunteers.\n",
    "# Please remember all copyrights apply, please give credit when applicable\n",
    "# The repo is maintained by the community effective January 01, 2022\n",
    "# There is ample opportunity to contribute, learn and teach\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# Dies ist ein Bericht, der die Daten von IQAASL verwendet.\n",
    "# IQAASL war ein von der Schweizerischen Eidgenossenschaft finanziertes Projekt.\n",
    "# Es erstellt eine Zusammenfassung der Ergebnisse der Littering-Umfrage für eine bestimmte Region.\n",
    "# Diese Grafiken dienten als Vorlage für die Entwicklung von plagespropres.ch.\n",
    "# Die Daten werden von Freiwilligen gesammelt.\n",
    "# Bitte denken Sie daran, dass alle Copyrights gelten, bitte geben Sie den Namen an, wenn zutreffend.\n",
    "# Das Repo wird ab dem 01. Januar 2022 von der Community gepflegt.\n",
    "# Es gibt reichlich Gelegenheit, etwas beizutragen, zu lernen und zu lehren.\n",
    "# Kontakt dev@hammerdirt.ch\n",
    "\n",
    "# Il s'agit d'un rapport utilisant les données de IQAASL.\n",
    "# IQAASL était un projet financé par la Confédération suisse.\n",
    "# Il produit un résumé des résultats de l'enquête sur les déchets sauvages pour une région définie.\n",
    "# Ces tableaux ont servi de modèles pour le développement de plagespropres.ch\n",
    "# Les données sont recueillies par des bénévoles.\n",
    "# N'oubliez pas que tous les droits d'auteur s'appliquent, veuillez indiquer le crédit lorsque cela est possible.\n",
    "# Le dépôt est maintenu par la communauté à partir du 1er janvier 2022.\n",
    "# Il y a de nombreuses possibilités de contribuer, d'apprendre et d'enseigner.\n",
    "# contact dev@hammerdirt.ch\n",
    "\n",
    "# sys, file and nav packages:\n",
    "import datetime as dt\n",
    "from datetime import date, datetime, time\n",
    "from babel.dates import format_date, format_datetime, format_time, get_month_names\n",
    "import locale\n",
    "\n",
    "# math packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from math import pi\n",
    "\n",
    "# charting:\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import ticker\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "\n",
    "# build report\n",
    "from reportlab.platypus import SimpleDocTemplate, Paragraph, Image, PageBreak, KeepTogether\n",
    "from reportlab.lib.pagesizes import A4\n",
    "from reportlab.lib.units import cm\n",
    "from reportlab.platypus import Table, TableStyle\n",
    "\n",
    "# the module that has all the methods for handling the data\n",
    "import resources.featuredata as featuredata\n",
    "from resources.featuredata import makeAParagraph, sectionParagraphs, makeAList, block_quote_style\n",
    "\n",
    "# home brew utitilties\n",
    "import resources.chart_kwargs as ck\n",
    "import resources.sr_ut as sut\n",
    "\n",
    "# images and display\n",
    "from IPython.display import Markdown as md\n",
    "from myst_nb import glue\n",
    "\n",
    "# chart style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# colors for gradients\n",
    "# colors_palette = ck.colors_palette\n",
    "bassin_pallette = featuredata.bassin_pallette\n",
    "\n",
    "# border and row shading for tables\n",
    "# table_row = \"saddlebrown\"\n",
    "\n",
    "# a place to save figures and a \n",
    "# method to choose formats\n",
    "save_fig_prefix = \"resources/output/\"\n",
    "\n",
    "# the arguments for formatting the image\n",
    "save_figure_kwargs = {\n",
    "    \"fname\": None,\n",
    "    \"dpi\": 300.0,\n",
    "    \"format\": \"jpeg\",\n",
    "    \"bbox_inches\": None,\n",
    "    \"pad_inches\": 0,\n",
    "    \"bbox_inches\": 'tight',\n",
    "    \"facecolor\": 'auto',\n",
    "    \"edgecolor\": 'auto',\n",
    "    \"backend\": None,\n",
    "}\n",
    "\n",
    "\n",
    "## !! Begin Note book variables !!\n",
    "# There are two language variants: german and english\n",
    "# change both: date_lang and language\n",
    "date_lang =  'de_DE.utf8'\n",
    "locale.setlocale(locale.LC_ALL, date_lang)\n",
    "\n",
    "# the date format of the survey data is defined in the module\n",
    "date_format = featuredata.date_format\n",
    "\n",
    "# the language setting use lower case: en or de\n",
    "# changing the language may require changing the unit label\n",
    "language = \"de\"\n",
    "unit_label = \"p/100 m\"\n",
    "\n",
    "# the standard date format is \"%Y-%m-%d\" if your date column is\n",
    "# not in this format it will not work.\n",
    "# these dates cover the duration of the IQAASL project\n",
    "start_date = \"2020-03-01\"\n",
    "end_date =\"2021-10-31\"\n",
    "start_end = [start_date, end_date]\n",
    "\n",
    "# the fail rate used to calculate the most common codes is\n",
    "# 50% it can be changed:\n",
    "fail_rate = 50\n",
    "\n",
    "# common aggregations\n",
    "agg_pcs_quantity = {unit_label:\"sum\", \"quantity\":\"sum\"}\n",
    "agg_pcs_median = {unit_label:\"median\", \"quantity\":\"sum\"}\n",
    "\n",
    "bassin_map = \"resources/maps/alpesvalaisannes.jpeg\"\n",
    "\n",
    "# the label for the aggregation of all data in the region\n",
    "# this is the chart label\n",
    "top = \"Alle Erhebungsgebiete\"\n",
    "\n",
    "# define the feature level and components\n",
    "# the label for charting is called 'name'\n",
    "this_feature = {'slug':'les-alpes', 'name':\"Alpen und Jura\", 'level':'river_bassin'}\n",
    "\n",
    "# these are the smallest aggregated components\n",
    "# choices are water_name_slug=lake or river, city or location at the scale of a river bassin \n",
    "# water body or lake maybe the most appropriate\n",
    "this_level = 'location'\n",
    "\n",
    "# location and object data\n",
    "dfBeaches = pd.read_csv(\"resources/beaches_with_land_use_rates.csv\")\n",
    "dfCodes = pd.read_csv(\"resources/codes_with_group_names_2015.csv\")\n",
    "\n",
    "# the dimensional data from each survey\n",
    "dfDims = pd.read_csv(\"resources/alpes_dims.csv\")\n",
    "\n",
    "# beach data\n",
    "dfBeaches.set_index(\"slug\", inplace=True)\n",
    "\n",
    "# index the code data\n",
    "dfCodes.set_index(\"code\", inplace=True)\n",
    "\n",
    "columns={\"% to agg\":\"% agg\", \"% to recreation\": \"% recreation\", \"% to woods\":\"% woods\", \"% to buildings\":\"% buildings\", \"p/100m\":\"p/100 m\"}\n",
    "\n",
    "# key word arguments to construct feature data\n",
    "# !Note the water type allows the selection of river or lakes\n",
    "# if None then the data is aggregated together. This selection\n",
    "# is only valid for survey-area reports or other aggregated data\n",
    "# that may have survey results from both lakes and rivers.\n",
    "fd_kwargs ={\n",
    "    \"filename\": \"resources/combined_alps_iqaasl.csv\",\n",
    "    \"feature_name\": this_feature['slug'], \n",
    "    \"feature_level\": this_feature['level'], \n",
    "    \"these_features\": this_feature['slug'], \n",
    "    \"component\": this_level, \n",
    "    \"columns\": columns, \n",
    "    \"language\": language, \n",
    "    \"unit_label\": unit_label, \n",
    "    \"fail_rate\": fail_rate,\n",
    "    \"code_data\":dfCodes,\n",
    "    \"date_range\": start_end,\n",
    "    \"water_type\": None,    \n",
    "}\n",
    "\n",
    "fdx = featuredata.Components(**fd_kwargs)\n",
    "\n",
    "# call the reports and languages\n",
    "fdx.adjustForLanguage()\n",
    "fdx.makeFeatureData()\n",
    "fdx.locationSampleTotals(columns=[\"loc_date\", \"location\", \"date\"])\n",
    "fdx.makeDailyTotalSummary()\n",
    "fdx.materialSummary()\n",
    "fdx.mostCommon()\n",
    "# !this is the feature data!\n",
    "fd = fdx.feature_data\n",
    "\n",
    "# this maps the slug-value to the proper name\n",
    "proper_names = dfBeaches[dfBeaches.river_bassin == 'les-alpes'][\"location\"]\n",
    "\n",
    "# a method to map proper name to slug when the proper name is the index\n",
    "# value of a dataframe.\n",
    "reverse_name_look_up = dfBeaches[dfBeaches.river_bassin == 'les-alpes'][\"location\"].reset_index()\n",
    "reverse_name_look_up = reverse_name_look_up.set_index(\"location\")\n",
    "\n",
    "# the sample totals are calculated by calling locationSampleTotals\n",
    "# this sets the index to location and allows acces to the sample totals by\n",
    "# location name instead of date. This is only valid because there is one \n",
    "# sample per location.\n",
    "unit_label_map = fdx.sample_totals[[\"location\", unit_label]].set_index(\"location\")\n",
    "\n",
    "\n",
    "# the period data is the data from the Iqaasl project\n",
    "# not collected in the alpes.\n",
    "mask = fdx.period_data.river_bassin != this_feature[\"slug\"]\n",
    "\n",
    "period_kwargs = {\n",
    "    \"period_data\": fdx.period_data[mask].copy(),\n",
    "    \"these_features\": this_feature['slug'],\n",
    "    \"feature_level\":this_feature['level'],\n",
    "    \"feature_parent\":this_feature['slug'],\n",
    "    \"parent_level\": this_feature['level'],\n",
    "    \"period_name\": top,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"most_common\": fdx.most_common.index\n",
    "}\n",
    "period_data = featuredata.PeriodResults(**period_kwargs)\n",
    "\n",
    "# collects the summarized values for the feature data\n",
    "# use this to generate the summary data for the survey area\n",
    "# and the section for the rivers\n",
    "admin_kwargs = {\n",
    "    \"data\":fd,\n",
    "    \"dims_data\":dfDims,\n",
    "    \"label\": this_feature[\"name\"],\n",
    "    \"feature_component\": this_level,\n",
    "    \"date_range\":start_end,\n",
    "    \"dfBeaches\":dfBeaches,\n",
    "}\n",
    "admin_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_summary = admin_details.summaryObject()\n",
    "\n",
    "# update the admin kwargs with river data to make the river summary\n",
    "admin_kwargs.update({\"data\":fdx.feature_data})\n",
    "admin_r_details = featuredata.AdministrativeSummary(**admin_kwargs)\n",
    "admin_r_summary = admin_r_details.summaryObject()\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;text-align:right;'}\n",
    "even_rows = {\"selector\": 'tr:nth-child(even)', 'props': f'background-color: rgba(139, 69, 19, 0.08);'}\n",
    "odd_rows = {'selector': 'tr:nth-child(odd)', 'props': 'background: #FFF;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 12px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding:6px;'}\n",
    "table_css_styles = [even_rows, odd_rows, table_font, header_row, table_data]\n",
    "\n",
    "# this defines the css rules for the note-book table displays\n",
    "header_row = {'selector': 'th:nth-child(1)', 'props': f'background-color: #FFF;text-align:right;'}\n",
    "table_font = {'selector': 'tr', 'props': 'font-size: 10px;'}\n",
    "table_data = {'selector': 'td', 'props': 'padding:4px;'}\n",
    "heat_map_css_styles = [table_font, header_row, table_data]\n",
    "\n",
    "glue(\"blank_caption\", \" \", display=False)\n",
    "\n",
    "# pdf download is an option \n",
    "# the .pdf output is generated in parallel\n",
    "# this is the same as if it were on the backend where we would\n",
    "# have a specific api endpoint for .pdf requests. \n",
    "# reportlab is used to produce the document\n",
    "# the components of the document are captured at run time\n",
    "# the pdf link gives the name and location of the future doc\n",
    "pdf_link = f'resources/pdfs/{this_feature[\"slug\"]}.pdf'\n",
    "source_prefix = \"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/\"\n",
    "source = \"alpes_valaisannes.html\"\n",
    "\n",
    "# the components are stored in an array and collected as the script runs\n",
    "pdfcomponents = []\n",
    "\n",
    "# pdf title and map\n",
    "pdf_title = Paragraph(this_feature[\"name\"], featuredata.title_style)\n",
    "map_image =  Image(bassin_map, width=cm*18, height=12.6*cm, kind=\"proportional\", hAlign= \"CENTER\")\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc([\n",
    "    pdf_title,    \n",
    "    featuredata.small_space,\n",
    "    map_image\n",
    "], pdfcomponents)"
   ]
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   "source": [
    "(lesalpes)=\n",
    "# Alpen und der Jura\n",
    "\n",
    "| {Download}`Download </resources/pdfs/les-alpes.pdf>` | [English](https://www.plagespropres.ch/alpes_valaisannes.html) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} resources/maps/alpesvalaisannes.jpeg\n",
    "---\n",
    "name: alpes_survey_area_map\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_survey_area_map>` Karte des Erhebungsgebiets Alpen und Jura, Clean-ups 2021."
   ]
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    "\n",
    "\n",
    "Die Verantwortung für die Erhebungen in den Alpen und im Jura lag bei der Summit Foundation. Die *Summit Foundation*  führt seit vielen Jahren  [Clean-ups](https://www.summit-foundation.org/en/) (Gruppenevents, bei denen Müll aus dem Gelände beseitigt wird) in Schweizer Bergregionen durch. Zu den Clean-ups im Jahr 2021 gehörten auch eine Reihe von Erhebungen zu Abfallobjekten, die parallel zu den regelmässig stattfindenden Clean-ups durchgeführt wurden. Die Summit Foundation hatte zwei Fragen in Bezug auf IQAASL:\n",
    "\n",
    "1. Wie kann die Datenerfassung in das aktuelle Geschäftsmodell integriert werden?\n",
    "2. Was ergeben die Erhebungen auf den Bergpfaden im Vergleich zu denen am Wasser?\n",
    "\n",
    "Der Zweck von Aufräumaktionen ist es, so viele Abfallobjekte wie möglich aus einem bestimmten Gebiet zu entfernen. Wie viel entfernt werden kann, hängt von den zur Verfügung stehenden Ressourcen ab. Eine Erhebung über Abfallobjekte dient der Identifizierung und Zählung der Objekte in einem bestimmten Gebiet. In diesem Sinne ist eine Aufräumaktion eine Annäherung an das Abfallproblem aus der Perspektive der Abschwächung oder Milderung, und Erhebungen liefern die notwendigen Daten zur Verbesserung der Prävention.   \n",
    "\n",
    "## Erhebungsmethoden\n",
    "\n",
    "Insgesamt wurden zwanzig Erhebungen zu Abfallobjekten von der Summit Foundation durchgeführt. Ursprünglich wurden zwei Methoden ausgewählt: \n",
    "\n",
    "1. Erhebungen entlang bestimmter Streckenabschnitte mit einer definierten Länge und Breite (inbesondere Wanderwege)\n",
    "2. Erhebungen im Umfeld der Liftinfrastrukur (insbesondere Wartebereiche Schneesport)\n",
    "\n",
    "Erhebungen im Umfeld der Liftinfrastrukur (insbesondere Wartebereiche Schneesport):\n",
    "\n",
    "1. Ein Abschnitt des Weges oder der Fläche wird gemessen\n",
    "2. Alle sichtbaren Verunreinigungen werden entfernt, gezählt und klassifiziert\n",
    "3. Die Ergebnisse und Abmessungen werden aufgezeichnet\n",
    "\n",
    "Der Unterschied zwischen den beiden Methoden liegt in der Art und Weise, wie die Grenzen des Vermessungsgebiets festgelegt werden. Wenn ein Weg benutzt wird, werden die Grenzen des Vermessungsgebiets durch den Weg selbst festgelegt, nicht durch die Person, die die Erhebung ausführt. Im Sommer sind die Barrieren und Schilder, welche Pisten etc. markieren, alle entfernt worden, so dass es für die Person, die die Erhebung ausführt, schwierig ist, die korrekten Grenzen genau zu bestimmen.  \n",
    "\n",
    "## Kumulierte Gesamtzahlen für das Erhebungsgebiet"
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    "sample_methods = \"Erhebungsmethoden\"\n",
    "\n",
    "alpes_opening = [\n",
    "    \"Die Verantwortung für die Erhebungen in den Alpen und im Jura lag bei der Summit Foundation. \",\n",
    "   'Die *Summit Foundation* führt seit vielen Jahren <a href=\"https://www.summit-foundation.org/en/\" color=\"blue\"> Clean-ups </a> ',\n",
    "    \"(Gruppenevents, bei denen Müll aus dem Gelände beseitigt wird) in Schweizer Bergregionen durch. Zu den \",\n",
    "    \"Clean-ups im Jahr 2021 gehörten auch eine Reihe von Erhebungen zu Abfallobjekten, die parallel zu den \",\n",
    "    \"regelmässig stattfindenden Clean-ups durchgeführt wurden. Die Summit Foundation hatte zwei Fragen in Bezug auf IQAASL: \"\n",
    "]\n",
    "\n",
    "alpes_questions = [\n",
    "    \"Wie kann die Datenerfassung in das aktuelle Geschäftsmodell integriert werden? \",\n",
    "    \"Was ergeben die Erhebungen auf den Bergpfaden im Vergleich zu denen am Wasser?\"\n",
    "]\n",
    "\n",
    "clean_up_event_purpose = [\n",
    "    \"Der Zweck von Aufräumaktionen ist es, so viele Abfallobjekte wie möglich aus einem \",\n",
    "    \"bestimmten Gebiet zu entfernen. Wie viel entfernt werden kann, hängt von den zur \",\n",
    "    \"Verfügung stehenden Ressourcen ab. Eine Erhebung über Abfallobjekte dient der Identifizierung \",\n",
    "    \"und Zählung der Objekte in einem bestimmten Gebiet. In diesem Sinne ist eine Aufräumaktion \",\n",
    "    \"eine Annäherung an das Abfallproblem aus der Perspektive der Abschwächung oder Milderung, und \",\n",
    "    \"Erhebungen liefern die notwendigen Daten zur Verbesserung der Prävention.\"\n",
    "]\n",
    "\n",
    "methods_one = [\n",
    "    \"Insgesamt wurden zwanzig Erhebungen zu Abfallobjekten von der Summit Foundation \",\n",
    "    \"durchgeführt. Ursprünglich wurden zwei Methoden ausgewählt:\"\n",
    "]\n",
    "\n",
    "methods_list_one = [\n",
    "    \"Erhebungen entlang bestimmter Streckenabschnitte mit einer definierten Länge und Breite (inbesondere Wanderwege)\",\n",
    "    \"Erhebungen im Umfeld der Liftinfrastrukur (insbesondere Wartebereiche Schneesport)\"\n",
    "]\n",
    "\n",
    "methods_two = [\"Erhebungen im Umfeld der Liftinfrastrukur (insbesondere Wartebereiche Schneesport):\"]\n",
    "\n",
    "methods_list_two = [\n",
    "    \"Ein Abschnitt des Weges oder der Fläche wird gemessen\",\n",
    "    \"Alle sichtbaren Verunreinigungen werden entfernt, gezählt und klassifiziert\",\n",
    "    \"Die Ergebnisse und Abmessungen werden aufgezeichnet\"\n",
    "]\n",
    "\n",
    "methods_three = [\n",
    "    \"Der Unterschied zwischen den beiden Methoden liegt in der Art und Weise, wie die \",\n",
    "    \"Grenzen des Vermessungsgebiets festgelegt werden. Wenn ein Weg benutzt wird, werden \",\n",
    "    \"die Grenzen des Vermessungsgebiets durch den Weg selbst festgelegt, nicht durch die \",\n",
    "    \"Person, die die Erhebung ausführt. Im Sommer sind die Barrieren und Schilder, welche \",\n",
    "    \"Pisten etc. markieren, alle entfernt worden, so dass es für die Person, die die Erhebung \",\n",
    "    \"ausführt, schwierig ist, die korrekten Grenzen genau zu bestimmen.\"\n",
    "]\n",
    "\n",
    "section_two = \"Kumulierte Gesamtzahlen für das Erhebungsgebiet\"\n",
    "\n",
    "smallspace = featuredata.small_space\n",
    "\n",
    "methods_title = Paragraph(sample_methods, featuredata.section_title)\n",
    "opening = makeAParagraph(alpes_opening)\n",
    "\n",
    "questions_list = makeAList(alpes_questions)\n",
    "a_purpose = makeAParagraph(clean_up_event_purpose)\n",
    "\n",
    "m_one = makeAParagraph(methods_one)\n",
    "m_list_one = makeAList(methods_list_one)\n",
    "m_two = makeAParagraph(methods_two)\n",
    "m_list_two = makeAList(methods_list_two)\n",
    "m_three = makeAParagraph(methods_three)\n",
    "section_two_title = Paragraph(section_two, featuredata.section_title)\n",
    "\n",
    "new_components = [    \n",
    "    smallspace,\n",
    "    opening,\n",
    "    smallspace,\n",
    "    questions_list,\n",
    "    smallspace,\n",
    "    a_purpose,\n",
    "    featuredata.large_space,\n",
    "    methods_title,\n",
    "    smallspace,\n",
    "    m_one,\n",
    "    smallspace,\n",
    "    m_list_one,\n",
    "    smallspace,\n",
    "    m_two,\n",
    "    smallspace,\n",
    "    m_list_two,\n",
    "    smallspace,\n",
    "    m_three,\n",
    "    featuredata.large_space,    \n",
    "    section_two_title,\n",
    "    smallspace   \n",
    "]"
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "Im Zeitraum von März 2020  bis Oktober 2021 wurden im Rahmen von 20 Datenerhebungen insgesamt 7 776 Objekte entfernt und identifiziert.  Die Ergebnisse des Alpen und Jura umfassen 20 Orte, 18 Gemeinden und eine Gesamtbevölkerung von etwa 70 606 Einwohnenden.\n",
       "\n",
       "*Gemeinden*\n",
       "\n",
       ">Airolo, Andermatt, Calanca, Châtel-Saint-Denis, Grindelwald, La Roche, Lens, Mesocco, Nendaz, Ollon, Ormont-Dessus, Riddes, Rovio, Troistorrents, Val de Bagnes, Val-d'Illiez, Val-de-Charmey, Val-de-Travers\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the admin summary can be converted into a standard text\n",
    "an_admin_summary = featuredata.makeAdminSummaryStateMent(start_date, end_date, this_feature[\"name\"], admin_summary=admin_summary)\n",
    "a_summary = Paragraph(an_admin_summary, featuredata.p_style)\n",
    "\n",
    "# add summary to pdf\n",
    "new_components.append(a_summary)\n",
    "\n",
    "# add the admin summary to the pdf\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "                      \n",
    "# collect component features and land marks\n",
    "# this collects the components of the feature of interest (city, lake, river)\n",
    "# a comma separated string of all the componenets and a heading for each component\n",
    "# type is produced\n",
    "feature_components = featuredata.collectComponentLandMarks(admin_details, language=\"de\")\n",
    "\n",
    "# markdown output\n",
    "components_markdown = \"\".join([f'*{x[0]}*\\n\\n>{x[1]}\\n\\n' for x in feature_components[1:]])\n",
    "\n",
    "# put that all together:\n",
    "lake_string = F\"\"\"\n",
    "{an_admin_summary}\n",
    "\n",
    "{\"\".join(components_markdown)}\n",
    "\"\"\"\n",
    "md(lake_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gesamtzahlen der Erhebungen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Die aggregierten Ergebnisse der Abfallerhebungen. Ein Teil der Daten befindet sich aus Platzgründen in einer zweiten Tabelle darunter.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_dims_table_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_22665 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_22665 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_22665 tr {\n  font-size: 12px;\n}\n#T_22665 th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_22665 td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_22665\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_22665_level0_col0\" class=\"col_heading level0 col0\" >Gesamtgewicht (Kg)</th>\n      <th id=\"T_22665_level0_col1\" class=\"col_heading level0 col1\" >Plastik (Kg)</th>\n      <th id=\"T_22665_level0_col2\" class=\"col_heading level0 col2\" >Fläche (m2)</th>\n      <th id=\"T_22665_level0_col3\" class=\"col_heading level0 col3\" >Länge (m)</th>\n      <th id=\"T_22665_level0_col4\" class=\"col_heading level0 col4\" >Erhebungen</th>\n      <th id=\"T_22665_level0_col5\" class=\"col_heading level0 col5\" >Objekte (St.)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_22665_level0_row0\" class=\"row_heading level0 row0\" >Alpen und Jura</th>\n      <td id=\"T_22665_row0_col0\" class=\"data row0 col0\" >18,85</td>\n      <td id=\"T_22665_row0_col1\" class=\"data row0 col1\" >6,35</td>\n      <td id=\"T_22665_row0_col2\" class=\"data row0 col2\" >6 856</td>\n      <td id=\"T_22665_row0_col3\" class=\"data row0 col3\" >1 237</td>\n      <td id=\"T_22665_row0_col4\" class=\"data row0 col4\" >20</td>\n      <td id=\"T_22665_row0_col5\" class=\"data row0 col5\" >7 776</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row1\" class=\"row_heading level0 row1\" >La Tzoumaz</th>\n      <td id=\"T_22665_row1_col0\" class=\"data row1 col0\" >3,42</td>\n      <td id=\"T_22665_row1_col1\" class=\"data row1 col1\" >0,74</td>\n      <td id=\"T_22665_row1_col2\" class=\"data row1 col2\" >1 200</td>\n      <td id=\"T_22665_row1_col3\" class=\"data row1 col3\" >80</td>\n      <td id=\"T_22665_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_22665_row1_col5\" class=\"data row1 col5\" >88</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row2\" class=\"row_heading level0 row2\" >Andermatt</th>\n      <td id=\"T_22665_row2_col0\" class=\"data row2 col0\" >1,8</td>\n      <td id=\"T_22665_row2_col1\" class=\"data row2 col1\" >0,1</td>\n      <td id=\"T_22665_row2_col2\" class=\"data row2 col2\" >800</td>\n      <td id=\"T_22665_row2_col3\" class=\"data row2 col3\" >100</td>\n      <td id=\"T_22665_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_22665_row2_col5\" class=\"data row2 col5\" >24</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row3\" class=\"row_heading level0 row3\" >San Bernardino</th>\n      <td id=\"T_22665_row3_col0\" class=\"data row3 col0\" >1,56</td>\n      <td id=\"T_22665_row3_col1\" class=\"data row3 col1\" >0,8</td>\n      <td id=\"T_22665_row3_col2\" class=\"data row3 col2\" >11</td>\n      <td id=\"T_22665_row3_col3\" class=\"data row3 col3\" >5</td>\n      <td id=\"T_22665_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_22665_row3_col5\" class=\"data row3 col5\" >431</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row4\" class=\"row_heading level0 row4\" >Val Calanca</th>\n      <td id=\"T_22665_row4_col0\" class=\"data row4 col0\" >1,5</td>\n      <td id=\"T_22665_row4_col1\" class=\"data row4 col1\" >0,06</td>\n      <td id=\"T_22665_row4_col2\" class=\"data row4 col2\" >240</td>\n      <td id=\"T_22665_row4_col3\" class=\"data row4 col3\" >80</td>\n      <td id=\"T_22665_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_22665_row4_col5\" class=\"data row4 col5\" >78</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row5\" class=\"row_heading level0 row5\" >Robella</th>\n      <td id=\"T_22665_row5_col0\" class=\"data row5 col0\" >1,5</td>\n      <td id=\"T_22665_row5_col1\" class=\"data row5 col1\" >0,28</td>\n      <td id=\"T_22665_row5_col2\" class=\"data row5 col2\" >1 200</td>\n      <td id=\"T_22665_row5_col3\" class=\"data row5 col3\" >80</td>\n      <td id=\"T_22665_row5_col4\" class=\"data row5 col4\" >1</td>\n      <td id=\"T_22665_row5_col5\" class=\"data row5 col5\" >47</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row6\" class=\"row_heading level0 row6\" >Nendaz</th>\n      <td id=\"T_22665_row6_col0\" class=\"data row6 col0\" >1,35</td>\n      <td id=\"T_22665_row6_col1\" class=\"data row6 col1\" >0,35</td>\n      <td id=\"T_22665_row6_col2\" class=\"data row6 col2\" >96</td>\n      <td id=\"T_22665_row6_col3\" class=\"data row6 col3\" >64</td>\n      <td id=\"T_22665_row6_col4\" class=\"data row6 col4\" >1</td>\n      <td id=\"T_22665_row6_col5\" class=\"data row6 col5\" >182</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row7\" class=\"row_heading level0 row7\" >Tour Les Crosets</th>\n      <td id=\"T_22665_row7_col0\" class=\"data row7 col0\" >1,27</td>\n      <td id=\"T_22665_row7_col1\" class=\"data row7 col1\" >0,68</td>\n      <td id=\"T_22665_row7_col2\" class=\"data row7 col2\" >141</td>\n      <td id=\"T_22665_row7_col3\" class=\"data row7 col3\" >94</td>\n      <td id=\"T_22665_row7_col4\" class=\"data row7 col4\" >1</td>\n      <td id=\"T_22665_row7_col5\" class=\"data row7 col5\" >518</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row8\" class=\"row_heading level0 row8\" >Les Diablerets</th>\n      <td id=\"T_22665_row8_col0\" class=\"data row8 col0\" >1,07</td>\n      <td id=\"T_22665_row8_col1\" class=\"data row8 col1\" >0,75</td>\n      <td id=\"T_22665_row8_col2\" class=\"data row8 col2\" >176</td>\n      <td id=\"T_22665_row8_col3\" class=\"data row8 col3\" >118</td>\n      <td id=\"T_22665_row8_col4\" class=\"data row8 col4\" >1</td>\n      <td id=\"T_22665_row8_col5\" class=\"data row8 col5\" >31</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row9\" class=\"row_heading level0 row9\" >Cabanes des Diablerets</th>\n      <td id=\"T_22665_row9_col0\" class=\"data row9 col0\" >1,05</td>\n      <td id=\"T_22665_row9_col1\" class=\"data row9 col1\" >0,02</td>\n      <td id=\"T_22665_row9_col2\" class=\"data row9 col2\" >18</td>\n      <td id=\"T_22665_row9_col3\" class=\"data row9 col3\" >12</td>\n      <td id=\"T_22665_row9_col4\" class=\"data row9 col4\" >1</td>\n      <td id=\"T_22665_row9_col5\" class=\"data row9 col5\" >165</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row10\" class=\"row_heading level0 row10\" >La Berra</th>\n      <td id=\"T_22665_row10_col0\" class=\"data row10 col0\" >1,01</td>\n      <td id=\"T_22665_row10_col1\" class=\"data row10 col1\" >0,01</td>\n      <td id=\"T_22665_row10_col2\" class=\"data row10 col2\" >1 500</td>\n      <td id=\"T_22665_row10_col3\" class=\"data row10 col3\" >60</td>\n      <td id=\"T_22665_row10_col4\" class=\"data row10 col4\" >1</td>\n      <td id=\"T_22665_row10_col5\" class=\"data row10 col5\" >34</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row11\" class=\"row_heading level0 row11\" >Villars</th>\n      <td id=\"T_22665_row11_col0\" class=\"data row11 col0\" >0,63</td>\n      <td id=\"T_22665_row11_col1\" class=\"data row11 col1\" >0,3</td>\n      <td id=\"T_22665_row11_col2\" class=\"data row11 col2\" >550</td>\n      <td id=\"T_22665_row11_col3\" class=\"data row11 col3\" >110</td>\n      <td id=\"T_22665_row11_col4\" class=\"data row11 col4\" >1</td>\n      <td id=\"T_22665_row11_col5\" class=\"data row11 col5\" >105</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row12\" class=\"row_heading level0 row12\" >Crans-Montana</th>\n      <td id=\"T_22665_row12_col0\" class=\"data row12 col0\" >0,56</td>\n      <td id=\"T_22665_row12_col1\" class=\"data row12 col1\" >0,36</td>\n      <td id=\"T_22665_row12_col2\" class=\"data row12 col2\" >64</td>\n      <td id=\"T_22665_row12_col3\" class=\"data row12 col3\" >43</td>\n      <td id=\"T_22665_row12_col4\" class=\"data row12 col4\" >1</td>\n      <td id=\"T_22665_row12_col5\" class=\"data row12 col5\" >47</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row13\" class=\"row_heading level0 row13\" >Grindelwald</th>\n      <td id=\"T_22665_row13_col0\" class=\"data row13 col0\" >0,39</td>\n      <td id=\"T_22665_row13_col1\" class=\"data row13 col1\" >0,15</td>\n      <td id=\"T_22665_row13_col2\" class=\"data row13 col2\" >92</td>\n      <td id=\"T_22665_row13_col3\" class=\"data row13 col3\" >61</td>\n      <td id=\"T_22665_row13_col4\" class=\"data row13 col4\" >1</td>\n      <td id=\"T_22665_row13_col5\" class=\"data row13 col5\" >169</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row14\" class=\"row_heading level0 row14\" >Verbier</th>\n      <td id=\"T_22665_row14_col0\" class=\"data row14 col0\" >0,37</td>\n      <td id=\"T_22665_row14_col1\" class=\"data row14 col1\" >0,16</td>\n      <td id=\"T_22665_row14_col2\" class=\"data row14 col2\" >74</td>\n      <td id=\"T_22665_row14_col3\" class=\"data row14 col3\" >50</td>\n      <td id=\"T_22665_row14_col4\" class=\"data row14 col4\" >1</td>\n      <td id=\"T_22665_row14_col5\" class=\"data row14 col5\" >205</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row15\" class=\"row_heading level0 row15\" >Morgins</th>\n      <td id=\"T_22665_row15_col0\" class=\"data row15 col0\" >0,33</td>\n      <td id=\"T_22665_row15_col1\" class=\"data row15 col1\" >0,23</td>\n      <td id=\"T_22665_row15_col2\" class=\"data row15 col2\" >112</td>\n      <td id=\"T_22665_row15_col3\" class=\"data row15 col3\" >75</td>\n      <td id=\"T_22665_row15_col4\" class=\"data row15 col4\" >1</td>\n      <td id=\"T_22665_row15_col5\" class=\"data row15 col5\" >123</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row16\" class=\"row_heading level0 row16\" >Charmey</th>\n      <td id=\"T_22665_row16_col0\" class=\"data row16 col0\" >0,33</td>\n      <td id=\"T_22665_row16_col1\" class=\"data row16 col1\" >0,91</td>\n      <td id=\"T_22665_row16_col2\" class=\"data row16 col2\" >150</td>\n      <td id=\"T_22665_row16_col3\" class=\"data row16 col3\" >30</td>\n      <td id=\"T_22665_row16_col4\" class=\"data row16 col4\" >1</td>\n      <td id=\"T_22665_row16_col5\" class=\"data row16 col5\" >174</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row17\" class=\"row_heading level0 row17\" >Les Paccots</th>\n      <td id=\"T_22665_row17_col0\" class=\"data row17 col0\" >0,22</td>\n      <td id=\"T_22665_row17_col1\" class=\"data row17 col1\" >0,09</td>\n      <td id=\"T_22665_row17_col2\" class=\"data row17 col2\" >96</td>\n      <td id=\"T_22665_row17_col3\" class=\"data row17 col3\" >48</td>\n      <td id=\"T_22665_row17_col4\" class=\"data row17 col4\" >1</td>\n      <td id=\"T_22665_row17_col5\" class=\"data row17 col5\" >118</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row18\" class=\"row_heading level0 row18\" >Airolo</th>\n      <td id=\"T_22665_row18_col0\" class=\"data row18 col0\" >0,2</td>\n      <td id=\"T_22665_row18_col1\" class=\"data row18 col1\" >0,14</td>\n      <td id=\"T_22665_row18_col2\" class=\"data row18 col2\" >57</td>\n      <td id=\"T_22665_row18_col3\" class=\"data row18 col3\" >38</td>\n      <td id=\"T_22665_row18_col4\" class=\"data row18 col4\" >1</td>\n      <td id=\"T_22665_row18_col5\" class=\"data row18 col5\" >224</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row19\" class=\"row_heading level0 row19\" >Veysonnaz</th>\n      <td id=\"T_22665_row19_col0\" class=\"data row19 col0\" >0,15</td>\n      <td id=\"T_22665_row19_col1\" class=\"data row19 col1\" >0,12</td>\n      <td id=\"T_22665_row19_col2\" class=\"data row19 col2\" >164</td>\n      <td id=\"T_22665_row19_col3\" class=\"data row19 col3\" >12</td>\n      <td id=\"T_22665_row19_col4\" class=\"data row19 col4\" >1</td>\n      <td id=\"T_22665_row19_col5\" class=\"data row19 col5\" >4 981</td>\n    </tr>\n    <tr>\n      <th id=\"T_22665_level0_row20\" class=\"row_heading level0 row20\" >Monte Generoso</th>\n      <td id=\"T_22665_row20_col0\" class=\"data row20 col0\" >0,14</td>\n      <td id=\"T_22665_row20_col1\" class=\"data row20 col1\" >0,1</td>\n      <td id=\"T_22665_row20_col2\" class=\"data row20 col2\" >115</td>\n      <td id=\"T_22665_row20_col3\" class=\"data row20 col3\" >77</td>\n      <td id=\"T_22665_row20_col4\" class=\"data row20 col4\" >1</td>\n      <td id=\"T_22665_row20_col5\" class=\"data row20 col5\" >32</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f544be7ef10>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_dims_table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the dimensional sumaries for each trash count\n",
    "dims_table = admin_details.dimensionalSummary()\n",
    "\n",
    "# for this report columns need to be added to standard dimensions\n",
    "# adding the pcs per unit column, add the region total first\n",
    "dims_table.loc[\"Alpen und Jura\", unit_label] = fdx.sample_totals[unit_label].median()\n",
    "\n",
    "for location in admin_details.locations_of_interest:\n",
    "    index_name = proper_names[location]\n",
    "    value = unit_label_map.loc[location, unit_label]\n",
    "    dims_table.loc[index_name, unit_label] = value\n",
    "\n",
    "# !surprise! the surveyors used the micro plastics weight in  field to log the sample total weights in grams\n",
    "# the total weight field was used to log the event total (in kilos), map these to the dims table\n",
    "# which means the total weight column needs to be dropped from this view\n",
    "new_columns = list(dims_table.columns)\n",
    "new_columns.remove(\"total_w\")\n",
    "dims_table = dims_table[new_columns]\n",
    "\n",
    "# map the mic_plas_w column back to the dims table\n",
    "weights_map = admin_details.dims_data[[\"location\", \"mic_plas_w\"]].set_index(\"location\")\n",
    "\n",
    "# add the cumulative total\n",
    "dims_table.loc[\"Alpen und Jura\", \"Gesamtgewicht (Kg)\"] = round(weights_map.mic_plas_w.sum()/1000, 2)\n",
    "\n",
    "\n",
    "for location in admin_details.locations_of_interest:\n",
    "    index_name = proper_names[location]\n",
    "    value = weights_map.loc[location, \"mic_plas_w\"]\n",
    "    dims_table.loc[index_name, \"Gesamtgewicht (Kg)\"] = round(value/1000, 2)\n",
    "\n",
    "# format the table for display\n",
    "# convert the plastic totals from grams to kilos\n",
    "dims_table.mac_plast_w = (dims_table.mac_plast_w/1000).round(2)\n",
    "dims_table = dims_table.sort_values(by=\"Gesamtgewicht (Kg)\", ascending=False)\n",
    "\n",
    "# convert samples, area, length and unit_label to integers\n",
    "# the thousands are separated by a space not a comma or decimal\n",
    "as_type_int = [\"samples\", \"quantity\", unit_label, \"area\", \"length\"]\n",
    "dims_table[as_type_int] = dims_table[as_type_int].astype(int)\n",
    "\n",
    "# rename the columns to german and reset the index\n",
    "dims_table = dims_table.rename(columns=featuredata.dims_table_columns_de)\n",
    "# dims_table.reset_index(inplace=True, drop=False)\n",
    "\n",
    "dims_table.index.name = None\n",
    "dims_table.columns.name = None\n",
    "\n",
    "dims_table_formatter = {\n",
    "    \"Plastik (Kg)\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"Gesamtgewicht (Kg)\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"Fläche (m2)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\"),\n",
    "    \"Länge (m)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\"),\n",
    "    \"Erhebungen\": lambda x: featuredata.thousandsSeparator(int(x), \"de\"),\n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\")\n",
    "    \n",
    "    \n",
    "}\n",
    "\n",
    "# the columns of interest\n",
    "alpes_columns = [\"Gesamtgewicht (Kg)\", \"Plastik (Kg)\", \"Fläche (m2)\", \"Länge (m)\", \"Erhebungen\", \"Objekte (St.)\"]\n",
    "dims_table = dims_table[alpes_columns].copy()\n",
    "\n",
    "# subsection title\n",
    "subsection_title = Paragraph(\"Gesamtzahlen der Erhebungen\", featuredata.subsection_title)\n",
    "\n",
    "# a caption for the figure\n",
    "dims_table_caption = 'Die aggregierten Ergebnisse der Abfallerhebungen. Ein Teil der Daten befindet sich aus Platzgründen in einer zweiten Tabelle darunter.'\n",
    "\n",
    "q = dims_table.style.format(formatter=dims_table_formatter).set_table_styles(table_css_styles)\n",
    "col_widths = [3.5*cm, 3*cm, *[2.2*cm]*(len(dims_table.columns)-1)]\n",
    "\n",
    "# pdf table                 \n",
    "d_chart = featuredata.aSingleStyledTable(dims_table, colWidths=col_widths)\n",
    "\n",
    "table_caption = Paragraph(dims_table_caption, featuredata.caption_style)\n",
    "table_and_caption = featuredata.tableAndCaption(d_chart, table_caption, col_widths)\n",
    "\n",
    "new_components = [\n",
    "    featuredata.large_space,\n",
    "    subsection_title,\n",
    "    featuredata.small_space,\n",
    "    table_and_caption\n",
    "]\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_dims_table_caption', dims_table_caption, display=False)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_dims_table', q, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_dims_table\n",
    "---\n",
    "name: 'les-alpes_dims_table'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <les-alpes_dims_table>` {glue:text}`les-alpes_dims_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gesamtzahlen in Bezug auf die Clean-ups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Die Gesamtmenge des gesammelten Mülls in Kilogramm, die Anzahl der Teilnehmenden und des Personals sowie die Zeit, die für die Durchführung der Erhebung benötigt wurde.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "event_summary_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_6ca7b tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_6ca7b tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_6ca7b tr {\n  font-size: 12px;\n}\n#T_6ca7b th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_6ca7b td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_6ca7b\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6ca7b_level0_col0\" class=\"col_heading level0 col0\" >Gesamtgewicht (Kg)</th>\n      <th id=\"T_6ca7b_level0_col1\" class=\"col_heading level0 col1\" >Teilnehmende</th>\n      <th id=\"T_6ca7b_level0_col2\" class=\"col_heading level0 col2\" >Mitarbeitende</th>\n      <th id=\"T_6ca7b_level0_col3\" class=\"col_heading level0 col3\" >Std.</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6ca7b_level0_row0\" class=\"row_heading level0 row0\" >Alpen und Jura</th>\n      <td id=\"T_6ca7b_row0_col0\" class=\"data row0 col0\" >3 120</td>\n      <td id=\"T_6ca7b_row0_col1\" class=\"data row0 col1\" >865</td>\n      <td id=\"T_6ca7b_row0_col2\" class=\"data row0 col2\" >20</td>\n      <td id=\"T_6ca7b_row0_col3\" class=\"data row0 col3\" >68,1</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row1\" class=\"row_heading level0 row1\" >Cabanes des Diablerets</th>\n      <td id=\"T_6ca7b_row1_col0\" class=\"data row1 col0\" >731</td>\n      <td id=\"T_6ca7b_row1_col1\" class=\"data row1 col1\" >20</td>\n      <td id=\"T_6ca7b_row1_col2\" class=\"data row1 col2\" >1</td>\n      <td id=\"T_6ca7b_row1_col3\" class=\"data row1 col3\" >3,3</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row2\" class=\"row_heading level0 row2\" >Andermatt</th>\n      <td id=\"T_6ca7b_row2_col0\" class=\"data row2 col0\" >263</td>\n      <td id=\"T_6ca7b_row2_col1\" class=\"data row2 col1\" >60</td>\n      <td id=\"T_6ca7b_row2_col2\" class=\"data row2 col2\" >1</td>\n      <td id=\"T_6ca7b_row2_col3\" class=\"data row2 col3\" >3,7</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row3\" class=\"row_heading level0 row3\" >Villars</th>\n      <td id=\"T_6ca7b_row3_col0\" class=\"data row3 col0\" >256</td>\n      <td id=\"T_6ca7b_row3_col1\" class=\"data row3 col1\" >125</td>\n      <td id=\"T_6ca7b_row3_col2\" class=\"data row3 col2\" >1</td>\n      <td id=\"T_6ca7b_row3_col3\" class=\"data row3 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row4\" class=\"row_heading level0 row4\" >Charmey</th>\n      <td id=\"T_6ca7b_row4_col0\" class=\"data row4 col0\" >235</td>\n      <td id=\"T_6ca7b_row4_col1\" class=\"data row4 col1\" >72</td>\n      <td id=\"T_6ca7b_row4_col2\" class=\"data row4 col2\" >1</td>\n      <td id=\"T_6ca7b_row4_col3\" class=\"data row4 col3\" >3,7</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row5\" class=\"row_heading level0 row5\" >Val Calanca</th>\n      <td id=\"T_6ca7b_row5_col0\" class=\"data row5 col0\" >228</td>\n      <td id=\"T_6ca7b_row5_col1\" class=\"data row5 col1\" >32</td>\n      <td id=\"T_6ca7b_row5_col2\" class=\"data row5 col2\" >1</td>\n      <td id=\"T_6ca7b_row5_col3\" class=\"data row5 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row6\" class=\"row_heading level0 row6\" >Tour Les Crosets</th>\n      <td id=\"T_6ca7b_row6_col0\" class=\"data row6 col0\" >205</td>\n      <td id=\"T_6ca7b_row6_col1\" class=\"data row6 col1\" >50</td>\n      <td id=\"T_6ca7b_row6_col2\" class=\"data row6 col2\" >1</td>\n      <td id=\"T_6ca7b_row6_col3\" class=\"data row6 col3\" >3,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row7\" class=\"row_heading level0 row7\" >La Tzoumaz</th>\n      <td id=\"T_6ca7b_row7_col0\" class=\"data row7 col0\" >200</td>\n      <td id=\"T_6ca7b_row7_col1\" class=\"data row7 col1\" >50</td>\n      <td id=\"T_6ca7b_row7_col2\" class=\"data row7 col2\" >1</td>\n      <td id=\"T_6ca7b_row7_col3\" class=\"data row7 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row8\" class=\"row_heading level0 row8\" >Crans-Montana</th>\n      <td id=\"T_6ca7b_row8_col0\" class=\"data row8 col0\" >185</td>\n      <td id=\"T_6ca7b_row8_col1\" class=\"data row8 col1\" >35</td>\n      <td id=\"T_6ca7b_row8_col2\" class=\"data row8 col2\" >1</td>\n      <td id=\"T_6ca7b_row8_col3\" class=\"data row8 col3\" >2,5</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row9\" class=\"row_heading level0 row9\" >Les Paccots</th>\n      <td id=\"T_6ca7b_row9_col0\" class=\"data row9 col0\" >160</td>\n      <td id=\"T_6ca7b_row9_col1\" class=\"data row9 col1\" >90</td>\n      <td id=\"T_6ca7b_row9_col2\" class=\"data row9 col2\" >1</td>\n      <td id=\"T_6ca7b_row9_col3\" class=\"data row9 col3\" >3,3</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row10\" class=\"row_heading level0 row10\" >Nendaz</th>\n      <td id=\"T_6ca7b_row10_col0\" class=\"data row10 col0\" >155</td>\n      <td id=\"T_6ca7b_row10_col1\" class=\"data row10 col1\" >70</td>\n      <td id=\"T_6ca7b_row10_col2\" class=\"data row10 col2\" >1</td>\n      <td id=\"T_6ca7b_row10_col3\" class=\"data row10 col3\" >4,8</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row11\" class=\"row_heading level0 row11\" >Morgins</th>\n      <td id=\"T_6ca7b_row11_col0\" class=\"data row11 col0\" >129</td>\n      <td id=\"T_6ca7b_row11_col1\" class=\"data row11 col1\" >60</td>\n      <td id=\"T_6ca7b_row11_col2\" class=\"data row11 col2\" >1</td>\n      <td id=\"T_6ca7b_row11_col3\" class=\"data row11 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row12\" class=\"row_heading level0 row12\" >La Berra</th>\n      <td id=\"T_6ca7b_row12_col0\" class=\"data row12 col0\" >80</td>\n      <td id=\"T_6ca7b_row12_col1\" class=\"data row12 col1\" >50</td>\n      <td id=\"T_6ca7b_row12_col2\" class=\"data row12 col2\" >1</td>\n      <td id=\"T_6ca7b_row12_col3\" class=\"data row12 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row13\" class=\"row_heading level0 row13\" >Verbier</th>\n      <td id=\"T_6ca7b_row13_col0\" class=\"data row13 col0\" >76</td>\n      <td id=\"T_6ca7b_row13_col1\" class=\"data row13 col1\" >27</td>\n      <td id=\"T_6ca7b_row13_col2\" class=\"data row13 col2\" >1</td>\n      <td id=\"T_6ca7b_row13_col3\" class=\"data row13 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row14\" class=\"row_heading level0 row14\" >Les Diablerets</th>\n      <td id=\"T_6ca7b_row14_col0\" class=\"data row14 col0\" >74</td>\n      <td id=\"T_6ca7b_row14_col1\" class=\"data row14 col1\" >40</td>\n      <td id=\"T_6ca7b_row14_col2\" class=\"data row14 col2\" >1</td>\n      <td id=\"T_6ca7b_row14_col3\" class=\"data row14 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row15\" class=\"row_heading level0 row15\" >Veysonnaz</th>\n      <td id=\"T_6ca7b_row15_col0\" class=\"data row15 col0\" >48</td>\n      <td id=\"T_6ca7b_row15_col1\" class=\"data row15 col1\" >12</td>\n      <td id=\"T_6ca7b_row15_col2\" class=\"data row15 col2\" >1</td>\n      <td id=\"T_6ca7b_row15_col3\" class=\"data row15 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row16\" class=\"row_heading level0 row16\" >Robella</th>\n      <td id=\"T_6ca7b_row16_col0\" class=\"data row16 col0\" >37</td>\n      <td id=\"T_6ca7b_row16_col1\" class=\"data row16 col1\" >23</td>\n      <td id=\"T_6ca7b_row16_col2\" class=\"data row16 col2\" >1</td>\n      <td id=\"T_6ca7b_row16_col3\" class=\"data row16 col3\" >3,3</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row17\" class=\"row_heading level0 row17\" >Grindelwald</th>\n      <td id=\"T_6ca7b_row17_col0\" class=\"data row17 col0\" >26</td>\n      <td id=\"T_6ca7b_row17_col1\" class=\"data row17 col1\" >21</td>\n      <td id=\"T_6ca7b_row17_col2\" class=\"data row17 col2\" >1</td>\n      <td id=\"T_6ca7b_row17_col3\" class=\"data row17 col3\" >3,3</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row18\" class=\"row_heading level0 row18\" >Monte Generoso</th>\n      <td id=\"T_6ca7b_row18_col0\" class=\"data row18 col0\" >22</td>\n      <td id=\"T_6ca7b_row18_col1\" class=\"data row18 col1\" >16</td>\n      <td id=\"T_6ca7b_row18_col2\" class=\"data row18 col2\" >1</td>\n      <td id=\"T_6ca7b_row18_col3\" class=\"data row18 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row19\" class=\"row_heading level0 row19\" >San Bernardino</th>\n      <td id=\"T_6ca7b_row19_col0\" class=\"data row19 col0\" >9</td>\n      <td id=\"T_6ca7b_row19_col1\" class=\"data row19 col1\" >12</td>\n      <td id=\"T_6ca7b_row19_col2\" class=\"data row19 col2\" >1</td>\n      <td id=\"T_6ca7b_row19_col3\" class=\"data row19 col3\" >4,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_6ca7b_level0_row20\" class=\"row_heading level0 row20\" >Airolo</th>\n      <td id=\"T_6ca7b_row20_col0\" class=\"data row20 col0\" >1</td>\n      <td id=\"T_6ca7b_row20_col1\" class=\"data row20 col1\" >0</td>\n      <td id=\"T_6ca7b_row20_col2\" class=\"data row20 col2\" >1</td>\n      <td id=\"T_6ca7b_row20_col3\" class=\"data row20 col3\" >1,6</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f544bd78a90>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_survey_area_event_summaries"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the event totals include the amount collected but not counted\n",
    "# the number of participants, the number of staff and the time to complete\n",
    "event_totals = admin_details.dims_data[[\"location\", \"total_w\", \"num_parts_other\", \"num_parts_staff\", \"time_minutes\"]].copy()\n",
    "\n",
    "# the names are changed to proper names and set as the index\n",
    "event_totals[\"location\"] = event_totals.location.map(lambda x: proper_names.loc[x])\n",
    "event_totals.set_index(\"location\", inplace=True)\n",
    "\n",
    "# sum all the events to give the total for the feauture\n",
    "event_totals.loc[this_feature[\"name\"]] = event_totals.sum(numeric_only=True, axis=0)\n",
    "\n",
    "# convert minutes to hours\n",
    "event_totals.time_minutes = (event_totals.time_minutes/60).round(1)\n",
    "event_totals = event_totals.sort_values(by=\"total_w\", ascending=False)\n",
    "\n",
    "# formatting for display\n",
    "thousands = [\"total_w\", \"num_parts_other\"]\n",
    "event_totals[thousands] = event_totals[thousands].applymap(lambda x: featuredata.thousandsSeparator(int(x), \"de\"))\n",
    "event_totals[\"time_minutes\"] = event_totals.time_minutes.map(lambda x: featuredata.replaceDecimal(str(x)))\n",
    "\n",
    "new_column_names = {\n",
    "    \"total_w\":  \"Gesamtgewicht (Kg)\",\n",
    "    \"num_parts_other\": \"Teilnehmende\", \n",
    "    \"num_parts_staff\": \"Mitarbeitende\",\n",
    "    \"time_minutes\": \"Std.\"\n",
    "}\n",
    "\n",
    "event_total_title = \"Gesamtzahlen in Bezug auf die Clean-ups\"\n",
    "clean_up_subsection_title = Paragraph(event_total_title, featuredata.subsection_title)   \n",
    "\n",
    "event_total_caption = [\n",
    "    \"Die Gesamtmenge des gesammelten Mülls in Kilogramm, die Anzahl \",\n",
    "    \"der Teilnehmenden und des Personals sowie die Zeit, die für die \",\n",
    "    \"Durchführung der Erhebung benötigt wurde.\"\n",
    "]\n",
    "\n",
    "event_caption = ''.join(event_total_caption)\n",
    "\n",
    "table_two = event_totals.rename(columns=new_column_names)\n",
    "col_widths=[3.5*cm, 3*cm, *[2.5*cm]*(len(table_two.columns)-2)]\n",
    "table_two.index.name = None\n",
    "table_two.columns.name = None\n",
    "\n",
    "table_caption = Paragraph(event_caption, featuredata.caption_style)\n",
    "d_chart = featuredata.aSingleStyledTable(table_two, colWidths=col_widths)\n",
    "table_and_caption = featuredata.tableAndCaption(d_chart, table_caption, col_widths)\n",
    "new_components = [\n",
    "    featuredata.large_space,\n",
    "    PageBreak(),\n",
    "    clean_up_subsection_title,\n",
    "    featuredata.small_space,\n",
    "    table_and_caption,\n",
    "    PageBreak()\n",
    "]\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "q = table_two.style.set_table_styles(table_css_styles)\n",
    "\n",
    "glue('event_summary_caption', event_caption, display=False)\n",
    "\n",
    "glue('alpes_survey_area_event_summaries', q, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_event_summaries\n",
    "---\n",
    "name: 'alpes_survey_area_event_summaries'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "\n",
    "{numref}`Abbildung %s: <alpes_survey_area_event_summaries>` {glue:text}`event_summary_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Landnutzungsprofil der Erhebungsorte\n",
    "\n",
    "Das Landnutzungsprofil zeigt, welche Nutzungen innerhalb eines Radius von 1500 m um jeden Erhebungsort dominieren. Flächen werden einer von den folgenden vier Kategorien zugewiesen:\n",
    "\n",
    "* Fläche, die von Gebäuden eingenommen wird in %\n",
    "* Fläche, die dem Wald vorbehalten ist in %\n",
    "* Fläche, die für Aktivitäten im Freien genutzt wird in %\n",
    "* Fläche, die von der Landwirtschaft genutzt wird in %\n",
    "\n",
    "Strassen (inkl. Wege) werden als Gesamtzahl der Strassenkilometer innerhalb eines Radius von 1500 m angegeben.\n",
    "\n",
    "Es wird zudem angegeben, wie viele Flüsse innerhalb eines Radius von 1500 m um den Erhebungsort herum in das Gewässer münden.\n",
    "\n",
    "Das Verhältnis der gefundenen Abfallobjekte unterscheidet sich je nach Landnutzungsprofil. Das Verhältnis gibt daher einen Hinweis auf die ökologischen und wirtschaftlichen Bedingungen um den Erhebungsort.\n",
    "\n",
    "Für weitere Informationen siehe *[17 Landnutzungsprofil](luseprofile)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "section_title =  \"Landnutzungsprofil der Erhebungsorte\"\n",
    "\n",
    "section_summary = [\n",
    "    \"Das Landnutzungsprofil zeigt, welche Nutzungen innerhalb eines Radius \",\n",
    "    \"von 1500 m um jeden Erhebungsort dominieren. Flächen werden einer von \",\n",
    "    \"den folgenden vier Kategorien zugewiesen:\"\n",
    "]\n",
    "\n",
    "section_definition = [\n",
    "    \"Fläche, die von Gebäuden eingenommen wird in %\",\n",
    "    \"Fläche, die dem Wald vorbehalten ist in %\",\n",
    "    \"Fläche, die für Aktivitäten im Freien genutzt wird in %\",\n",
    "    \"Fläche, die von der Landwirtschaft genutzt wird in %\"\n",
    "]\n",
    "\n",
    "p_one = [\n",
    "    \"Strassen (inkl. Wege) werden als Gesamtzahl der Strassenkilometer \",\n",
    "    \"innerhalb eines Radius von 1500 m angegeben.\"\n",
    "]\n",
    "\n",
    "p_two = [\n",
    "    \"Es wird zudem angegeben, wie viele Flüsse innerhalb eines Radius von \",\n",
    "    \"1500 m um den Erhebungsort herum in das Gewässer münden.\"\n",
    "]\n",
    "\n",
    "p_three = [\n",
    "    \"Das Verhältnis der gefundenen Abfallobjekte unterscheidet sich \",\n",
    "    \"je nach Landnutzungsprofil. Das Verhältnis gibt daher einen Hinweis \",\n",
    "    \"auf die ökologischen und wirtschaftlichen Bedingungen um den Erhebungsort. \",\n",
    "    'Für weitere Informationen siehe ',\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/land_use_correlation.html\" color=\"blue\"> Landnutzungsprofil </a>.'\n",
    "]\n",
    "\n",
    "# make paragraphs\n",
    "land_use_subtitle = Paragraph(section_title, featuredata.section_title)\n",
    "land_use_summary = sectionParagraphs([section_summary])\n",
    "definition_list = makeAList(section_definition)\n",
    "land_use_one_to_three = sectionParagraphs([p_one, p_two, p_three])\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        land_use_subtitle,\n",
    "        featuredata.small_space,\n",
    "        *land_use_summary,\n",
    "        featuredata.smallest_space,\n",
    "        definition_list,\n",
    "        featuredata.smallest_space,\n",
    "        *land_use_one_to_three\n",
    "    ])\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 900x800 with 6 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_survey_area_landuse"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "'Die Erhebungsorte in den Alpen und im Jura wiesen im Vergleich zu den Ergebungsorten IQAASL einen höheren Prozentsatz an forst- und landwirtschaftlichen Flächen und einen geringeren Prozentsatz an bebauter Fläche (Gebäude) und an Fläche, die für Aktivitäten im Freien genutzt werden, auf.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_land_use_caption"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this gets all the data for the project\n",
    "land_use_kwargs = {\n",
    "    \"data\": period_data.period_data,\n",
    "    \"index_column\":\"loc_date\",\n",
    "    \"these_features\": this_feature['slug'],\n",
    "    \"feature_level\":this_feature['level']   \n",
    "}\n",
    "\n",
    "# the landuse profile of the project\n",
    "project_profile = featuredata.LandUseProfile(**land_use_kwargs).byIndexColumn()\n",
    "\n",
    "# update the kwargs for the feature data\n",
    "land_use_kwargs.update({\"data\":fdx.feature_data})\n",
    "\n",
    "# build the landuse profile of the feature\n",
    "feature_profile = featuredata.LandUseProfile(**land_use_kwargs)\n",
    "\n",
    "# this is the component features of the report\n",
    "feature_landuse = feature_profile.featureOfInterest()\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(9,8), sharey=\"row\")\n",
    "\n",
    "for i, n in enumerate(featuredata.default_land_use_columns):\n",
    "    r = i%2\n",
    "    c = i%3\n",
    "    ax=axs[r,c]\n",
    "    \n",
    "    # the value of land use feature n\n",
    "    # for each element of the feature\n",
    "    for element in feature_landuse:\n",
    "        # the land use data for a feature\n",
    "        data = element[n].values\n",
    "        # the name of the element\n",
    "        element_name = element[feature_profile.feature_level].unique()\n",
    "        # proper name for chart\n",
    "        label = featuredata.river_basin_de[element_name[0]]\n",
    "        # cumulative distribution        \n",
    "        xs, ys = featuredata.empiricalCDF(data)\n",
    "        # the plot of landuse n for this element\n",
    "        sns.lineplot(x=xs, y=ys, ax=ax, label=label, color=featuredata.bassin_pallette[element_name[0]])\n",
    "    \n",
    "    # the value of the land use feature n for the project\n",
    "    testx, testy = featuredata.empiricalCDF(project_profile[n].values)\n",
    "    sns.lineplot(x=testx, y=testy, ax=ax, label=top, color=\"magenta\")\n",
    "    \n",
    "    # get the median landuse for the feature\n",
    "    the_median = np.median(data)\n",
    "    \n",
    "    # plot the median and drop horizontal and vertical lines\n",
    "    ax.scatter([the_median], 0.5, color=\"red\",s=40, linewidth=1, zorder=100, label=\"Median\")\n",
    "    ax.vlines(x=the_median, ymin=0, ymax=0.5, color=\"red\", linewidth=1)\n",
    "    ax.hlines(xmax=the_median, xmin=0, y=0.5, color=\"red\", linewidth=1)\n",
    "    \n",
    "    if i <= 3:\n",
    "        if c == 0:            \n",
    "            ax.yaxis.set_major_locator(MultipleLocator(.1))\n",
    "        ax.xaxis.set_major_formatter(ticker.PercentFormatter(1.0, 0, \"%\"))        \n",
    "    else:\n",
    "        pass      \n",
    "    \n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.get_legend().remove()    \n",
    "    ax.set_title(featuredata.luse_de[n], loc='left')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=.91, hspace=.4)\n",
    "plt.suptitle(\"Landnutzung im Umkries von 1 500 m um den Erhebungsort\", ha=\"center\", y=1, fontsize=16)\n",
    "fig.legend(handles, labels, bbox_to_anchor=(.5,.5), loc=\"upper center\", ncol=3)\n",
    "\n",
    "figure_caption = [\n",
    "    \"Die Erhebungsorte in den Alpen und im Jura wiesen im Vergleich zu den Ergebungsorten IQAASL \",\n",
    "    \"einen höheren Prozentsatz an forst- und landwirtschaftlichen Flächen und einen geringeren \",\n",
    "    \"Prozentsatz an bebauter Fläche (Gebäude) und an Fläche, die für Aktivitäten im Freien genutzt werden, auf.\"\n",
    "]\n",
    "\n",
    "figure_caption = ''.join(figure_caption)\n",
    "    \n",
    "figure_name = f\"{this_feature['slug']}_survey_area_landuse\"\n",
    "land_use_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":land_use_file_name})\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=.91, hspace=.4)\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "# capture the output\n",
    "glue(figure_name, fig, display=False)\n",
    "glue(\"les-alpes_land_use_caption\", figure_caption, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_survey_area_landuse\n",
    "---\n",
    "name: 'les-alpes_survey_area_landuse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "\n",
    "{numref}`Abbildung %s: <les-alpes_survey_area_landuse>` {glue:text}`les-alpes_land_use_caption`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "# add previous chart and caption to .pdf\n",
    "land_use_caption = Paragraph(figure_caption, featuredata.caption_style)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\": land_use_file_name,\n",
    "    \"caption\": land_use_caption,\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\": 1.7,\n",
    "    \"original_height\": 8,\n",
    "    \"original_width\": 9,\n",
    "}\n",
    "\n",
    "figure_and_caption = featuredata.figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    figure_and_caption\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die aggregierten Ergebnisse zeigen den Unterschied zwischen den beiden Erhebungsmethoden. Die drei Erhebungsorte mit dem höchsten p/100 m haben auch die kürzeste Länge. Im Fall von Cabanes-des-Diablerets entspricht die Fläche (in m2) der Länge (in m), was darauf hindeutet, dass ein kleiner Bereich um eine Struktur oder ein Gebäude herum vermessen wurde. In Veysonnaz befindet sich die Talstation der Seilbahn nach Thyon (Wintersportgebiet Veysonnaz / 4 Vallées).\n",
    "\n",
    "Der Unterschied in den Methoden führt zu abweichenden Ergebnissen. Ausserdem wurden diese beiden Orte aufgrund der früheren Erfahrungen der Person, die die Erhebung ausführt, speziell für die Bestandsaufnahme ausgewählt. Wegen der unterschiedlichen Dimensionen und Methoden werden die Erhebungsergebnisse aus Veysonnaz, San-Beranardino und Cabanes-des-Diablerets in der weiteren Analyse nicht berücksichtigt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "land_use_one = [\n",
    "    \"Die aggregierten Ergebnisse zeigen den Unterschied zwischen den beiden Erhebungsmethoden. \",\n",
    "    \"Die drei Erhebungsorte mit dem höchsten p/100 m haben auch die kürzeste Länge. Im Fall von \",\n",
    "    \"Cabanes-des-Diablerets entspricht die Fläche (in m2) der Länge (in m), was darauf hindeutet, \",\n",
    "    \"dass ein kleiner Bereich um eine Struktur oder ein Gebäude herum vermessen wurde. In Veysonnaz \",\n",
    "    \"befindet sich die Talstation der Seilbahn nach Thyon (Wintersportgebiet Veysonnaz / 4 Vallées).\"\n",
    "]\n",
    "\n",
    "land_use_two = [\n",
    "    \"Der Unterschied in den Methoden führt zu abweichenden Ergebnissen. Ausserdem wurden diese beiden \",\n",
    "    \"Orte aufgrund der früheren Erfahrungen der Person, die die Erhebung ausführt, speziell für die \",\n",
    "    \"Bestandsaufnahme ausgewählt. Wegen der unterschiedlichen Dimensionen und Methoden werden die Erhebungsergebnisse \",\n",
    "    \"aus Veysonnaz, San-Beranardino und Cabanes-des-Diablerets in der weiteren Analyse nicht berücksichtigt.\"\n",
    "]\n",
    "\n",
    "lu_one = makeAParagraph(land_use_one)\n",
    "lu_two = makeAParagraph(land_use_two)\n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    lu_one,\n",
    "    smallspace,\n",
    "    lu_two\n",
    "]\n",
    "    \n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verteilung der Erhebungsergebnisse¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Links: Zusammenfassung der Daten aller Erhebungen Erhebungsgebiet Alpes März 2020 bis September 2021, n=20. Rechts: Gefundene Materialarten im Erhebungsgebiet Alpes in Stückzahlen und als prozentuale Anteile (stückzahlbezogen). '"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_sample_total_notes"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 1000x500 with 2 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_sample_totals"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dx = period_data.parentSampleTotals(parent=False)\n",
    "\n",
    "not_trail_samples = [\"veysonnaz\", \"cabanes-des-diablerets\", \"san-bernardino\"]\n",
    "\n",
    "fdt = fd[~fd.location.isin(not_trail_samples)].copy()\n",
    "fdt_samples = fdx.sample_totals[~fdx.sample_totals.location.isin(not_trail_samples)].copy()\n",
    "\n",
    "material_map = fdx.mMap\n",
    "description_map = fdx.dMap\n",
    "\n",
    "fig, axs = plt.subplots(1,2, figsize=(10,5))\n",
    "\n",
    "months = mdates.MonthLocator(interval=1)\n",
    "months_fmt = mdates.DateFormatter(\"%b\")\n",
    "days = mdates.DayLocator(interval=7)\n",
    "\n",
    "ax = axs[0]\n",
    "ax.set_ylim(-50, 2000)\n",
    "\n",
    "# the cumlative distributions:\n",
    "axtwo = axs[1]\n",
    "\n",
    "# feature surveys\n",
    "sns.scatterplot(data=dx, x=\"date\", y=unit_label, label=top, color=\"black\", alpha=0.3,  ax=ax)\n",
    "# all other surveys\n",
    "sns.scatterplot(data=fdt_samples, x=\"date\", y=unit_label, label=this_feature[\"name\"], color=bassin_pallette[this_feature[\"slug\"]], edgecolor=\"white\", ax=ax)\n",
    "\n",
    "ax.set_ylabel(unit_label, **ck.xlab_k14)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.xaxis.set_minor_locator(days)\n",
    "ax.xaxis.set_major_formatter(months_fmt)\n",
    "\n",
    "axtwo = axs[1]\n",
    "\n",
    "box_props = {\n",
    "    \"boxprops\":{\"facecolor\":\"none\", \"edgecolor\":\"magenta\"},\n",
    "    \"medianprops\":{\"color\":\"magenta\"},\n",
    "    \"whiskerprops\":{\"color\":\"magenta\"},\n",
    "    \"capprops\":{\"color\":\"magenta\"}\n",
    "}\n",
    "sns.boxplot(data=dx, y=unit_label, color=\"black\",  ax=axtwo, showfliers=False, **box_props, zorder=1)\n",
    "sns.stripplot(data=dx[dx[unit_label] <= 1000], s=10, y=unit_label, color=\"black\", ax=axtwo, alpha=0.5, jitter=0.3, zorder=0)\n",
    "sns.stripplot(data=fdt_samples[fdt_samples[unit_label] <= 1000], y=unit_label, color=bassin_pallette[this_feature[\"slug\"]], s=10, edgecolor=\"white\",linewidth=1, ax=axtwo, jitter=0.3, zorder=2)\n",
    "\n",
    "axtwo.set_xlabel(\"\")\n",
    "axtwo.set_ylabel(unit_label, **ck.xlab_k14)\n",
    "axtwo.tick_params(which=\"both\", axis=\"x\", bottom=False)\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "figure_name = f\"{this_feature['slug']}_sample_totals\"\n",
    "sample_totals_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":sample_totals_file_name})\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "# figure caption\n",
    "sample_total_notes = [\n",
    "    \"Links: Zusammenfassung der Daten aller Erhebungen Erhebungsgebiet Alpes März 2020 bis September 2021, n=20. \",\n",
    "    \"Rechts: Gefundene Materialarten im Erhebungsgebiet Alpes in Stückzahlen und als prozentuale Anteile (stückzahlbezogen). \",\n",
    "]\n",
    "\n",
    "sample_total_notes = ''.join(sample_total_notes)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_sample_total_notes', sample_total_notes, display=False)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_sample_totals', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_sample_totals\n",
    "---\n",
    "name: 'les-alpes_sample_totals'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "\n",
    "{numref}`Abbildung %s: <les-alpes_sample_totals>` {glue:text}`les-alpes_sample_total_notes`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zusammenfassende Daten und Materialtypen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Links: Zusammenfassung der erhebungen entlang der Wanderwege. Rechts: Aufschlüsselung nach Materialart (Stückzahlen und prozentuale Verteilung).'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_sample_summaries_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 640x480 with 2 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_sample_material_tables"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def columnsAndOperations(column_operations: list = None, columns: list = None, unit_label: str = None):\n",
    "    if column_operations is None:\n",
    "        column_operation = {unit_label: \"median\", \"quantity\": \"sum\"}\n",
    "    else:\n",
    "        column_operation = {x[0]: x[1] for x in column_operations}\n",
    "    if columns is None:\n",
    "        columns = [\"loc_date\", \"groupname\"]\n",
    "    \n",
    "    return columns, column_operation\n",
    "\n",
    "def changeSeriesIndexLabels(a_series: pd.Series = None, change_names: {} = None):\n",
    "    \"\"\"A convenience function to change the index labels of a series x.\n",
    "    Change_names is keyed to the series index.\n",
    "\n",
    "    :param a_series: A pandas series\n",
    "    :param change_names: A dictionary that has keys = x.index and values = new x.index label\n",
    "    :return: The series with the new labels\n",
    "    \"\"\"\n",
    "    \n",
    "    new_dict = {}\n",
    "    for param in a_series.index:\n",
    "        new_dict.update({change_names[param]: a_series[param]})\n",
    "    return pd.Series(new_dict)\n",
    "\n",
    "def createSummaryTableIndex(unit_label, language=\"en\"):\n",
    "    \"\"\"Assigns the current units to the keys and creates\n",
    "    custom key values based on language selection.\n",
    "\n",
    "    :param unit_label: The current value of unit label\n",
    "    :param language: the two letter abbreviation for the current language\n",
    "    :return: the pd.describe dict with custom labels\n",
    "    \"\"\"\n",
    "    \n",
    "    if language == \"en\":\n",
    "        new_data = {\"count\": \"# samples\",\n",
    "                    \"mean\": f\"average {unit_label}\",\n",
    "                    \"std\": \"standard deviation\",\n",
    "                    \"min\": f\"min {unit_label}\",\n",
    "                    \"max\": f\"max {unit_label}\",\n",
    "                    \"25%\": \"25%\",\n",
    "                    \"50%\": \"50%\",\n",
    "                    \"75%\": \"75%\",\n",
    "                    \"total objects\": \"total objects\",\n",
    "                    \"# locations\": \"# locations\",\n",
    "                    }\n",
    "    elif language == \"de\":\n",
    "        new_data = {\"count\": \"Erhebungen\",\n",
    "                    \"mean\": f\"Durchschnitt {unit_label}\",\n",
    "                    \"std\": \"Standardabweichung\",\n",
    "                    \"min\": f\"min {unit_label}\",\n",
    "                    \"max\": f\"max {unit_label}\",\n",
    "                    \"25%\": \"25%\",\n",
    "                    \"50%\": \"50%\",\n",
    "                    \"75%\": \"75%\",\n",
    "                    \"total objects\": \"Abfallobjekte\",\n",
    "                    \"# locations\": \"Anzahl der Standorte\",\n",
    "                    }\n",
    "    else:\n",
    "        print(f\"ERROR {language} is not an option\")\n",
    "        new_data = {}\n",
    "    \n",
    "    return new_data\n",
    "\n",
    "def makeDailyTotalSummary(\n",
    "    sample_totals: pd.DataFrame = None, unit_label: str = None, language: str = None):\n",
    "\n",
    "    # the summary of the dependent variable\n",
    "    a = sample_totals[unit_label].describe().round(2)\n",
    "    a[\"total objects\"] = sample_totals.quantity.sum()\n",
    "\n",
    "    # assign appropriate language to index names\n",
    "    # retrieve the appropriate index names based on language\n",
    "    table_index = createSummaryTableIndex(unit_label, language=language)\n",
    "\n",
    "    # assign the new index\n",
    "    summary_table = changeSeriesIndexLabels(a, table_index)\n",
    "\n",
    "    return summary_table\n",
    "\n",
    "def codeSummary(\n",
    "    feature_data: pd.DataFrame = None, unit_label: str = None, column_operations=None,\n",
    "    material_map: pd.DataFrame = None, description_map: pd.DataFrame = None):\n",
    "\n",
    "        \n",
    "    if column_operations is None:\n",
    "        column_operations = {\n",
    "            \"column_operations\": [(unit_label, \"median\"), (\"quantity\", \"sum\")],\n",
    "            \"columns\": [\"code\"],\n",
    "            \"unit_label\": unit_label\n",
    "        }\n",
    "\n",
    "    columns, column_operation = columnsAndOperations(**column_operations)\n",
    "\n",
    "    # apply the column operations\n",
    "    code_totals = feature_data.groupby(columns, as_index=False).agg(column_operation)\n",
    "    # percent of total\n",
    "    code_totals[\"% of total\"] = ((code_totals.quantity / code_totals.quantity.sum()) * 100).round(2)\n",
    "    # fail and fail-rate\n",
    "    code_totals[\"fail\"] = code_totals.code.map(lambda x: feature_data[\n",
    "        (feature_data.code == x) & (feature_data.quantity > 0)].loc_date.nunique())\n",
    "    \n",
    "    code_totals[\"fail rate\"] = ((code_totals.fail / feature_data.loc_date.nunique()) * 100).astype(\"int\")\n",
    "\n",
    "    # the code data comes from the feature data (survey results)\n",
    "    # Add the description of the code and the material\n",
    "    code_totals.set_index(columns, inplace=True)\n",
    "    code_totals[\"item\"] = code_totals.index.map(lambda x: description_map[x])\n",
    "    code_totals[\"material\"] = code_totals.index.map(lambda x: material_map[x])\n",
    "\n",
    "    code_totals = code_totals[[\"item\", \"quantity\", \"% of total\", unit_label, \"fail rate\", \"material\"]]\n",
    "\n",
    "    return code_totals\n",
    "\n",
    "def materialSummary(code_summary: pd.DataFrame = None):\n",
    "    a = code_summary.groupby(\"material\", as_index=False).quantity.sum()\n",
    "    a[\"% of total\"] = a['quantity'] / a['quantity'].sum()\n",
    "    b = a.sort_values(by=\"quantity\", ascending=False)\n",
    "    \n",
    "    return b\n",
    "\n",
    "def mostCommon(code_summary: pd.DataFrame = None, fail_rate: int = None, limit: int = 10):\n",
    "    # the top ten by quantity\n",
    "    most_abundant = code_summary.sort_values(by=\"quantity\", ascending=False)[:limit]\n",
    "    # the most common\n",
    "    most_common = code_summary[code_summary[\"fail rate\"] >= fail_rate].sort_values(by=\"quantity\", ascending=False)\n",
    "    # merge with most_common and drop duplicates\n",
    "    # it is possible (likely) that a code will be abundant and common\n",
    "    m_common = pd.concat([most_abundant, most_common]).drop_duplicates()\n",
    "\n",
    "    return m_common\n",
    "\n",
    "s_table_kwargs = {\n",
    "    \"sample_totals\": fdt_samples,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"language\": language\n",
    "}\n",
    "\n",
    "code_summary_kwargs = {\n",
    "    \"feature_data\": fdt,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"column_operations\": None,\n",
    "    \"description_map\": description_map,\n",
    "    \"material_map\": material_map\n",
    "}\n",
    "\n",
    "\n",
    "code_summary = codeSummary(**code_summary_kwargs)\n",
    "cs = makeDailyTotalSummary(**s_table_kwargs)\n",
    "\n",
    "combined_summary =[(x, featuredata.thousandsSeparator(int(cs[x]), language)) for x in cs.index]\n",
    "\n",
    "\n",
    "# the materials table\n",
    "fd_mat_totals = materialSummary(code_summary=code_summary)\n",
    "fd_mat_totals = featuredata.fmtPctOfTotal(fd_mat_totals, around=0)\n",
    "\n",
    "# applly new column names for printing\n",
    "cols_to_use = {\"material\":\"Material\",\"quantity\":\"Objekte (St.)\", \"% of total\":\"Anteil\"}\n",
    "fd_mat_t = fd_mat_totals[cols_to_use.keys()].values\n",
    "fd_mat_t = [(x[0], featuredata.thousandsSeparator(int(x[1]), language), x[2]) for x in fd_mat_t]\n",
    "\n",
    "# make tables\n",
    "fig, axs = plt.subplots(1,2)\n",
    "\n",
    "# names for the table columns\n",
    "a_col = [this_feature[\"name\"], \"Total\"]\n",
    "\n",
    "axone = axs[0]\n",
    "sut.hide_spines_ticks_grids(axone)\n",
    "\n",
    "table_two = sut.make_a_table(axone, combined_summary,  colLabels=a_col, colWidths=[.75,.25],  bbox=[0,0,1,1], **{\"loc\":\"lower center\"})\n",
    "table_two.get_celld()[(0,0)].get_text().set_text(\" \")\n",
    "table_two.set_fontsize(12)\n",
    "\n",
    "# material table\n",
    "axtwo = axs[1]\n",
    "axtwo.set_xlabel(\" \")\n",
    "sut.hide_spines_ticks_grids(axtwo)\n",
    "\n",
    "table_three = sut.make_a_table(axtwo, fd_mat_t,  colLabels=list(cols_to_use.values()), colWidths=[.4, .4,.2],  bbox=[0,0,1,1], **{\"loc\":\"lower center\"})\n",
    "table_three.get_celld()[(0,0)].get_text().set_text(\" \")\n",
    "table_three.set_fontsize(12)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.1)\n",
    "# figure caption\n",
    "summary_of_survey_totals = [\n",
    "    \"Links: Zusammenfassung der erhebungen entlang der Wanderwege. \",\n",
    "    \"Rechts: Aufschlüsselung nach Materialart (Stückzahlen und prozentuale Verteilung).\"\n",
    "]\n",
    "\n",
    "summary_of_survey_totals = ''.join(summary_of_survey_totals)\n",
    "glue(f'{this_feature[\"slug\"]}_sample_summaries_caption', summary_of_survey_totals, display=False)\n",
    "\n",
    "figure_name = f'{this_feature[\"slug\"]}_sample_summaries'\n",
    "sample_summaries_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":sample_summaries_file_name})\n",
    "\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "glue('les-alpes_sample_material_tables', fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_sample_material_tables\n",
    "---\n",
    "name: 'les-alpes_sample_material_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "\n",
    "{numref}`Abbildung %s: <les-alpes_sample_material_tables>` {glue:text}`les-alpes_sample_summaries_caption`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "figure_kwargs = {\n",
    "    \"image_file\": sample_totals_file_name,\n",
    "    \"caption\": Paragraph(sample_total_notes, featuredata.caption_style),\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\": 1.7,\n",
    "    \"original_height\": 5,\n",
    "    \"original_width\": 10,\n",
    "}\n",
    "\n",
    "s_section_title = Paragraph(\"Verteilung der Erhebungsergebnisse\", featuredata.subsection_title)\n",
    "\n",
    "s_figure_and_caption = featuredata.figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    s_section_title,\n",
    "    smallspace,\n",
    "    s_figure_and_caption,\n",
    "    smallspace\n",
    "]\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\": sample_summaries_file_name,\n",
    "    \"caption\":  Paragraph(summary_of_survey_totals, featuredata.caption_style),\n",
    "    \"desired_width\": 12,\n",
    "    \"original_height\": 11,\n",
    "    \"original_width\": 15.24,\n",
    "    \"caption_height\": 1,\n",
    "    \"style\": featuredata.figure_table_style\n",
    "}\n",
    "\n",
    "m_section_title = Paragraph(\"Zusammenfassende Daten und Materialtypen\", featuredata.subsection_title)\n",
    "\n",
    "m_figure_and_caption = featuredata.figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [*new_components, m_section_title, smallspace, m_figure_and_caption, PageBreak()]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Die am häufigsten gefundenen Objekte\n",
    "\n",
    "Die am häufigsten gefundenen Objekte sind die zehn mengenmässig am meisten vorkommenden Objekte und/oder Objekte, die in mindestens 50 % aller Erhebungen identifiziert wurden (Häufigkeitsrate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_e4b0a tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_e4b0a tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_e4b0a tr {\n  font-size: 12px;\n}\n#T_e4b0a th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_e4b0a td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_e4b0a\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_e4b0a_level0_col0\" class=\"col_heading level0 col0\" >Objekte (St.)</th>\n      <th id=\"T_e4b0a_level0_col1\" class=\"col_heading level0 col1\" >Anteil</th>\n      <th id=\"T_e4b0a_level0_col2\" class=\"col_heading level0 col2\" >Häufigkeitsrate</th>\n      <th id=\"T_e4b0a_level0_col3\" class=\"col_heading level0 col3\" >p/100 m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e4b0a_level0_row0\" class=\"row_heading level0 row0\" >Seilbahnbürsten</th>\n      <td id=\"T_e4b0a_row0_col0\" class=\"data row0 col0\" >461</td>\n      <td id=\"T_e4b0a_row0_col1\" class=\"data row0 col1\" >20%</td>\n      <td id=\"T_e4b0a_row0_col2\" class=\"data row0 col2\" >5%</td>\n      <td id=\"T_e4b0a_row0_col3\" class=\"data row0 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row1\" class=\"row_heading level0 row1\" >Zigarettenfilter</th>\n      <td id=\"T_e4b0a_row1_col0\" class=\"data row1 col0\" >270</td>\n      <td id=\"T_e4b0a_row1_col1\" class=\"data row1 col1\" >12%</td>\n      <td id=\"T_e4b0a_row1_col2\" class=\"data row1 col2\" >94%</td>\n      <td id=\"T_e4b0a_row1_col3\" class=\"data row1 col3\" >11,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row2\" class=\"row_heading level0 row2\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_e4b0a_row2_col0\" class=\"data row2 col0\" >222</td>\n      <td id=\"T_e4b0a_row2_col1\" class=\"data row2 col1\" >10%</td>\n      <td id=\"T_e4b0a_row2_col2\" class=\"data row2 col2\" >100%</td>\n      <td id=\"T_e4b0a_row2_col3\" class=\"data row2 col3\" >12,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row3\" class=\"row_heading level0 row3\" >Snack-Verpackungen</th>\n      <td id=\"T_e4b0a_row3_col0\" class=\"data row3 col0\" >187</td>\n      <td id=\"T_e4b0a_row3_col1\" class=\"data row3 col1\" >8%</td>\n      <td id=\"T_e4b0a_row3_col2\" class=\"data row3 col2\" >100%</td>\n      <td id=\"T_e4b0a_row3_col3\" class=\"data row3 col3\" >11,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row4\" class=\"row_heading level0 row4\" >Schrauben und Bolzen</th>\n      <td id=\"T_e4b0a_row4_col0\" class=\"data row4 col0\" >81</td>\n      <td id=\"T_e4b0a_row4_col1\" class=\"data row4 col1\" >3%</td>\n      <td id=\"T_e4b0a_row4_col2\" class=\"data row4 col2\" >64%</td>\n      <td id=\"T_e4b0a_row4_col3\" class=\"data row4 col3\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row5\" class=\"row_heading level0 row5\" >Kabelbinder</th>\n      <td id=\"T_e4b0a_row5_col0\" class=\"data row5 col0\" >73</td>\n      <td id=\"T_e4b0a_row5_col1\" class=\"data row5 col1\" >3%</td>\n      <td id=\"T_e4b0a_row5_col2\" class=\"data row5 col2\" >64%</td>\n      <td id=\"T_e4b0a_row5_col3\" class=\"data row5 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row6\" class=\"row_heading level0 row6\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_e4b0a_row6_col0\" class=\"data row6 col0\" >67</td>\n      <td id=\"T_e4b0a_row6_col1\" class=\"data row6 col1\" >3%</td>\n      <td id=\"T_e4b0a_row6_col2\" class=\"data row6 col2\" >41%</td>\n      <td id=\"T_e4b0a_row6_col3\" class=\"data row6 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row7\" class=\"row_heading level0 row7\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_e4b0a_row7_col0\" class=\"data row7 col0\" >67</td>\n      <td id=\"T_e4b0a_row7_col1\" class=\"data row7 col1\" >3%</td>\n      <td id=\"T_e4b0a_row7_col2\" class=\"data row7 col2\" >64%</td>\n      <td id=\"T_e4b0a_row7_col3\" class=\"data row7 col3\" >2,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row8\" class=\"row_heading level0 row8\" >Andere Kunststoff</th>\n      <td id=\"T_e4b0a_row8_col0\" class=\"data row8 col0\" >60</td>\n      <td id=\"T_e4b0a_row8_col1\" class=\"data row8 col1\" >2%</td>\n      <td id=\"T_e4b0a_row8_col2\" class=\"data row8 col2\" >5%</td>\n      <td id=\"T_e4b0a_row8_col3\" class=\"data row8 col3\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_e4b0a_level0_row9\" class=\"row_heading level0 row9\" >Abdeckklebeband/Verpackungsklebeband</th>\n      <td id=\"T_e4b0a_row9_col0\" class=\"data row9 col0\" >48</td>\n      <td id=\"T_e4b0a_row9_col1\" class=\"data row9 col1\" >2%</td>\n      <td id=\"T_e4b0a_row9_col2\" class=\"data row9 col2\" >64%</td>\n      <td id=\"T_e4b0a_row9_col3\" class=\"data row9 col3\" >3,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5443a869d0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_most_common_tables"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/plain": "'Häufigste Objekte auf Wanderwegen: Objekte mit einer Häufigkeitsrate von mindestens 50 % und/oder die 10 häufigsten Objekte. Zusammengenommen stellen die zehn häufigsten Objekte 69 % aller gefundenen Objekte dar. p/100 m: Medianwert der Erhebung.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_most_common_caption"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcc = mostCommon(code_summary=code_summary, fail_rate=fail_rate)\n",
    "mcc[\"item\"] = mcc.index.map(lambda x: description_map.loc[x])\n",
    "\n",
    "# make a copy for string formatting\n",
    "most_common = mcc.copy()\n",
    "\n",
    "aformatter = {\n",
    "    \"Anteil\":lambda x: f\"{int(x)}%\",\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"Häufigkeitsrate\": lambda x: f\"{int(x)}%\",   \n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\")\n",
    "}\n",
    "\n",
    "cols_to_use = featuredata.most_common_objects_table_de\n",
    "cols_to_use.update({unit_label:unit_label})\n",
    "table_data = most_common[cols_to_use.keys()].copy()\n",
    "table_data.rename(columns=cols_to_use, inplace=True)\n",
    "table_data.set_index(\"Objekte\", inplace=True)\n",
    "mcd =table_data.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "\n",
    "# .pdf output\n",
    "data = table_data.copy()\n",
    "data[\"Anteil\"] = data[\"Anteil\"].map(lambda x: f\"{int(x)}%\")\n",
    "data['Objekte (St.)'] = data['Objekte (St.)'].map(lambda x:featuredata.thousandsSeparator(x, language))\n",
    "data['Häufigkeitsrate'] = data['Häufigkeitsrate'].map(lambda x: f\"{x}%\")\n",
    "data[unit_label] = data[unit_label].map(lambda x: featuredata.replaceDecimal(round(x,1)))\n",
    "\n",
    "# make caption\n",
    "# get percent of total to make the caption string\n",
    "mc_caption_string = [\n",
    "    \"Häufigste Objekte auf Wanderwegen: Objekte mit einer Häufigkeitsrate von mindestens 50 % \",\n",
    "    \"und/oder die 10 häufigsten Objekte. Zusammengenommen stellen die zehn häufigsten Objekte 69 % \",\n",
    "    \"aller gefundenen Objekte dar. p/100 m: Medianwert der Erhebung.\"\n",
    "]\n",
    "\n",
    "mc_caption_string = \"\".join(mc_caption_string)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_most_common_tables', mcd, display=False)\n",
    "glue(f'{this_feature[\"slug\"]}_most_common_caption', mc_caption_string, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_most_common_tables\n",
    "---\n",
    "name: 'les-alpes_most_common_tables'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <les-alpes_most_common_tables>` {glue:text}`les-alpes_most_common_caption`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "mc_section_title = Paragraph(\"Die am häufigsten gefundenen Gegenstände\", featuredata.section_title)\n",
    "para_g =  [\n",
    "    \"Die am häufigsten gefundenen Objekte sind die zehn mengenmässig am meisten \",\n",
    "    f\"vorkommenden Objekte und/oder Objekte, die in mindestens 50 % aller \",\n",
    "    \"Datenerhebungen identifiziert wurden (Häufigkeitsrate)\"\n",
    "]\n",
    "\n",
    "para_g = ''.join(para_g)\n",
    "mc_section_para = Paragraph(para_g, featuredata.p_style)\n",
    "\n",
    "mc_caption_string = \"\".join(mc_caption_string)\n",
    "col_widths = [4.5*cm, 2.2*cm, 2*cm, 2.8*cm, 2*cm]\n",
    "\n",
    "mc_table = featuredata.aSingleStyledTable(data, colWidths=col_widths)\n",
    "mc_table_caption = Paragraph(mc_caption_string, featuredata.caption_style)\n",
    "\n",
    "t_and_c = featuredata.tableAndCaption(mc_table, mc_table_caption,col_widths)\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        mc_section_title,\n",
    "        featuredata.small_space,\n",
    "        mc_section_para\n",
    "    ]),\n",
    "    featuredata.small_space,\n",
    "    t_and_c\n",
    "       \n",
    "]\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Die am häufigsten gefundenen Gegenstände nach Erhebungsort   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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#000000;\n}\n#T_26a8c_row0_col2, #T_26a8c_row0_col3, #T_26a8c_row0_col4, #T_26a8c_row0_col8, #T_26a8c_row0_col9, #T_26a8c_row0_col11, #T_26a8c_row0_col12, #T_26a8c_row0_col13, #T_26a8c_row0_col15, #T_26a8c_row0_col16, #T_26a8c_row0_col17, #T_26a8c_row1_col1, #T_26a8c_row1_col4, #T_26a8c_row1_col5, #T_26a8c_row1_col6, #T_26a8c_row1_col8, #T_26a8c_row1_col9, #T_26a8c_row1_col11, #T_26a8c_row1_col14, #T_26a8c_row1_col18, #T_26a8c_row2_col0, #T_26a8c_row2_col1, #T_26a8c_row2_col2, #T_26a8c_row2_col3, #T_26a8c_row2_col4, #T_26a8c_row2_col5, #T_26a8c_row2_col6, #T_26a8c_row2_col7, #T_26a8c_row2_col8, #T_26a8c_row2_col9, #T_26a8c_row2_col10, #T_26a8c_row2_col11, #T_26a8c_row2_col12, #T_26a8c_row2_col14, #T_26a8c_row2_col15, #T_26a8c_row2_col16, #T_26a8c_row2_col17, #T_26a8c_row2_col18, #T_26a8c_row3_col6, #T_26a8c_row3_col8, #T_26a8c_row4_col1, #T_26a8c_row4_col5, #T_26a8c_row4_col6, #T_26a8c_row4_col7, #T_26a8c_row4_col8, #T_26a8c_row4_col14, #T_26a8c_row4_col16, #T_26a8c_row4_col18, #T_26a8c_row5_col1, #T_26a8c_row5_col5, #T_26a8c_row5_col6, #T_26a8c_row5_col7, #T_26a8c_row5_col9, #T_26a8c_row5_col11, #T_26a8c_row5_col16, #T_26a8c_row5_col18, #T_26a8c_row6_col0, #T_26a8c_row6_col1, #T_26a8c_row6_col2, #T_26a8c_row6_col3, #T_26a8c_row6_col4, #T_26a8c_row6_col5, #T_26a8c_row6_col6, #T_26a8c_row6_col7, #T_26a8c_row6_col9, #T_26a8c_row6_col10, #T_26a8c_row6_col11, #T_26a8c_row6_col12, #T_26a8c_row6_col13, #T_26a8c_row6_col14, #T_26a8c_row6_col15, #T_26a8c_row6_col16, #T_26a8c_row6_col17, #T_26a8c_row6_col18, #T_26a8c_row7_col1, #T_26a8c_row7_col14, #T_26a8c_row8_col1, #T_26a8c_row8_col3, #T_26a8c_row8_col6, #T_26a8c_row8_col8, #T_26a8c_row8_col12, #T_26a8c_row8_col13, #T_26a8c_row8_col14, #T_26a8c_row8_col16, #T_26a8c_row8_col18, #T_26a8c_row9_col8 {\n  background-color: #ffffe5;\n  color: #000000;\n}\n#T_26a8c_row0_col6 {\n  background-color: #fffbd0;\n  color: #000000;\n}\n#T_26a8c_row0_col14, #T_26a8c_row3_col15, #T_26a8c_row7_col13, #T_26a8c_row8_col2 {\n  background-color: #fffbd3;\n  color: #000000;\n}\n#T_26a8c_row1_col0, #T_26a8c_row9_col18 {\n  background-color: #fffcd8;\n  color: #000000;\n}\n#T_26a8c_row1_col2, #T_26a8c_row7_col12, #T_26a8c_row9_col3, #T_26a8c_row9_col14 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_26a8c_row1_col3, #T_26a8c_row3_col11, #T_26a8c_row3_col14, #T_26a8c_row3_col16, #T_26a8c_row8_col7, #T_26a8c_row9_col6 {\n  background-color: #fffee2;\n  color: #000000;\n}\n#T_26a8c_row1_col7, #T_26a8c_row7_col7, #T_26a8c_row7_col17, #T_26a8c_row8_col10, #T_26a8c_row9_col16, #T_26a8c_row9_col17 {\n  background-color: #fffedf;\n  color: #000000;\n}\n#T_26a8c_row1_col10, #T_26a8c_row1_col13, #T_26a8c_row3_col4, #T_26a8c_row3_col5, #T_26a8c_row3_col17, #T_26a8c_row5_col8, #T_26a8c_row5_col12, #T_26a8c_row7_col10, #T_26a8c_row8_col15, #T_26a8c_row9_col10 {\n  background-color: #fffddd;\n  color: #000000;\n}\n#T_26a8c_row1_col15, #T_26a8c_row4_col0, #T_26a8c_row4_col15, #T_26a8c_row5_col3, #T_26a8c_row7_col3, #T_26a8c_row7_col18, #T_26a8c_row9_col7 {\n  background-color: #fffee0;\n  color: #000000;\n}\n#T_26a8c_row2_col13 {\n  background-color: #feeca5;\n  color: #000000;\n}\n#T_26a8c_row3_col0 {\n  background-color: #fff4b6;\n  color: #000000;\n}\n#T_26a8c_row3_col2 {\n  background-color: #fed778;\n  color: #000000;\n}\n#T_26a8c_row3_col3, #T_26a8c_row4_col12, #T_26a8c_row5_col2, #T_26a8c_row7_col8, #T_26a8c_row7_col16 {\n  background-color: #fffee1;\n  color: #000000;\n}\n#T_26a8c_row3_col7 {\n  background-color: #fffcd7;\n  color: #000000;\n}\n#T_26a8c_row3_col10 {\n  background-color: #fff5b8;\n  color: #000000;\n}\n#T_26a8c_row3_col12 {\n  background-color: #fff9c5;\n  color: #000000;\n}\n#T_26a8c_row3_col13, #T_26a8c_row7_col15, #T_26a8c_row9_col12 {\n  background-color: #fffcd6;\n  color: #000000;\n}\n#T_26a8c_row3_col18 {\n  background-color: #fffdd9;\n  color: #000000;\n}\n#T_26a8c_row4_col2 {\n  background-color: #fffacd;\n  color: #000000;\n}\n#T_26a8c_row4_col10 {\n  background-color: #fffbd2;\n  color: #000000;\n}\n#T_26a8c_row4_col13 {\n  background-color: #fff9c7;\n  color: #000000;\n}\n#T_26a8c_row5_col10, #T_26a8c_row5_col13 {\n  background-color: #fffcd4;\n  color: #000000;\n}\n#T_26a8c_row5_col15 {\n  background-color: #fffaca;\n  color: #000000;\n}\n#T_26a8c_row6_col8 {\n  background-color: #662506;\n  color: #f1f1f1;\n}\n#T_26a8c_row7_col0 {\n  background-color: #fff0ad;\n  color: #000000;\n}\n#T_26a8c_row7_col2 {\n  background-color: #fff7bc;\n  color: #000000;\n}\n#T_26a8c_row7_col4 {\n  background-color: #fff4b5;\n  color: #000000;\n}\n#T_26a8c_row7_col5, #T_26a8c_row8_col4 {\n  background-color: #fffddc;\n  color: #000000;\n}\n#T_26a8c_row8_col0 {\n  background-color: #fff3b4;\n  color: #000000;\n}\n#T_26a8c_row9_col0 {\n  background-color: #fff6b9;\n  color: #000000;\n}\n#T_26a8c_row9_col2 {\n  background-color: #fff8c4;\n  color: #000000;\n}\n#T_26a8c_row9_col4 {\n  background-color: #fee28e;\n  color: #000000;\n}\n#T_26a8c_row9_col15 {\n  background-color: #fed471;\n  color: #000000;\n}\n#T_26a8c_level0_col0, #T_26a8c_level0_col1, #T_26a8c_level0_col2, #T_26a8c_level0_col3, #T_26a8c_level0_col4, #T_26a8c_level0_col5, #T_26a8c_level0_col6, #T_26a8c_level0_col7, #T_26a8c_level0_col8, #T_26a8c_level0_col9, #T_26a8c_level0_col10, #T_26a8c_level0_col11, #T_26a8c_level0_col12, #T_26a8c_level0_col13, #T_26a8c_level0_col14, #T_26a8c_level0_col15, #T_26a8c_level0_col16, #T_26a8c_level0_col17, #T_26a8c_level0_col18 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_26a8c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_26a8c_level0_col0\" class=\"col_heading level0 col0\" >Airolo</th>\n      <th id=\"T_26a8c_level0_col1\" class=\"col_heading level0 col1\" >Andermatt</th>\n      <th id=\"T_26a8c_level0_col2\" class=\"col_heading level0 col2\" >Charmey</th>\n      <th id=\"T_26a8c_level0_col3\" class=\"col_heading level0 col3\" >Crans-Montana</th>\n      <th id=\"T_26a8c_level0_col4\" class=\"col_heading level0 col4\" >Grindelwald</th>\n      <th id=\"T_26a8c_level0_col5\" class=\"col_heading level0 col5\" >La Berra</th>\n      <th id=\"T_26a8c_level0_col6\" class=\"col_heading level0 col6\" >Robella</th>\n      <th id=\"T_26a8c_level0_col7\" class=\"col_heading level0 col7\" >La Tzoumaz</th>\n      <th id=\"T_26a8c_level0_col8\" class=\"col_heading level0 col8\" >Tour Les Crosets</th>\n      <th id=\"T_26a8c_level0_col9\" class=\"col_heading level0 col9\" >Les Diablerets</th>\n      <th id=\"T_26a8c_level0_col10\" class=\"col_heading level0 col10\" >Les Paccots</th>\n      <th id=\"T_26a8c_level0_col11\" class=\"col_heading level0 col11\" >Monte Generoso</th>\n      <th id=\"T_26a8c_level0_col12\" class=\"col_heading level0 col12\" >Morgins</th>\n      <th id=\"T_26a8c_level0_col13\" class=\"col_heading level0 col13\" >Nendaz</th>\n      <th id=\"T_26a8c_level0_col14\" class=\"col_heading level0 col14\" >Val Calanca</th>\n      <th id=\"T_26a8c_level0_col15\" class=\"col_heading level0 col15\" >Verbier</th>\n      <th id=\"T_26a8c_level0_col16\" class=\"col_heading level0 col16\" >Villars</th>\n      <th id=\"T_26a8c_level0_col17\" class=\"col_heading level0 col17\" >Alpen und Jura</th>\n      <th id=\"T_26a8c_level0_col18\" class=\"col_heading level0 col18\" >IQAASL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_26a8c_level0_row0\" class=\"row_heading level0 row0\" > Getränkeflaschen aus Glas, Glasfragmente</th>\n      <td id=\"T_26a8c_row0_col0\" class=\"data row0 col0\" >34,0</td>\n      <td id=\"T_26a8c_row0_col1\" class=\"data row0 col1\" >2,0</td>\n      <td id=\"T_26a8c_row0_col2\" class=\"data row0 col2\" >0,0</td>\n      <td id=\"T_26a8c_row0_col3\" class=\"data row0 col3\" >0,0</td>\n      <td id=\"T_26a8c_row0_col4\" class=\"data row0 col4\" >0,0</td>\n      <td id=\"T_26a8c_row0_col5\" class=\"data row0 col5\" >3,0</td>\n      <td id=\"T_26a8c_row0_col6\" class=\"data row0 col6\" >31,0</td>\n      <td id=\"T_26a8c_row0_col7\" class=\"data row0 col7\" >3,0</td>\n      <td id=\"T_26a8c_row0_col8\" class=\"data row0 col8\" >0,0</td>\n      <td id=\"T_26a8c_row0_col9\" class=\"data row0 col9\" >0,0</td>\n      <td id=\"T_26a8c_row0_col10\" class=\"data row0 col10\" >2,0</td>\n      <td id=\"T_26a8c_row0_col11\" class=\"data row0 col11\" >0,0</td>\n      <td id=\"T_26a8c_row0_col12\" class=\"data row0 col12\" >0,0</td>\n      <td id=\"T_26a8c_row0_col13\" class=\"data row0 col13\" >0,0</td>\n      <td id=\"T_26a8c_row0_col14\" class=\"data row0 col14\" >28,0</td>\n      <td id=\"T_26a8c_row0_col15\" class=\"data row0 col15\" >0,0</td>\n      <td id=\"T_26a8c_row0_col16\" class=\"data row0 col16\" >0,0</td>\n      <td id=\"T_26a8c_row0_col17\" class=\"data row0 col17\" >0,0</td>\n      <td id=\"T_26a8c_row0_col18\" class=\"data row0 col18\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row1\" class=\"row_heading level0 row1\" >Abdeckklebeband/Verpackungsklebeband</th>\n      <td id=\"T_26a8c_row1_col0\" class=\"data row1 col0\" >21,0</td>\n      <td id=\"T_26a8c_row1_col1\" class=\"data row1 col1\" >1,0</td>\n      <td id=\"T_26a8c_row1_col2\" class=\"data row1 col2\" >17,0</td>\n      <td id=\"T_26a8c_row1_col3\" class=\"data row1 col3\" >5,0</td>\n      <td id=\"T_26a8c_row1_col4\" class=\"data row1 col4\" >0,0</td>\n      <td id=\"T_26a8c_row1_col5\" class=\"data row1 col5\" >0,0</td>\n      <td id=\"T_26a8c_row1_col6\" class=\"data row1 col6\" >1,0</td>\n      <td id=\"T_26a8c_row1_col7\" class=\"data row1 col7\" >10,0</td>\n      <td id=\"T_26a8c_row1_col8\" class=\"data row1 col8\" >0,0</td>\n      <td id=\"T_26a8c_row1_col9\" class=\"data row1 col9\" >0,0</td>\n      <td id=\"T_26a8c_row1_col10\" class=\"data row1 col10\" >12,0</td>\n      <td id=\"T_26a8c_row1_col11\" class=\"data row1 col11\" >0,0</td>\n      <td id=\"T_26a8c_row1_col12\" class=\"data row1 col12\" >3,0</td>\n      <td id=\"T_26a8c_row1_col13\" class=\"data row1 col13\" >12,0</td>\n      <td id=\"T_26a8c_row1_col14\" class=\"data row1 col14\" >0,0</td>\n      <td id=\"T_26a8c_row1_col15\" class=\"data row1 col15\" >8,0</td>\n      <td id=\"T_26a8c_row1_col16\" class=\"data row1 col16\" >3,0</td>\n      <td id=\"T_26a8c_row1_col17\" class=\"data row1 col17\" >3,0</td>\n      <td id=\"T_26a8c_row1_col18\" class=\"data row1 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row2\" class=\"row_heading level0 row2\" >Andere Kunststoff</th>\n      <td id=\"T_26a8c_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_26a8c_row2_col1\" class=\"data row2 col1\" >0,0</td>\n      <td id=\"T_26a8c_row2_col2\" class=\"data row2 col2\" >0,0</td>\n      <td id=\"T_26a8c_row2_col3\" class=\"data row2 col3\" >0,0</td>\n      <td id=\"T_26a8c_row2_col4\" class=\"data row2 col4\" >0,0</td>\n      <td id=\"T_26a8c_row2_col5\" class=\"data row2 col5\" >0,0</td>\n      <td id=\"T_26a8c_row2_col6\" class=\"data row2 col6\" >0,0</td>\n      <td id=\"T_26a8c_row2_col7\" class=\"data row2 col7\" >0,0</td>\n      <td id=\"T_26a8c_row2_col8\" class=\"data row2 col8\" >0,0</td>\n      <td id=\"T_26a8c_row2_col9\" class=\"data row2 col9\" >0,0</td>\n      <td id=\"T_26a8c_row2_col10\" class=\"data row2 col10\" >0,0</td>\n      <td id=\"T_26a8c_row2_col11\" class=\"data row2 col11\" >0,0</td>\n      <td id=\"T_26a8c_row2_col12\" class=\"data row2 col12\" >0,0</td>\n      <td id=\"T_26a8c_row2_col13\" class=\"data row2 col13\" >94,0</td>\n      <td id=\"T_26a8c_row2_col14\" class=\"data row2 col14\" >0,0</td>\n      <td id=\"T_26a8c_row2_col15\" class=\"data row2 col15\" >0,0</td>\n      <td id=\"T_26a8c_row2_col16\" class=\"data row2 col16\" >0,0</td>\n      <td id=\"T_26a8c_row2_col17\" class=\"data row2 col17\" >0,0</td>\n      <td id=\"T_26a8c_row2_col18\" class=\"data row2 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row3\" class=\"row_heading level0 row3\" >Fragmentierte Kunststoffe</th>\n      <td id=\"T_26a8c_row3_col0\" class=\"data row3 col0\" >69,0</td>\n      <td id=\"T_26a8c_row3_col1\" class=\"data row3 col1\" >3,0</td>\n      <td id=\"T_26a8c_row3_col2\" class=\"data row3 col2\" >147,0</td>\n      <td id=\"T_26a8c_row3_col3\" class=\"data row3 col3\" >7,0</td>\n      <td id=\"T_26a8c_row3_col4\" class=\"data row3 col4\" >12,0</td>\n      <td id=\"T_26a8c_row3_col5\" class=\"data row3 col5\" >12,0</td>\n      <td id=\"T_26a8c_row3_col6\" class=\"data row3 col6\" >1,0</td>\n      <td id=\"T_26a8c_row3_col7\" class=\"data row3 col7\" >22,0</td>\n      <td id=\"T_26a8c_row3_col8\" class=\"data row3 col8\" >1,0</td>\n      <td id=\"T_26a8c_row3_col9\" class=\"data row3 col9\" >3,0</td>\n      <td id=\"T_26a8c_row3_col10\" class=\"data row3 col10\" >67,0</td>\n      <td id=\"T_26a8c_row3_col11\" class=\"data row3 col11\" >4,0</td>\n      <td id=\"T_26a8c_row3_col12\" class=\"data row3 col12\" >49,0</td>\n      <td id=\"T_26a8c_row3_col13\" class=\"data row3 col13\" >23,0</td>\n      <td id=\"T_26a8c_row3_col14\" class=\"data row3 col14\" >5,0</td>\n      <td id=\"T_26a8c_row3_col15\" class=\"data row3 col15\" >28,0</td>\n      <td id=\"T_26a8c_row3_col16\" class=\"data row3 col16\" >4,0</td>\n      <td id=\"T_26a8c_row3_col17\" class=\"data row3 col17\" >12,0</td>\n      <td id=\"T_26a8c_row3_col18\" class=\"data row3 col18\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row4\" class=\"row_heading level0 row4\" >Kabelbinder</th>\n      <td id=\"T_26a8c_row4_col0\" class=\"data row4 col0\" >8,0</td>\n      <td id=\"T_26a8c_row4_col1\" class=\"data row4 col1\" >0,0</td>\n      <td id=\"T_26a8c_row4_col2\" class=\"data row4 col2\" >37,0</td>\n      <td id=\"T_26a8c_row4_col3\" class=\"data row4 col3\" >2,0</td>\n      <td id=\"T_26a8c_row4_col4\" class=\"data row4 col4\" >2,0</td>\n      <td id=\"T_26a8c_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_26a8c_row4_col6\" class=\"data row4 col6\" >0,0</td>\n      <td id=\"T_26a8c_row4_col7\" class=\"data row4 col7\" >0,0</td>\n      <td id=\"T_26a8c_row4_col8\" class=\"data row4 col8\" >0,0</td>\n      <td id=\"T_26a8c_row4_col9\" class=\"data row4 col9\" >2,0</td>\n      <td id=\"T_26a8c_row4_col10\" class=\"data row4 col10\" >29,0</td>\n      <td id=\"T_26a8c_row4_col11\" class=\"data row4 col11\" >3,0</td>\n      <td id=\"T_26a8c_row4_col12\" class=\"data row4 col12\" >7,0</td>\n      <td id=\"T_26a8c_row4_col13\" class=\"data row4 col13\" >45,0</td>\n      <td id=\"T_26a8c_row4_col14\" class=\"data row4 col14\" >0,0</td>\n      <td id=\"T_26a8c_row4_col15\" class=\"data row4 col15\" >8,0</td>\n      <td id=\"T_26a8c_row4_col16\" class=\"data row4 col16\" >1,0</td>\n      <td id=\"T_26a8c_row4_col17\" class=\"data row4 col17\" >2,0</td>\n      <td id=\"T_26a8c_row4_col18\" class=\"data row4 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row5\" class=\"row_heading level0 row5\" >Schrauben und Bolzen</th>\n      <td id=\"T_26a8c_row5_col0\" class=\"data row5 col0\" >3,0</td>\n      <td id=\"T_26a8c_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_26a8c_row5_col2\" class=\"data row5 col2\" >7,0</td>\n      <td id=\"T_26a8c_row5_col3\" class=\"data row5 col3\" >9,0</td>\n      <td id=\"T_26a8c_row5_col4\" class=\"data row5 col4\" >3,0</td>\n      <td id=\"T_26a8c_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_26a8c_row5_col6\" class=\"data row5 col6\" >0,0</td>\n      <td id=\"T_26a8c_row5_col7\" class=\"data row5 col7\" >0,0</td>\n      <td id=\"T_26a8c_row5_col8\" class=\"data row5 col8\" >12,0</td>\n      <td id=\"T_26a8c_row5_col9\" class=\"data row5 col9\" >0,0</td>\n      <td id=\"T_26a8c_row5_col10\" class=\"data row5 col10\" >25,0</td>\n      <td id=\"T_26a8c_row5_col11\" class=\"data row5 col11\" >0,0</td>\n      <td id=\"T_26a8c_row5_col12\" class=\"data row5 col12\" >12,0</td>\n      <td id=\"T_26a8c_row5_col13\" class=\"data row5 col13\" >25,0</td>\n      <td id=\"T_26a8c_row5_col14\" class=\"data row5 col14\" >3,0</td>\n      <td id=\"T_26a8c_row5_col15\" class=\"data row5 col15\" >42,0</td>\n      <td id=\"T_26a8c_row5_col16\" class=\"data row5 col16\" >1,0</td>\n      <td id=\"T_26a8c_row5_col17\" class=\"data row5 col17\" >3,0</td>\n      <td id=\"T_26a8c_row5_col18\" class=\"data row5 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row6\" class=\"row_heading level0 row6\" >Seilbahnbürsten</th>\n      <td id=\"T_26a8c_row6_col0\" class=\"data row6 col0\" >0,0</td>\n      <td id=\"T_26a8c_row6_col1\" class=\"data row6 col1\" >0,0</td>\n      <td id=\"T_26a8c_row6_col2\" class=\"data row6 col2\" >0,0</td>\n      <td id=\"T_26a8c_row6_col3\" class=\"data row6 col3\" >0,0</td>\n      <td id=\"T_26a8c_row6_col4\" class=\"data row6 col4\" >0,0</td>\n      <td id=\"T_26a8c_row6_col5\" class=\"data row6 col5\" >0,0</td>\n      <td id=\"T_26a8c_row6_col6\" class=\"data row6 col6\" >0,0</td>\n      <td id=\"T_26a8c_row6_col7\" class=\"data row6 col7\" >0,0</td>\n      <td id=\"T_26a8c_row6_col8\" class=\"data row6 col8\" >490,0</td>\n      <td id=\"T_26a8c_row6_col9\" class=\"data row6 col9\" >0,0</td>\n      <td id=\"T_26a8c_row6_col10\" class=\"data row6 col10\" >0,0</td>\n      <td id=\"T_26a8c_row6_col11\" class=\"data row6 col11\" >0,0</td>\n      <td id=\"T_26a8c_row6_col12\" class=\"data row6 col12\" >0,0</td>\n      <td id=\"T_26a8c_row6_col13\" class=\"data row6 col13\" >0,0</td>\n      <td id=\"T_26a8c_row6_col14\" class=\"data row6 col14\" >0,0</td>\n      <td id=\"T_26a8c_row6_col15\" class=\"data row6 col15\" >0,0</td>\n      <td id=\"T_26a8c_row6_col16\" class=\"data row6 col16\" >0,0</td>\n      <td id=\"T_26a8c_row6_col17\" class=\"data row6 col17\" >0,0</td>\n      <td id=\"T_26a8c_row6_col18\" class=\"data row6 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row7\" class=\"row_heading level0 row7\" >Snack-Verpackungen</th>\n      <td id=\"T_26a8c_row7_col0\" class=\"data row7 col0\" >84,0</td>\n      <td id=\"T_26a8c_row7_col1\" class=\"data row7 col1\" >1,0</td>\n      <td id=\"T_26a8c_row7_col2\" class=\"data row7 col2\" >63,0</td>\n      <td id=\"T_26a8c_row7_col3\" class=\"data row7 col3\" >9,0</td>\n      <td id=\"T_26a8c_row7_col4\" class=\"data row7 col4\" >72,0</td>\n      <td id=\"T_26a8c_row7_col5\" class=\"data row7 col5\" >15,0</td>\n      <td id=\"T_26a8c_row7_col6\" class=\"data row7 col6\" >3,0</td>\n      <td id=\"T_26a8c_row7_col7\" class=\"data row7 col7\" >11,0</td>\n      <td id=\"T_26a8c_row7_col8\" class=\"data row7 col8\" >6,0</td>\n      <td id=\"T_26a8c_row7_col9\" class=\"data row7 col9\" >2,0</td>\n      <td id=\"T_26a8c_row7_col10\" class=\"data row7 col10\" >12,0</td>\n      <td id=\"T_26a8c_row7_col11\" class=\"data row7 col11\" >3,0</td>\n      <td id=\"T_26a8c_row7_col12\" class=\"data row7 col12\" >17,0</td>\n      <td id=\"T_26a8c_row7_col13\" class=\"data row7 col13\" >27,0</td>\n      <td id=\"T_26a8c_row7_col14\" class=\"data row7 col14\" >1,0</td>\n      <td id=\"T_26a8c_row7_col15\" class=\"data row7 col15\" >24,0</td>\n      <td id=\"T_26a8c_row7_col16\" class=\"data row7 col16\" >7,0</td>\n      <td id=\"T_26a8c_row7_col17\" class=\"data row7 col17\" >11,0</td>\n      <td id=\"T_26a8c_row7_col18\" class=\"data row7 col18\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row8\" class=\"row_heading level0 row8\" >Verpackungen aus Aluminiumfolie</th>\n      <td id=\"T_26a8c_row8_col0\" class=\"data row8 col0\" >74,0</td>\n      <td id=\"T_26a8c_row8_col1\" class=\"data row8 col1\" >0,0</td>\n      <td id=\"T_26a8c_row8_col2\" class=\"data row8 col2\" >27,0</td>\n      <td id=\"T_26a8c_row8_col3\" class=\"data row8 col3\" >0,0</td>\n      <td id=\"T_26a8c_row8_col4\" class=\"data row8 col4\" >15,0</td>\n      <td id=\"T_26a8c_row8_col5\" class=\"data row8 col5\" >3,0</td>\n      <td id=\"T_26a8c_row8_col6\" class=\"data row8 col6\" >0,0</td>\n      <td id=\"T_26a8c_row8_col7\" class=\"data row8 col7\" >4,0</td>\n      <td id=\"T_26a8c_row8_col8\" class=\"data row8 col8\" >0,0</td>\n      <td id=\"T_26a8c_row8_col9\" class=\"data row8 col9\" >2,0</td>\n      <td id=\"T_26a8c_row8_col10\" class=\"data row8 col10\" >10,0</td>\n      <td id=\"T_26a8c_row8_col11\" class=\"data row8 col11\" >3,0</td>\n      <td id=\"T_26a8c_row8_col12\" class=\"data row8 col12\" >1,0</td>\n      <td id=\"T_26a8c_row8_col13\" class=\"data row8 col13\" >0,0</td>\n      <td id=\"T_26a8c_row8_col14\" class=\"data row8 col14\" >0,0</td>\n      <td id=\"T_26a8c_row8_col15\" class=\"data row8 col15\" >12,0</td>\n      <td id=\"T_26a8c_row8_col16\" class=\"data row8 col16\" >1,0</td>\n      <td id=\"T_26a8c_row8_col17\" class=\"data row8 col17\" >2,0</td>\n      <td id=\"T_26a8c_row8_col18\" class=\"data row8 col18\" >0,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_26a8c_level0_row9\" class=\"row_heading level0 row9\" >Zigarettenfilter</th>\n      <td id=\"T_26a8c_row9_col0\" class=\"data row9 col0\" >66,0</td>\n      <td id=\"T_26a8c_row9_col1\" class=\"data row9 col1\" >3,0</td>\n      <td id=\"T_26a8c_row9_col2\" class=\"data row9 col2\" >50,0</td>\n      <td id=\"T_26a8c_row9_col3\" class=\"data row9 col3\" >16,0</td>\n      <td id=\"T_26a8c_row9_col4\" class=\"data row9 col4\" >125,0</td>\n      <td id=\"T_26a8c_row9_col5\" class=\"data row9 col5\" >2,0</td>\n      <td id=\"T_26a8c_row9_col6\" class=\"data row9 col6\" >5,0</td>\n      <td id=\"T_26a8c_row9_col7\" class=\"data row9 col7\" >9,0</td>\n      <td id=\"T_26a8c_row9_col8\" class=\"data row9 col8\" >0,0</td>\n      <td id=\"T_26a8c_row9_col9\" class=\"data row9 col9\" >2,0</td>\n      <td id=\"T_26a8c_row9_col10\" class=\"data row9 col10\" >12,0</td>\n      <td id=\"T_26a8c_row9_col11\" class=\"data row9 col11\" >3,0</td>\n      <td id=\"T_26a8c_row9_col12\" class=\"data row9 col12\" >24,0</td>\n      <td id=\"T_26a8c_row9_col13\" class=\"data row9 col13\" >3,0</td>\n      <td id=\"T_26a8c_row9_col14\" class=\"data row9 col14\" >17,0</td>\n      <td id=\"T_26a8c_row9_col15\" class=\"data row9 col15\" >152,0</td>\n      <td id=\"T_26a8c_row9_col16\" class=\"data row9 col16\" >11,0</td>\n      <td id=\"T_26a8c_row9_col17\" class=\"data row9 col17\" >11,0</td>\n      <td id=\"T_26a8c_row9_col18\" class=\"data row9 col18\" >20,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5441dfda90>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_survey_area_most_common_heat_map"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def componentMostCommonPcsM(\n",
    "    feature_data: pd.DataFrame = None, most_common: pd.DataFrame = None, columns: list = None, unit_label: str = None,\n",
    "    feature_component: str = None, column_operations: list = None, description_map: pd.DataFrame = None):\n",
    "\n",
    "    codes = most_common.index\n",
    "    \n",
    "    if column_operations is None:        \n",
    "        column_operations = {\n",
    "            \"column_operations\":  [(unit_label, \"sum\"), (\"quantity\", \"sum\")],\n",
    "            \"columns\":  [feature_component, \"loc_date\", \"code\"],\n",
    "            \"unit_label\": unit_label\n",
    "        }\n",
    "\n",
    "    columns, column_operation = columnsAndOperations(**column_operations)\n",
    "\n",
    "    mask = feature_data.code.isin(codes)    \n",
    "\n",
    "    a = feature_data[mask].groupby(columns, as_index=False).agg(column_operation)\n",
    "    a = a.groupby([feature_component, \"code\"], as_index=False)[unit_label].median()\n",
    "\n",
    "    # map the description to the code\n",
    "    a[\"item\"] = a.code.map(lambda x: description_map.loc[x])\n",
    "\n",
    "    return a\n",
    "\n",
    "       \n",
    "def parentMostCommon(parent_data: pd.DataFrame = None, most_common: list = None, unit_label: str = None, \n",
    "                     percent: bool = False, name: str = None):\n",
    "    \n",
    "    data = parent_data[parent_data.code.isin(most_common)]   \n",
    "\n",
    "    if percent:\n",
    "        data = data.groupby('code', as_index=False).quantity.sum()\n",
    "        data.set_index('code', inplace=True)\n",
    "        data[name] = (data.quantity / data.quantity.sum()).round(2)\n",
    "\n",
    "        return data[name]\n",
    "\n",
    "    else:\n",
    "        data = data.groupby([\"loc_date\", 'code'], as_index=False)[unit_label].sum()\n",
    "        data = data.groupby('code', as_index=False)[unit_label].median()\n",
    "        data.set_index('code', inplace=True)\n",
    "        data[name] = data[unit_label]\n",
    "\n",
    "        return data[name]\n",
    "    \n",
    "aggregate_kwargs = {\n",
    "    \"parent_data\": period_data.period_data,\n",
    "    \"most_common\": mcc.index,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"percent\": False,\n",
    "    \"name\":\"IQAASL\",\n",
    "}\n",
    "\n",
    "component_kwargs = {\n",
    "    \"feature_data\": fdt,\n",
    "    \"most_common\": most_common,\n",
    "    \"feature_component\": fdx.feature_component,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"description_map\": description_map,\n",
    "}\n",
    "    \n",
    "# the columns needed to build the heat map\n",
    "heat_map_columns = [\"item\", \"location\", unit_label]\n",
    "\n",
    "# the results for the most common codes at the component level\n",
    "components = componentMostCommonPcsM(**component_kwargs)\n",
    "\n",
    "# pivot that to make a heat map layouy\n",
    "mc_comp = components[heat_map_columns].pivot(columns='location', index='item')\n",
    "\n",
    "# drop any indexing above the component name\n",
    "mc_comp.columns = mc_comp.columns.get_level_values(1)\n",
    "mc_comp.rename(columns={x:proper_names[x] for x in mc_comp.columns}, inplace=True)\n",
    "\n",
    "# switch the index to the item description\n",
    "mc_feature = mcc[[\"item\", unit_label]].set_index(\"item\")\n",
    "\n",
    "# add a column for the results of all the feature data\n",
    "mc_comp[this_feature[\"name\"]] = mc_feature\n",
    "\n",
    "# get the resluts of the feature most common from IQAASL results\n",
    "iqaasl_results = parentMostCommon(**aggregate_kwargs)\n",
    "\n",
    "# replace the code with a description\n",
    "new_labels = {x : description_map[x] for x in iqaasl_results.index}\n",
    "mc_iqaasl = featuredata.changeSeriesIndexLabels(a_series=iqaasl_results, change_names=new_labels)\n",
    "\n",
    "# add a column for the results of all the IQAASL data\n",
    "mc_comp[\"IQAASL\"] = mc_iqaasl\n",
    "\n",
    "caption_prefix =  f\"Verwendungszweck der gefundenen Objekte an Wanderwegen: Median {unit_label} am \"\n",
    "col_widths=[4.5*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "mc_heatmap_title = Paragraph(\"Die am häufigsten gefundenen Gegenstände nach Erhebungsort\", featuredata.subsection_title)\n",
    "mc_heat_map_caption = \"Verwendungszweck der gefundenen Objekte an Wanderwegen: Median p/100 m.\"\n",
    "tables = featuredata.splitTableWidth(mc_comp, gradient=True, caption_prefix=caption_prefix, caption=mc_heat_map_caption,\n",
    "                    this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths)\n",
    "\n",
    "# identify the tables variable as either a list or a Flowable:\n",
    "if isinstance(tables, (list, np.ndarray)):\n",
    "    grouped_pdf_components = [mc_heatmap_title, featuredata.large_space, *tables]\n",
    "else:\n",
    "    grouped_pdf_components = [mc_heatmap_title, featuredata.large_space, tables]    \n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    *grouped_pdf_components\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "aformatter = {x: featuredata.replaceDecimal for x in mc_comp.columns}\n",
    "mcd = mc_comp.style.format(aformatter).set_table_styles(heat_map_css_styles)\n",
    "mcd = mcd.background_gradient(axis=None, vmin=mc_comp.min().min(), vmax=mc_comp.max().max(), cmap=\"YlOrBr\")\n",
    "mcd = mcd.applymap_index(featuredata.rotateText, axis=1)\n",
    "mcd.index.name = None\n",
    "mcd.columns.name = None\n",
    "glue('alpes_survey_area_most_common_heat_map', mcd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_most_common_heat_map\n",
    "---\n",
    "name: 'alpes_survey_area_most_common_heat_map'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "\n",
    "{numref}`Abbildung %s: <alpes_survey_area_most_common_heat_map>` Häufigste Objekte an Wanderwegen: Median p/100 m."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seilbahnbürsten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} resources/images/alpes_valaisanne/veysonnaz_brosse.jpg\n",
    "---\n",
    "name: alpes_survey_area_teleski_brush\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_survey_area_teleski_brush>` Seilbahnbürsten, die verwendet werden, um Eis und Schnee von Skiliften zu entfernen, können sich von der Anlage lösen und Tausende von schweren Kunststofffäden erzeugen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "caption = [\n",
    "    \"Seilbahnbürsten, die verwendet werden, um Eis und Schnee von Skiliften zu entfernen, \",\n",
    "    \"können sich von der Anlage lösen und Tausende von schweren Kunststofffäden erzeugen.\"\n",
    "]\n",
    "\n",
    "image_caption = ''.join(caption)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\": \"resources/images/alpes_valaisanne/veysonnaz_brosse.jpg\",\n",
    "    \"caption\": Paragraph(image_caption, featuredata.caption_style),\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\": 1.2,\n",
    "    \"original_height\": 22.86,\n",
    "    \"original_width\": 40.64\n",
    "}\n",
    "\n",
    "s_section_title = Paragraph(\"Seilbahnbürsten\", featuredata.bold_block)\n",
    "\n",
    "s_figure_and_caption = featuredata.figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc([smallspace, s_section_title, s_figure_and_caption], pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verwendungszweck der gefundenen Objekte\n",
    "\n",
    "Der Verwendungszweck basiert auf der Verwendung des Objekts, bevor es weggeworfen wurde, oder auf der Artikelbeschreibung, wenn die ursprüngliche Verwendung unbestimmt ist. Identifizierte Objekte werden einer der 260 vordefinierten Kategorien zugeordnet. Die Kategorien werden je nach Verwendung oder Artikelbeschreibung gruppiert.\n",
    "\n",
    "* Abwasser: Objekte, die aus Kläranlagen freigesetzt werden, sprich Objekte, die wahrscheinlich über die Toilette entsorgt werden\n",
    "* Mikroplastik (< 5 mm): fragmentierte Kunststoffe und Kunststoffharze aus der Vorproduktion\n",
    "* Infrastruktur: Artikel im Zusammenhang mit dem Bau und der Instandhaltung von Gebäuden, Strassen und der Wasser-/Stromversorgung\n",
    "* Essen und Trinken: alle Materialien, die mit dem Konsum von Essen und Trinken in Zusammenhang stehen\n",
    "* Landwirtschaft: Materialien z. B. für Mulch und Reihenabdeckungen, Gewächshäuser, Bodenbegasung, Ballenverpackungen. Einschliesslich Hartkunststoffe für landwirtschaftliche Zäune, Blumentöpfe usw.\n",
    "* Tabakwaren: hauptsächlich Zigarettenfilter, einschliesslich aller mit dem Rauchen verbundenen Materialien\n",
    "* Freizeit und Erholung: Objekte, die mit Sport und Freizeit zu tun haben, z. B. Angeln, Jagen, Wandern usw.\n",
    "* Verpackungen ausser Lebensmittel und Tabak: Verpackungsmaterial, das nicht lebensmittel- oder tabakbezogen ist\n",
    "* Plastikfragmente: Plastikteile unbestimmter Herkunft oder Verwendung\n",
    "* Persönliche Gegenstände: Accessoires, Hygieneartikel und Kleidung\n",
    "\n",
    "Im Anhang (Kapitel 3.6.3) befindet sich die vollständige Liste der identifizierten Objekte, einschliesslich Beschreibungen und Gruppenklassifizierung. Das Kapitel [16 Codegruppen](codegroups) beschreibt jede Codegruppe im Detail und bietet eine umfassende Liste aller Objekte in einer Gruppe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "section_title = Paragraph(\"Verwendungszweck der gefundenen Objekte\", featuredata.section_title)\n",
    "\n",
    "para_one = [\n",
    "    \"Der Verwendungszweck basiert auf der Verwendung des Objekts, bevor es weggeworfen wurde, \",\n",
    "    \"oder auf der Artikelbeschreibung, wenn die ursprüngliche Verwendung unbestimmt ist. \",\n",
    "    \"Identifizierte Objekte werden einer der 260 vordefinierten Kategorien zugeordnet. Die \",\n",
    "    \"Kategorien werden je nach Verwendung oder Artikelbeschreibung gruppiert.\"\n",
    "]\n",
    "\n",
    "p_one = makeAParagraph(para_one)\n",
    "\n",
    "group_list = [\n",
    "    \"Abwasser: Objekte, die aus Kläranlagen freigesetzt werden, sprich Objekte, die wahrscheinlich über die Toilette entsorgt werden\",\n",
    "    \"Mikroplastik (< 5 mm): fragmentierte Kunststoffe und Kunststoffharze aus der Vorproduktion\",\n",
    "    \"Infrastruktur: Artikel im Zusammenhang mit dem Bau und der Instandhaltung von Gebäuden, Strassen und der Wasser-/Stromversorgung\",\n",
    "    \"Essen und Trinken: alle Materialien, die mit dem Konsum von Essen und Trinken in Zusammenhang stehen\",\n",
    "    \"Landwirtschaft: Materialien z. B. für Mulch und Reihenabdeckungen, Gewächshäuser, Bodenbegasung, Ballenverpackungen. Einschliesslich Hartkunststoffe für landwirtschaftliche Zäune, Blumentöpfe usw.\",\n",
    "    \"Tabakwaren: hauptsächlich Zigarettenfilter, einschliesslich aller mit dem Rauchen verbundenen Materialien\",\n",
    "    \"Freizeit und Erholung: Objekte, die mit Sport und Freizeit zu tun haben, z. B. Angeln, Jagen, Wandern usw.\",\n",
    "    \"Verpackungen ausser Lebensmittel und Tabak: Verpackungsmaterial, das nicht lebensmittel- oder tabakbezogen ist\",\n",
    "    \"Plastikfragmente: Plastikteile unbestimmter Herkunft oder Verwendung\",\n",
    "    \"Persönliche Gegenstände: Accessoires, Hygieneartikel und Kleidung\",\n",
    "]\n",
    "\n",
    "group_list = makeAList(group_list)\n",
    "    \n",
    "para_two = [\n",
    "    \"Im Anhang (Kapitel 3.6.3) befindet sich die vollständige Liste der identifizierten Objekte, einschliesslich Beschreibungen und \",\n",
    "    'Gruppenklassifizierung. Das Kapitel <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/code_groups.html\"> Codegruppen </a> ',\n",
    "    \"beschreibt jede Codegruppe im Detail und bietet eine umfassende \",\n",
    "    \"Liste aller Objekte in einer Gruppe.\"\n",
    "]\n",
    "\n",
    "p_two = makeAParagraph(para_two)\n",
    "\n",
    "new_components = [\n",
    "    featuredata.large_space,\n",
    "    section_title,\n",
    "    smallspace,\n",
    "    p_one,\n",
    "    smallspace,\n",
    "    group_list,\n",
    "    smallspace,\n",
    "    p_two\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "source": [
    "*__Unten:__ Wanderwege Nutzen der gefundenen Objekte: % der Gesamtzahl nach Wassermerkmal. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, bleiben nach Grösse klassifiziert.*  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_6a445\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6a445_level0_col0\" class=\"col_heading level0 col0\" >Airolo</th>\n",
       "      <th id=\"T_6a445_level0_col1\" class=\"col_heading level0 col1\" >Andermatt</th>\n",
       "      <th id=\"T_6a445_level0_col2\" class=\"col_heading level0 col2\" >Charmey</th>\n",
       "      <th id=\"T_6a445_level0_col3\" class=\"col_heading level0 col3\" >Crans-Montana</th>\n",
       "      <th id=\"T_6a445_level0_col4\" class=\"col_heading level0 col4\" >Grindelwald</th>\n",
       "      <th id=\"T_6a445_level0_col5\" class=\"col_heading level0 col5\" >La Berra</th>\n",
       "      <th id=\"T_6a445_level0_col6\" class=\"col_heading level0 col6\" >Robella</th>\n",
       "      <th id=\"T_6a445_level0_col7\" class=\"col_heading level0 col7\" >La Tzoumaz</th>\n",
       "      <th id=\"T_6a445_level0_col8\" class=\"col_heading level0 col8\" >Tour Les Crosets</th>\n",
       "      <th id=\"T_6a445_level0_col9\" class=\"col_heading level0 col9\" >Les Diablerets</th>\n",
       "      <th id=\"T_6a445_level0_col10\" class=\"col_heading level0 col10\" >Les Paccots</th>\n",
       "      <th id=\"T_6a445_level0_col11\" class=\"col_heading level0 col11\" >Monte Generoso</th>\n",
       "      <th id=\"T_6a445_level0_col12\" class=\"col_heading level0 col12\" >Morgins</th>\n",
       "      <th id=\"T_6a445_level0_col13\" class=\"col_heading level0 col13\" >Nendaz</th>\n",
       "      <th id=\"T_6a445_level0_col14\" class=\"col_heading level0 col14\" >Val Calanca</th>\n",
       "      <th id=\"T_6a445_level0_col15\" class=\"col_heading level0 col15\" >Verbier</th>\n",
       "      <th id=\"T_6a445_level0_col16\" class=\"col_heading level0 col16\" >Villars</th>\n",
       "      <th id=\"T_6a445_level0_col17\" class=\"col_heading level0 col17\" >Alpen und Jura</th>\n",
       "      <th id=\"T_6a445_level0_col18\" class=\"col_heading level0 col18\" >IQAASL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n",
       "      <td id=\"T_6a445_row0_col0\" class=\"data row0 col0\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col1\" class=\"data row0 col1\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col2\" class=\"data row0 col2\" >1%</td>\n",
       "      <td id=\"T_6a445_row0_col3\" class=\"data row0 col3\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col4\" class=\"data row0 col4\" >1%</td>\n",
       "      <td id=\"T_6a445_row0_col5\" class=\"data row0 col5\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col6\" class=\"data row0 col6\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col7\" class=\"data row0 col7\" >1%</td>\n",
       "      <td id=\"T_6a445_row0_col8\" class=\"data row0 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col9\" class=\"data row0 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col10\" class=\"data row0 col10\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col11\" class=\"data row0 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col12\" class=\"data row0 col12\" >2%</td>\n",
       "      <td id=\"T_6a445_row0_col13\" class=\"data row0 col13\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col14\" class=\"data row0 col14\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col15\" class=\"data row0 col15\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col16\" class=\"data row0 col16\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col17\" class=\"data row0 col17\" >0%</td>\n",
       "      <td id=\"T_6a445_row0_col18\" class=\"data row0 col18\" >5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n",
       "      <td id=\"T_6a445_row1_col0\" class=\"data row1 col0\" >50%</td>\n",
       "      <td id=\"T_6a445_row1_col1\" class=\"data row1 col1\" >29%</td>\n",
       "      <td id=\"T_6a445_row1_col2\" class=\"data row1 col2\" >16%</td>\n",
       "      <td id=\"T_6a445_row1_col3\" class=\"data row1 col3\" >23%</td>\n",
       "      <td id=\"T_6a445_row1_col4\" class=\"data row1 col4\" >34%</td>\n",
       "      <td id=\"T_6a445_row1_col5\" class=\"data row1 col5\" >41%</td>\n",
       "      <td id=\"T_6a445_row1_col6\" class=\"data row1 col6\" >70%</td>\n",
       "      <td id=\"T_6a445_row1_col7\" class=\"data row1 col7\" >19%</td>\n",
       "      <td id=\"T_6a445_row1_col8\" class=\"data row1 col8\" >3%</td>\n",
       "      <td id=\"T_6a445_row1_col9\" class=\"data row1 col9\" >19%</td>\n",
       "      <td id=\"T_6a445_row1_col10\" class=\"data row1 col10\" >12%</td>\n",
       "      <td id=\"T_6a445_row1_col11\" class=\"data row1 col11\" >22%</td>\n",
       "      <td id=\"T_6a445_row1_col12\" class=\"data row1 col12\" >16%</td>\n",
       "      <td id=\"T_6a445_row1_col13\" class=\"data row1 col13\" >13%</td>\n",
       "      <td id=\"T_6a445_row1_col14\" class=\"data row1 col14\" >33%</td>\n",
       "      <td id=\"T_6a445_row1_col15\" class=\"data row1 col15\" >16%</td>\n",
       "      <td id=\"T_6a445_row1_col16\" class=\"data row1 col16\" >21%</td>\n",
       "      <td id=\"T_6a445_row1_col17\" class=\"data row1 col17\" >20%</td>\n",
       "      <td id=\"T_6a445_row1_col18\" class=\"data row1 col18\" >19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n",
       "      <td id=\"T_6a445_row2_col0\" class=\"data row2 col0\" >4%</td>\n",
       "      <td id=\"T_6a445_row2_col1\" class=\"data row2 col1\" >0%</td>\n",
       "      <td id=\"T_6a445_row2_col2\" class=\"data row2 col2\" >3%</td>\n",
       "      <td id=\"T_6a445_row2_col3\" class=\"data row2 col3\" >11%</td>\n",
       "      <td id=\"T_6a445_row2_col4\" class=\"data row2 col4\" >2%</td>\n",
       "      <td id=\"T_6a445_row2_col5\" class=\"data row2 col5\" >9%</td>\n",
       "      <td id=\"T_6a445_row2_col6\" class=\"data row2 col6\" >2%</td>\n",
       "      <td id=\"T_6a445_row2_col7\" class=\"data row2 col7\" >7%</td>\n",
       "      <td id=\"T_6a445_row2_col8\" class=\"data row2 col8\" >1%</td>\n",
       "      <td id=\"T_6a445_row2_col9\" class=\"data row2 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row2_col10\" class=\"data row2 col10\" >3%</td>\n",
       "      <td id=\"T_6a445_row2_col11\" class=\"data row2 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row2_col12\" class=\"data row2 col12\" >9%</td>\n",
       "      <td id=\"T_6a445_row2_col13\" class=\"data row2 col13\" >1%</td>\n",
       "      <td id=\"T_6a445_row2_col14\" class=\"data row2 col14\" >3%</td>\n",
       "      <td id=\"T_6a445_row2_col15\" class=\"data row2 col15\" >3%</td>\n",
       "      <td id=\"T_6a445_row2_col16\" class=\"data row2 col16\" >6%</td>\n",
       "      <td id=\"T_6a445_row2_col17\" class=\"data row2 col17\" >3%</td>\n",
       "      <td id=\"T_6a445_row2_col18\" class=\"data row2 col18\" >4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n",
       "      <td id=\"T_6a445_row3_col0\" class=\"data row3 col0\" >7%</td>\n",
       "      <td id=\"T_6a445_row3_col1\" class=\"data row3 col1\" >21%</td>\n",
       "      <td id=\"T_6a445_row3_col2\" class=\"data row3 col2\" >24%</td>\n",
       "      <td id=\"T_6a445_row3_col3\" class=\"data row3 col3\" >21%</td>\n",
       "      <td id=\"T_6a445_row3_col4\" class=\"data row3 col4\" >2%</td>\n",
       "      <td id=\"T_6a445_row3_col5\" class=\"data row3 col5\" >9%</td>\n",
       "      <td id=\"T_6a445_row3_col6\" class=\"data row3 col6\" >9%</td>\n",
       "      <td id=\"T_6a445_row3_col7\" class=\"data row3 col7\" >33%</td>\n",
       "      <td id=\"T_6a445_row3_col8\" class=\"data row3 col8\" >91%</td>\n",
       "      <td id=\"T_6a445_row3_col9\" class=\"data row3 col9\" >39%</td>\n",
       "      <td id=\"T_6a445_row3_col10\" class=\"data row3 col10\" >44%</td>\n",
       "      <td id=\"T_6a445_row3_col11\" class=\"data row3 col11\" >38%</td>\n",
       "      <td id=\"T_6a445_row3_col12\" class=\"data row3 col12\" >16%</td>\n",
       "      <td id=\"T_6a445_row3_col13\" class=\"data row3 col13\" >32%</td>\n",
       "      <td id=\"T_6a445_row3_col14\" class=\"data row3 col14\" >29%</td>\n",
       "      <td id=\"T_6a445_row3_col15\" class=\"data row3 col15\" >14%</td>\n",
       "      <td id=\"T_6a445_row3_col16\" class=\"data row3 col16\" >6%</td>\n",
       "      <td id=\"T_6a445_row3_col17\" class=\"data row3 col17\" >36%</td>\n",
       "      <td id=\"T_6a445_row3_col18\" class=\"data row3 col18\" >18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n",
       "      <td id=\"T_6a445_row4_col0\" class=\"data row4 col0\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col1\" class=\"data row4 col1\" >4%</td>\n",
       "      <td id=\"T_6a445_row4_col2\" class=\"data row4 col2\" >1%</td>\n",
       "      <td id=\"T_6a445_row4_col3\" class=\"data row4 col3\" >2%</td>\n",
       "      <td id=\"T_6a445_row4_col4\" class=\"data row4 col4\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col5\" class=\"data row4 col5\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col6\" class=\"data row4 col6\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col7\" class=\"data row4 col7\" >2%</td>\n",
       "      <td id=\"T_6a445_row4_col8\" class=\"data row4 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col9\" class=\"data row4 col9\" >26%</td>\n",
       "      <td id=\"T_6a445_row4_col10\" class=\"data row4 col10\" >2%</td>\n",
       "      <td id=\"T_6a445_row4_col11\" class=\"data row4 col11\" >25%</td>\n",
       "      <td id=\"T_6a445_row4_col12\" class=\"data row4 col12\" >1%</td>\n",
       "      <td id=\"T_6a445_row4_col13\" class=\"data row4 col13\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col14\" class=\"data row4 col14\" >3%</td>\n",
       "      <td id=\"T_6a445_row4_col15\" class=\"data row4 col15\" >0%</td>\n",
       "      <td id=\"T_6a445_row4_col16\" class=\"data row4 col16\" >3%</td>\n",
       "      <td id=\"T_6a445_row4_col17\" class=\"data row4 col17\" >1%</td>\n",
       "      <td id=\"T_6a445_row4_col18\" class=\"data row4 col18\" >6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n",
       "      <td id=\"T_6a445_row5_col0\" class=\"data row5 col0\" >4%</td>\n",
       "      <td id=\"T_6a445_row5_col1\" class=\"data row5 col1\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col2\" class=\"data row5 col2\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col3\" class=\"data row5 col3\" >9%</td>\n",
       "      <td id=\"T_6a445_row5_col4\" class=\"data row5 col4\" >8%</td>\n",
       "      <td id=\"T_6a445_row5_col5\" class=\"data row5 col5\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col6\" class=\"data row5 col6\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col7\" class=\"data row5 col7\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col8\" class=\"data row5 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col9\" class=\"data row5 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col10\" class=\"data row5 col10\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col11\" class=\"data row5 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col12\" class=\"data row5 col12\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col13\" class=\"data row5 col13\" >1%</td>\n",
       "      <td id=\"T_6a445_row5_col14\" class=\"data row5 col14\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col15\" class=\"data row5 col15\" >3%</td>\n",
       "      <td id=\"T_6a445_row5_col16\" class=\"data row5 col16\" >0%</td>\n",
       "      <td id=\"T_6a445_row5_col17\" class=\"data row5 col17\" >2%</td>\n",
       "      <td id=\"T_6a445_row5_col18\" class=\"data row5 col18\" >8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n",
       "      <td id=\"T_6a445_row6_col0\" class=\"data row6 col0\" >4%</td>\n",
       "      <td id=\"T_6a445_row6_col1\" class=\"data row6 col1\" >8%</td>\n",
       "      <td id=\"T_6a445_row6_col2\" class=\"data row6 col2\" >8%</td>\n",
       "      <td id=\"T_6a445_row6_col3\" class=\"data row6 col3\" >6%</td>\n",
       "      <td id=\"T_6a445_row6_col4\" class=\"data row6 col4\" >1%</td>\n",
       "      <td id=\"T_6a445_row6_col5\" class=\"data row6 col5\" >18%</td>\n",
       "      <td id=\"T_6a445_row6_col6\" class=\"data row6 col6\" >2%</td>\n",
       "      <td id=\"T_6a445_row6_col7\" class=\"data row6 col7\" >6%</td>\n",
       "      <td id=\"T_6a445_row6_col8\" class=\"data row6 col8\" >3%</td>\n",
       "      <td id=\"T_6a445_row6_col9\" class=\"data row6 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row6_col10\" class=\"data row6 col10\" >1%</td>\n",
       "      <td id=\"T_6a445_row6_col11\" class=\"data row6 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row6_col12\" class=\"data row6 col12\" >4%</td>\n",
       "      <td id=\"T_6a445_row6_col13\" class=\"data row6 col13\" >3%</td>\n",
       "      <td id=\"T_6a445_row6_col14\" class=\"data row6 col14\" >4%</td>\n",
       "      <td id=\"T_6a445_row6_col15\" class=\"data row6 col15\" >0%</td>\n",
       "      <td id=\"T_6a445_row6_col16\" class=\"data row6 col16\" >45%</td>\n",
       "      <td id=\"T_6a445_row6_col17\" class=\"data row6 col17\" >5%</td>\n",
       "      <td id=\"T_6a445_row6_col18\" class=\"data row6 col18\" >3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n",
       "      <td id=\"T_6a445_row7_col0\" class=\"data row7 col0\" >12%</td>\n",
       "      <td id=\"T_6a445_row7_col1\" class=\"data row7 col1\" >12%</td>\n",
       "      <td id=\"T_6a445_row7_col2\" class=\"data row7 col2\" >25%</td>\n",
       "      <td id=\"T_6a445_row7_col3\" class=\"data row7 col3\" >6%</td>\n",
       "      <td id=\"T_6a445_row7_col4\" class=\"data row7 col4\" >4%</td>\n",
       "      <td id=\"T_6a445_row7_col5\" class=\"data row7 col5\" >21%</td>\n",
       "      <td id=\"T_6a445_row7_col6\" class=\"data row7 col6\" >2%</td>\n",
       "      <td id=\"T_6a445_row7_col7\" class=\"data row7 col7\" >20%</td>\n",
       "      <td id=\"T_6a445_row7_col8\" class=\"data row7 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row7_col9\" class=\"data row7 col9\" >10%</td>\n",
       "      <td id=\"T_6a445_row7_col10\" class=\"data row7 col10\" >27%</td>\n",
       "      <td id=\"T_6a445_row7_col11\" class=\"data row7 col11\" >9%</td>\n",
       "      <td id=\"T_6a445_row7_col12\" class=\"data row7 col12\" >30%</td>\n",
       "      <td id=\"T_6a445_row7_col13\" class=\"data row7 col13\" >8%</td>\n",
       "      <td id=\"T_6a445_row7_col14\" class=\"data row7 col14\" >5%</td>\n",
       "      <td id=\"T_6a445_row7_col15\" class=\"data row7 col15\" >7%</td>\n",
       "      <td id=\"T_6a445_row7_col16\" class=\"data row7 col16\" >4%</td>\n",
       "      <td id=\"T_6a445_row7_col17\" class=\"data row7 col17\" >10%</td>\n",
       "      <td id=\"T_6a445_row7_col18\" class=\"data row7 col18\" >14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n",
       "      <td id=\"T_6a445_row8_col0\" class=\"data row8 col0\" >12%</td>\n",
       "      <td id=\"T_6a445_row8_col1\" class=\"data row8 col1\" >12%</td>\n",
       "      <td id=\"T_6a445_row8_col2\" class=\"data row8 col2\" >11%</td>\n",
       "      <td id=\"T_6a445_row8_col3\" class=\"data row8 col3\" >17%</td>\n",
       "      <td id=\"T_6a445_row8_col4\" class=\"data row8 col4\" >47%</td>\n",
       "      <td id=\"T_6a445_row8_col5\" class=\"data row8 col5\" >3%</td>\n",
       "      <td id=\"T_6a445_row8_col6\" class=\"data row8 col6\" >9%</td>\n",
       "      <td id=\"T_6a445_row8_col7\" class=\"data row8 col7\" >9%</td>\n",
       "      <td id=\"T_6a445_row8_col8\" class=\"data row8 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row8_col9\" class=\"data row8 col9\" >6%</td>\n",
       "      <td id=\"T_6a445_row8_col10\" class=\"data row8 col10\" >7%</td>\n",
       "      <td id=\"T_6a445_row8_col11\" class=\"data row8 col11\" >6%</td>\n",
       "      <td id=\"T_6a445_row8_col12\" class=\"data row8 col12\" >15%</td>\n",
       "      <td id=\"T_6a445_row8_col13\" class=\"data row8 col13\" >2%</td>\n",
       "      <td id=\"T_6a445_row8_col14\" class=\"data row8 col14\" >18%</td>\n",
       "      <td id=\"T_6a445_row8_col15\" class=\"data row8 col15\" >37%</td>\n",
       "      <td id=\"T_6a445_row8_col16\" class=\"data row8 col16\" >12%</td>\n",
       "      <td id=\"T_6a445_row8_col17\" class=\"data row8 col17\" >13%</td>\n",
       "      <td id=\"T_6a445_row8_col18\" class=\"data row8 col18\" >17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n",
       "      <td id=\"T_6a445_row9_col0\" class=\"data row9 col0\" >5%</td>\n",
       "      <td id=\"T_6a445_row9_col1\" class=\"data row9 col1\" >4%</td>\n",
       "      <td id=\"T_6a445_row9_col2\" class=\"data row9 col2\" >7%</td>\n",
       "      <td id=\"T_6a445_row9_col3\" class=\"data row9 col3\" >2%</td>\n",
       "      <td id=\"T_6a445_row9_col4\" class=\"data row9 col4\" >0%</td>\n",
       "      <td id=\"T_6a445_row9_col5\" class=\"data row9 col5\" >0%</td>\n",
       "      <td id=\"T_6a445_row9_col6\" class=\"data row9 col6\" >6%</td>\n",
       "      <td id=\"T_6a445_row9_col7\" class=\"data row9 col7\" >2%</td>\n",
       "      <td id=\"T_6a445_row9_col8\" class=\"data row9 col8\" >1%</td>\n",
       "      <td id=\"T_6a445_row9_col9\" class=\"data row9 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row9_col10\" class=\"data row9 col10\" >4%</td>\n",
       "      <td id=\"T_6a445_row9_col11\" class=\"data row9 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row9_col12\" class=\"data row9 col12\" >7%</td>\n",
       "      <td id=\"T_6a445_row9_col13\" class=\"data row9 col13\" >6%</td>\n",
       "      <td id=\"T_6a445_row9_col14\" class=\"data row9 col14\" >5%</td>\n",
       "      <td id=\"T_6a445_row9_col15\" class=\"data row9 col15\" >16%</td>\n",
       "      <td id=\"T_6a445_row9_col16\" class=\"data row9 col16\" >4%</td>\n",
       "      <td id=\"T_6a445_row9_col17\" class=\"data row9 col17\" >5%</td>\n",
       "      <td id=\"T_6a445_row9_col18\" class=\"data row9 col18\" >5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a445_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n",
       "      <td id=\"T_6a445_row10_col0\" class=\"data row10 col0\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col1\" class=\"data row10 col1\" >8%</td>\n",
       "      <td id=\"T_6a445_row10_col2\" class=\"data row10 col2\" >5%</td>\n",
       "      <td id=\"T_6a445_row10_col3\" class=\"data row10 col3\" >2%</td>\n",
       "      <td id=\"T_6a445_row10_col4\" class=\"data row10 col4\" >1%</td>\n",
       "      <td id=\"T_6a445_row10_col5\" class=\"data row10 col5\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col6\" class=\"data row10 col6\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col7\" class=\"data row10 col7\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col8\" class=\"data row10 col8\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col9\" class=\"data row10 col9\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col10\" class=\"data row10 col10\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col11\" class=\"data row10 col11\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col12\" class=\"data row10 col12\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col13\" class=\"data row10 col13\" >34%</td>\n",
       "      <td id=\"T_6a445_row10_col14\" class=\"data row10 col14\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col15\" class=\"data row10 col15\" >3%</td>\n",
       "      <td id=\"T_6a445_row10_col16\" class=\"data row10 col16\" >0%</td>\n",
       "      <td id=\"T_6a445_row10_col17\" class=\"data row10 col17\" >4%</td>\n",
       "      <td id=\"T_6a445_row10_col18\" class=\"data row10 col18\" >1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f54b003a970>"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "alpes_survey_area_codegroup_percent"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def parentGroupTotals(data: pd.DataFrame = None, most_common: list = None, unit_label: str = None, \n",
    "                     percent: bool = False, name: str = None):\n",
    "    \n",
    "    if percent:\n",
    "        data = data.groupby('groupname', as_index=False).quantity.sum()\n",
    "        data.set_index('groupname', inplace=True)\n",
    "        data[name] = (data.quantity / data.quantity.sum()).round(2)\n",
    "\n",
    "        return data[name]\n",
    "\n",
    "    else:\n",
    "        data = data.groupby([\"loc_date\", 'groupname'], as_index=False)[unit_label].sum()\n",
    "        data = data.groupby('groupname', as_index=False)[unit_label].median()\n",
    "        data.set_index('groupname', inplace=True)\n",
    "        data[name] = data[unit_label]\n",
    "\n",
    "        return data[name]\n",
    "\n",
    "\n",
    "\n",
    "# the results are a callable for the components\n",
    "components = fdx.componentCodeGroupResults()\n",
    "pt_comp = components[~components.location.isin(not_trail_samples)][[\"location\", \"groupname\", '% of total' ]].pivot(columns=\"location\", index=\"groupname\")\n",
    "\n",
    "# pivot that and reomve any hierarchal column index\n",
    "pt_comp.columns = pt_comp.columns.get_level_values(1)\n",
    "pt_comp.rename(columns = {x:proper_names[x] for x in pt_comp.columns}, inplace=True)\n",
    "\n",
    "aggregate_kwargs = {\n",
    "    \"data\": period_data.period_data,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"percent\": True,\n",
    "    \"name\":\"IQAASL\",\n",
    "}        \n",
    "\n",
    "    \n",
    "component_kwargs = {\n",
    "    \"data\":fdt,    \n",
    "    \"unit_label\": unit_label,\n",
    "    \"name\": this_feature[\"name\"],\n",
    "    \"percent\": True\n",
    "    \n",
    "}\n",
    "\n",
    "pt_comp[this_feature[\"name\"]] = parentGroupTotals(**component_kwargs)\n",
    "pt_comp[\"IQAASL\"] = parentGroupTotals(**aggregate_kwargs)\n",
    "\n",
    "# pdf out put\n",
    "\n",
    "caption_prefix =  f\"Verwendungszweck der gefundenen Objekte an Wanderwegen: \"\n",
    "col_widths=[4.5*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "codegroup_heatmap_title = [\n",
    "    \"Unten: Wanderwege Nutzen der gefundenen Objekte: % der Gesamtzahl nach Wassermerkmal. Fragmentierte Objekte, \",\n",
    "    \"die nicht eindeutig identifiziert werden können, bleiben nach Grösse klassifiziert.\"\n",
    "]\n",
    "\n",
    "codegroup_heatmap_title = ''.join(codegroup_heatmap_title)\n",
    "table_title = Paragraph(codegroup_heatmap_title, featuredata.p_style)\n",
    "\n",
    "mc_heat_map_caption = \"Verwendungszweck der gefundenen Objekte an Wanderwegen: Median p/100 m.\"\n",
    "tables = featuredata.splitTableWidth(pt_comp, gradient=True, caption_prefix=caption_prefix, caption=mc_heat_map_caption,\n",
    "                                     this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths)\n",
    "\n",
    "# identify the tables variable as either a list or a Flowable:\n",
    "if isinstance(tables, (list, np.ndarray)):\n",
    "    grouped_pdf_components = [table_title, featuredata.small_space, *tables]\n",
    "else:\n",
    "    grouped_pdf_components = [table_title, featuredata.small_space, tables]\n",
    "    \n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    *grouped_pdf_components\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "pt_comp.index.name = None\n",
    "pt_comp.columns.name = None\n",
    "aformatter = {x: '{:.0%}' for x in pt_comp.columns}\n",
    "\n",
    "ptd = pt_comp.style.format(aformatter).set_table_styles(heat_map_css_styles).background_gradient(axis=None, vmin=pt_comp.min().min(), vmax=pt_comp.max().max(), cmap=\"YlOrBr\")\n",
    "ptd = ptd.applymap_index(featuredata.rotateText, axis=1)\n",
    "glue('alpes_survey_area_codegroup_percent', ptd, display=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_codegroup_percent\n",
    "---\n",
    "name: 'alpes_survey_area_codegroup_percent'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_survey_area_codegroup_percent>` Verwendungszweck der gefundenen Objekte an Wanderwegen: prozentualer Anteil an der Gesamtzahl nach Verwendungszweck. Fragmentierte Objekte, die nicht eindeutig identifiziert werden können, bleiben nach Grösse klassifiziert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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#T_29fd1_level0_col15, #T_29fd1_level0_col16, #T_29fd1_level0_col17, #T_29fd1_level0_col18 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_29fd1\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_29fd1_level0_col0\" class=\"col_heading level0 col0\" >Airolo</th>\n      <th id=\"T_29fd1_level0_col1\" class=\"col_heading level0 col1\" >Andermatt</th>\n      <th id=\"T_29fd1_level0_col2\" class=\"col_heading level0 col2\" >Charmey</th>\n      <th id=\"T_29fd1_level0_col3\" class=\"col_heading level0 col3\" >Crans-Montana</th>\n      <th id=\"T_29fd1_level0_col4\" class=\"col_heading level0 col4\" >Grindelwald</th>\n      <th id=\"T_29fd1_level0_col5\" class=\"col_heading level0 col5\" >La Berra</th>\n      <th id=\"T_29fd1_level0_col6\" class=\"col_heading level0 col6\" >Robella</th>\n      <th id=\"T_29fd1_level0_col7\" class=\"col_heading level0 col7\" >La Tzoumaz</th>\n      <th id=\"T_29fd1_level0_col8\" class=\"col_heading level0 col8\" >Tour Les Crosets</th>\n      <th id=\"T_29fd1_level0_col9\" class=\"col_heading level0 col9\" >Les Diablerets</th>\n      <th id=\"T_29fd1_level0_col10\" class=\"col_heading level0 col10\" >Les Paccots</th>\n      <th id=\"T_29fd1_level0_col11\" class=\"col_heading level0 col11\" >Monte Generoso</th>\n      <th id=\"T_29fd1_level0_col12\" class=\"col_heading level0 col12\" >Morgins</th>\n      <th id=\"T_29fd1_level0_col13\" class=\"col_heading level0 col13\" >Nendaz</th>\n      <th id=\"T_29fd1_level0_col14\" class=\"col_heading level0 col14\" >Val Calanca</th>\n      <th id=\"T_29fd1_level0_col15\" class=\"col_heading level0 col15\" >Verbier</th>\n      <th id=\"T_29fd1_level0_col16\" class=\"col_heading level0 col16\" >Villars</th>\n      <th id=\"T_29fd1_level0_col17\" class=\"col_heading level0 col17\" >Alpen und Jura</th>\n      <th id=\"T_29fd1_level0_col18\" class=\"col_heading level0 col18\" >IQAASL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_29fd1_level0_row0\" class=\"row_heading level0 row0\" >Abwasser</th>\n      <td id=\"T_29fd1_row0_col0\" class=\"data row0 col0\" >3,0</td>\n      <td id=\"T_29fd1_row0_col1\" class=\"data row0 col1\" >0,0</td>\n      <td id=\"T_29fd1_row0_col2\" class=\"data row0 col2\" >3,0</td>\n      <td id=\"T_29fd1_row0_col3\" class=\"data row0 col3\" >0,0</td>\n      <td id=\"T_29fd1_row0_col4\" class=\"data row0 col4\" >4,0</td>\n      <td id=\"T_29fd1_row0_col5\" class=\"data row0 col5\" >0,0</td>\n      <td id=\"T_29fd1_row0_col6\" class=\"data row0 col6\" >0,0</td>\n      <td id=\"T_29fd1_row0_col7\" class=\"data row0 col7\" >1,0</td>\n      <td id=\"T_29fd1_row0_col8\" class=\"data row0 col8\" >0,0</td>\n      <td id=\"T_29fd1_row0_col9\" class=\"data row0 col9\" >0,0</td>\n      <td id=\"T_29fd1_row0_col10\" class=\"data row0 col10\" >0,0</td>\n      <td id=\"T_29fd1_row0_col11\" class=\"data row0 col11\" >0,0</td>\n      <td id=\"T_29fd1_row0_col12\" class=\"data row0 col12\" >3,0</td>\n      <td id=\"T_29fd1_row0_col13\" class=\"data row0 col13\" >0,0</td>\n      <td id=\"T_29fd1_row0_col14\" class=\"data row0 col14\" >0,0</td>\n      <td id=\"T_29fd1_row0_col15\" class=\"data row0 col15\" >0,0</td>\n      <td id=\"T_29fd1_row0_col16\" class=\"data row0 col16\" >0,0</td>\n      <td id=\"T_29fd1_row0_col17\" class=\"data row0 col17\" >0,0</td>\n      <td id=\"T_29fd1_row0_col18\" class=\"data row0 col18\" >3,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row1\" class=\"row_heading level0 row1\" >Essen und Trinken</th>\n      <td id=\"T_29fd1_row1_col0\" class=\"data row1 col0\" >296,0</td>\n      <td id=\"T_29fd1_row1_col1\" class=\"data row1 col1\" >7,0</td>\n      <td id=\"T_29fd1_row1_col2\" class=\"data row1 col2\" >93,0</td>\n      <td id=\"T_29fd1_row1_col3\" class=\"data row1 col3\" >25,0</td>\n      <td id=\"T_29fd1_row1_col4\" class=\"data row1 col4\" >95,0</td>\n      <td id=\"T_29fd1_row1_col5\" class=\"data row1 col5\" >23,0</td>\n      <td id=\"T_29fd1_row1_col6\" class=\"data row1 col6\" >42,0</td>\n      <td id=\"T_29fd1_row1_col7\" class=\"data row1 col7\" >22,0</td>\n      <td id=\"T_29fd1_row1_col8\" class=\"data row1 col8\" >17,0</td>\n      <td id=\"T_29fd1_row1_col9\" class=\"data row1 col9\" >6,0</td>\n      <td id=\"T_29fd1_row1_col10\" class=\"data row1 col10\" >28,0</td>\n      <td id=\"T_29fd1_row1_col11\" class=\"data row1 col11\" >9,0</td>\n      <td id=\"T_29fd1_row1_col12\" class=\"data row1 col12\" >26,0</td>\n      <td id=\"T_29fd1_row1_col13\" class=\"data row1 col13\" >39,0</td>\n      <td id=\"T_29fd1_row1_col14\" class=\"data row1 col14\" >33,0</td>\n      <td id=\"T_29fd1_row1_col15\" class=\"data row1 col15\" >66,0</td>\n      <td id=\"T_29fd1_row1_col16\" class=\"data row1 col16\" >20,0</td>\n      <td id=\"T_29fd1_row1_col17\" class=\"data row1 col17\" >0,2</td>\n      <td id=\"T_29fd1_row1_col18\" class=\"data row1 col18\" >37,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row2\" class=\"row_heading level0 row2\" >Freizeit und Erholung</th>\n      <td id=\"T_29fd1_row2_col0\" class=\"data row2 col0\" >24,0</td>\n      <td id=\"T_29fd1_row2_col1\" class=\"data row2 col1\" >0,0</td>\n      <td id=\"T_29fd1_row2_col2\" class=\"data row2 col2\" >20,0</td>\n      <td id=\"T_29fd1_row2_col3\" class=\"data row2 col3\" >11,0</td>\n      <td id=\"T_29fd1_row2_col4\" class=\"data row2 col4\" >6,0</td>\n      <td id=\"T_29fd1_row2_col5\" class=\"data row2 col5\" >6,0</td>\n      <td id=\"T_29fd1_row2_col6\" class=\"data row2 col6\" >1,0</td>\n      <td id=\"T_29fd1_row2_col7\" class=\"data row2 col7\" >7,0</td>\n      <td id=\"T_29fd1_row2_col8\" class=\"data row2 col8\" >3,0</td>\n      <td id=\"T_29fd1_row2_col9\" class=\"data row2 col9\" >0,0</td>\n      <td id=\"T_29fd1_row2_col10\" class=\"data row2 col10\" >8,0</td>\n      <td id=\"T_29fd1_row2_col11\" class=\"data row2 col11\" >0,0</td>\n      <td id=\"T_29fd1_row2_col12\" class=\"data row2 col12\" >14,0</td>\n      <td id=\"T_29fd1_row2_col13\" class=\"data row2 col13\" >3,0</td>\n      <td id=\"T_29fd1_row2_col14\" class=\"data row2 col14\" >3,0</td>\n      <td id=\"T_29fd1_row2_col15\" class=\"data row2 col15\" >14,0</td>\n      <td id=\"T_29fd1_row2_col16\" class=\"data row2 col16\" >6,0</td>\n      <td id=\"T_29fd1_row2_col17\" class=\"data row2 col17\" >0,03</td>\n      <td id=\"T_29fd1_row2_col18\" class=\"data row2 col18\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row3\" class=\"row_heading level0 row3\" >Infrastruktur</th>\n      <td id=\"T_29fd1_row3_col0\" class=\"data row3 col0\" >42,0</td>\n      <td id=\"T_29fd1_row3_col1\" class=\"data row3 col1\" >5,0</td>\n      <td id=\"T_29fd1_row3_col2\" class=\"data row3 col2\" >137,0</td>\n      <td id=\"T_29fd1_row3_col3\" class=\"data row3 col3\" >23,0</td>\n      <td id=\"T_29fd1_row3_col4\" class=\"data row3 col4\" >7,0</td>\n      <td id=\"T_29fd1_row3_col5\" class=\"data row3 col5\" >5,0</td>\n      <td id=\"T_29fd1_row3_col6\" class=\"data row3 col6\" >5,0</td>\n      <td id=\"T_29fd1_row3_col7\" class=\"data row3 col7\" >37,0</td>\n      <td id=\"T_29fd1_row3_col8\" class=\"data row3 col8\" >503,0</td>\n      <td id=\"T_29fd1_row3_col9\" class=\"data row3 col9\" >10,0</td>\n      <td id=\"T_29fd1_row3_col10\" class=\"data row3 col10\" >108,0</td>\n      <td id=\"T_29fd1_row3_col11\" class=\"data row3 col11\" >16,0</td>\n      <td id=\"T_29fd1_row3_col12\" class=\"data row3 col12\" >27,0</td>\n      <td id=\"T_29fd1_row3_col13\" class=\"data row3 col13\" >93,0</td>\n      <td id=\"T_29fd1_row3_col14\" class=\"data row3 col14\" >28,0</td>\n      <td id=\"T_29fd1_row3_col15\" class=\"data row3 col15\" >58,0</td>\n      <td id=\"T_29fd1_row3_col16\" class=\"data row3 col16\" >6,0</td>\n      <td id=\"T_29fd1_row3_col17\" class=\"data row3 col17\" >0,36</td>\n      <td id=\"T_29fd1_row3_col18\" class=\"data row3 col18\" >20,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row4\" class=\"row_heading level0 row4\" >Landwirtschaft</th>\n      <td id=\"T_29fd1_row4_col0\" class=\"data row4 col0\" >3,0</td>\n      <td id=\"T_29fd1_row4_col1\" class=\"data row4 col1\" >1,0</td>\n      <td id=\"T_29fd1_row4_col2\" class=\"data row4 col2\" >3,0</td>\n      <td id=\"T_29fd1_row4_col3\" class=\"data row4 col3\" >2,0</td>\n      <td id=\"T_29fd1_row4_col4\" class=\"data row4 col4\" >0,0</td>\n      <td id=\"T_29fd1_row4_col5\" class=\"data row4 col5\" >0,0</td>\n      <td id=\"T_29fd1_row4_col6\" class=\"data row4 col6\" >0,0</td>\n      <td id=\"T_29fd1_row4_col7\" class=\"data row4 col7\" >3,0</td>\n      <td id=\"T_29fd1_row4_col8\" class=\"data row4 col8\" >2,0</td>\n      <td id=\"T_29fd1_row4_col9\" class=\"data row4 col9\" >7,0</td>\n      <td id=\"T_29fd1_row4_col10\" class=\"data row4 col10\" >4,0</td>\n      <td id=\"T_29fd1_row4_col11\" class=\"data row4 col11\" >10,0</td>\n      <td id=\"T_29fd1_row4_col12\" class=\"data row4 col12\" >1,0</td>\n      <td id=\"T_29fd1_row4_col13\" class=\"data row4 col13\" >0,0</td>\n      <td id=\"T_29fd1_row4_col14\" class=\"data row4 col14\" >2,0</td>\n      <td id=\"T_29fd1_row4_col15\" class=\"data row4 col15\" >0,0</td>\n      <td id=\"T_29fd1_row4_col16\" class=\"data row4 col16\" >3,0</td>\n      <td id=\"T_29fd1_row4_col17\" class=\"data row4 col17\" >0,01</td>\n      <td id=\"T_29fd1_row4_col18\" class=\"data row4 col18\" >7,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row5\" class=\"row_heading level0 row5\" >Mikroplastik (< 5mm)</th>\n      <td id=\"T_29fd1_row5_col0\" class=\"data row5 col0\" >26,0</td>\n      <td id=\"T_29fd1_row5_col1\" class=\"data row5 col1\" >0,0</td>\n      <td id=\"T_29fd1_row5_col2\" class=\"data row5 col2\" >0,0</td>\n      <td id=\"T_29fd1_row5_col3\" class=\"data row5 col3\" >9,0</td>\n      <td id=\"T_29fd1_row5_col4\" class=\"data row5 col4\" >21,0</td>\n      <td id=\"T_29fd1_row5_col5\" class=\"data row5 col5\" >0,0</td>\n      <td id=\"T_29fd1_row5_col6\" class=\"data row5 col6\" >0,0</td>\n      <td id=\"T_29fd1_row5_col7\" class=\"data row5 col7\" >0,0</td>\n      <td id=\"T_29fd1_row5_col8\" class=\"data row5 col8\" >0,0</td>\n      <td id=\"T_29fd1_row5_col9\" class=\"data row5 col9\" >0,0</td>\n      <td id=\"T_29fd1_row5_col10\" class=\"data row5 col10\" >0,0</td>\n      <td id=\"T_29fd1_row5_col11\" class=\"data row5 col11\" >0,0</td>\n      <td id=\"T_29fd1_row5_col12\" class=\"data row5 col12\" >0,0</td>\n      <td id=\"T_29fd1_row5_col13\" class=\"data row5 col13\" >3,0</td>\n      <td id=\"T_29fd1_row5_col14\" class=\"data row5 col14\" >0,0</td>\n      <td id=\"T_29fd1_row5_col15\" class=\"data row5 col15\" >14,0</td>\n      <td id=\"T_29fd1_row5_col16\" class=\"data row5 col16\" >0,0</td>\n      <td id=\"T_29fd1_row5_col17\" class=\"data row5 col17\" >0,02</td>\n      <td id=\"T_29fd1_row5_col18\" class=\"data row5 col18\" >1,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row6\" class=\"row_heading level0 row6\" >Persönliche Gegenstände</th>\n      <td id=\"T_29fd1_row6_col0\" class=\"data row6 col0\" >25,0</td>\n      <td id=\"T_29fd1_row6_col1\" class=\"data row6 col1\" >2,0</td>\n      <td id=\"T_29fd1_row6_col2\" class=\"data row6 col2\" >47,0</td>\n      <td id=\"T_29fd1_row6_col3\" class=\"data row6 col3\" >7,0</td>\n      <td id=\"T_29fd1_row6_col4\" class=\"data row6 col4\" >4,0</td>\n      <td id=\"T_29fd1_row6_col5\" class=\"data row6 col5\" >10,0</td>\n      <td id=\"T_29fd1_row6_col6\" class=\"data row6 col6\" >1,0</td>\n      <td id=\"T_29fd1_row6_col7\" class=\"data row6 col7\" >6,0</td>\n      <td id=\"T_29fd1_row6_col8\" class=\"data row6 col8\" >17,0</td>\n      <td id=\"T_29fd1_row6_col9\" class=\"data row6 col9\" >0,0</td>\n      <td id=\"T_29fd1_row6_col10\" class=\"data row6 col10\" >2,0</td>\n      <td id=\"T_29fd1_row6_col11\" class=\"data row6 col11\" >0,0</td>\n      <td id=\"T_29fd1_row6_col12\" class=\"data row6 col12\" >7,0</td>\n      <td id=\"T_29fd1_row6_col13\" class=\"data row6 col13\" >8,0</td>\n      <td id=\"T_29fd1_row6_col14\" class=\"data row6 col14\" >3,0</td>\n      <td id=\"T_29fd1_row6_col15\" class=\"data row6 col15\" >0,0</td>\n      <td id=\"T_29fd1_row6_col16\" class=\"data row6 col16\" >44,0</td>\n      <td id=\"T_29fd1_row6_col17\" class=\"data row6 col17\" >0,05</td>\n      <td id=\"T_29fd1_row6_col18\" class=\"data row6 col18\" >6,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row7\" class=\"row_heading level0 row7\" >Plastikfragmente</th>\n      <td id=\"T_29fd1_row7_col0\" class=\"data row7 col0\" >69,0</td>\n      <td id=\"T_29fd1_row7_col1\" class=\"data row7 col1\" >3,0</td>\n      <td id=\"T_29fd1_row7_col2\" class=\"data row7 col2\" >147,0</td>\n      <td id=\"T_29fd1_row7_col3\" class=\"data row7 col3\" >7,0</td>\n      <td id=\"T_29fd1_row7_col4\" class=\"data row7 col4\" >12,0</td>\n      <td id=\"T_29fd1_row7_col5\" class=\"data row7 col5\" >12,0</td>\n      <td id=\"T_29fd1_row7_col6\" class=\"data row7 col6\" >1,0</td>\n      <td id=\"T_29fd1_row7_col7\" class=\"data row7 col7\" >22,0</td>\n      <td id=\"T_29fd1_row7_col8\" class=\"data row7 col8\" >1,0</td>\n      <td id=\"T_29fd1_row7_col9\" class=\"data row7 col9\" >3,0</td>\n      <td id=\"T_29fd1_row7_col10\" class=\"data row7 col10\" >67,0</td>\n      <td id=\"T_29fd1_row7_col11\" class=\"data row7 col11\" >4,0</td>\n      <td id=\"T_29fd1_row7_col12\" class=\"data row7 col12\" >49,0</td>\n      <td id=\"T_29fd1_row7_col13\" class=\"data row7 col13\" >23,0</td>\n      <td id=\"T_29fd1_row7_col14\" class=\"data row7 col14\" >5,0</td>\n      <td id=\"T_29fd1_row7_col15\" class=\"data row7 col15\" >28,0</td>\n      <td id=\"T_29fd1_row7_col16\" class=\"data row7 col16\" >4,0</td>\n      <td id=\"T_29fd1_row7_col17\" class=\"data row7 col17\" >0,1</td>\n      <td id=\"T_29fd1_row7_col18\" class=\"data row7 col18\" >18,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row8\" class=\"row_heading level0 row8\" >Tabakwaren</th>\n      <td id=\"T_29fd1_row8_col0\" class=\"data row8 col0\" >74,0</td>\n      <td id=\"T_29fd1_row8_col1\" class=\"data row8 col1\" >3,0</td>\n      <td id=\"T_29fd1_row8_col2\" class=\"data row8 col2\" >63,0</td>\n      <td id=\"T_29fd1_row8_col3\" class=\"data row8 col3\" >18,0</td>\n      <td id=\"T_29fd1_row8_col4\" class=\"data row8 col4\" >130,0</td>\n      <td id=\"T_29fd1_row8_col5\" class=\"data row8 col5\" >2,0</td>\n      <td id=\"T_29fd1_row8_col6\" class=\"data row8 col6\" >5,0</td>\n      <td id=\"T_29fd1_row8_col7\" class=\"data row8 col7\" >10,0</td>\n      <td id=\"T_29fd1_row8_col8\" class=\"data row8 col8\" >1,0</td>\n      <td id=\"T_29fd1_row8_col9\" class=\"data row8 col9\" >2,0</td>\n      <td id=\"T_29fd1_row8_col10\" class=\"data row8 col10\" >16,0</td>\n      <td id=\"T_29fd1_row8_col11\" class=\"data row8 col11\" >3,0</td>\n      <td id=\"T_29fd1_row8_col12\" class=\"data row8 col12\" >24,0</td>\n      <td id=\"T_29fd1_row8_col13\" class=\"data row8 col13\" >5,0</td>\n      <td id=\"T_29fd1_row8_col14\" class=\"data row8 col14\" >17,0</td>\n      <td id=\"T_29fd1_row8_col15\" class=\"data row8 col15\" >152,0</td>\n      <td id=\"T_29fd1_row8_col16\" class=\"data row8 col16\" >12,0</td>\n      <td id=\"T_29fd1_row8_col17\" class=\"data row8 col17\" >0,13</td>\n      <td id=\"T_29fd1_row8_col18\" class=\"data row8 col18\" >25,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row9\" class=\"row_heading level0 row9\" >Verpackungen ohne Lebensmittel/Tabak</th>\n      <td id=\"T_29fd1_row9_col0\" class=\"data row9 col0\" >30,0</td>\n      <td id=\"T_29fd1_row9_col1\" class=\"data row9 col1\" >1,0</td>\n      <td id=\"T_29fd1_row9_col2\" class=\"data row9 col2\" >40,0</td>\n      <td id=\"T_29fd1_row9_col3\" class=\"data row9 col3\" >2,0</td>\n      <td id=\"T_29fd1_row9_col4\" class=\"data row9 col4\" >0,0</td>\n      <td id=\"T_29fd1_row9_col5\" class=\"data row9 col5\" >0,0</td>\n      <td id=\"T_29fd1_row9_col6\" class=\"data row9 col6\" >4,0</td>\n      <td id=\"T_29fd1_row9_col7\" class=\"data row9 col7\" >2,0</td>\n      <td id=\"T_29fd1_row9_col8\" class=\"data row9 col8\" >5,0</td>\n      <td id=\"T_29fd1_row9_col9\" class=\"data row9 col9\" >0,0</td>\n      <td id=\"T_29fd1_row9_col10\" class=\"data row9 col10\" >10,0</td>\n      <td id=\"T_29fd1_row9_col11\" class=\"data row9 col11\" >0,0</td>\n      <td id=\"T_29fd1_row9_col12\" class=\"data row9 col12\" >12,0</td>\n      <td id=\"T_29fd1_row9_col13\" class=\"data row9 col13\" >17,0</td>\n      <td id=\"T_29fd1_row9_col14\" class=\"data row9 col14\" >5,0</td>\n      <td id=\"T_29fd1_row9_col15\" class=\"data row9 col15\" >64,0</td>\n      <td id=\"T_29fd1_row9_col16\" class=\"data row9 col16\" >4,0</td>\n      <td id=\"T_29fd1_row9_col17\" class=\"data row9 col17\" >0,05</td>\n      <td id=\"T_29fd1_row9_col18\" class=\"data row9 col18\" >9,0</td>\n    </tr>\n    <tr>\n      <th id=\"T_29fd1_level0_row10\" class=\"row_heading level0 row10\" >nicht klassifiziert</th>\n      <td id=\"T_29fd1_row10_col0\" class=\"data row10 col0\" >3,0</td>\n      <td id=\"T_29fd1_row10_col1\" class=\"data row10 col1\" >2,0</td>\n      <td id=\"T_29fd1_row10_col2\" class=\"data row10 col2\" >27,0</td>\n      <td id=\"T_29fd1_row10_col3\" class=\"data row10 col3\" >2,0</td>\n      <td id=\"T_29fd1_row10_col4\" class=\"data row10 col4\" >2,0</td>\n      <td id=\"T_29fd1_row10_col5\" class=\"data row10 col5\" >0,0</td>\n      <td id=\"T_29fd1_row10_col6\" class=\"data row10 col6\" >0,0</td>\n      <td id=\"T_29fd1_row10_col7\" class=\"data row10 col7\" >0,0</td>\n      <td id=\"T_29fd1_row10_col8\" class=\"data row10 col8\" >0,0</td>\n      <td id=\"T_29fd1_row10_col9\" class=\"data row10 col9\" >0,0</td>\n      <td id=\"T_29fd1_row10_col10\" class=\"data row10 col10\" >0,0</td>\n      <td id=\"T_29fd1_row10_col11\" class=\"data row10 col11\" >0,0</td>\n      <td id=\"T_29fd1_row10_col12\" class=\"data row10 col12\" >0,0</td>\n      <td id=\"T_29fd1_row10_col13\" class=\"data row10 col13\" >96,0</td>\n      <td id=\"T_29fd1_row10_col14\" class=\"data row10 col14\" >0,0</td>\n      <td id=\"T_29fd1_row10_col15\" class=\"data row10 col15\" >14,0</td>\n      <td id=\"T_29fd1_row10_col16\" class=\"data row10 col16\" >0,0</td>\n      <td id=\"T_29fd1_row10_col17\" class=\"data row10 col17\" >0,04</td>\n      <td id=\"T_29fd1_row10_col18\" class=\"data row10 col18\" >0,0</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5441f23f10>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_survey_area_codegroup_pcsm"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggregate_kwargs = {\n",
    "    \"data\": period_data.period_data,\n",
    "    \"unit_label\": unit_label,\n",
    "    \"percent\": False,\n",
    "    \"name\":\"IQAASL\",\n",
    "}        \n",
    "\n",
    "pt_comp =  components[~components.location.isin(not_trail_samples)][[\"location\", \"groupname\", unit_label]].pivot(columns=\"location\", index=\"groupname\")\n",
    "\n",
    "# quash the hierarchal column index\n",
    "pt_comp.columns = pt_comp.columns.get_level_values(1)\n",
    "pt_comp.rename(columns = {x:proper_names[x] for x in pt_comp.columns}, inplace=True)\n",
    "pt_comp[this_feature[\"name\"]] = parentGroupTotals(**component_kwargs)\n",
    "pt_comp[\"IQAASL\"] = parentGroupTotals(**aggregate_kwargs)\n",
    "\n",
    "caption_prefix =  f\"Verwendungszweck der gefundenen Objekte an Wanderwegen: \"\n",
    "col_widths=[4.5*cm, *[1.2*cm]*(len(mc_comp.columns)-1)]\n",
    "codegroup_heatmap_title = [\n",
    "    f\"Unten: Wanderwege Nutzen der gefundenen Objekte: {unit_label} der Gesamtzahl nach Wassermerkmal. Fragmentierte Objekte, \",\n",
    "    \"die nicht eindeutig identifiziert werden können, bleiben nach Grösse klassifiziert.\"\n",
    "]\n",
    "\n",
    "codegroup_heatmap_title = ''.join(codegroup_heatmap_title)\n",
    "table_title = Paragraph(codegroup_heatmap_title, featuredata.p_style)\n",
    "\n",
    "mc_heat_map_caption = \"Verwendungszweck der gefundenen Objekte an Wanderwegen: Median p/100 m.\"\n",
    "tables = featuredata.splitTableWidth(pt_comp, gradient=True, caption_prefix=caption_prefix, caption=mc_heat_map_caption,\n",
    "                    this_feature=this_feature[\"name\"], vertical_header=True, colWidths=col_widths)\n",
    "\n",
    "# identify the tables variable as either a list or a Flowable:\n",
    "if isinstance(tables, (list, np.ndarray)):\n",
    "    grouped_pdf_components = [table_title, featuredata.small_space, *tables]\n",
    "else:\n",
    "    grouped_pdf_components = [table_title, featuredata.small_space, tables]\n",
    "    \n",
    "\n",
    "new_components = [\n",
    "    smallspace,\n",
    "    *grouped_pdf_components\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# format for display\n",
    "pt_comp.index.name = None\n",
    "pt_comp.columns.name = None\n",
    "aformatter = {x: featuredata.replaceDecimal for x in pt_comp.columns}\n",
    "ptd = pt_comp.style.format(aformatter).set_table_styles(heat_map_css_styles).background_gradient(axis=None, vmin=pt_comp.min().min(), vmax=pt_comp.max().max(), cmap=\"YlOrBr\")\n",
    "ptd = ptd.applymap_index(featuredata.rotateText, axis=1)\n",
    "glue('alpes_survey_area_codegroup_pcsm', ptd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_codegroup_pcsm\n",
    "---\n",
    "name: 'alpes_survey_area_codegroup_pcsm'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{numref}`Abbildung %s: <alpes_survey_area_codegroup_pcsm>` *__Unten:__ Verwendungszweck der gefundenen Objekte an Wanderwegen: Median p/100 m.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perzentil-Rangfolge der Erhebungsergebnisse in Bezug auf die Landnutzung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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#T_a2077_row0_col14, #T_a2077_row1_col7, #T_a2077_row2_col7 {\n  background-color: #fff1b0;\n  color: #000000;\n}\n#T_a2077_row0_col8, #T_a2077_row2_col8 {\n  background-color: #fff9c9;\n  color: #000000;\n}\n#T_a2077_row0_col9, #T_a2077_row0_col12 {\n  background-color: #fffddb;\n  color: #000000;\n}\n#T_a2077_row0_col10, #T_a2077_row1_col2, #T_a2077_row1_col10 {\n  background-color: #fff7bd;\n  color: #000000;\n}\n#T_a2077_row0_col13, #T_a2077_row1_col13, #T_a2077_row2_col13 {\n  background-color: #fffee1;\n  color: #000000;\n}\n#T_a2077_row0_col15, #T_a2077_row1_col15, #T_a2077_row2_col15 {\n  background-color: #fffedf;\n  color: #000000;\n}\n#T_a2077_row0_col16, #T_a2077_row1_col16, #T_a2077_row2_col16 {\n  background-color: #fff8c0;\n  color: #000000;\n}\n#T_a2077_row1_col5, #T_a2077_row1_col11, #T_a2077_row2_col11 {\n  background-color: #fffbcf;\n  color: #000000;\n}\n#T_a2077_row1_col8 {\n  background-color: #fff9c7;\n  color: #000000;\n}\n#T_a2077_row1_col9, #T_a2077_row1_col12 {\n  background-color: #fffcd8;\n  color: #000000;\n}\n#T_a2077_row2_col0, #T_a2077_row2_col6 {\n  background-color: #fffbd2;\n  color: #000000;\n}\n#T_a2077_row2_col9, #T_a2077_row2_col12 {\n  background-color: #fffdd9;\n  color: #000000;\n}\n#T_a2077_level0_col0, #T_a2077_level0_col1, #T_a2077_level0_col2, #T_a2077_level0_col3, #T_a2077_level0_col4, #T_a2077_level0_col5, #T_a2077_level0_col6, #T_a2077_level0_col7, #T_a2077_level0_col8, #T_a2077_level0_col9, #T_a2077_level0_col10, #T_a2077_level0_col11, #T_a2077_level0_col12, #T_a2077_level0_col13, #T_a2077_level0_col14, #T_a2077_level0_col15, #T_a2077_level0_col16 {\n  writing-mode: vertical-lr;\n  transform: rotate(-180deg);\n  padding: 10px;\n  margins: 0;\n  vertical-align: baseline;\n}\n</style>\n<table id=\"T_a2077\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a2077_level0_col0\" class=\"col_heading level0 col0\" >Val Calanca</th>\n      <th id=\"T_a2077_level0_col1\" class=\"col_heading level0 col1\" >Airolo</th>\n      <th id=\"T_a2077_level0_col2\" class=\"col_heading level0 col2\" >Nendaz</th>\n      <th id=\"T_a2077_level0_col3\" class=\"col_heading level0 col3\" >Andermatt</th>\n      <th id=\"T_a2077_level0_col4\" class=\"col_heading level0 col4\" >Verbier</th>\n      <th id=\"T_a2077_level0_col5\" class=\"col_heading level0 col5\" >Crans-Montana</th>\n      <th id=\"T_a2077_level0_col6\" class=\"col_heading level0 col6\" >Villars</th>\n      <th id=\"T_a2077_level0_col7\" class=\"col_heading level0 col7\" >Tour Les Crosets</th>\n      <th id=\"T_a2077_level0_col8\" class=\"col_heading level0 col8\" >Morgins</th>\n      <th id=\"T_a2077_level0_col9\" class=\"col_heading level0 col9\" >La Berra</th>\n      <th id=\"T_a2077_level0_col10\" class=\"col_heading level0 col10\" >Grindelwald</th>\n      <th id=\"T_a2077_level0_col11\" class=\"col_heading level0 col11\" >La Tzoumaz</th>\n      <th id=\"T_a2077_level0_col12\" class=\"col_heading level0 col12\" >Robella</th>\n      <th id=\"T_a2077_level0_col13\" class=\"col_heading level0 col13\" >Les Diablerets</th>\n      <th id=\"T_a2077_level0_col14\" class=\"col_heading level0 col14\" >Charmey</th>\n      <th id=\"T_a2077_level0_col15\" class=\"col_heading level0 col15\" >Monte Generoso</th>\n      <th id=\"T_a2077_level0_col16\" class=\"col_heading level0 col16\" >Les Paccots</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a2077_level0_row0\" class=\"row_heading level0 row0\" >Landwirtschaftlich</th>\n      <td id=\"T_a2077_row0_col0\" class=\"data row0 col0\" >29</td>\n      <td id=\"T_a2077_row0_col1\" class=\"data row0 col1\" >83</td>\n      <td id=\"T_a2077_row0_col2\" class=\"data row0 col2\" >63</td>\n      <td id=\"T_a2077_row0_col3\" class=\"data row0 col3\" >4</td>\n      <td id=\"T_a2077_row0_col4\" class=\"data row0 col4\" >75</td>\n      <td id=\"T_a2077_row0_col5\" class=\"data row0 col5\" >32</td>\n      <td id=\"T_a2077_row0_col6\" class=\"data row0 col6\" >29</td>\n      <td id=\"T_a2077_row0_col7\" class=\"data row0 col7\" >82</td>\n      <td id=\"T_a2077_row0_col8\" class=\"data row0 col8\" >44</td>\n      <td id=\"T_a2077_row0_col9\" class=\"data row0 col9\" >17</td>\n      <td id=\"T_a2077_row0_col10\" class=\"data row0 col10\" >62</td>\n      <td id=\"T_a2077_row0_col11\" class=\"data row0 col11\" >33</td>\n      <td id=\"T_a2077_row0_col12\" class=\"data row0 col12\" >17</td>\n      <td id=\"T_a2077_row0_col13\" class=\"data row0 col13\" >6</td>\n      <td id=\"T_a2077_row0_col14\" class=\"data row0 col14\" >82</td>\n      <td id=\"T_a2077_row0_col15\" class=\"data row0 col15\" >11</td>\n      <td id=\"T_a2077_row0_col16\" class=\"data row0 col16\" >57</td>\n    </tr>\n    <tr>\n      <th id=\"T_a2077_level0_row1\" class=\"row_heading level0 row1\" >Wald</th>\n      <td id=\"T_a2077_row1_col0\" class=\"data row1 col0\" >32</td>\n      <td id=\"T_a2077_row1_col1\" class=\"data row1 col1\" >83</td>\n      <td id=\"T_a2077_row1_col2\" class=\"data row1 col2\" >61</td>\n      <td id=\"T_a2077_row1_col3\" class=\"data row1 col3\" >4</td>\n      <td id=\"T_a2077_row1_col4\" class=\"data row1 col4\" >76</td>\n      <td id=\"T_a2077_row1_col5\" class=\"data row1 col5\" >34</td>\n      <td id=\"T_a2077_row1_col6\" class=\"data row1 col6\" >32</td>\n      <td id=\"T_a2077_row1_col7\" class=\"data row1 col7\" >82</td>\n      <td id=\"T_a2077_row1_col8\" class=\"data row1 col8\" >47</td>\n      <td id=\"T_a2077_row1_col9\" class=\"data row1 col9\" >20</td>\n      <td id=\"T_a2077_row1_col10\" class=\"data row1 col10\" >61</td>\n      <td id=\"T_a2077_row1_col11\" class=\"data row1 col11\" >34</td>\n      <td id=\"T_a2077_row1_col12\" class=\"data row1 col12\" >20</td>\n      <td id=\"T_a2077_row1_col13\" class=\"data row1 col13\" >6</td>\n      <td id=\"T_a2077_row1_col14\" class=\"data row1 col14\" >83</td>\n      <td id=\"T_a2077_row1_col15\" class=\"data row1 col15\" >11</td>\n      <td id=\"T_a2077_row1_col16\" class=\"data row1 col16\" >57</td>\n    </tr>\n    <tr>\n      <th id=\"T_a2077_level0_row2\" class=\"row_heading level0 row2\" >population</th>\n      <td id=\"T_a2077_row2_col0\" class=\"data row2 col0\" >30</td>\n      <td id=\"T_a2077_row2_col1\" class=\"data row2 col1\" >83</td>\n      <td id=\"T_a2077_row2_col2\" class=\"data row2 col2\" >63</td>\n      <td id=\"T_a2077_row2_col3\" class=\"data row2 col3\" >5</td>\n      <td id=\"T_a2077_row2_col4\" class=\"data row2 col4\" >75</td>\n      <td id=\"T_a2077_row2_col5\" class=\"data row2 col5\" >32</td>\n      <td id=\"T_a2077_row2_col6\" class=\"data row2 col6\" >30</td>\n      <td id=\"T_a2077_row2_col7\" class=\"data row2 col7\" >82</td>\n      <td id=\"T_a2077_row2_col8\" class=\"data row2 col8\" >45</td>\n      <td id=\"T_a2077_row2_col9\" class=\"data row2 col9\" >18</td>\n      <td id=\"T_a2077_row2_col10\" class=\"data row2 col10\" >63</td>\n      <td id=\"T_a2077_row2_col11\" class=\"data row2 col11\" >34</td>\n      <td id=\"T_a2077_row2_col12\" class=\"data row2 col12\" >18</td>\n      <td id=\"T_a2077_row2_col13\" class=\"data row2 col13\" >6</td>\n      <td id=\"T_a2077_row2_col14\" class=\"data row2 col14\" >83</td>\n      <td id=\"T_a2077_row2_col15\" class=\"data row2 col15\" >11</td>\n      <td id=\"T_a2077_row2_col16\" class=\"data row2 col16\" >58</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5441f0acd0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_survey_area_pranking_luse"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nvsn = fdt.location.unique()\n",
    "\n",
    "l_f = [\"% agg\", \"% woods\", \"population\"]\n",
    "\n",
    "this_range = (fdt[l_f[0]].min(), fdt[l_f[0]].max())\n",
    "this_range_w = (fdt[l_f[1]].min(), fdt[l_f[1]].max())\n",
    "this_range_p = (fdt[l_f[2]].min(), fdt[l_f[2]].max())\n",
    "\n",
    "a_data = period_data.period_data.copy()\n",
    "\n",
    "# apply them to all the data\n",
    "# one test for agg -- a dominant land use feature of the sample data\n",
    "some_data = a_data[(a_data[l_f[0]] >= this_range[0])&(a_data[l_f[0]] <= this_range[1])].copy()\n",
    "\n",
    "# one test for woods -- the dominant land use feature of the sample data\n",
    "some_data_w = a_data[(a_data[l_f[1]] >= this_range_w[0])&(a_data[l_f[1]] <= this_range_w[1])].copy()\n",
    "\n",
    "# one test for population -- \n",
    "some_data_p = a_data[(a_data[l_f[2]] >= this_range_p[0])&(a_data[l_f[2]] <= this_range_p[1])].copy()\n",
    "\n",
    "# remove Alps valaisannes\n",
    "some_data = some_data[~some_data.location.isin(fd.location.unique())].copy()\n",
    "some_data_w = some_data_w[~some_data_w.location.isin(fd.location.unique())].copy()\n",
    "some_data_p = some_data_p[~some_data_p.location.isin(fd.location.unique())].copy()\n",
    "\n",
    "# the number of samples and locations that have similar land use profiles as AV:\n",
    "# agg to loc_date for each criteria\n",
    "# data for charting and comparing\n",
    "data=some_data.groupby([\"loc_date\",\"location\",l_f[0]], as_index=False)[unit_label].sum()\n",
    "data_w =some_data_w.groupby([\"loc_date\",\"location\",l_f[1]], as_index=False)[unit_label].sum()\n",
    "data_p = some_data_p.groupby([\"loc_date\",\"location\",*l_f[1:]], as_index=False)[unit_label].sum()\n",
    "\n",
    "# get the percentile ranking for each location under each condtion:\n",
    "table_data = {}\n",
    "for i,x in enumerate(nvsn):\n",
    "    this_num = fdt_samples.loc[fdt_samples.location == x, unit_label].values[0]\n",
    "    a = (stats.percentileofscore(data[unit_label].to_numpy(), this_num))\n",
    "    b = (stats.percentileofscore(data_p[unit_label].to_numpy(), this_num))\n",
    "    c = (stats.percentileofscore(data_w[unit_label].to_numpy(), this_num))\n",
    "    table_data.update({x:{\"Landwirtschaftlich\":a, \"Wald\":b, \"population\":c}})\n",
    "\n",
    "# make df and format\n",
    "t_data = pd.DataFrame(table_data)\n",
    "t_data = t_data.astype(\"int\")\n",
    "t_data.reset_index(inplace=True)\n",
    "t_data.rename(columns={\"index\":\"variable\"}, inplace=True)\n",
    "t_data.set_index(\"variable\", inplace=True, drop=True)\n",
    "t_data.rename(columns={x:proper_names.loc[x] for x in t_data.columns}, inplace=True)\n",
    "t_data.index.name = None\n",
    "ptd = t_data.style.set_table_styles(heat_map_css_styles).background_gradient(axis=None, vmin=pt_comp.min().min(), vmax=pt_comp.max().max(), cmap=\"YlOrBr\")\n",
    "ptd = ptd.applymap_index(featuredata.rotateText, axis=1)\n",
    "\n",
    "glue('alpes_survey_area_pranking_luse', ptd, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_pranking_luse\n",
    "---\n",
    "name: 'alpes_survey_area_pranking_luse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_survey_area_pranking_luse>` Die Rangfolge der Erhebungsorte in den Alpen und im Jura in Bezug auf die Landnutzung. Die Erhebungsergebnisse in Airolo waren zum Beispiel höher als 83 % aller Erhebungen (Seen, Fliessgewässer, Alpen und Jura). In Andermatt liegen die Erhebungsergebnisse unter 95 % aller Erhebungen mit einem vergleichbaren Landnutzungsprofil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "f = t_data.T.sort_index()\n",
    "fone = f.loc[[\"Airolo\", \"Andermatt\", \"Charmey\", \"Crans-Montana\", \"Grindelwald\", \"La Berra\", \"La Tzoumaz\", \"Les Diablerets\", \"Les Paccots\"], :]\n",
    "col_widths = [3*cm, 4*cm, 1.2*cm, 3*cm]\n",
    "table_one = featuredata.aSingleStyledTable(f, colWidths=col_widths)\n",
    "\n",
    "ftwo = f.loc[[\"Monte Generoso\", \"Morgins\", \"Nendaz\", \"Robella\", \"Tour Les Crosets\", \"Val Calanca\", \"Verbier\", \"Villars\"], :]\n",
    "table_two = featuredata.aSingleStyledTable(ftwo, colWidths=col_widths)\n",
    "\n",
    "description = [\n",
    "    \"Die Rangfolge der Erhebungsorte in den Alpen und im Jura in Bezug auf die Landnutzung. \",\n",
    "    \"Die Erhebungsergebnisse in Airolo waren zum Beispiel höher als 83 % aller Erhebungen \",\n",
    "    \"(Seen, Fliessgewässer, Alpen und Jura). In Andermatt liegen die Erhebungsergebnisse unter 95 % \",\n",
    "    \"aller Erhebungen mit einem vergleichbaren Landnutzungsprofil.\",\n",
    "]\n",
    "\n",
    "description = ''.join(description)\n",
    "\n",
    "title = Paragraph(\"Perzentil-Rangfolge der Erhebungsergebnisse in Bezug auf die Landnutzung\", featuredata.section_title)\n",
    "\n",
    "caption = Paragraph(description, featuredata.p_style)\n",
    "\n",
    "three = [[table_one, caption ]]\n",
    "col_widths = [ sum(col_widths), 6*cm]\n",
    "table_three = Table(three, colWidths=col_widths, style=featuredata.figure_table_style)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    title,\n",
    "    smallspace, \n",
    "    table_three\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Diskussion\n",
    "\n",
    "### Vergleich der Ergebnisse: Alpen und Jura versus Seen und Fliessgewässer\n",
    "\n",
    "Der Medianwert beträgt 110 p/100 m für die 17 Erhebungsorte, die die Kriterien für Länge und Breite im Erhebungsgebiet Alpen und Jura erfüllen, und liegt damit unter dem Medianwert aller anderen Erhebungsgebiete (189 p/100 m). Objekte, die mit dem Konsum von Nahrungsmitteln, Getränken und Tabakwaren in Verbindung stehen, machten einen geringeren Prozentsatz der Gesamtzahl aus und wiesen eine niedrigere p/100 m-Rate auf als Erhebungsorte entlang von Wassersystemen. Dieser Unterschied könnte zum Teil auf die geringe Verstädterung zurückzuführen sein, die das Erhebungsgebiet Alpen und Jura im Vergleich zu allen anderen Erhebungsgebieten kennzeichnet.\n",
    "\n",
    "Der Anteil von Objekten, die mit der Infrastruktur zusammenhängen, ist mit 36 % doppelt so hoch wie in allen Untersuchungsgebieten zusammen. Dies ist grösstenteils auf die Fäden von Seilbahnbürsten zurückzuführen, die in Les-Crosets in grossen Mengen gefunden wurden. Seilbahnbürsten werden verwendet, um den Schnee von der Oberseite der abgedeckten Seilbahnkabinen zu entfernen, wenn diese sich dem Einstiegsort nähern. Ähnlich wie Industriepellets oder Schaumstoffkügelchen in der aquatischen Umwelt werden Teile von Seilbahnbürsten wahrscheinlich immer wieder in gelegentlich grossen Mengen an ganz bestimmten Orten gefunden.\n",
    "\n",
    "Das Verhältnis von Objekten, die mit der Infrastruktur zusammenhängen zu solchen, die mit dem Konsum von Lebensmitteln und Tabakwaren in Verbindung stehen, ist fast 1:1. Solche Ergebnisse sind typisch für Umgebungen mit einer besser entwickelten Infrastruktur, siehe  [Gemeinsame Verantwortung] (Verkehr). Fragmentierte Kunststoffe werden in ähnlichem Umfang wie in den anderen Untersuchungsgebieten gefunden. Neu auf der Liste der häufigsten Objekte sind Kabelbinder und Abdeckband. Beide Objekte werden auch an Seen und Fliessgewässern gefunden, allerdings mit einer Häufigkeit von unter 50 %.\n",
    "\n",
    "Es sei daran erinnert, dass diese Erhebungen entlang von Wintersportinfrastrukturen (Pisten, Wartebereiche bei den Liften etc.) oder eines Wanderweges in einem Skigebiet durchgeführt wurden. Auch wenn die Nutzung im Winter erhöht sein mag, sind viele Gebiete auch im Sommer hervorragende Wandergebiete, so dass eine ganzjährige Nutzung dieser Regionen möglich ist.\n",
    "\n",
    "#### Die am häufigsten gefundenen Objekte\n",
    "\n",
    "Die am häufigsten vorkommenden Objekte machen 74 % sämtlicher gefundener Objekte aus. Die Zigarettenstummel lagen im Erhebungsgebiet Alpen und Jura nicht über dem nationalen Median, allerdings wurden in Verbier, Grindelwald und Airolo signifikante Werte festgestellt. Zudem sind spezifische Objekte aus der Gruppe der Infrastruktur vertreten, wie z. B.:   \n",
    "\n",
    "1. Schrauben und Bolzen \n",
    "2. Kabelbinder\n",
    "3. Abdeckband\n",
    "4. Seilbahnbürste \n",
    "\n",
    "Das Fehlen von expandiertem oder extrudiertem Polystyrol in der Liste der am häufigsten vorkommenden Objekte im Erhebungsgebiet Alpen und Jura steht in scharfem Kontrast zu den anderen Erhebungsgebieten, in denen expandiertes oder extrudiertes Polystyrol etwa 13 % der Gesamtmenge ausmacht, siehe [_Seen und Flüsse_](allsurveys).\n",
    "\n",
    "### Implementierung von Abfallerhebungen in das bestehende Geschäftsmodell\n",
    "\n",
    "Im Vergleich zu einer Abfallerhebung an Seen und Fliessgewässern deckt eine Aufräumaktion ein relativ grosses geographisches Gebiet ab. Freiwillige, die an einem solchen Clean-up teilnehmen, werden von der Möglichkeit angezogen, sich um die Umwelt zu kümmern und sich in der Gesellschaft anderer (in den Bergen) zu bewegen. Erhebungen zu Abfallobjekten an Seen und Fliessgewässern bieten nicht dasselbe Aktivitätsniveau und sind möglicherweise nicht für alle Freiwilligen von Interesse.\n",
    "\n",
    "Wer Abfallerhebungen durchführt, gibt Freiwilligen die Möglichkeit, vor Ort Erfahrungen zu sammeln, muss aber auch intern die Ressourcen bereitstellen, um sicherzustellen, dass die Untersuchung gemäss dem Protokoll durchgeführt wird. Dazu gehören das Identifizieren, Zählen und Eingeben von Daten. Die Summit Foundation war in der Lage, dies zu tun, indem sie dafür sorgte, dass bei jedem Clean-up eine Person anwesend war, die die Erhebung durchführen konnte.\n",
    "\n",
    "Die Personen, die die Erhebung ausführten, zogen es vor, die Proben entlang der Wintersportinfrastrukturen zu nehmen und bei den Bergstationen zu beginnen. Die auf diese Weise entnommenen Proben folgen dem Verlauf des Clean-ups: bergab und in den Bereichen mit hohem Verkehrsaufkommen.\n",
    "\n",
    "Proben, die in der Nähe von Gebäuden oder anderen Einrichtungen genommen wurden, ergaben höhere Erhebungsergebnisse. Damit bestätigte sich, was die Mitglieder der Summit Foundation in den vergangenen Jahren festgestellt hatten. Aus diesen Erfahrungen erklärte der Projektleiter, Téo Gürsoy:    \n",
    "\n",
    "> Die Personen, die Erhebung ausführen, konzentrieren sich nämlich hauptsächlich auf die Abschnitte unter den Sesselliften, Gondeln oder bei der Abfahrt und Ankunft dieser Anlagen, die stark frequentierte Orte sind.\n",
    "\n",
    "In einigen Fällen ist die Dichte der Objekte so gross, dass sich die Person, welche die Erhebung ausführt, gezwungen sah, sich auf einen Bereich zu konzentrieren. Téo Gürsoy beschrieb, was passierte, als eine Person, welche die Erhebung ausführt, auf einen Ort stiess, der grosse Mengen von Skiliftbürsten enthielt:  \n",
    "\n",
    "> Die Person, welche die Erhebung ausführt, begann den Streckenabschnitt […] an der Ankunftsstation der Gondel. Die Skiliftbürsten erregten schnell die Aufmerksamkeit der Person, die beschloss, sich nur auf den betroffenen Bereich zu konzentrieren, um herauszufinden, wie viele von ihnen zu finden waren.\n",
    "\n",
    "Die Erhebungsergebnisse rund um Infrastruktur oder Gebäude sind kein Indikator für den Zustand der Umwelt im gesamten Gebiet. Erhebungen in der Umgebung dieser Strukturen weisen tendenziell höhere Werte auf, machen aber nur einen kleinen Teil der gesamten Landnutzung aus.  \n",
    "\n",
    "Es mussten Anpassungen an der Software und dem Berichtsschema vorgenommen werden, um die verschiedenen Arten von Daten zu verarbeiten, die bei Aufräumarbeiten anfallen. Dazu gehörte auch die Schaffung neuer Identifikationscodes für bestimmte Objekte, die im Untersuchungsgebiet Alpen und Jura gefunden werden. Ausserdem stellte die Summit Foundation die Ressourcen zur Verfügung, damit ein Mitarbeiter der Stiftung in der Anwendung des Projektprotokolls und der Software geschult werden konnte. \n",
    "\n",
    "#### Schlussfolgerungen\n",
    "\n",
    "Die Erhebungen, die entlang der Wege und Wintersportinfrastrukturen im Untersuchungsgebiet Alpen und Jura durchgeführt wurden, ergaben Daten, die den Daten der Erhebungen entlang von Seen und Fliessgewässern sehr ähnlich waren. Wenn sich die Personen, die die Erhebungen durchgeführt haben, jedoch auf bestimmte Infrastruktureinrichtungen konzentrierten, wurden extreme Werte ermittelt. Die Erhebungen Seen und Fliessgewässern würden zu den gleichen Ergebnissen führen, wenn die Erhebungen nur an Orten durchgeführt würden, an denen einen hohen Anteil an Abfallobjekten wahrscheinlicher sind.   \n",
    "\n",
    "Objekte aus dem Bereich Essen und Trinken machen nur 11 % der insgesamt gefundenen Objekte aus, verglichen mit 36 % in den anderen Untersuchungsgebieten. Der Anteil an Abfallobjekten aus dem Bereich Infrastruktur beträgt in den Alpen und im Jura jedoch 75 % gegenüber 18 % in allen anderen Untersuchungsgebieten. Dies ist zum Teil auf den Unterschied in der menschlichen Präsenz im Vergleich zu Orten in niedrigeren Höhenlagen zurückzuführen, wo die menschliche Präsenz das ganze Jahr über konstant ist, so dass der Druck durch Nahrungs- und Genussmittel im Gegensatz zur Infrastruktur grösser ist.  \n",
    "\n",
    "Dieses erste Projekt hat auch gezeigt, dass es möglich ist, die Erhebung mit Clean-ups zu kombinieren. In Vorbereitung auf die Erhebung tauschten die Mitglieder beider Teams Ideen aus und sortierten gemeinsam Proben. Dies ermöglichte es beiden Organisationen, sich gegenseitig besser zu verstehen und Basisleistungen zu bestimmen, die bei der Datenerfassung für einen nationalen Bericht erbracht werden konnten:  \n",
    "\n",
    "1. Unterstützung bei der Erfassung und Identifizierung von Abfallobjekten\n",
    "2. Unterstützung bei der Dateneingabe\n",
    "3. Erstellung von Diagrammen, Grafiken und Daten, die von den teilnehmenden Organisationen verwendet werden können \n",
    "\n",
    "Eine Erhebung von Abfällen an Seen und Fliessgewässern dauert 2–4 Stunden, je nachdem, wie viele verschiedene Objekte es gibt. Diese Ressourcen waren im Betriebsbudget der beiden Organisationen nicht vorgesehen. Daher stellte die Summit Foundation die Koordination und Infrastruktur zur Verfügung und Hammerdirt eine zusätzliche Person, die die Erhebung ausführt, sowie IT-Unterstützung. \n",
    "\n",
    "Die zur Verfügung gestellten Daten ermöglichen direkte Vergleiche zwischen den Orten, vorausgesetzt, es wird die gleiche Erhebungsmethode verwendet. Eine grosse Anzahl von Abfallobjekten mit Infrastrukturbezug im Vergleich zu Objekten aus dem Bereich Lebensmittel und Tabakwaren ist typisch für ländliche Gebiete. Wie gut die Daten aus dem Erhebungsgebiet Alpen und Jura mit jenen an Seen und Fliessgewässern vergleichbar sind, muss noch weiter untersucht werden. Zigarettenstummel, Glasscherben, Plastiksplitter und Snack-Verpackungen gehören jedoch zu den häufigsten Objekten, die in Wassernähe gefunden werden.\n",
    "\n",
    "Wir danken allen Mitgliedern der Summit Foundation für ihre Hilfe, insbesondere Olivier Kressmann und Téo Gürsoy. "
   ]
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    "section_title = Paragraph(\"Diskussion\", featuredata.section_title)\n",
    "subsection_one = Paragraph(\"ergleich der Ergebnisse: Alpen und Jura versus Seen und Fliessgewässer\", featuredata.subsection_title)\n",
    "\n",
    "\n",
    "pone = [\n",
    "    f\"Der Medianwert beträgt 110 {unit_label} für die 17 Erhebungsorte, die die Kriterien für Länge \",\n",
    "    \"und Breite im Erhebungsgebiet Alpen und Jura erfüllen, und liegt damit unter dem Medianwert aller \",\n",
    "    f\"anderen Erhebungsgebiete (189 {unit_label}). Objekte, die mit dem Konsum von Nahrungsmitteln, Getränken \",\n",
    "    \"und Tabakwaren in Verbindung stehen, machten einen geringeren Prozentsatz der Gesamtzahl aus und wiesen \",\n",
    "    f\"eine niedrigere {unit_label} m-Rate auf als Erhebungsorte entlang von Wassersystemen. Dieser Unterschied \",\n",
    "    \"könnte zum Teil auf die geringe Verstädterung zurückzuführen sein, die das Erhebungsgebiet Alpen und Jura im \",\n",
    "    \"Vergleich zu allen anderen Erhebungsgebieten kennzeichnet. \"\n",
    "]\n",
    "\n",
    "\n",
    "ptwo = [\n",
    "    \"Der Anteil von Objekten, die mit der Infrastruktur zusammenhängen, ist mit 36 % doppelt so hoch wie in allen \",\n",
    "    \"Untersuchungsgebieten zusammen. Dies ist grösstenteils auf die Fäden von Seilbahnbürsten zurückzuführen, die \",\n",
    "    \"in Les-Crosets in grossen Mengen gefunden wurden. Seilbahnbürsten werden verwendet, um den Schnee von der \",\n",
    "    \"Oberseite der abgedeckten Seilbahnkabinen zu entfernen, wenn diese sich dem Einstiegsort nähern. Ähnlich wie \",\n",
    "    \"Industriepellets oder Schaumstoffkügelchen in der aquatischen Umwelt werden Teile von Seilbahnbürsten wahrscheinlich \",\n",
    "    \"immer wieder in gelegentlich grossen Mengen an ganz bestimmten Orten gefunden.\"\n",
    "]\n",
    "\n",
    "pthree = [\n",
    "    \"Das Verhältnis von Objekten, die mit der Infrastruktur zusammenhängen zu solchen, die mit dem Konsum von Lebensmitteln \",\n",
    "    \"und Tabakwaren in Verbindung stehen, ist fast 1:1. Solche Ergebnisse sind typisch für Umgebungen mit einer besser entwickelten Infrastruktur, \",\n",
    "    'siehe <a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/shared_responsibility.html\" color=\"blue\">Gemeinsame Verantwortung</a>. ',\n",
    "    \"Fragmentierte Kunststoffe werden in ähnlichem , Umfang wie in den anderen Untersuchungsgebieten gefunden. Neu auf der Liste der häufigsten \",\n",
    "    \"Objekte sind Kabelbinder und Abdeckband. Beide Objekte werden auch an Seen und Fliessgewässern gefunden, allerdings mit einer Häufigkeit von unter 50 %.\"\n",
    "]\n",
    "\n",
    "pfour = [\n",
    "    \"Es sei daran erinnert, dass diese Erhebungen entlang von Wintersportinfrastrukturen (Pisten, Wartebereiche bei den Liften etc.) \",\n",
    "    \"oder eines Wanderweges in einem Skigebiet durchgeführt wurden. Auch wenn die Nutzung im Winter erhöht sein mag, sind viele Gebiete \",\n",
    "    \"auch im Sommer hervorragende Wandergebiete, so dass eine ganzjährige Nutzung dieser Regionen möglich ist.\"\n",
    "]\n",
    "\n",
    "pone_to_four = sectionParagraphs([pone, ptwo, pthree, pfour])\n",
    "\n",
    "new_components = [\n",
    "    featuredata.large_space,\n",
    "    section_title,\n",
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    "    subsection_title,\n",
    "    smallspace,\n",
    "    *pone_to_four,\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "# new section\n",
    "\n",
    "subsection_two_title = Paragraph(\"Implementierung von Abfallerhebungen in das bestehende Geschäftsmodell\", featuredata.subsection_title)\n",
    "\n",
    "most_common_summary = Paragraph(\"Die am häufigsten gefundenen Objekte\", featuredata.bold_block)\n",
    "\n",
    "p_2_one = [\n",
    "    \"Die am häufigsten vorkommenden Objekte machen 74 % sämtlicher gefundener Objekte aus. Die Zigarettenstummel lagen im Erhebungsgebiet \",\n",
    "    \"Alpen und Jura nicht über dem nationalen Median, allerdings wurden in Verbier, Grindelwald und Airolo signifikante Werte festgestellt. \",\n",
    "    \"Zudem sind spezifische Objekte aus der Gruppe der Infrastruktur vertreten, wie z. B.:\"\n",
    "]\n",
    "\n",
    "first_list = [\n",
    "    \"Schrauben und Bolzen\",\n",
    "    \"Kabelbinder\",\n",
    "    \"Abdeckband\",\n",
    "    \"Seilbahnbürste\"\n",
    "]\n",
    "\n",
    "p_six = [\n",
    "    \"Das Fehlen von expandiertem oder extrudiertem Polystyrol in der Liste der am häufigsten vorkommenden Objekte im \",\n",
    "    \"Erhebungsgebiet Alpen und Jura steht in scharfem Kontrast zu den anderen Erhebungsgebieten, in denen expandiertes \",\n",
    "    \"oder extrudiertes Polystyrol etwa 13 % der Gesamtmenge ausmacht, siehe \",\n",
    "    '<a href=\"https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/lakes_rivers.html\" color=\"blue\">Seen und Flüsse</a>.'\n",
    "]\n",
    "\n",
    "\n",
    "pfive = sectionParagraphs([p_2_one])\n",
    "items_list = makeAList(first_list)\n",
    "psix = sectionParagraphs([p_six])\n",
    "\n",
    "new_components = [\n",
    "    most_common_summary,\n",
    "    smallspace,\n",
    "    *pfive,\n",
    "    items_list,\n",
    "    smallspace,\n",
    "    *psix,\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "p_seven = [\n",
    "    \"Im Vergleich zu einer Abfallerhebung an Seen und Fliessgewässern deckt eine Aufräumaktion ein relativ grosses \",\n",
    "    \"geographisches Gebiet ab. Freiwillige, die an einem solchen Clean-up teilnehmen, werden von der Möglichkeit angezogen, \",\n",
    "    \"sich um die Umwelt zu kümmern und sich in der Gesellschaft anderer (in den Bergen) zu bewegen. Erhebungen zu Abfallobjekten \",\n",
    "    \"an Seen und Fliessgewässern bieten nicht dasselbe Aktivitätsniveau und sind möglicherweise nicht für alle Freiwilligen von Interesse.\"\n",
    "]\n",
    "\n",
    "p_eight = [\n",
    "    \"Wer Abfallerhebungen durchführt, gibt Freiwilligen die Möglichkeit, vor Ort Erfahrungen zu sammeln, muss aber \",\n",
    "    \"auch intern die Ressourcen bereitstellen, um sicherzustellen, dass die Untersuchung gemäss dem Protokoll \",\n",
    "    \"durchgeführt wird. Dazu gehören das Identifizieren, Zählen und Eingeben von Daten. Die Summit Foundation war in \",\n",
    "    \"der Lage, dies zu tun, indem sie dafür sorgte, dass bei jedem Clean-up eine Person anwesend war, die die Erhebung durchführen konnte.\"\n",
    "]\n",
    "\n",
    "p_nine = [\n",
    "    \"Die Personen, die die Erhebung ausführten, zogen es vor, die Proben entlang der Wintersportinfrastrukturen zu nehmen \",\n",
    "    \"und bei den Bergstationen zu beginnen. Die auf diese Weise entnommenen Proben folgen dem Verlauf des Clean-ups: \",\n",
    "    \"bergab und in den Bereichen mit hohem Verkehrsaufkommen.\"\n",
    "]\n",
    "\n",
    "p_ten = [\n",
    "    \"Proben, die in der Nähe von Gebäuden oder anderen Einrichtungen genommen wurden, ergaben höhere Erhebungsergebnisse. \",\n",
    "    \"Damit bestätigte sich, was die Mitglieder der Summit Foundation in den vergangenen Jahren festgestellt hatten. \",\n",
    "    \"Aus diesen Erfahrungen erklärte der Projektleiter, Téo Gürsoy:\"\n",
    "]\n",
    "\n",
    "p_seven_to_ten = sectionParagraphs([p_seven, p_eight, p_nine, p_ten])\n",
    "\n",
    "bquote_one = [\n",
    "    '\"Die Personen, die Erhebung ausführen, konzentrieren sich nämlich hauptsächlich auf die Abschnitte unter den ',\n",
    "    'Sesselliften, Gondeln oder bei der Abfahrt und Ankunft dieser Anlagen, die stark frequentierte Orte sind.\"'\n",
    "]\n",
    "\n",
    "bquote_one = ''.join(bquote_one)\n",
    "\n",
    "quote_one = Paragraph(bquote_one, block_quote_style)\n",
    "\n",
    "peleven = [\n",
    "    \"In einigen Fällen ist die Dichte der Objekte so gross, dass sich die Person, welche die Erhebung ausführt, \",\n",
    "    \"gezwungen sah, sich auf einen Bereich zu konzentrieren. Téo Gürsoy beschrieb, was passierte, als eine Person, \",\n",
    "    \"welche die Erhebung ausführt, auf einen Ort stiess, der grosse Mengen von Skiliftbürsten enthielt: \"\n",
    "]\n",
    "\n",
    "p_eleven = sectionParagraphs([peleven])\n",
    "\n",
    "bquote_two = [\n",
    "    '\"Die Person, welche die Erhebung ausführt, begann den Streckenabschnitt […] an der Ankunftsstation der Gondel. ',\n",
    "    \"Die Skiliftbürsten erregten schnell die Aufmerksamkeit der Person, die beschloss, sich nur auf den betroffenen \",\n",
    "    'Bereich zu konzentrieren, um herauszufinden, wie viele von ihnen zu finden waren.\"'\n",
    "]\n",
    "\n",
    "bquote_two = ''.join(bquote_two)\n",
    "\n",
    "quote_two = Paragraph(bquote_two, block_quote_style)\n",
    "\n",
    "p_twelve = [\n",
    "    \"Die Erhebungsergebnisse rund um Infrastruktur oder Gebäude sind kein Indikator für den Zustand der Umwelt \",\n",
    "    \"im gesamten Gebiet. Erhebungen in der Umgebung dieser Strukturen weisen tendenziell höhere Werte auf, \",\n",
    "    \"machen aber nur einen kleinen Teil der gesamten Landnutzung aus.\"\n",
    "]\n",
    "\n",
    "p_thirteen = [\n",
    "    \"Es mussten Anpassungen an der Software und dem Berichtsschema vorgenommen werden, um die verschiedenen Arten von Daten \",\n",
    "    \"zu verarbeiten, die bei Aufräumarbeiten anfallen. Dazu gehörte auch die Schaffung neuer Identifikationscodes für bestimmte \",\n",
    "    \"Objekte, die im Untersuchungsgebiet Alpen und Jura gefunden werden. Ausserdem stellte die Summit Foundation die Ressourcen \",\n",
    "    \"zur Verfügung, damit ein Mitarbeiter der Stiftung in der Anwendung des Projektprotokolls und der Software geschult werden konnte.\"\n",
    "]\n",
    "\n",
    "p_twelve_to_thirteen = sectionParagraphs([p_twelve, p_thirteen])\n",
    "new_components = [\n",
    "    subsection_two_title,\n",
    "    smallspace,\n",
    "    *p_seven_to_ten,\n",
    "    quote_one,\n",
    "    smallspace,\n",
    "    *p_eleven,\n",
    "    quote_two,\n",
    "    smallspace,\n",
    "    *p_twelve_to_thirteen\n",
    "    \n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "new_subsection = Paragraph(\"Schlussfolgerungen\", featuredata.subsection_title)\n",
    "\n",
    "p_fourteen = [\n",
    "    \"Die Erhebungen, die entlang der Wege und Wintersportinfrastrukturen im Untersuchungsgebiet Alpen und Jura durchgeführt wurden, \",\n",
    "    \"ergaben Daten, die den Daten der Erhebungen entlang von Seen und Fliessgewässern sehr ähnlich waren. Wenn sich die Personen, \",\n",
    "    \"die die Erhebungen durchgeführt haben, jedoch auf bestimmte Infrastruktureinrichtungen konzentrierten, wurden extreme Werte \",\n",
    "    \"ermittelt. Die Erhebungen Seen und Fliessgewässern würden zu den gleichen Ergebnissen führen, wenn die Erhebungen nur an Orten \",\n",
    "    \"durchgeführt würden, an denen einen hohen Anteil an Abfallobjekten wahrscheinlicher sind.\"\n",
    "]\n",
    "\n",
    "p_fifteen = [\n",
    "    \"Objekte aus dem Bereich Essen und Trinken machen nur 11 % der insgesamt gefundenen Objekte aus, verglichen mit 36 % \",\n",
    "    \"in den anderen Untersuchungsgebieten. Der Anteil an Abfallobjekten aus dem Bereich Infrastruktur beträgt in den Alpen \",\n",
    "    \"und im Jura jedoch 75 % gegenüber 18 % in allen anderen Untersuchungsgebieten. Dies ist zum Teil auf den Unterschied \",\n",
    "    \"in der menschlichen Präsenz im Vergleich zu Orten in niedrigeren Höhenlagen zurückzuführen, wo die menschliche Präsenz \",\n",
    "    \"das ganze Jahr über konstant ist, so dass der Druck durch Nahrungs- und Genussmittel im Gegensatz zur Infrastruktur grösser ist.\"\n",
    "]\n",
    "\n",
    "p_sixteen = [\n",
    "    \"Dieses erste Projekt hat auch gezeigt, dass es möglich ist, die Erhebung mit Clean-ups zu kombinieren. In Vorbereitung auf die \",\n",
    "    \"Erhebung tauschten die Mitglieder beider Teams Ideen aus und sortierten gemeinsam Proben. Dies ermöglichte es beiden Organisationen, \",\n",
    "    \"sich gegenseitig besser zu verstehen und Basisleistungen zu bestimmen, die bei der Datenerfassung für einen nationalen Bericht erbracht \",\n",
    "    \"werden konnten:\"\n",
    "]\n",
    "\n",
    "p_fourteen_to_sixteen = sectionParagraphs([p_fourteen, p_fifteen, p_sixteen])\n",
    "\n",
    "\n",
    "numb_list = [\n",
    "    \"Unterstützung bei der Erfassung und Identifizierung von Abfallobjekten\",\n",
    "    \"Unterstützung bei der Dateneingabe\",\n",
    "    \"Erstellung von Diagrammen, Grafiken und Daten, die von den teilnehmenden Organisationen verwendet werden können\"\n",
    "]\n",
    "\n",
    "third_list = makeAList(numb_list)\n",
    "\n",
    "p_seventeen = [\n",
    "    \"Eine Erhebung von Abfällen an Seen und Fliessgewässern dauert 2–4 Stunden, je nachdem, wie viele verschiedene Objekte \",\n",
    "    \"es gibt. Diese Ressourcen waren im Betriebsbudget der beiden Organisationen nicht vorgesehen. Daher stellte die Summit \",\n",
    "    \"Foundation die Koordination und Infrastruktur zur Verfügung und Hammerdirt eine zusätzliche Person, die die Erhebung \",\n",
    "    \"ausführt, sowie IT-Unterstützung.\"\n",
    "]\n",
    "\n",
    "p_eighteen = [\n",
    "    \"Die zur Verfügung gestellten Daten ermöglichen direkte Vergleiche zwischen den Orten, vorausgesetzt, es wird die gleiche \",\n",
    "    \"Erhebungsmethode verwendet. Eine grosse Anzahl von Abfallobjekten mit Infrastrukturbezug im Vergleich zu Objekten aus dem \",\n",
    "    \"Bereich Lebensmittel und Tabakwaren ist typisch für ländliche Gebiete. Wie gut die Daten aus dem Erhebungsgebiet Alpen und \",\n",
    "    \"Jura mit jenen an Seen und Fliessgewässern vergleichbar sind, muss noch weiter untersucht werden. Zigarettenstummel, Glasscherben, \"\n",
    "    \"Plastiksplitter und Snack-Verpackungen gehören jedoch zu den häufigsten Objekten, die in Wassernähe gefunden werden.\"\n",
    "]\n",
    "\n",
    "p_nineteen = [\n",
    "    \"Wir danken allen Mitgliedern der Summit Foundation für ihre Hilfe, insbesondere Olivier Kressmann und Téo Gürsoy.\"\n",
    "]\n",
    "\n",
    "p_seventeen_to_nineteen = sectionParagraphs([p_seventeen, p_eighteen, p_nineteen])    \n",
    "\n",
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    "    smallspace,\n",
    "    *p_fourteen_to_sixteen,\n",
    "    third_list,\n",
    "    smallspace,\n",
    "    *p_seventeen_to_nineteen\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
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    "## Anhang\n",
    "\n",
    "### Schaumstoffe und Kunststoffe nach Grösse\n",
    "\n",
    "Die folgende Tabelle enthält die Komponenten “Gfoam” und “Gfrags”, die für die Analyse gruppiert wurden. Objekte, die als Schaumstoffe gekennzeichnet sind, werden als Gfoam gruppiert und umfassen alle geschäumten Polystyrol-Kunststoffe > 0,5 cm. Kunststoffteile und Objekte aus kombinierten Kunststoff- und Schaumstoffmaterialien > 0,5 cm werden für die Analyse als Gfrags gruppiert. "
   ]
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    {
     "data": {
      "application/papermill.record/text/plain": "'Fragmentierte und geschäumte Kunststoffe nach Grösse am Alpen und JuraMedian p/100 m, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_frag_table_caption"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_892dc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_892dc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_892dc tr {\n  font-size: 12px;\n}\n#T_892dc th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_892dc td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_892dc\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_892dc_level0_col0\" class=\"col_heading level0 col0\" >p/100 m</th>\n      <th id=\"T_892dc_level0_col1\" class=\"col_heading level0 col1\" >Objekte (St.)</th>\n      <th id=\"T_892dc_level0_col2\" class=\"col_heading level0 col2\" >Anteil</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_892dc_level0_row0\" class=\"row_heading level0 row0\" >Schaumstoffverpackungen/Isolierung</th>\n      <td id=\"T_892dc_row0_col0\" class=\"data row0 col0\" >0,0</td>\n      <td id=\"T_892dc_row0_col1\" class=\"data row0 col1\" >0</td>\n      <td id=\"T_892dc_row0_col2\" class=\"data row0 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row1\" class=\"row_heading level0 row1\" >Objekte aus Kunststoff/Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_892dc_row1_col0\" class=\"data row1 col0\" >0,0</td>\n      <td id=\"T_892dc_row1_col1\" class=\"data row1 col1\" >1</td>\n      <td id=\"T_892dc_row1_col2\" class=\"data row1 col2\" >1%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row2\" class=\"row_heading level0 row2\" >Objekte aus Kunststoff/Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_892dc_row2_col0\" class=\"data row2 col0\" >0,0</td>\n      <td id=\"T_892dc_row2_col1\" class=\"data row2 col1\" >0</td>\n      <td id=\"T_892dc_row2_col2\" class=\"data row2 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row3\" class=\"row_heading level0 row3\" >Objekte aus Kunststoff 0,5 - 2,5 cm</th>\n      <td id=\"T_892dc_row3_col0\" class=\"data row3 col0\" >6,0</td>\n      <td id=\"T_892dc_row3_col1\" class=\"data row3 col1\" >64</td>\n      <td id=\"T_892dc_row3_col2\" class=\"data row3 col2\" >82%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row4\" class=\"row_heading level0 row4\" >Objekte aus Kunststoff 2,5 - 50 cm</th>\n      <td id=\"T_892dc_row4_col0\" class=\"data row4 col0\" >1,0</td>\n      <td id=\"T_892dc_row4_col1\" class=\"data row4 col1\" >43</td>\n      <td id=\"T_892dc_row4_col2\" class=\"data row4 col2\" >55%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row5\" class=\"row_heading level0 row5\" >Objekte aus Kunststoff > 50 cm</th>\n      <td id=\"T_892dc_row5_col0\" class=\"data row5 col0\" >0,0</td>\n      <td id=\"T_892dc_row5_col1\" class=\"data row5 col1\" >0</td>\n      <td id=\"T_892dc_row5_col2\" class=\"data row5 col2\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row6\" class=\"row_heading level0 row6\" >Objekte aus expandiertem Polystyrol 0,5 - 2,5 cm</th>\n      <td id=\"T_892dc_row6_col0\" class=\"data row6 col0\" >0,0</td>\n      <td id=\"T_892dc_row6_col1\" class=\"data row6 col1\" >10</td>\n      <td id=\"T_892dc_row6_col2\" class=\"data row6 col2\" >13%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row7\" class=\"row_heading level0 row7\" >Objekte aus expandiertem Polystyrol 2,5 - 50 cm</th>\n      <td id=\"T_892dc_row7_col0\" class=\"data row7 col0\" >0,0</td>\n      <td id=\"T_892dc_row7_col1\" class=\"data row7 col1\" >2</td>\n      <td id=\"T_892dc_row7_col2\" class=\"data row7 col2\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_892dc_level0_row8\" class=\"row_heading level0 row8\" >Objekte aus expandiertem Polystyrol > 50 cm</th>\n      <td id=\"T_892dc_row8_col0\" class=\"data row8 col0\" >0,0</td>\n      <td id=\"T_892dc_row8_col1\" class=\"data row8 col1\" >0</td>\n      <td id=\"T_892dc_row8_col2\" class=\"data row8 col2\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5441f2f5b0>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_frags_table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annex_title = Paragraph(\"Anhang\", featuredata.section_title)\n",
    "frag_sub_title = Paragraph(\"Schaumstoffe und Kunststoffe nach Grösse\", featuredata.subsection_title)\n",
    "\n",
    "frag_paras = [\n",
    "    \"Die folgende Tabelle enthält die Komponenten «Gfoam» und «Gfrag», die für die Analyse gruppiert wurden. \",\n",
    "    \"Objekte, die als Schaumstoffe gekennzeichnet sind, werden als Gfoam gruppiert und umfassen alle geschäumten \",\n",
    "    \"Polystyrol-Kunststoffe > 0,5 cm. Kunststoffteile und Objekte aus kombinierten Kunststoff - und Schaumstoffmaterialien > 0,5 \",\n",
    "    \"cm werden für die Analyse als Gfrags gruppiert.\"\n",
    "]\n",
    "\n",
    "frag_p = \"\".join(frag_paras)\n",
    "frag = Paragraph(frag_p, featuredata.p_style)\n",
    "\n",
    "frag_caption = [\n",
    "    f'Fragmentierte und geschäumte Kunststoffe nach Grösse am {this_feature[\"name\"]}',\n",
    "    f'Median {unit_label}, Anzahl der gefundenen Objekte und Prozent der Gesamtmenge.'\n",
    "]\n",
    "frag_captions = ''.join(frag_caption)\n",
    "\n",
    "# collect the data before aggregating foams for all locations in the survye area\n",
    "h=pd.read_csv(\"resources/checked_alpes_survey_data_be.csv\")\n",
    "\n",
    "# remove prefix\n",
    "h[\"location\"] = h[\"location\"].map(lambda x: sut.get_rid_of_ix(x, prefix=\"clean-up-tour-\"))\n",
    "\n",
    "# remove prefixes from survey data\n",
    "lAlps = h[h.river_bassin == this_feature[\"slug\"]][[\"loc_date\",\"code\",\"location\",\"river_bassin\",\"groupname\", \"quantity\", \"pcs_m\"]].copy()\n",
    "\n",
    "# convert to reporting unit\n",
    "lAlps[unit_label]= (lAlps.pcs_m*100).round(2)\n",
    "\n",
    "# the fragmented plastics and foams\n",
    "some_foams = [\"G81\", \"G82\", \"G83\", \"G74\"]\n",
    "some_frag_plas = list(lAlps[lAlps.groupname == \"plastic pieces\"].code.unique())\n",
    "\n",
    "# get just the foams and plastics and aggregate to code\n",
    "conditions = ((lAlps.code.isin([*some_frag_plas, *some_foams]))&(~lAlps.location.isin(not_trail_samples)))\n",
    "fd_frags_foams = lAlps[conditions].groupby([\"loc_date\",\"code\"], as_index=False).agg(agg_pcs_quantity)\n",
    "fd_frags_foams = fd_frags_foams.groupby(\"code\", as_index=False).agg(agg_pcs_median)\n",
    "\n",
    "# add code description and format for printing\n",
    "fd_frags_foams[\"item\"] = fd_frags_foams.code.map(lambda x: description_map.loc[x])\n",
    "fd_frags_foams[\"% of total\"] = (fd_frags_foams.quantity/fd.quantity.sum()*100).round(2)\n",
    "\n",
    "# table data\n",
    "data = fd_frags_foams[[\"item\",unit_label, \"quantity\", \"% of total\"]].copy()\n",
    "data.rename(columns={\"item\":\"Objekt\", \"quantity\":\"Objekte (St.)\", \"% of total\":\"Anteil\"}, inplace=True)\n",
    "data.set_index(\"Objekt\", inplace=True, drop=True)\n",
    "data.index.name = None\n",
    "\n",
    "aformatter = {\n",
    "    f\"{unit_label}\": lambda x: featuredata.replaceDecimal(x, \"de\"),\n",
    "    \"Objekte (St.)\": lambda x: featuredata.thousandsSeparator(int(x), \"de\"),\n",
    "    \"Anteil\":'{:.0%}',\n",
    "   \n",
    "}\n",
    "\n",
    "frags_table = data.style.format(aformatter).set_table_styles(table_css_styles)\n",
    "\n",
    "glue(\"alpes_frag_table_caption\", frag_captions, display=False)\n",
    "glue(\"alpes_frags_table\", frags_table, display=False)\n",
    "col_widths = [7*cm, *[2*cm]*(len(dims_table.columns)-1)]\n",
    "frag_table = featuredata.aSingleStyledTable(data, colWidths=col_widths)\n",
    "frag_caption = Paragraph(frag_captions, featuredata.caption_style)\n",
    "\n",
    "frag_table_and_caption = featuredata.tableAndCaption(frag_table, frag_caption, col_widths)\n",
    "\n",
    "frag_table = featuredata.aStyledTable(data, caption=frag_captions, colWidths=[7*cm, *[2*cm]*(len(dims_table.columns)-1)])\n",
    "\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        annex_title,\n",
    "        featuredata.small_space,\n",
    "        frag_sub_title,\n",
    "        featuredata.smaller_space,\n",
    "        frag,\n",
    "        featuredata.small_space\n",
    "    ]),\n",
    "    frag_table_and_caption\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_frags_table\n",
    "---\n",
    "name: 'alpes_frags_table'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_frags_table>` {glue:text}`alpes_frag_table_caption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Landnutzungsprofil der Erhebungsorte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_770fd tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_770fd tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_770fd tr {\n  font-size: 12px;\n}\n#T_770fd th:nth-child(1) {\n  background-color: #FFF;\n  text-align: right;\n}\n#T_770fd td {\n  padding: 6px;\n}\n</style>\n<table id=\"T_770fd\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_770fd_level0_col0\" class=\"col_heading level0 col0\" >Gebäude</th>\n      <th id=\"T_770fd_level0_col1\" class=\"col_heading level0 col1\" >Aktivitäten im Freien</th>\n      <th id=\"T_770fd_level0_col2\" class=\"col_heading level0 col2\" >Landwirtschaft</th>\n      <th id=\"T_770fd_level0_col3\" class=\"col_heading level0 col3\" >Wald</th>\n      <th id=\"T_770fd_level0_col4\" class=\"col_heading level0 col4\" >Bevölkerung</th>\n      <th id=\"T_770fd_level0_col5\" class=\"col_heading level0 col5\" >Strassen km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_770fd_level0_row0\" class=\"row_heading level0 row0\" >cabanes-des-diablerets</th>\n      <td id=\"T_770fd_row0_col0\" class=\"data row0 col0\" >0%</td>\n      <td id=\"T_770fd_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_770fd_row0_col2\" class=\"data row0 col2\" >4%</td>\n      <td id=\"T_770fd_row0_col3\" class=\"data row0 col3\" >8%</td>\n      <td id=\"T_770fd_row0_col4\" class=\"data row0 col4\" >1 448</td>\n      <td id=\"T_770fd_row0_col5\" class=\"data row0 col5\" >9</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row1\" class=\"row_heading level0 row1\" >val-calanca</th>\n      <td id=\"T_770fd_row1_col0\" class=\"data row1 col0\" >2%</td>\n      <td id=\"T_770fd_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_770fd_row1_col2\" class=\"data row1 col2\" >8%</td>\n      <td id=\"T_770fd_row1_col3\" class=\"data row1 col3\" >82%</td>\n      <td id=\"T_770fd_row1_col4\" class=\"data row1 col4\" >199</td>\n      <td id=\"T_770fd_row1_col5\" class=\"data row1 col5\" >38</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row2\" class=\"row_heading level0 row2\" >san-bernardino</th>\n      <td id=\"T_770fd_row2_col0\" class=\"data row2 col0\" >2%</td>\n      <td id=\"T_770fd_row2_col1\" class=\"data row2 col1\" >0%</td>\n      <td id=\"T_770fd_row2_col2\" class=\"data row2 col2\" >13%</td>\n      <td id=\"T_770fd_row2_col3\" class=\"data row2 col3\" >69%</td>\n      <td id=\"T_770fd_row2_col4\" class=\"data row2 col4\" >1 347</td>\n      <td id=\"T_770fd_row2_col5\" class=\"data row2 col5\" >43</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row3\" class=\"row_heading level0 row3\" >airolo</th>\n      <td id=\"T_770fd_row3_col0\" class=\"data row3 col0\" >2%</td>\n      <td id=\"T_770fd_row3_col1\" class=\"data row3 col1\" >0%</td>\n      <td id=\"T_770fd_row3_col2\" class=\"data row3 col2\" >27%</td>\n      <td id=\"T_770fd_row3_col3\" class=\"data row3 col3\" >45%</td>\n      <td id=\"T_770fd_row3_col4\" class=\"data row3 col4\" >1 501</td>\n      <td id=\"T_770fd_row3_col5\" class=\"data row3 col5\" >51</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row4\" class=\"row_heading level0 row4\" >nendaz</th>\n      <td id=\"T_770fd_row4_col0\" class=\"data row4 col0\" >1%</td>\n      <td id=\"T_770fd_row4_col1\" class=\"data row4 col1\" >0%</td>\n      <td id=\"T_770fd_row4_col2\" class=\"data row4 col2\" >19%</td>\n      <td id=\"T_770fd_row4_col3\" class=\"data row4 col3\" >59%</td>\n      <td id=\"T_770fd_row4_col4\" class=\"data row4 col4\" >6 623</td>\n      <td id=\"T_770fd_row4_col5\" class=\"data row4 col5\" >49</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row5\" class=\"row_heading level0 row5\" >veysonnaz</th>\n      <td id=\"T_770fd_row5_col0\" class=\"data row5 col0\" >2%</td>\n      <td id=\"T_770fd_row5_col1\" class=\"data row5 col1\" >0%</td>\n      <td id=\"T_770fd_row5_col2\" class=\"data row5 col2\" >25%</td>\n      <td id=\"T_770fd_row5_col3\" class=\"data row5 col3\" >59%</td>\n      <td id=\"T_770fd_row5_col4\" class=\"data row5 col4\" >6 623</td>\n      <td id=\"T_770fd_row5_col5\" class=\"data row5 col5\" >65</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row6\" class=\"row_heading level0 row6\" >andermatt</th>\n      <td id=\"T_770fd_row6_col0\" class=\"data row6 col0\" >0%</td>\n      <td id=\"T_770fd_row6_col1\" class=\"data row6 col1\" >0%</td>\n      <td id=\"T_770fd_row6_col2\" class=\"data row6 col2\" >24%</td>\n      <td id=\"T_770fd_row6_col3\" class=\"data row6 col3\" >25%</td>\n      <td id=\"T_770fd_row6_col4\" class=\"data row6 col4\" >1 390</td>\n      <td id=\"T_770fd_row6_col5\" class=\"data row6 col5\" >22</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row7\" class=\"row_heading level0 row7\" >verbier</th>\n      <td id=\"T_770fd_row7_col0\" class=\"data row7 col0\" >27%</td>\n      <td id=\"T_770fd_row7_col1\" class=\"data row7 col1\" >4%</td>\n      <td id=\"T_770fd_row7_col2\" class=\"data row7 col2\" >34%</td>\n      <td id=\"T_770fd_row7_col3\" class=\"data row7 col3\" >21%</td>\n      <td id=\"T_770fd_row7_col4\" class=\"data row7 col4\" >10 185</td>\n      <td id=\"T_770fd_row7_col5\" class=\"data row7 col5\" >84</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row8\" class=\"row_heading level0 row8\" >crans-montana</th>\n      <td id=\"T_770fd_row8_col0\" class=\"data row8 col0\" >0%</td>\n      <td id=\"T_770fd_row8_col1\" class=\"data row8 col1\" >0%</td>\n      <td id=\"T_770fd_row8_col2\" class=\"data row8 col2\" >48%</td>\n      <td id=\"T_770fd_row8_col3\" class=\"data row8 col3\" >28%</td>\n      <td id=\"T_770fd_row8_col4\" class=\"data row8 col4\" >4 198</td>\n      <td id=\"T_770fd_row8_col5\" class=\"data row8 col5\" >44</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row9\" class=\"row_heading level0 row9\" >villars</th>\n      <td id=\"T_770fd_row9_col0\" class=\"data row9 col0\" >0%</td>\n      <td id=\"T_770fd_row9_col1\" class=\"data row9 col1\" >5%</td>\n      <td id=\"T_770fd_row9_col2\" class=\"data row9 col2\" >41%</td>\n      <td id=\"T_770fd_row9_col3\" class=\"data row9 col3\" >39%</td>\n      <td id=\"T_770fd_row9_col4\" class=\"data row9 col4\" >7 463</td>\n      <td id=\"T_770fd_row9_col5\" class=\"data row9 col5\" >38</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row10\" class=\"row_heading level0 row10\" >les-crosets</th>\n      <td id=\"T_770fd_row10_col0\" class=\"data row10 col0\" >1%</td>\n      <td id=\"T_770fd_row10_col1\" class=\"data row10 col1\" >0%</td>\n      <td id=\"T_770fd_row10_col2\" class=\"data row10 col2\" >66%</td>\n      <td id=\"T_770fd_row10_col3\" class=\"data row10 col3\" >18%</td>\n      <td id=\"T_770fd_row10_col4\" class=\"data row10 col4\" >1 961</td>\n      <td id=\"T_770fd_row10_col5\" class=\"data row10 col5\" >50</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row11\" class=\"row_heading level0 row11\" >morgins</th>\n      <td id=\"T_770fd_row11_col0\" class=\"data row11 col0\" >8%</td>\n      <td id=\"T_770fd_row11_col1\" class=\"data row11 col1\" >0%</td>\n      <td id=\"T_770fd_row11_col2\" class=\"data row11 col2\" >20%</td>\n      <td id=\"T_770fd_row11_col3\" class=\"data row11 col3\" >65%</td>\n      <td id=\"T_770fd_row11_col4\" class=\"data row11 col4\" >4 684</td>\n      <td id=\"T_770fd_row11_col5\" class=\"data row11 col5\" >66</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row12\" class=\"row_heading level0 row12\" >la-berra</th>\n      <td id=\"T_770fd_row12_col0\" class=\"data row12 col0\" >0%</td>\n      <td id=\"T_770fd_row12_col1\" class=\"data row12 col1\" >0%</td>\n      <td id=\"T_770fd_row12_col2\" class=\"data row12 col2\" >33%</td>\n      <td id=\"T_770fd_row12_col3\" class=\"data row12 col3\" >61%</td>\n      <td id=\"T_770fd_row12_col4\" class=\"data row12 col4\" >1 711</td>\n      <td id=\"T_770fd_row12_col5\" class=\"data row12 col5\" >26</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row13\" class=\"row_heading level0 row13\" >grindelwald</th>\n      <td id=\"T_770fd_row13_col0\" class=\"data row13 col0\" >0%</td>\n      <td id=\"T_770fd_row13_col1\" class=\"data row13 col1\" >0%</td>\n      <td id=\"T_770fd_row13_col2\" class=\"data row13 col2\" >66%</td>\n      <td id=\"T_770fd_row13_col3\" class=\"data row13 col3\" >19%</td>\n      <td id=\"T_770fd_row13_col4\" class=\"data row13 col4\" >3 802</td>\n      <td id=\"T_770fd_row13_col5\" class=\"data row13 col5\" >34</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row14\" class=\"row_heading level0 row14\" >la-tzoumaz</th>\n      <td id=\"T_770fd_row14_col0\" class=\"data row14 col0\" >13%</td>\n      <td id=\"T_770fd_row14_col1\" class=\"data row14 col1\" >0%</td>\n      <td id=\"T_770fd_row14_col2\" class=\"data row14 col2\" >12%</td>\n      <td id=\"T_770fd_row14_col3\" class=\"data row14 col3\" >68%</td>\n      <td id=\"T_770fd_row14_col4\" class=\"data row14 col4\" >3 133</td>\n      <td id=\"T_770fd_row14_col5\" class=\"data row14 col5\" >74</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row15\" class=\"row_heading level0 row15\" >la-robella</th>\n      <td id=\"T_770fd_row15_col0\" class=\"data row15 col0\" >2%</td>\n      <td id=\"T_770fd_row15_col1\" class=\"data row15 col1\" >0%</td>\n      <td id=\"T_770fd_row15_col2\" class=\"data row15 col2\" >35%</td>\n      <td id=\"T_770fd_row15_col3\" class=\"data row15 col3\" >60%</td>\n      <td id=\"T_770fd_row15_col4\" class=\"data row15 col4\" >10 668</td>\n      <td id=\"T_770fd_row15_col5\" class=\"data row15 col5\" >52</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row16\" class=\"row_heading level0 row16\" >les-diablerets</th>\n      <td id=\"T_770fd_row16_col0\" class=\"data row16 col0\" >13%</td>\n      <td id=\"T_770fd_row16_col1\" class=\"data row16 col1\" >0%</td>\n      <td id=\"T_770fd_row16_col2\" class=\"data row16 col2\" >26%</td>\n      <td id=\"T_770fd_row16_col3\" class=\"data row16 col3\" >53%</td>\n      <td id=\"T_770fd_row16_col4\" class=\"data row16 col4\" >1 448</td>\n      <td id=\"T_770fd_row16_col5\" class=\"data row16 col5\" >63</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row17\" class=\"row_heading level0 row17\" >charmey</th>\n      <td id=\"T_770fd_row17_col0\" class=\"data row17 col0\" >4%</td>\n      <td id=\"T_770fd_row17_col1\" class=\"data row17 col1\" >0%</td>\n      <td id=\"T_770fd_row17_col2\" class=\"data row17 col2\" >44%</td>\n      <td id=\"T_770fd_row17_col3\" class=\"data row17 col3\" >48%</td>\n      <td id=\"T_770fd_row17_col4\" class=\"data row17 col4\" >2 483</td>\n      <td id=\"T_770fd_row17_col5\" class=\"data row17 col5\" >36</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row18\" class=\"row_heading level0 row18\" >monte-generoso</th>\n      <td id=\"T_770fd_row18_col0\" class=\"data row18 col0\" >0%</td>\n      <td id=\"T_770fd_row18_col1\" class=\"data row18 col1\" >0%</td>\n      <td id=\"T_770fd_row18_col2\" class=\"data row18 col2\" >0%</td>\n      <td id=\"T_770fd_row18_col3\" class=\"data row18 col3\" >0%</td>\n      <td id=\"T_770fd_row18_col4\" class=\"data row18 col4\" >839</td>\n      <td id=\"T_770fd_row18_col5\" class=\"data row18 col5\" >17</td>\n    </tr>\n    <tr>\n      <th id=\"T_770fd_level0_row19\" class=\"row_heading level0 row19\" >les-paccots</th>\n      <td id=\"T_770fd_row19_col0\" class=\"data row19 col0\" >7%</td>\n      <td id=\"T_770fd_row19_col1\" class=\"data row19 col1\" >0%</td>\n      <td id=\"T_770fd_row19_col2\" class=\"data row19 col2\" >28%</td>\n      <td id=\"T_770fd_row19_col3\" class=\"data row19 col3\" >61%</td>\n      <td id=\"T_770fd_row19_col4\" class=\"data row19 col4\" >6 971</td>\n      <td id=\"T_770fd_row19_col5\" class=\"data row19 col5\" >35</td>\n    </tr>\n  </tbody>\n</table>\n",
      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7f5441c29580>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "alpes_survey_area_luse_commune"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the land use profile of AV\n",
    "# lu_prof = fd[[\"location\",\"% buildings\", \"% recreation\", \"% agg\", \"% woods\", \"population\", \"streets\"]].drop_duplicates()\n",
    "lu_prof = fd[[\"location\",\"% buildings\", \"% recreation\", \"% agg\", \"% woods\", \"population\", \"streets\"]].drop_duplicates()\n",
    "percents = [\"% buildings\", \"% recreation\", \"% agg\", \"% woods\"] \n",
    "thousands = [\"population\"]\n",
    "\n",
    "lu_prof[\"streets\"] = (lu_prof[\"streets\"]/1000).astype(int)\n",
    "\n",
    "# format for printing\n",
    "lu_prof[percents] = lu_prof[percents].applymap(lambda x: f\"{int((x*100))}%\")\n",
    "lu_prof[thousands] = lu_prof[thousands].applymap(lambda x: featuredata.thousandsSeparator(int(x), \"de\"))\n",
    "lu_prof.rename(columns=featuredata.luse_de, inplace=True)\n",
    "lu_prof.rename(columns={\"Aktivitäten im Freien\":\"Aktivitäten im Freien\"}, inplace=True)\n",
    "\n",
    "# put that to a table\n",
    "data=lu_prof.set_index(\"location\", drop=True)\n",
    "data.index.name = None\n",
    "\n",
    "landuse_subsection_title = Paragraph(\"Landnutzungsprofil der Erhebungsorte\", featuredata.subsection_title)\n",
    "\n",
    "a_caption = \"Landnutzungsprofil der ErhebungsorteGesamtmenge.\"\n",
    "a_caption = Paragraph(a_caption, featuredata.caption_style)\n",
    "col_widths = [2.5*cm, 2*cm, 2*cm, 2.4*cm, *[2*cm]*3]\n",
    "\n",
    "a_table = featuredata.aSingleStyledTable(data, colWidths=col_widths)\n",
    "\n",
    "landuse_table_and_caption = featuredata.tableAndCaption(a_table, a_caption, col_widths)\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        smallspace,\n",
    "        landuse_subsection_title,\n",
    "        smallspace,\n",
    "        landuse_table_and_caption\n",
    "    ])\n",
    "   \n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "land_use_display = data.style.set_table_styles(table_css_styles)\n",
    "\n",
    "glue('alpes_survey_area_luse_commune', land_use_display, display=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} alpes_survey_area_luse_commune\n",
    "---\n",
    "name: 'alpes_survey_area_luse_commune'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <alpes_survey_area_luse_commune>` Landnutzungsprofil der ErhebungsorteGesamtmenge. LWS = Landwirtschaft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alpen und Jura in Bezug auf die Landnutzung\n",
    "\n",
    "Die Ergebnisse aus den Alpen und dem Jura werden mit den anderen Erhebungsergebnissen verglichen, die entweder % bis Wald oder % bis Landwirtschaft (LWS) innerhalb der gleichen Spanne wie AV liegen. Die Bereiche für Alpen und Jura sind:\n",
    "\n",
    "* Landwirtschaft: 0 to 66\\%\n",
    "* Wald: 0 to 83\\%\n",
    "* Bevölkerung: 199 to 10 668"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/text/plain": "'Oben links: Gesamtsumme der Erhebung in Bezug auf den prozentualen Anteil an landwirtschaftlich genutzter Fläche, Bereich = (0 %, 66 %). Oben rechts: Gesamtzahl der Erhebungen in Bezug auf den Waldanteil, Bereich=(0 %, 65 %). Unten links: Gesamtzahl der Erhebungen in Bezug auf die Bevölkerung, Bereich=(199, 10668).'"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_compare_notes"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/papermill.record/image/png": 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",
      "application/papermill.record/text/plain": "<Figure size 1000x800 with 4 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "les-alpes_compare_luse"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define land use ranges based on the sample data\n",
    "this_range = (fd[l_f[0]].min(), fd[l_f[0]].max())\n",
    "this_range_w = (fd[l_f[1]].min(), fd[l_f[1]].max())\n",
    "this_range_p = (fd[l_f[2]].min(), fd[l_f[2]].max())\n",
    "\n",
    "# apply them to all the data\n",
    "# one test for agg -- a dominant land use feature of the sample data\n",
    "some_data = a_data[(a_data[l_f[0]] >= this_range[0])&(a_data[l_f[0]] <= this_range[1])].copy()\n",
    "\n",
    "# one test for woods -- the dominant land use feature of the sample data\n",
    "some_data_w = a_data[(a_data[l_f[1]] >= this_range_w[0])&(a_data[l_f[1]] <= this_range_w[1])].copy()\n",
    "\n",
    "# one test for population -- \n",
    "some_data_p = a_data[(a_data[l_f[2]] >= this_range_p[0])&(a_data[l_f[2]] <= this_range_p[1])].copy()\n",
    "\n",
    "# remove Alps valaisannes\n",
    "some_data = some_data[~some_data.location.isin(fd.location.unique())].copy()\n",
    "some_data_w = some_data_w[~some_data_w.location.isin(fd.location.unique())].copy()\n",
    "some_data_p = some_data_p[~some_data_p.location.isin(fd.location.unique())].copy()\n",
    "\n",
    "# the number of samples and locations that have similar land use profiles as AV:\n",
    "# agg to loc_date for each criteria\n",
    "# data for charting and comparing\n",
    "data=some_data.groupby([\"loc_date\",\"location\",l_f[0]], as_index=False)[unit_label].sum()\n",
    "data_w =some_data_w.groupby([\"loc_date\",\"location\", l_f[1]], as_index=False)[unit_label].sum()\n",
    "data_p = some_data_p.groupby([\"loc_date\",\"location\",l_f[1], \"population\"], as_index=False)[unit_label].sum()\n",
    "regional = fd.groupby([\"loc_date\",\"location\", *l_f], as_index=False)[unit_label].sum()\n",
    "\n",
    "# locations that share the characteristics\n",
    "commonsamps = set(data.loc_date.unique()) & set(data_w.loc_date.unique())& set(data_p.loc_date.unique())\n",
    "commonlocs = set(data.location.unique()) & set(data_w.location.unique())&set(data_p.location.unique())\n",
    "\n",
    "# print these out to get the comparison\n",
    "\n",
    "# print(\"agg\")\n",
    "# print(this_range)\n",
    "# print(len(data.location.unique()))\n",
    "# print(data.loc_date.nunique())\n",
    "# print(\"woods\")\n",
    "# print(this_range_w)\n",
    "# print(len(data_w.location.unique()))\n",
    "# print(data_w.loc_date.nunique())\n",
    "# print(\"p\")\n",
    "# print(this_range_p)\n",
    "# print(len(data_p.location.unique()))\n",
    "# print(data_p.loc_date.nunique())\n",
    "# print(len(commonsamps))\n",
    "# print(commonlocs)\n",
    "\n",
    "# make a categorical df for mapping\n",
    "mat_agg = dfBeaches.loc[data.location.unique()]\n",
    "mat_agg[\"match\"] = \"agg\"\n",
    "mat_w = dfBeaches.loc[data_w.location.unique()]\n",
    "mat_w[\"match\"] = \"woods\"\n",
    "mat_p = dfBeaches.loc[data_p.location.unique()]\n",
    "mat_p[\"match\"]=\"pop\"\n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(10,8), sharey=True)\n",
    "\n",
    "axone=axs[0,0]\n",
    "axtwo=axs[0,1]\n",
    "axthree=axs[1,0]\n",
    "axfour=axs[1,1]\n",
    "\n",
    "# plot the samples from all the data that meet the x criteria\n",
    "sns.scatterplot(data=data, x=l_f[0], y=unit_label, color=\"black\", alpha=1, linewidth=1, label=\"All surveys\", ax=axone, zorder=1)\n",
    "\n",
    "# point estimates of the percentile ranking based off the edcf of all surveys\n",
    "# place to store the rankings\n",
    "rankings = {}\n",
    "\n",
    "# plot the values for AV\n",
    "for x in regional.location.unique():\n",
    "    this_y = regional[regional.location == x][unit_label]\n",
    "    this_x = regional[regional.location == x][l_f[0]]\n",
    "    axone.scatter(this_x, this_y, color=bassin_pallette[this_feature[\"slug\"]], s=60, zorder=2)\n",
    "\n",
    "# handle extreme values\n",
    "axone.set_ylim(0, max(data[unit_label].to_numpy()))\n",
    "\n",
    "# set labels\n",
    "axone.set_ylabel(unit_label, **ck.xlab_k14)\n",
    "axone.set_xlabel(featuredata.luse_de[l_f[0]], **ck.xlab_k14)\n",
    "\n",
    "# gather up legend handles\n",
    "axone.get_legend().remove()\n",
    "\n",
    "# start axtwo\n",
    "# plot the samples from all the data that meet the x criteria\n",
    "sns.scatterplot(data=data_w, x=l_f[1], y=unit_label, color=\"black\", alpha=1, linewidth=1, label=\"All surveys\", ax=axtwo, zorder=1)\n",
    "\n",
    "# plot the values from AV\n",
    "for x in regional.location.unique():\n",
    "    this_y = regional[regional.location == x][unit_label]\n",
    "    this_x = regional[regional.location == x][l_f[1]]\n",
    "    rankings.update({x:(this_x, this_y)})\n",
    "    axtwo.scatter(this_x, this_y, color=bassin_pallette[this_feature[\"slug\"]], s=60, zorder=2)\n",
    "\n",
    "# handle extreme values\n",
    "axtwo.set_ylim(0, max(data[unit_label].to_numpy()))\n",
    "\n",
    "# set labels\n",
    "axtwo.set_ylabel(unit_label, **ck.xlab_k14)\n",
    "axtwo.set_xlabel(featuredata.luse_de[l_f[1]], **ck.xlab_k14)\n",
    "axtwo.get_legend().remove()\n",
    "\n",
    "# start axthree\n",
    "# plot the samples from all the data that meet the x criteria\n",
    "sns.scatterplot(data=data_p, x=l_f[2], y=unit_label, color=\"black\", alpha=1, linewidth=1, label=top, ax=axthree, zorder=1)\n",
    "\n",
    "# plot the values from AV\n",
    "for x in regional.location.unique():\n",
    "    this_y = regional[regional.location == x][unit_label]\n",
    "    this_x = regional[regional.location == x][l_f[2]]\n",
    "    rankings.update({x:(this_x, this_y)})\n",
    "    axthree.scatter(this_x, this_y, color=bassin_pallette[this_feature[\"slug\"]], s=60, label=this_feature[\"name\"], zorder=2)\n",
    "\n",
    "# handle extreme values\n",
    "axthree.set_ylim(-100, max(data[unit_label].to_numpy()))\n",
    "\n",
    "# start axfour,# clear axfour\n",
    "sut.hide_spines_ticks_grids(axfour)\n",
    "\n",
    "# set labels\n",
    "axthree.set_ylabel(unit_label, **ck.xlab_k14)\n",
    "axthree.set_xlabel(l_f[2], **ck.xlab_k14)\n",
    "handles, labels = axthree.get_legend_handles_labels()\n",
    "axthree.get_legend().remove()\n",
    "\n",
    "fig.legend(handles[:2], labels[:2], bbox_to_anchor=(.75,.25), loc=\"lower center\",  fontsize=12)\n",
    "plt.tight_layout()\n",
    "figure_name = f\"{this_feature['slug']}_compare_luse\"\n",
    "compare_land_use_file_name = f'{save_fig_prefix}{figure_name}.jpeg'\n",
    "save_figure_kwargs.update({\"fname\":compare_land_use_file_name})\n",
    "plt.savefig(**save_figure_kwargs)\n",
    "\n",
    "# figure caption\n",
    "compare_notes = [\n",
    "    f'Oben links: Gesamtsumme der Erhebung in Bezug auf den prozentualen Anteil an landwirtschaftlich genutzter Fläche, Bereich = ({0} {\"%\"}, {66} {\"%\"}). ',\n",
    "    f'Oben rechts: Gesamtzahl der Erhebungen in Bezug auf den Waldanteil, Bereich=({0} {\"%\"}, {65} {\"%\"}). ',\n",
    "    f'Unten links: Gesamtzahl der Erhebungen in Bezug auf die Bevölkerung, Bereich=({199}, {10668}).'\n",
    "]\n",
    "\n",
    "compare_notes = ''.join(compare_notes)\n",
    "\n",
    "glue(f'{this_feature[\"slug\"]}_compare_notes', compare_notes, display=False)\n",
    "glue(\"les-alpes_compare_luse\", fig, display=False)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} les-alpes_compare_luse\n",
    "---\n",
    "name: 'les-alpes_compare_luse'\n",
    "---\n",
    "{glue:text}`blank_caption` \n",
    "```\n",
    "{numref}`Abbildung %s: <les-alpes_compare_luse>` {glue:text}`les-alpes_compare_notes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "compare_title = Paragraph(\"Alpen und Jura in Bezug auf die Landnutzung\", featuredata.subsection_title)\n",
    "ptwenty = [\n",
    "    \"Die Ergebnisse aus den Alpen und dem Jura werden mit den anderen Erhebungsergebnissen verglichen, die entweder % bis Wald oder % bis Landwirtschaft \",\n",
    "    \"innerhalb der gleichen Spanne wie AV liegen. Die Bereiche für Alpen und Jura sind:\"\n",
    "]\n",
    "\n",
    "p_twenty = makeAParagraph(ptwenty)\n",
    "\n",
    "landuse_ranges = [\n",
    "    \"Landwirtschaft: 0 to 66 %\", \n",
    "    \"Wald: 0 to 83 %\",\n",
    "    \"Bevölkerung: 199 to 10 668\"\n",
    "]\n",
    "\n",
    "landuse_list = makeAList(landuse_ranges)\n",
    "\n",
    "figure_kwargs = {\n",
    "    \"image_file\": compare_land_use_file_name,\n",
    "    \"caption\": Paragraph(compare_notes, featuredata.caption_style),\n",
    "    \"desired_width\": 14,\n",
    "    \"caption_height\": 1.7,\n",
    "    \"original_height\": 8,\n",
    "    \"original_width\": 10,\n",
    "}\n",
    "\n",
    "s_figure_and_caption = featuredata.figureAndCaptionTable(**figure_kwargs)\n",
    "\n",
    "new_components = [\n",
    "    PageBreak(),\n",
    "    smallspace,\n",
    "    compare_title,\n",
    "    smallspace,\n",
    "    p_twenty,\n",
    "    smallspace,\n",
    "    landuse_list,\n",
    "    smallspace,\n",
    "    s_figure_and_caption,\n",
    "]\n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organisation und Durchführung\n",
    "\n",
    "Summit foundation: Téo Gürsoy\n",
    "\n",
    "Hammerdirt: Bettina Siegenthaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Die Erhebungsorte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standort</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cabanes-des-diablerets</th>\n",
       "      <td>46.338604</td>\n",
       "      <td>7.215525</td>\n",
       "      <td>Ormont-Dessus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val-calanca</th>\n",
       "      <td>46.330718</td>\n",
       "      <td>9.120103</td>\n",
       "      <td>Calanca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>san-bernardino</th>\n",
       "      <td>46.445947</td>\n",
       "      <td>9.191344</td>\n",
       "      <td>Mesocco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airolo</th>\n",
       "      <td>46.514257</td>\n",
       "      <td>8.607884</td>\n",
       "      <td>Airolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nendaz</th>\n",
       "      <td>46.162099</td>\n",
       "      <td>7.283486</td>\n",
       "      <td>Nendaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veysonnaz</th>\n",
       "      <td>46.180334</td>\n",
       "      <td>7.366005</td>\n",
       "      <td>Nendaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>andermatt</th>\n",
       "      <td>46.618327</td>\n",
       "      <td>8.598803</td>\n",
       "      <td>Andermatt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verbier</th>\n",
       "      <td>46.103981</td>\n",
       "      <td>7.224760</td>\n",
       "      <td>Val de Bagnes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crans-montana</th>\n",
       "      <td>46.334214</td>\n",
       "      <td>7.479386</td>\n",
       "      <td>Lens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>villars</th>\n",
       "      <td>46.321937</td>\n",
       "      <td>7.073841</td>\n",
       "      <td>Ollon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>les-crosets</th>\n",
       "      <td>46.183591</td>\n",
       "      <td>6.832916</td>\n",
       "      <td>Val-d'Illiez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>morgins</th>\n",
       "      <td>46.236681</td>\n",
       "      <td>6.858132</td>\n",
       "      <td>Troistorrents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la-berra</th>\n",
       "      <td>46.677399</td>\n",
       "      <td>7.178738</td>\n",
       "      <td>La Roche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grindelwald</th>\n",
       "      <td>46.658523</td>\n",
       "      <td>8.065323</td>\n",
       "      <td>Grindelwald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la-tzoumaz</th>\n",
       "      <td>46.144287</td>\n",
       "      <td>7.232897</td>\n",
       "      <td>Riddes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>la-robella</th>\n",
       "      <td>46.875905</td>\n",
       "      <td>6.548577</td>\n",
       "      <td>Val-de-Travers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>les-diablerets</th>\n",
       "      <td>46.346141</td>\n",
       "      <td>7.151228</td>\n",
       "      <td>Ormont-Dessus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charmey</th>\n",
       "      <td>46.622641</td>\n",
       "      <td>7.189341</td>\n",
       "      <td>Val-de-Charmey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monte-generoso</th>\n",
       "      <td>45.929625</td>\n",
       "      <td>9.019921</td>\n",
       "      <td>Rovio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>les-paccots</th>\n",
       "      <td>46.512592</td>\n",
       "      <td>6.949421</td>\n",
       "      <td>Châtel-Saint-Denis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         latitude  longitude                stat\n",
       "standort                                                        \n",
       "cabanes-des-diablerets  46.338604   7.215525       Ormont-Dessus\n",
       "val-calanca             46.330718   9.120103             Calanca\n",
       "san-bernardino          46.445947   9.191344             Mesocco\n",
       "airolo                  46.514257   8.607884              Airolo\n",
       "nendaz                  46.162099   7.283486              Nendaz\n",
       "veysonnaz               46.180334   7.366005              Nendaz\n",
       "andermatt               46.618327   8.598803           Andermatt\n",
       "verbier                 46.103981   7.224760       Val de Bagnes\n",
       "crans-montana           46.334214   7.479386                Lens\n",
       "villars                 46.321937   7.073841               Ollon\n",
       "les-crosets             46.183591   6.832916        Val-d'Illiez\n",
       "morgins                 46.236681   6.858132       Troistorrents\n",
       "la-berra                46.677399   7.178738            La Roche\n",
       "grindelwald             46.658523   8.065323         Grindelwald\n",
       "la-tzoumaz              46.144287   7.232897              Riddes\n",
       "la-robella              46.875905   6.548577      Val-de-Travers\n",
       "les-diablerets          46.346141   7.151228       Ormont-Dessus\n",
       "charmey                 46.622641   7.189341      Val-de-Charmey\n",
       "monte-generoso          45.929625   9.019921               Rovio\n",
       "les-paccots             46.512592   6.949421  Châtel-Saint-Denis"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the survey locations\n",
    "disp_columns = [\"latitude\", \"longitude\", \"city\"]\n",
    "disp_beaches = admin_details.df_beaches.loc[admin_summary[\"locations_of_interest\"]][disp_columns]\n",
    "disp_beaches.reset_index(inplace=True)\n",
    "disp_beaches.rename(columns={\"city\":\"stat\", \"slug\":\"standort\"}, inplace=True)\n",
    "disp_beaches.set_index(\"standort\", inplace=True, drop=True)\n",
    "\n",
    "# make this into a pdf table\n",
    "location_subsection = Paragraph(\"Die Erhebungsorte und Inventar der Objekte\", featuredata.subsection_title)\n",
    "col_widths = [6*cm, 2.2*cm, 2.2*cm, 3*cm]\n",
    "pdf_table = featuredata.aStyledTableWithTitleRow(disp_beaches, title=\"Die Erhebungsorte\", colWidths=col_widths)\n",
    "\n",
    "disp_beaches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inventar der Objekte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Objekte</th>\n",
       "      <th>Objekte (St.)</th>\n",
       "      <th>Anteil</th>\n",
       "      <th>p/100 m</th>\n",
       "      <th>Häufigkeitsrate</th>\n",
       "      <th>Material</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G704</th>\n",
       "      <td>Seilbahnbürsten</td>\n",
       "      <td>5 181</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G27</th>\n",
       "      <td>Zigarettenfilter</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>95</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfrags</th>\n",
       "      <td>Fragmentierte Kunststoffe</td>\n",
       "      <td>294</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G30</th>\n",
       "      <td>Snack-Verpackungen</td>\n",
       "      <td>253</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>95</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G705</th>\n",
       "      <td>Schrauben und Bolzen</td>\n",
       "      <td>206</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G936</th>\n",
       "      <td>Folien für Gewächshäuser</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfoam</th>\n",
       "      <td>Expandiertes Polystyrol</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G93</th>\n",
       "      <td>Kabelbinder</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G200</th>\n",
       "      <td>Getränkeflaschen aus Glas, Glasfragmente</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G177</th>\n",
       "      <td>Verpackungen aus Aluminiumfolie</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G124</th>\n",
       "      <td>Andere Kunststoff</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G941</th>\n",
       "      <td>Verpackungsfolien, nicht für Lebensmittel</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G87</th>\n",
       "      <td>Abdeckklebeband/Verpackungsklebeband</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G204</th>\n",
       "      <td>Baumaterial;  Ziegel, Rohre, Zement</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G117</th>\n",
       "      <td>Styropor &lt; 5mm</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G33</th>\n",
       "      <td>Einwegartikel; Tassen/Becher &amp; Deckel</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G12</th>\n",
       "      <td>Kosmetika, Behälter für Körperpflegeprodukte, ...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G89</th>\n",
       "      <td>Kunststoff-Bauabfälle</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G142</th>\n",
       "      <td>Seil, Schnur oder Netze</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G122</th>\n",
       "      <td>Kunststofffragmente (&gt;1mm)</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G919</th>\n",
       "      <td>Nägel, Schrauben, Bolzen usw.</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G922</th>\n",
       "      <td>Etiketten, Strichcodes</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G923</th>\n",
       "      <td>Taschentücher, Toilettenpapier, Servietten, Pa...</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G703</th>\n",
       "      <td>Pistenmarkierungspfosten (Plastik)</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G24</th>\n",
       "      <td>Ringe von Plastikflaschen/Behältern</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G178</th>\n",
       "      <td>Kronkorken, Lasche von Dose/Ausfreisslachen</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G146</th>\n",
       "      <td>Papier, Karton</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G926</th>\n",
       "      <td>Kaugummi, enthält oft Kunststoffe</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G934</th>\n",
       "      <td>Sandsäcke, Kunststoff für Hochwasser- und Eros...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>Lebensmittelbehälter zum einmaligen Gebrauch a...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G34</th>\n",
       "      <td>Besteck, Teller und Tabletts</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G25</th>\n",
       "      <td>Tabak; Kunststoffverpackungen, Behälter</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G905</th>\n",
       "      <td>Haarspangen, Haargummis, persönliche Accessoir...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G710</th>\n",
       "      <td>Ski / Snowboards (Ski, Befestigungen und ander...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G707</th>\n",
       "      <td>Skiausrüstungsetikett</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G208</th>\n",
       "      <td>Glas oder Keramikfragmente &gt;2.5 cm</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G901</th>\n",
       "      <td>Medizinische Masken, synthetische</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G198</th>\n",
       "      <td>Andere Metallteile &lt; 50 cm</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G170</th>\n",
       "      <td>Holz (verarbeitet)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G145</th>\n",
       "      <td>Andere Textilien</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G152</th>\n",
       "      <td>Papier &amp;Karton;Zigarettenschachteln, Papier/Ka...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>Getränkeflaschen &lt; = 0,5 l</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G709</th>\n",
       "      <td>Teller von Skistock (runder Teil aus Plastik u...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G66</th>\n",
       "      <td>Umreifungsbänder; Hartplastik für Verpackung f...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G48</th>\n",
       "      <td>Seile, synthetische</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G100</th>\n",
       "      <td>Medizin; Behälter/Röhrchen/Verpackungen</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G31</th>\n",
       "      <td>Schleckstengel, Stengel von Lutscher</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G702</th>\n",
       "      <td>Pistenmarkierungspfosten (Holz)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G74</th>\n",
       "      <td>Schaumstoffverpackungen/Isolierung</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G129</th>\n",
       "      <td>Schläuche und Gummiplatten</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G921</th>\n",
       "      <td>Keramikfliesen und Bruchstücke</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G105</th>\n",
       "      <td>Kunststofffragmente subangulär &lt;5mm</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G23</th>\n",
       "      <td>unidentifizierte Deckel</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G20</th>\n",
       "      <td>Laschen und Deckel</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G156</th>\n",
       "      <td>Papierfragmente</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G123</th>\n",
       "      <td>Polyurethan-Granulat &lt; 5mm</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G918</th>\n",
       "      <td>Sicherheitsnadeln, Büroklammern, kleine Gebrau...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G706</th>\n",
       "      <td>Skiabonnement</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G149</th>\n",
       "      <td>Papierverpackungen</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G191</th>\n",
       "      <td>Draht und Gitter</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G943</th>\n",
       "      <td>Zäune Landwirtschaft, Kunststoff</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G915</th>\n",
       "      <td>Reflektoren, Mobilitätsartikel aus Kunststoff</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G175</th>\n",
       "      <td>Getränkedosen (Dosen, Getränke)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G73</th>\n",
       "      <td>Gegenstände aus Schaumstoff &amp; Teilstücke (nich...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G211</th>\n",
       "      <td>Sonstiges medizinisches Material</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G35</th>\n",
       "      <td>Strohhalme und Rührstäbchen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G158</th>\n",
       "      <td>Sonstige Gegenstände aus Papier</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G95</th>\n",
       "      <td>Wattestäbchen/Tupfer</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G171</th>\n",
       "      <td>Sonstiges Holz &lt; 50cm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G194</th>\n",
       "      <td>Kabel, Metalldraht oft in Gummi- oder Kunststo...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G712</th>\n",
       "      <td>Skihandschuhe</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G711</th>\n",
       "      <td>Handwärmer</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G139</th>\n",
       "      <td>Rucksäcke</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>Einkaufstaschen, Shoppingtaschen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G159</th>\n",
       "      <td>Kork</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G210</th>\n",
       "      <td>Sonstiges Glas/Keramik Materialien</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G90</th>\n",
       "      <td>Blumentöpfe aus Plastik</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G28</th>\n",
       "      <td>Stifte, Deckel, Druckbleistifte usw.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G928</th>\n",
       "      <td>Bänder und Schleifen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G931</th>\n",
       "      <td>(Absperr)band für Absperrungen, Polizei, Baust...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G713</th>\n",
       "      <td>Skiförderband</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G26</th>\n",
       "      <td>Feuerzeug</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G155</th>\n",
       "      <td>Feuerwerkspapierhülsen und -fragmente</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G181</th>\n",
       "      <td>Geschirr aus Metall, Tassen, Besteck usw.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G167</th>\n",
       "      <td>Streichhölzer oder Feuerwerke</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G168</th>\n",
       "      <td>Holzbretter</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G935</th>\n",
       "      <td>Gummi-Puffer für Geh- und Wanderstöcke und Tei...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G71</th>\n",
       "      <td>Schuhe Sandalen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G67</th>\n",
       "      <td>Industriefolie (Kunststoff)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G148</th>\n",
       "      <td>Kartonkisten und Stücke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G65</th>\n",
       "      <td>Eimer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G131</th>\n",
       "      <td>Gummibänder</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G8</th>\n",
       "      <td>Getränkeflaschen &gt; 0,5L</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G128</th>\n",
       "      <td>Reifen und Antriebsriemen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G916</th>\n",
       "      <td>Bleistifte und Bruchstücke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G917</th>\n",
       "      <td>Blähton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G929</th>\n",
       "      <td>Elektronik und Teile; Sensoren, Headsets usw.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G21</th>\n",
       "      <td>Getränke-Deckel, Getränkeverschluss</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G708</th>\n",
       "      <td>Skistöcke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G195</th>\n",
       "      <td>Batterien (Haushalt)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101</th>\n",
       "      <td>Robidog Hundekot-Säcklein, andere Hundekotsäck...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G199</th>\n",
       "      <td>Andere Metallteile &gt; 50 cm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G96</th>\n",
       "      <td>Hygienebinden/Höscheneinlagen/Tampons und Appl...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G902</th>\n",
       "      <td>Medizinische Masken, Stoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G9</th>\n",
       "      <td>Flaschen und Behälter für Reinigungsmittel und...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G126</th>\n",
       "      <td>Bälle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G900</th>\n",
       "      <td>Handschuhe Latex, persönliche Schutzausrüstung</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G125</th>\n",
       "      <td>Luftballons und Luftballonstäbchen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G940</th>\n",
       "      <td>Schaumstoff EVA (flexibler Kunststoff)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G88</th>\n",
       "      <td>Telefon inkl. Teile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G904</th>\n",
       "      <td>Feuerwerkskörper; Raketenkappen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G119</th>\n",
       "      <td>Folienartiger Kunststoff (&gt;1mm)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G906</th>\n",
       "      <td>Kaffeekapseln Aluminium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G907</th>\n",
       "      <td>Kaffeekapseln aus Kunststoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G908</th>\n",
       "      <td>Klebeband; elektrisch, isolierend</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G903</th>\n",
       "      <td>Behälter und Packungen für Handdesinfektionsmi...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G84</th>\n",
       "      <td>CD oder CD-Hülle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G13</th>\n",
       "      <td>Flaschen, Behälter, Fässer zum Transportieren ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G913</th>\n",
       "      <td>Schnuller</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G97</th>\n",
       "      <td>Behälter von Toilettenerfrischer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G98</th>\n",
       "      <td>Windeln – Feuchttücher</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G99</th>\n",
       "      <td>Spritzen - Nadeln</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G999</th>\n",
       "      <td>Keine Gegenstände bei dieser Erhebung/Studie g...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unbekannt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G132</th>\n",
       "      <td>Schwimmer (Angeln)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G133</th>\n",
       "      <td>Kondome einschließlich Verpackungen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G134</th>\n",
       "      <td>Sonstiges Gummi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G135</th>\n",
       "      <td>Kleidung, Fussbekleidung, Kopfbedeckung, Hands...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G136</th>\n",
       "      <td>Schuhe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G137</th>\n",
       "      <td>Kleidung, Handtücher und Lappen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G91</th>\n",
       "      <td>Bio-Filtermaterial  / Trägermaterial aus Kunst...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G914</th>\n",
       "      <td>Büroklammern, Wäscheklammern, Gebrauchsgegenst...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G94</th>\n",
       "      <td>Tischtuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G118</th>\n",
       "      <td>Kleine Industrielle Kügelchen &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G939</th>\n",
       "      <td>Kunststoffblumen, Kunststoffpflanzen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G938</th>\n",
       "      <td>Zahnstocher, Zahnseide Kunststoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G937</th>\n",
       "      <td>Pheromonköder für Weinberge</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G106</th>\n",
       "      <td>Kunststofffragmente eckig &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G104</th>\n",
       "      <td>Kunststofffragmente abgerundet / rundlich  &lt;5m...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G942</th>\n",
       "      <td>Kunststoffspäne von Drehbänken, CNC-Bearbeitung</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G107</th>\n",
       "      <td>Zylindrische Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G933</th>\n",
       "      <td>Taschen, Etuis für Zubehör, Brillen, Elektroni...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G932</th>\n",
       "      <td>Bio-Beads, Mikroplastik für die Abwasserbehand...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G108</th>\n",
       "      <td>Scheibenförmige Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G930</th>\n",
       "      <td>Schaumstoff-Ohrstöpsel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G103</th>\n",
       "      <td>Kunststofffragmente rund &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G109</th>\n",
       "      <td>Flache Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G944</th>\n",
       "      <td>Pelletmasse aus dem Spritzgussverfahren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11</th>\n",
       "      <td>Kosmetika für den Strand, z.B. Sonnencreme</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G927</th>\n",
       "      <td>Plastikschnur von Rasentrimmern, die zum Schne...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G111</th>\n",
       "      <td>Kugelförmige Pellets &lt; 5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G925</th>\n",
       "      <td>Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G112</th>\n",
       "      <td>Industriepellets (Nurdles)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G138</th>\n",
       "      <td>Schuhe und Sandalen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G113</th>\n",
       "      <td>Fäden &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G114</th>\n",
       "      <td>Folien &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G92</th>\n",
       "      <td>Köderbehälter</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G115</th>\n",
       "      <td>Schaumstoff &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G116</th>\n",
       "      <td>Granulat &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G945</th>\n",
       "      <td>Rasierklingen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G102</th>\n",
       "      <td>Flip-Flops</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G183</th>\n",
       "      <td>Teile von Angelhaken</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G144</th>\n",
       "      <td>Tampons</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G14</th>\n",
       "      <td>Flaschen für Motoröl (Motorölflaschen)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G22</th>\n",
       "      <td>Deckel für Chemikalien, Reinigungsmittel (Ohne...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G207</th>\n",
       "      <td>Tonkrüge zum Fangen von Kraken</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G173</th>\n",
       "      <td>Sonstiges</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G172</th>\n",
       "      <td>Sonstiges Holz &gt; 50 cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G17</th>\n",
       "      <td>Kartuschen von Kartuschen-spritzpistolen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G213</th>\n",
       "      <td>Paraffinwachs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G214</th>\n",
       "      <td>Öl/Teer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G166</th>\n",
       "      <td>Farbpinsel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G140</th>\n",
       "      <td>Sack oder Beutel (Tragetasche), Jute oder Hanf</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G165</th>\n",
       "      <td>Glacestengel (Eisstiele), Zahnstocher, Essstäb...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G162</th>\n",
       "      <td>Kisten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G161</th>\n",
       "      <td>Verarbeitetes Holz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G29</th>\n",
       "      <td>Kämme, Bürsten und Sonnenbrillen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G160</th>\n",
       "      <td>Paletten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G157</th>\n",
       "      <td>Papier</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G205</th>\n",
       "      <td>Leuchtstoffröhren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G174</th>\n",
       "      <td>Aerosolspraydosen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G203</th>\n",
       "      <td>Geschirr aus Keramik oder Glas, Tassen, Teller...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G202</th>\n",
       "      <td>Glühbirnen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G201</th>\n",
       "      <td>Gläser, einschliesslich Stücke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G176</th>\n",
       "      <td>Konservendosen (Lebensmitteldosen)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G179</th>\n",
       "      <td>Einweg Grill</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>Säcke, Taschen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G180</th>\n",
       "      <td>Haushaltsgeräte</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G197</th>\n",
       "      <td>sonstiges Metall</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G196</th>\n",
       "      <td>Grosse metallische Gegenstände</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G193</th>\n",
       "      <td>Teile von Autos und Autobatterien</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G182</th>\n",
       "      <td>Angeln; Haken, Gewichte, Köder, Senkblei, usw.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G190</th>\n",
       "      <td>Farbtöpfe, Farbbüchsen,  ( Farbeimer)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G19</th>\n",
       "      <td>Autoteile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G188</th>\n",
       "      <td>Andere Kanister/Behälter &lt; 4 L</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G186</th>\n",
       "      <td>Industrieschrott</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G32</th>\n",
       "      <td>Spielzeug und Partyartikel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G154</th>\n",
       "      <td>Zeitungen oder Zeitschriften</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G153</th>\n",
       "      <td>Papierbecher, Lebensmittelverpackungen aus Pap...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G55</th>\n",
       "      <td>Angelschnur (verwickelt)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G141</th>\n",
       "      <td>Teppiche</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G143</th>\n",
       "      <td>Segel und Segeltuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G185</th>\n",
       "      <td>Behälter mittlerer Grösse</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G70</th>\n",
       "      <td>Schrotflintenpatronen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G147</th>\n",
       "      <td>Papiertragetaschen, (Papiertüten)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G68</th>\n",
       "      <td>Fiberglas-Fragmente</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G64</th>\n",
       "      <td>Kotflügel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G63</th>\n",
       "      <td>Bojen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G62</th>\n",
       "      <td>Schwimmer für Netze</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G61</th>\n",
       "      <td>Sonstiges Angelzubehör</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G60</th>\n",
       "      <td>Lichtstab, Knicklicht, Glow-sticks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6</th>\n",
       "      <td>Flaschen und Behälter aus Kunststoff, nicht fü...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G59</th>\n",
       "      <td>Monofile Angelschnur (Angeln)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G56</th>\n",
       "      <td>Verhedderte Netze</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G53</th>\n",
       "      <td>Netze und Teilstücke &lt; 50cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G151</th>\n",
       "      <td>Tetrapack, Kartons</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G52</th>\n",
       "      <td>Netze und Teilstücke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G51</th>\n",
       "      <td>Fischernetz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G50</th>\n",
       "      <td>Schnur &lt; 1cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G5</th>\n",
       "      <td>Plastiksäcke/ Plastiktüten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G49</th>\n",
       "      <td>Seile &gt; 1cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G150</th>\n",
       "      <td>Milchkartons, Tetrapack</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G43</th>\n",
       "      <td>Sicherheitsetiketten, Siegel für Fischerei ode...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G41</th>\n",
       "      <td>Handschuhe Industriell/Professionell</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G40</th>\n",
       "      <td>Handschuhe Haushalt/Garten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G4</th>\n",
       "      <td>Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G39</th>\n",
       "      <td>Handschuhe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G38</th>\n",
       "      <td>Abdeckungen; Kunststoffverpackungen, Folien zu...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G37</th>\n",
       "      <td>Netzbeutel, Netztasche, Netzsäcke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G36</th>\n",
       "      <td>Säcke aus strapazierfähigem Kunststoff für 25 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>Sixpack-Ringe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Objekte Objekte (St.)   \n",
       "code                                                                      \n",
       "G704                                      Seilbahnbürsten         5 181  \\\n",
       "G27                                      Zigarettenfilter           317   \n",
       "Gfrags                          Fragmentierte Kunststoffe           294   \n",
       "G30                                    Snack-Verpackungen           253   \n",
       "G705                                 Schrauben und Bolzen           206   \n",
       "G936                             Folien für Gewächshäuser           132   \n",
       "Gfoam                             Expandiertes Polystyrol           121   \n",
       "G93                                           Kabelbinder           104   \n",
       "G200             Getränkeflaschen aus Glas, Glasfragmente            84   \n",
       "G177                      Verpackungen aus Aluminiumfolie            68   \n",
       "G124                                    Andere Kunststoff            60   \n",
       "G941            Verpackungsfolien, nicht für Lebensmittel            57   \n",
       "G87                  Abdeckklebeband/Verpackungsklebeband            53   \n",
       "G204                  Baumaterial;  Ziegel, Rohre, Zement            49   \n",
       "G117                                       Styropor < 5mm            37   \n",
       "G33                 Einwegartikel; Tassen/Becher & Deckel            36   \n",
       "G12     Kosmetika, Behälter für Körperpflegeprodukte, ...            34   \n",
       "G89                                 Kunststoff-Bauabfälle            34   \n",
       "G142                              Seil, Schnur oder Netze            33   \n",
       "G122                           Kunststofffragmente (>1mm)            32   \n",
       "G919                       Nägel, Schrauben, Bolzen usw.             31   \n",
       "G922                               Etiketten, Strichcodes            27   \n",
       "G923    Taschentücher, Toilettenpapier, Servietten, Pa...            26   \n",
       "G703                   Pistenmarkierungspfosten (Plastik)            21   \n",
       "G24                   Ringe von Plastikflaschen/Behältern            20   \n",
       "G178          Kronkorken, Lasche von Dose/Ausfreisslachen            18   \n",
       "G146                                       Papier, Karton            18   \n",
       "G926                    Kaugummi, enthält oft Kunststoffe            17   \n",
       "G934    Sandsäcke, Kunststoff für Hochwasser- und Eros...            17   \n",
       "G10     Lebensmittelbehälter zum einmaligen Gebrauch a...            17   \n",
       "G34                          Besteck, Teller und Tabletts            16   \n",
       "G25               Tabak; Kunststoffverpackungen, Behälter            16   \n",
       "G905    Haarspangen, Haargummis, persönliche Accessoir...            16   \n",
       "G710    Ski / Snowboards (Ski, Befestigungen und ander...            15   \n",
       "G707                                Skiausrüstungsetikett            13   \n",
       "G208                   Glas oder Keramikfragmente >2.5 cm            13   \n",
       "G901                    Medizinische Masken, synthetische            13   \n",
       "G198                           Andere Metallteile < 50 cm            12   \n",
       "G170                                   Holz (verarbeitet)            11   \n",
       "G145                                     Andere Textilien            11   \n",
       "G152    Papier &Karton;Zigarettenschachteln, Papier/Ka...            11   \n",
       "G7                             Getränkeflaschen < = 0,5 l            11   \n",
       "G709    Teller von Skistock (runder Teil aus Plastik u...            11   \n",
       "G66     Umreifungsbänder; Hartplastik für Verpackung f...            10   \n",
       "G48                                   Seile, synthetische            10   \n",
       "G100              Medizin; Behälter/Röhrchen/Verpackungen             9   \n",
       "G31                  Schleckstengel, Stengel von Lutscher             9   \n",
       "G702                      Pistenmarkierungspfosten (Holz)             9   \n",
       "G74                    Schaumstoffverpackungen/Isolierung             8   \n",
       "G129                           Schläuche und Gummiplatten             8   \n",
       "G921                       Keramikfliesen und Bruchstücke             7   \n",
       "G105                  Kunststofffragmente subangulär <5mm             6   \n",
       "G23                               unidentifizierte Deckel             6   \n",
       "G20                                    Laschen und Deckel             6   \n",
       "G156                                      Papierfragmente             6   \n",
       "G123                           Polyurethan-Granulat < 5mm             6   \n",
       "G918    Sicherheitsnadeln, Büroklammern, kleine Gebrau...             6   \n",
       "G706                                        Skiabonnement             5   \n",
       "G149                                   Papierverpackungen             5   \n",
       "G191                                     Draht und Gitter             5   \n",
       "G943                     Zäune Landwirtschaft, Kunststoff             4   \n",
       "G915        Reflektoren, Mobilitätsartikel aus Kunststoff             4   \n",
       "G175                      Getränkedosen (Dosen, Getränke)             4   \n",
       "G73     Gegenstände aus Schaumstoff & Teilstücke (nich...             4   \n",
       "G211                     Sonstiges medizinisches Material             4   \n",
       "G35                           Strohhalme und Rührstäbchen             3   \n",
       "G158                      Sonstige Gegenstände aus Papier             3   \n",
       "G95                                  Wattestäbchen/Tupfer             3   \n",
       "G171                                Sonstiges Holz < 50cm             3   \n",
       "G194    Kabel, Metalldraht oft in Gummi- oder Kunststo...             3   \n",
       "G712                                        Skihandschuhe             3   \n",
       "G711                                          Handwärmer              3   \n",
       "G139                                            Rucksäcke             3   \n",
       "G3                       Einkaufstaschen, Shoppingtaschen             3   \n",
       "G159                                                 Kork             3   \n",
       "G210                   Sonstiges Glas/Keramik Materialien             2   \n",
       "G90                               Blumentöpfe aus Plastik             2   \n",
       "G28                  Stifte, Deckel, Druckbleistifte usw.             2   \n",
       "G928                                 Bänder und Schleifen             2   \n",
       "G931    (Absperr)band für Absperrungen, Polizei, Baust...             2   \n",
       "G713                                        Skiförderband             2   \n",
       "G26                                             Feuerzeug             2   \n",
       "G155                Feuerwerkspapierhülsen und -fragmente             2   \n",
       "G181            Geschirr aus Metall, Tassen, Besteck usw.             2   \n",
       "G167                        Streichhölzer oder Feuerwerke             2   \n",
       "G168                                          Holzbretter             2   \n",
       "G935    Gummi-Puffer für Geh- und Wanderstöcke und Tei...             1   \n",
       "G71                                       Schuhe Sandalen             1   \n",
       "G67                           Industriefolie (Kunststoff)             1   \n",
       "G148                              Kartonkisten und Stücke             1   \n",
       "G65                                                 Eimer             1   \n",
       "G131                                          Gummibänder             1   \n",
       "G8                                Getränkeflaschen > 0,5L             1   \n",
       "G128                            Reifen und Antriebsriemen             1   \n",
       "G916                           Bleistifte und Bruchstücke             1   \n",
       "G917                                              Blähton             1   \n",
       "G929       Elektronik und Teile; Sensoren, Headsets usw.              1   \n",
       "G21                   Getränke-Deckel, Getränkeverschluss             1   \n",
       "G708                                            Skistöcke             1   \n",
       "G195                                 Batterien (Haushalt)             1   \n",
       "G101    Robidog Hundekot-Säcklein, andere Hundekotsäck...             1   \n",
       "G199                           Andere Metallteile > 50 cm             1   \n",
       "G96     Hygienebinden/Höscheneinlagen/Tampons und Appl...             1   \n",
       "G902                           Medizinische Masken, Stoff             0   \n",
       "G9      Flaschen und Behälter für Reinigungsmittel und...             0   \n",
       "G126                                                Bälle             0   \n",
       "G900       Handschuhe Latex, persönliche Schutzausrüstung             0   \n",
       "G125                   Luftballons und Luftballonstäbchen             0   \n",
       "G940               Schaumstoff EVA (flexibler Kunststoff)             0   \n",
       "G88                                   Telefon inkl. Teile             0   \n",
       "G904                      Feuerwerkskörper; Raketenkappen             0   \n",
       "G119                      Folienartiger Kunststoff (>1mm)             0   \n",
       "G906                              Kaffeekapseln Aluminium             0   \n",
       "G907                         Kaffeekapseln aus Kunststoff             0   \n",
       "G908                    Klebeband; elektrisch, isolierend             0   \n",
       "G903    Behälter und Packungen für Handdesinfektionsmi...             0   \n",
       "G84                                      CD oder CD-Hülle             0   \n",
       "G13     Flaschen, Behälter, Fässer zum Transportieren ...             0   \n",
       "G913                                            Schnuller             0   \n",
       "G97                     Behälter von Toilettenerfrischer              0   \n",
       "G98                                Windeln – Feuchttücher             0   \n",
       "G99                                     Spritzen - Nadeln             0   \n",
       "G999    Keine Gegenstände bei dieser Erhebung/Studie g...             0   \n",
       "G132                                   Schwimmer (Angeln)             0   \n",
       "G133                  Kondome einschließlich Verpackungen             0   \n",
       "G134                                      Sonstiges Gummi             0   \n",
       "G135    Kleidung, Fussbekleidung, Kopfbedeckung, Hands...             0   \n",
       "G136                                               Schuhe             0   \n",
       "G137                      Kleidung, Handtücher und Lappen             0   \n",
       "G91     Bio-Filtermaterial  / Trägermaterial aus Kunst...             0   \n",
       "G914    Büroklammern, Wäscheklammern, Gebrauchsgegenst...             0   \n",
       "G94                                             Tischtuch             0   \n",
       "G118                   Kleine Industrielle Kügelchen <5mm             0   \n",
       "G939                 Kunststoffblumen, Kunststoffpflanzen             0   \n",
       "G938                    Zahnstocher, Zahnseide Kunststoff             0   \n",
       "G937                          Pheromonköder für Weinberge             0   \n",
       "G106                       Kunststofffragmente eckig <5mm             0   \n",
       "G104    Kunststofffragmente abgerundet / rundlich  <5m...             0   \n",
       "G942      Kunststoffspäne von Drehbänken, CNC-Bearbeitung             0   \n",
       "G107                            Zylindrische Pellets <5mm             0   \n",
       "G933    Taschen, Etuis für Zubehör, Brillen, Elektroni...             0   \n",
       "G932    Bio-Beads, Mikroplastik für die Abwasserbehand...             0   \n",
       "G108                         Scheibenförmige Pellets <5mm             0   \n",
       "G930                               Schaumstoff-Ohrstöpsel             0   \n",
       "G103                        Kunststofffragmente rund <5mm             0   \n",
       "G109                                  Flache Pellets <5mm             0   \n",
       "G944              Pelletmasse aus dem Spritzgussverfahren             0   \n",
       "G11            Kosmetika für den Strand, z.B. Sonnencreme             0   \n",
       "G927    Plastikschnur von Rasentrimmern, die zum Schne...             0   \n",
       "G111                           Kugelförmige Pellets < 5mm             0   \n",
       "G925    Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...             0   \n",
       "G112                           Industriepellets (Nurdles)             0   \n",
       "G138                                  Schuhe und Sandalen             0   \n",
       "G113                                           Fäden <5mm             0   \n",
       "G114                                          Folien <5mm             0   \n",
       "G92                                         Köderbehälter             0   \n",
       "G115                                     Schaumstoff <5mm             0   \n",
       "G116                                        Granulat <5mm             0   \n",
       "G945                                        Rasierklingen             0   \n",
       "G102                                           Flip-Flops             0   \n",
       "G183                                 Teile von Angelhaken             0   \n",
       "G144                                              Tampons             0   \n",
       "G14                Flaschen für Motoröl (Motorölflaschen)             0   \n",
       "G22     Deckel für Chemikalien, Reinigungsmittel (Ohne...             0   \n",
       "G207                       Tonkrüge zum Fangen von Kraken             0   \n",
       "G173                                            Sonstiges             0   \n",
       "G172                               Sonstiges Holz > 50 cm             0   \n",
       "G17              Kartuschen von Kartuschen-spritzpistolen             0   \n",
       "G213                                       Paraffinwachs              0   \n",
       "G214                                              Öl/Teer             0   \n",
       "G166                                           Farbpinsel             0   \n",
       "G140       Sack oder Beutel (Tragetasche), Jute oder Hanf             0   \n",
       "G165    Glacestengel (Eisstiele), Zahnstocher, Essstäb...             0   \n",
       "G162                                               Kisten             0   \n",
       "G161                                   Verarbeitetes Holz             0   \n",
       "G29                      Kämme, Bürsten und Sonnenbrillen             0   \n",
       "G160                                             Paletten             0   \n",
       "G157                                               Papier             0   \n",
       "G205                                    Leuchtstoffröhren             0   \n",
       "G174                                    Aerosolspraydosen             0   \n",
       "G203    Geschirr aus Keramik oder Glas, Tassen, Teller...             0   \n",
       "G202                                           Glühbirnen             0   \n",
       "G201                       Gläser, einschliesslich Stücke             0   \n",
       "G176                   Konservendosen (Lebensmitteldosen)             0   \n",
       "G179                                         Einweg Grill             0   \n",
       "G2                                         Säcke, Taschen             0   \n",
       "G180                                      Haushaltsgeräte             0   \n",
       "G197                                     sonstiges Metall             0   \n",
       "G196                       Grosse metallische Gegenstände             0   \n",
       "G193                    Teile von Autos und Autobatterien             0   \n",
       "G182       Angeln; Haken, Gewichte, Köder, Senkblei, usw.             0   \n",
       "G190                Farbtöpfe, Farbbüchsen,  ( Farbeimer)             0   \n",
       "G19                                             Autoteile             0   \n",
       "G188                       Andere Kanister/Behälter < 4 L             0   \n",
       "G186                                     Industrieschrott             0   \n",
       "G32                            Spielzeug und Partyartikel             0   \n",
       "G154                         Zeitungen oder Zeitschriften             0   \n",
       "G153    Papierbecher, Lebensmittelverpackungen aus Pap...             0   \n",
       "G55                              Angelschnur (verwickelt)             0   \n",
       "G141                                             Teppiche             0   \n",
       "G143                                  Segel und Segeltuch             0   \n",
       "G185                            Behälter mittlerer Grösse             0   \n",
       "G70                                 Schrotflintenpatronen             0   \n",
       "G147                    Papiertragetaschen, (Papiertüten)             0   \n",
       "G68                                   Fiberglas-Fragmente             0   \n",
       "G64                                             Kotflügel             0   \n",
       "G63                                                 Bojen             0   \n",
       "G62                                   Schwimmer für Netze             0   \n",
       "G61                                Sonstiges Angelzubehör             0   \n",
       "G60                    Lichtstab, Knicklicht, Glow-sticks             0   \n",
       "G6      Flaschen und Behälter aus Kunststoff, nicht fü...             0   \n",
       "G59                         Monofile Angelschnur (Angeln)             0   \n",
       "G56                                     Verhedderte Netze             0   \n",
       "G53                           Netze und Teilstücke < 50cm             0   \n",
       "G151                                   Tetrapack, Kartons             0   \n",
       "G52                                  Netze und Teilstücke             0   \n",
       "G51                                           Fischernetz             0   \n",
       "G50                                          Schnur < 1cm             0   \n",
       "G5                             Plastiksäcke/ Plastiktüten             0   \n",
       "G49                                           Seile > 1cm             0   \n",
       "G150                              Milchkartons, Tetrapack             0   \n",
       "G43     Sicherheitsetiketten, Siegel für Fischerei ode...             0   \n",
       "G41                  Handschuhe Industriell/Professionell             0   \n",
       "G40                            Handschuhe Haushalt/Garten             0   \n",
       "G4      Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...             0   \n",
       "G39                                            Handschuhe             0   \n",
       "G38     Abdeckungen; Kunststoffverpackungen, Folien zu...             0   \n",
       "G37                     Netzbeutel, Netztasche, Netzsäcke             0   \n",
       "G36     Säcke aus strapazierfähigem Kunststoff für 25 ...             0   \n",
       "G1                                          Sixpack-Ringe             0   \n",
       "\n",
       "        Anteil  p/100 m  Häufigkeitsrate     Material  \n",
       "code                                                   \n",
       "G704        66        0               10      Plastik  \n",
       "G27          4       14               95      Plastik  \n",
       "Gfrags       3       12              100      Plastik  \n",
       "G30          3       11               95      Plastik  \n",
       "G705         2        3               65       Metall  \n",
       "G936         1        0               15      Plastik  \n",
       "Gfoam        1        0               20      Plastik  \n",
       "G93          1        2               60      Plastik  \n",
       "G200         1        0               45         Glas  \n",
       "G177         0        1               60       Metall  \n",
       "G124         0        0                5      Plastik  \n",
       "G941         0        0               30      Plastik  \n",
       "G87          0        2               60      Plastik  \n",
       "G204         0        0               40         Glas  \n",
       "G117         0        0                5      Plastik  \n",
       "G33          0        0               40      Plastik  \n",
       "G12          0        0                5      Plastik  \n",
       "G89          0        0               15      Plastik  \n",
       "G142         0        0               40        Stoff  \n",
       "G122         0        0               30      Plastik  \n",
       "G919         0        0               20       Metall  \n",
       "G922         0        0               40      Plastik  \n",
       "G923         0        0               40       Papier  \n",
       "G703         0        0               10      Plastik  \n",
       "G24          0        0               35      Plastik  \n",
       "G178         0        0               40       Metall  \n",
       "G146         0        0               25       Papier  \n",
       "G926         0        0               25      Plastik  \n",
       "G934         0        0               15      Plastik  \n",
       "G10          0        0               15      Plastik  \n",
       "G34          0        0               20      Plastik  \n",
       "G25          0        0               25      Plastik  \n",
       "G905         0        0               40      Plastik  \n",
       "G710         0        0               30      Plastik  \n",
       "G707         0        0               25      Plastik  \n",
       "G208         0        0               25         Glas  \n",
       "G901         0        0               20      Plastik  \n",
       "G198         0        0               40       Metall  \n",
       "G170         0        0               10         Holz  \n",
       "G145         0        0               20        Stoff  \n",
       "G152         0        0               25       Papier  \n",
       "G7           0        0               40      Plastik  \n",
       "G709         0        0               30      Plastik  \n",
       "G66          0        0               10      Plastik  \n",
       "G48          0        0               20      Plastik  \n",
       "G100         0        0               40      Plastik  \n",
       "G31          0        0               25      Plastik  \n",
       "G702         0        0               20         Holz  \n",
       "G74          0        0               10      Plastik  \n",
       "G129         0        0                5        Gummi  \n",
       "G921         0        0               10         Glas  \n",
       "G105         0        0                5      Plastik  \n",
       "G23          0        0               10      Plastik  \n",
       "G20          0        0               20      Plastik  \n",
       "G156         0        0               25       Papier  \n",
       "G123         0        0                5      Plastik  \n",
       "G918         0        0               15       Metall  \n",
       "G706         0        0               25      Plastik  \n",
       "G149         0        0               10       Papier  \n",
       "G191         0        0               20       Metall  \n",
       "G943         0        0               10      Plastik  \n",
       "G915         0        0                5      Plastik  \n",
       "G175         0        0               15       Metall  \n",
       "G73          0        0                5      Plastik  \n",
       "G211         0        0               15      Plastik  \n",
       "G35          0        0               15      Plastik  \n",
       "G158         0        0               15       Papier  \n",
       "G95          0        0                5      Plastik  \n",
       "G171         0        0               15         Holz  \n",
       "G194         0        0               10       Metall  \n",
       "G712         0        0               10        Stoff  \n",
       "G711         0        0               10      Plastik  \n",
       "G139         0        0                5        Stoff  \n",
       "G3           0        0               10      Plastik  \n",
       "G159         0        0               10         Holz  \n",
       "G210         0        0                5         Glas  \n",
       "G90          0        0               10      Plastik  \n",
       "G28          0        0               10      Plastik  \n",
       "G928         0        0                5      Plastik  \n",
       "G931         0        0               10      Plastik  \n",
       "G713         0        0                5      Plastik  \n",
       "G26          0        0               10      Plastik  \n",
       "G155         0        0                5       Papier  \n",
       "G181         0        0                5       Metall  \n",
       "G167         0        0                5         Holz  \n",
       "G168         0        0                5         Holz  \n",
       "G935         0        0                5      Plastik  \n",
       "G71          0        0                5      Plastik  \n",
       "G67          0        0                5      Plastik  \n",
       "G148         0        0                5       Papier  \n",
       "G65          0        0                5      Plastik  \n",
       "G131         0        0                5        Gummi  \n",
       "G8           0        0                5      Plastik  \n",
       "G128         0        0                5        Gummi  \n",
       "G916         0        0                5         Holz  \n",
       "G917         0        0                5         Glas  \n",
       "G929         0        0                5       Metall  \n",
       "G21          0        0                5      Plastik  \n",
       "G708         0        0                5       Metall  \n",
       "G195         0        0                5       Metall  \n",
       "G101         0        0                5      Plastik  \n",
       "G199         0        0                5       Metall  \n",
       "G96          0        0                5      Plastik  \n",
       "G902         0        0                0        Stoff  \n",
       "G9           0        0                0      Plastik  \n",
       "G126         0        0                0        Gummi  \n",
       "G900         0        0                0        Gummi  \n",
       "G125         0        0                0        Gummi  \n",
       "G940         0        0                0      Plastik  \n",
       "G88          0        0                0      Plastik  \n",
       "G904         0        0                0      Plastik  \n",
       "G119         0        0                0      Plastik  \n",
       "G906         0        0                0       Metall  \n",
       "G907         0        0                0      Plastik  \n",
       "G908         0        0                0      Plastik  \n",
       "G903         0        0                0      Plastik  \n",
       "G84          0        0                0      Plastik  \n",
       "G13          0        0                0      Plastik  \n",
       "G913         0        0                0      Plastik  \n",
       "G97          0        0                0      Plastik  \n",
       "G98          0        0                0      Plastik  \n",
       "G99          0        0                0      Plastik  \n",
       "G999         0        0                0    Unbekannt  \n",
       "G132         0        0                0        Gummi  \n",
       "G133         0        0                0        Gummi  \n",
       "G134         0        0                0        Gummi  \n",
       "G135         0        0                0        Stoff  \n",
       "G136         0        0                0        Stoff  \n",
       "G137         0        0                0        Stoff  \n",
       "G91          0        0                0      Plastik  \n",
       "G914         0        0                0      Plastik  \n",
       "G94          0        0                0      Plastik  \n",
       "G118         0        0                0      Plastik  \n",
       "G939         0        0                0      Plastik  \n",
       "G938         0        0                0      Plastik  \n",
       "G937         0        0                0      Plastik  \n",
       "G106         0        0                0      Plastik  \n",
       "G104         0        0                0      Plastik  \n",
       "G942         0        0                0      Plastik  \n",
       "G107         0        0                0      Plastik  \n",
       "G933         0        0                0      Plastik  \n",
       "G932         0        0                0      Plastik  \n",
       "G108         0        0                0      Plastik  \n",
       "G930         0        0                0      Plastik  \n",
       "G103         0        0                0      Plastik  \n",
       "G109         0        0                0      Plastik  \n",
       "G944         0        0                0      Plastik  \n",
       "G11          0        0                0      Plastik  \n",
       "G927         0        0                0      Plastik  \n",
       "G111         0        0                0      Plastik  \n",
       "G925         0        0                0      Plastik  \n",
       "G112         0        0                0      Plastik  \n",
       "G138         0        0                0        Stoff  \n",
       "G113         0        0                0      Plastik  \n",
       "G114         0        0                0      Plastik  \n",
       "G92          0        0                0      Plastik  \n",
       "G115         0        0                0      Plastik  \n",
       "G116         0        0                0      Plastik  \n",
       "G945         0        0                0       Metall  \n",
       "G102         0        0                0      Plastik  \n",
       "G183         0        0                0       Metall  \n",
       "G144         0        0                0        Stoff  \n",
       "G14          0        0                0      Plastik  \n",
       "G22          0        0                0      Plastik  \n",
       "G207         0        0                0         Glas  \n",
       "G173         0        0                0         Holz  \n",
       "G172         0        0                0         Holz  \n",
       "G17          0        0                0      Plastik  \n",
       "G213         0        0                0  Chemikalien  \n",
       "G214         0        0                0  Chemikalien  \n",
       "G166         0        0                0         Holz  \n",
       "G140         0        0                0        Stoff  \n",
       "G165         0        0                0         Holz  \n",
       "G162         0        0                0         Holz  \n",
       "G161         0        0                0         Holz  \n",
       "G29          0        0                0      Plastik  \n",
       "G160         0        0                0         Holz  \n",
       "G157         0        0                0       Papier  \n",
       "G205         0        0                0         Glas  \n",
       "G174         0        0                0       Metall  \n",
       "G203         0        0                0         Glas  \n",
       "G202         0        0                0         Glas  \n",
       "G201         0        0                0         Glas  \n",
       "G176         0        0                0       Metall  \n",
       "G179         0        0                0       Metall  \n",
       "G2           0        0                0      Plastik  \n",
       "G180         0        0                0       Metall  \n",
       "G197         0        0                0       Metall  \n",
       "G196         0        0                0       Metall  \n",
       "G193         0        0                0       Metall  \n",
       "G182         0        0                0       Metall  \n",
       "G190         0        0                0       Metall  \n",
       "G19          0        0                0      Plastik  \n",
       "G188         0        0                0       Metall  \n",
       "G186         0        0                0       Metall  \n",
       "G32          0        0                0      Plastik  \n",
       "G154         0        0                0       Papier  \n",
       "G153         0        0                0       Papier  \n",
       "G55          0        0                0      Plastik  \n",
       "G141         0        0                0        Stoff  \n",
       "G143         0        0                0        Stoff  \n",
       "G185         0        0                0       Metall  \n",
       "G70          0        0                0      Plastik  \n",
       "G147         0        0                0       Papier  \n",
       "G68          0        0                0      Plastik  \n",
       "G64          0        0                0      Plastik  \n",
       "G63          0        0                0      Plastik  \n",
       "G62          0        0                0      Plastik  \n",
       "G61          0        0                0      Plastik  \n",
       "G60          0        0                0      Plastik  \n",
       "G6           0        0                0      Plastik  \n",
       "G59          0        0                0      Plastik  \n",
       "G56          0        0                0      Plastik  \n",
       "G53          0        0                0      Plastik  \n",
       "G151         0        0                0       Papier  \n",
       "G52          0        0                0      Plastik  \n",
       "G51          0        0                0      Plastik  \n",
       "G50          0        0                0      Plastik  \n",
       "G5           0        0                0      Plastik  \n",
       "G49          0        0                0      Plastik  \n",
       "G150         0        0                0       Papier  \n",
       "G43          0        0                0      Plastik  \n",
       "G41          0        0                0      Plastik  \n",
       "G40          0        0                0      Plastik  \n",
       "G4           0        0                0      Plastik  \n",
       "G39          0        0                0      Plastik  \n",
       "G38          0        0                0      Plastik  \n",
       "G37          0        0                0      Plastik  \n",
       "G36          0        0                0      Plastik  \n",
       "G1           0        0                0      Plastik  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "complete_inventory = fdx.code_summary.copy()\n",
    "complete_inventory.sort_values(by=\"quantity\", ascending=False, inplace=True)\n",
    "complete_inventory[\"quantity\"] = complete_inventory[\"quantity\"].map(lambda x: featuredata.thousandsSeparator(x, language))\n",
    "complete_inventory[\"% of total\"] = complete_inventory[\"% of total\"].astype(int)\n",
    "complete_inventory[unit_label] = complete_inventory[unit_label].astype(int)\n",
    "complete_inventory.rename(columns=featuredata.inventory_table_de, inplace=True)\n",
    "\n",
    "    \n",
    "inventory_subsection = Paragraph(\"Inventar der Objekte\", featuredata.subsection_title)\n",
    "col_widths=[1.2*cm, 4.5*cm, 2.2*cm, 1.5*cm, 1.5*cm, 2.4*cm, 1.5*cm]\n",
    "inventory_table = featuredata.aStyledTableWithTitleRow(complete_inventory, title=\"Inventar der Objekte\", colWidths=col_widths)\n",
    "\n",
    "map_caption = featuredata.defaultMapCaption(language=\"de\")\n",
    "\n",
    "new_map_image =  Image('resources/maps/alpesvalaisannes.jpeg', width=cm*18, height=12.6*cm, kind=\"proportional\", hAlign= \"CENTER\")\n",
    "new_components = [\n",
    "    KeepTogether([\n",
    "        featuredata.large_space,\n",
    "        location_subsection,\n",
    "        featuredata.small_space,\n",
    "        new_map_image,\n",
    "        Paragraph(map_caption, featuredata.caption_style),\n",
    "        featuredata.smaller_space,\n",
    "        pdf_table,\n",
    "        \n",
    "    ]),\n",
    "    featuredata.large_space,\n",
    "    inventory_table\n",
    "]    \n",
    "\n",
    "pdfcomponents = featuredata.addToDoc(new_components, pdfcomponents)\n",
    "\n",
    "complete_inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Objekte</th>\n",
       "      <th>Objekte (St.)</th>\n",
       "      <th>Anteil</th>\n",
       "      <th>p/100 m</th>\n",
       "      <th>Häufigkeitsrate</th>\n",
       "      <th>Material</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G702</th>\n",
       "      <td>Pistenmarkierungspfosten (Holz)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G100</th>\n",
       "      <td>Medizin; Behälter/Röhrchen/Verpackungen</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G31</th>\n",
       "      <td>Schleckstengel, Stengel von Lutscher</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G200</th>\n",
       "      <td>Getränkeflaschen aus Glas, Glasfragmente</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G74</th>\n",
       "      <td>Schaumstoffverpackungen/Isolierung</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G129</th>\n",
       "      <td>Schläuche und Gummiplatten</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G921</th>\n",
       "      <td>Keramikfliesen und Bruchstücke</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G177</th>\n",
       "      <td>Verpackungen aus Aluminiumfolie</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G124</th>\n",
       "      <td>Andere Kunststoff</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G20</th>\n",
       "      <td>Laschen und Deckel</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G156</th>\n",
       "      <td>Papierfragmente</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G105</th>\n",
       "      <td>Kunststofffragmente subangulär &lt;5mm</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G918</th>\n",
       "      <td>Sicherheitsnadeln, Büroklammern, kleine Gebrau...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G123</th>\n",
       "      <td>Polyurethan-Granulat &lt; 5mm</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G23</th>\n",
       "      <td>unidentifizierte Deckel</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G941</th>\n",
       "      <td>Verpackungsfolien, nicht für Lebensmittel</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G87</th>\n",
       "      <td>Abdeckklebeband/Verpackungsklebeband</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G704</th>\n",
       "      <td>Seilbahnbürsten</td>\n",
       "      <td>5 181</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G191</th>\n",
       "      <td>Draht und Gitter</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G149</th>\n",
       "      <td>Papierverpackungen</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G706</th>\n",
       "      <td>Skiabonnement</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G204</th>\n",
       "      <td>Baumaterial;  Ziegel, Rohre, Zement</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G73</th>\n",
       "      <td>Gegenstände aus Schaumstoff &amp; Teilstücke (nich...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G211</th>\n",
       "      <td>Sonstiges medizinisches Material</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G175</th>\n",
       "      <td>Getränkedosen (Dosen, Getränke)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G943</th>\n",
       "      <td>Zäune Landwirtschaft, Kunststoff</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G915</th>\n",
       "      <td>Reflektoren, Mobilitätsartikel aus Kunststoff</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G117</th>\n",
       "      <td>Styropor &lt; 5mm</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G33</th>\n",
       "      <td>Einwegartikel; Tassen/Becher &amp; Deckel</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G12</th>\n",
       "      <td>Kosmetika, Behälter für Körperpflegeprodukte, ...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G89</th>\n",
       "      <td>Kunststoff-Bauabfälle</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G142</th>\n",
       "      <td>Seil, Schnur oder Netze</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G122</th>\n",
       "      <td>Kunststofffragmente (&gt;1mm)</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G27</th>\n",
       "      <td>Zigarettenfilter</td>\n",
       "      <td>317</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>95</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G919</th>\n",
       "      <td>Nägel, Schrauben, Bolzen usw.</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G194</th>\n",
       "      <td>Kabel, Metalldraht oft in Gummi- oder Kunststo...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G711</th>\n",
       "      <td>Handwärmer</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G712</th>\n",
       "      <td>Skihandschuhe</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G35</th>\n",
       "      <td>Strohhalme und Rührstäbchen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G171</th>\n",
       "      <td>Sonstiges Holz &lt; 50cm</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>Einkaufstaschen, Shoppingtaschen</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G139</th>\n",
       "      <td>Rucksäcke</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G158</th>\n",
       "      <td>Sonstige Gegenstände aus Papier</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G95</th>\n",
       "      <td>Wattestäbchen/Tupfer</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G159</th>\n",
       "      <td>Kork</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfrags</th>\n",
       "      <td>Fragmentierte Kunststoffe</td>\n",
       "      <td>294</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G922</th>\n",
       "      <td>Etiketten, Strichcodes</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G923</th>\n",
       "      <td>Taschentücher, Toilettenpapier, Servietten, Pa...</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G30</th>\n",
       "      <td>Snack-Verpackungen</td>\n",
       "      <td>253</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>95</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G703</th>\n",
       "      <td>Pistenmarkierungspfosten (Plastik)</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G705</th>\n",
       "      <td>Schrauben und Bolzen</td>\n",
       "      <td>206</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G24</th>\n",
       "      <td>Ringe von Plastikflaschen/Behältern</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G210</th>\n",
       "      <td>Sonstiges Glas/Keramik Materialien</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G28</th>\n",
       "      <td>Stifte, Deckel, Druckbleistifte usw.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G167</th>\n",
       "      <td>Streichhölzer oder Feuerwerke</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G713</th>\n",
       "      <td>Skiförderband</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G181</th>\n",
       "      <td>Geschirr aus Metall, Tassen, Besteck usw.</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G90</th>\n",
       "      <td>Blumentöpfe aus Plastik</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G928</th>\n",
       "      <td>Bänder und Schleifen</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G168</th>\n",
       "      <td>Holzbretter</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G931</th>\n",
       "      <td>(Absperr)band für Absperrungen, Polizei, Baust...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G26</th>\n",
       "      <td>Feuerzeug</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G155</th>\n",
       "      <td>Feuerwerkspapierhülsen und -fragmente</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G178</th>\n",
       "      <td>Kronkorken, Lasche von Dose/Ausfreisslachen</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G146</th>\n",
       "      <td>Papier, Karton</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G934</th>\n",
       "      <td>Sandsäcke, Kunststoff für Hochwasser- und Eros...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G926</th>\n",
       "      <td>Kaugummi, enthält oft Kunststoffe</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>Lebensmittelbehälter zum einmaligen Gebrauch a...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G25</th>\n",
       "      <td>Tabak; Kunststoffverpackungen, Behälter</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G905</th>\n",
       "      <td>Haarspangen, Haargummis, persönliche Accessoir...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G34</th>\n",
       "      <td>Besteck, Teller und Tabletts</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G710</th>\n",
       "      <td>Ski / Snowboards (Ski, Befestigungen und ander...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G936</th>\n",
       "      <td>Folien für Gewächshäuser</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G208</th>\n",
       "      <td>Glas oder Keramikfragmente &gt;2.5 cm</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G901</th>\n",
       "      <td>Medizinische Masken, synthetische</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G707</th>\n",
       "      <td>Skiausrüstungsetikett</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gfoam</th>\n",
       "      <td>Expandiertes Polystyrol</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G198</th>\n",
       "      <td>Andere Metallteile &lt; 50 cm</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G709</th>\n",
       "      <td>Teller von Skistock (runder Teil aus Plastik u...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G152</th>\n",
       "      <td>Papier &amp;Karton;Zigarettenschachteln, Papier/Ka...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G145</th>\n",
       "      <td>Andere Textilien</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>Getränkeflaschen &lt; = 0,5 l</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G170</th>\n",
       "      <td>Holz (verarbeitet)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G93</th>\n",
       "      <td>Kabelbinder</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G66</th>\n",
       "      <td>Umreifungsbänder; Hartplastik für Verpackung f...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G48</th>\n",
       "      <td>Seile, synthetische</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G21</th>\n",
       "      <td>Getränke-Deckel, Getränkeverschluss</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G917</th>\n",
       "      <td>Blähton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G916</th>\n",
       "      <td>Bleistifte und Bruchstücke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G148</th>\n",
       "      <td>Kartonkisten und Stücke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G929</th>\n",
       "      <td>Elektronik und Teile; Sensoren, Headsets usw.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G199</th>\n",
       "      <td>Andere Metallteile &gt; 50 cm</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G195</th>\n",
       "      <td>Batterien (Haushalt)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G8</th>\n",
       "      <td>Getränkeflaschen &gt; 0,5L</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G71</th>\n",
       "      <td>Schuhe Sandalen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G131</th>\n",
       "      <td>Gummibänder</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G67</th>\n",
       "      <td>Industriefolie (Kunststoff)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G96</th>\n",
       "      <td>Hygienebinden/Höscheneinlagen/Tampons und Appl...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101</th>\n",
       "      <td>Robidog Hundekot-Säcklein, andere Hundekotsäck...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G65</th>\n",
       "      <td>Eimer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G708</th>\n",
       "      <td>Skistöcke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G128</th>\n",
       "      <td>Reifen und Antriebsriemen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G935</th>\n",
       "      <td>Gummi-Puffer für Geh- und Wanderstöcke und Tei...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G126</th>\n",
       "      <td>Bälle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G115</th>\n",
       "      <td>Schaumstoff &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G116</th>\n",
       "      <td>Granulat &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G904</th>\n",
       "      <td>Feuerwerkskörper; Raketenkappen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G903</th>\n",
       "      <td>Behälter und Packungen für Handdesinfektionsmi...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G902</th>\n",
       "      <td>Medizinische Masken, Stoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G118</th>\n",
       "      <td>Kleine Industrielle Kügelchen &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G119</th>\n",
       "      <td>Folienartiger Kunststoff (&gt;1mm)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G84</th>\n",
       "      <td>CD oder CD-Hülle</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G13</th>\n",
       "      <td>Flaschen, Behälter, Fässer zum Transportieren ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G88</th>\n",
       "      <td>Telefon inkl. Teile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G900</th>\n",
       "      <td>Handschuhe Latex, persönliche Schutzausrüstung</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G113</th>\n",
       "      <td>Fäden &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G9</th>\n",
       "      <td>Flaschen und Behälter für Reinigungsmittel und...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G906</th>\n",
       "      <td>Kaffeekapseln Aluminium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G125</th>\n",
       "      <td>Luftballons und Luftballonstäbchen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G114</th>\n",
       "      <td>Folien &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G161</th>\n",
       "      <td>Verarbeitetes Holz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G92</th>\n",
       "      <td>Köderbehälter</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G907</th>\n",
       "      <td>Kaffeekapseln aus Kunststoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G942</th>\n",
       "      <td>Kunststoffspäne von Drehbänken, CNC-Bearbeitung</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G937</th>\n",
       "      <td>Pheromonköder für Weinberge</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G938</th>\n",
       "      <td>Zahnstocher, Zahnseide Kunststoff</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G939</th>\n",
       "      <td>Kunststoffblumen, Kunststoffpflanzen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G94</th>\n",
       "      <td>Tischtuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G940</th>\n",
       "      <td>Schaumstoff EVA (flexibler Kunststoff)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G103</th>\n",
       "      <td>Kunststofffragmente rund &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G102</th>\n",
       "      <td>Flip-Flops</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G908</th>\n",
       "      <td>Klebeband; elektrisch, isolierend</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G944</th>\n",
       "      <td>Pelletmasse aus dem Spritzgussverfahren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G945</th>\n",
       "      <td>Rasierklingen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G97</th>\n",
       "      <td>Behälter von Toilettenerfrischer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G98</th>\n",
       "      <td>Windeln – Feuchttücher</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G99</th>\n",
       "      <td>Spritzen - Nadeln</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G999</th>\n",
       "      <td>Keine Gegenstände bei dieser Erhebung/Studie g...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unbekannt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G933</th>\n",
       "      <td>Taschen, Etuis für Zubehör, Brillen, Elektroni...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G932</th>\n",
       "      <td>Bio-Beads, Mikroplastik für die Abwasserbehand...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G104</th>\n",
       "      <td>Kunststofffragmente abgerundet / rundlich  &lt;5m...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G930</th>\n",
       "      <td>Schaumstoff-Ohrstöpsel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G106</th>\n",
       "      <td>Kunststofffragmente eckig &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G927</th>\n",
       "      <td>Plastikschnur von Rasentrimmern, die zum Schne...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G925</th>\n",
       "      <td>Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G107</th>\n",
       "      <td>Zylindrische Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G108</th>\n",
       "      <td>Scheibenförmige Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G109</th>\n",
       "      <td>Flache Pellets &lt;5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G133</th>\n",
       "      <td>Kondome einschließlich Verpackungen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11</th>\n",
       "      <td>Kosmetika für den Strand, z.B. Sonnencreme</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G111</th>\n",
       "      <td>Kugelförmige Pellets &lt; 5mm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G112</th>\n",
       "      <td>Industriepellets (Nurdles)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G914</th>\n",
       "      <td>Büroklammern, Wäscheklammern, Gebrauchsgegenst...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G913</th>\n",
       "      <td>Schnuller</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G91</th>\n",
       "      <td>Bio-Filtermaterial  / Trägermaterial aus Kunst...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G132</th>\n",
       "      <td>Schwimmer (Angeln)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G61</th>\n",
       "      <td>Sonstiges Angelzubehör</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G70</th>\n",
       "      <td>Schrotflintenpatronen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G214</th>\n",
       "      <td>Öl/Teer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G19</th>\n",
       "      <td>Autoteile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G190</th>\n",
       "      <td>Farbtöpfe, Farbbüchsen,  ( Farbeimer)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G151</th>\n",
       "      <td>Tetrapack, Kartons</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G193</th>\n",
       "      <td>Teile von Autos und Autobatterien</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G150</th>\n",
       "      <td>Milchkartons, Tetrapack</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G196</th>\n",
       "      <td>Grosse metallische Gegenstände</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G197</th>\n",
       "      <td>sonstiges Metall</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>Säcke, Taschen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G147</th>\n",
       "      <td>Papiertragetaschen, (Papiertüten)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G201</th>\n",
       "      <td>Gläser, einschliesslich Stücke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G202</th>\n",
       "      <td>Glühbirnen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G203</th>\n",
       "      <td>Geschirr aus Keramik oder Glas, Tassen, Teller...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G205</th>\n",
       "      <td>Leuchtstoffröhren</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G207</th>\n",
       "      <td>Tonkrüge zum Fangen von Kraken</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Glas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G144</th>\n",
       "      <td>Tampons</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G188</th>\n",
       "      <td>Andere Kanister/Behälter &lt; 4 L</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G186</th>\n",
       "      <td>Industrieschrott</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G185</th>\n",
       "      <td>Behälter mittlerer Grösse</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G173</th>\n",
       "      <td>Sonstiges</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G165</th>\n",
       "      <td>Glacestengel (Eisstiele), Zahnstocher, Essstäb...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G166</th>\n",
       "      <td>Farbpinsel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G160</th>\n",
       "      <td>Paletten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G157</th>\n",
       "      <td>Papier</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G17</th>\n",
       "      <td>Kartuschen von Kartuschen-spritzpistolen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G172</th>\n",
       "      <td>Sonstiges Holz &gt; 50 cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G174</th>\n",
       "      <td>Aerosolspraydosen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G183</th>\n",
       "      <td>Teile von Angelhaken</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G154</th>\n",
       "      <td>Zeitungen oder Zeitschriften</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G176</th>\n",
       "      <td>Konservendosen (Lebensmitteldosen)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G153</th>\n",
       "      <td>Papierbecher, Lebensmittelverpackungen aus Pap...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Papier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G179</th>\n",
       "      <td>Einweg Grill</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G180</th>\n",
       "      <td>Haushaltsgeräte</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G182</th>\n",
       "      <td>Angeln; Haken, Gewichte, Köder, Senkblei, usw.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Metall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G213</th>\n",
       "      <td>Paraffinwachs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chemikalien</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G22</th>\n",
       "      <td>Deckel für Chemikalien, Reinigungsmittel (Ohne...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G68</th>\n",
       "      <td>Fiberglas-Fragmente</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G143</th>\n",
       "      <td>Segel und Segeltuch</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G49</th>\n",
       "      <td>Seile &gt; 1cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G5</th>\n",
       "      <td>Plastiksäcke/ Plastiktüten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G50</th>\n",
       "      <td>Schnur &lt; 1cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G51</th>\n",
       "      <td>Fischernetz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G52</th>\n",
       "      <td>Netze und Teilstücke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G53</th>\n",
       "      <td>Netze und Teilstücke &lt; 50cm</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G55</th>\n",
       "      <td>Angelschnur (verwickelt)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G56</th>\n",
       "      <td>Verhedderte Netze</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G59</th>\n",
       "      <td>Monofile Angelschnur (Angeln)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6</th>\n",
       "      <td>Flaschen und Behälter aus Kunststoff, nicht fü...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G60</th>\n",
       "      <td>Lichtstab, Knicklicht, Glow-sticks</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G162</th>\n",
       "      <td>Kisten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Holz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G62</th>\n",
       "      <td>Schwimmer für Netze</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G63</th>\n",
       "      <td>Bojen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G64</th>\n",
       "      <td>Kotflügel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G43</th>\n",
       "      <td>Sicherheitsetiketten, Siegel für Fischerei ode...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G41</th>\n",
       "      <td>Handschuhe Industriell/Professionell</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G40</th>\n",
       "      <td>Handschuhe Haushalt/Garten</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G136</th>\n",
       "      <td>Schuhe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G141</th>\n",
       "      <td>Teppiche</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G140</th>\n",
       "      <td>Sack oder Beutel (Tragetasche), Jute oder Hanf</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G14</th>\n",
       "      <td>Flaschen für Motoröl (Motorölflaschen)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G29</th>\n",
       "      <td>Kämme, Bürsten und Sonnenbrillen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G138</th>\n",
       "      <td>Schuhe und Sandalen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G137</th>\n",
       "      <td>Kleidung, Handtücher und Lappen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G32</th>\n",
       "      <td>Spielzeug und Partyartikel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G4</th>\n",
       "      <td>Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G135</th>\n",
       "      <td>Kleidung, Fussbekleidung, Kopfbedeckung, Hands...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Stoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G134</th>\n",
       "      <td>Sonstiges Gummi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gummi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G36</th>\n",
       "      <td>Säcke aus strapazierfähigem Kunststoff für 25 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G37</th>\n",
       "      <td>Netzbeutel, Netztasche, Netzsäcke</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G38</th>\n",
       "      <td>Abdeckungen; Kunststoffverpackungen, Folien zu...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G39</th>\n",
       "      <td>Handschuhe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>Sixpack-Ringe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Plastik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Objekte Objekte (St.)   \n",
       "code                                                                      \n",
       "G702                      Pistenmarkierungspfosten (Holz)             9  \\\n",
       "G100              Medizin; Behälter/Röhrchen/Verpackungen             9   \n",
       "G31                  Schleckstengel, Stengel von Lutscher             9   \n",
       "G200             Getränkeflaschen aus Glas, Glasfragmente            84   \n",
       "G74                    Schaumstoffverpackungen/Isolierung             8   \n",
       "G129                           Schläuche und Gummiplatten             8   \n",
       "G921                       Keramikfliesen und Bruchstücke             7   \n",
       "G177                      Verpackungen aus Aluminiumfolie            68   \n",
       "G124                                    Andere Kunststoff            60   \n",
       "G20                                    Laschen und Deckel             6   \n",
       "G156                                      Papierfragmente             6   \n",
       "G105                  Kunststofffragmente subangulär <5mm             6   \n",
       "G918    Sicherheitsnadeln, Büroklammern, kleine Gebrau...             6   \n",
       "G123                           Polyurethan-Granulat < 5mm             6   \n",
       "G23                               unidentifizierte Deckel             6   \n",
       "G941            Verpackungsfolien, nicht für Lebensmittel            57   \n",
       "G87                  Abdeckklebeband/Verpackungsklebeband            53   \n",
       "G704                                      Seilbahnbürsten         5 181   \n",
       "G191                                     Draht und Gitter             5   \n",
       "G149                                   Papierverpackungen             5   \n",
       "G706                                        Skiabonnement             5   \n",
       "G204                  Baumaterial;  Ziegel, Rohre, Zement            49   \n",
       "G73     Gegenstände aus Schaumstoff & Teilstücke (nich...             4   \n",
       "G211                     Sonstiges medizinisches Material             4   \n",
       "G175                      Getränkedosen (Dosen, Getränke)             4   \n",
       "G943                     Zäune Landwirtschaft, Kunststoff             4   \n",
       "G915        Reflektoren, Mobilitätsartikel aus Kunststoff             4   \n",
       "G117                                       Styropor < 5mm            37   \n",
       "G33                 Einwegartikel; Tassen/Becher & Deckel            36   \n",
       "G12     Kosmetika, Behälter für Körperpflegeprodukte, ...            34   \n",
       "G89                                 Kunststoff-Bauabfälle            34   \n",
       "G142                              Seil, Schnur oder Netze            33   \n",
       "G122                           Kunststofffragmente (>1mm)            32   \n",
       "G27                                      Zigarettenfilter           317   \n",
       "G919                       Nägel, Schrauben, Bolzen usw.             31   \n",
       "G194    Kabel, Metalldraht oft in Gummi- oder Kunststo...             3   \n",
       "G711                                          Handwärmer              3   \n",
       "G712                                        Skihandschuhe             3   \n",
       "G35                           Strohhalme und Rührstäbchen             3   \n",
       "G171                                Sonstiges Holz < 50cm             3   \n",
       "G3                       Einkaufstaschen, Shoppingtaschen             3   \n",
       "G139                                            Rucksäcke             3   \n",
       "G158                      Sonstige Gegenstände aus Papier             3   \n",
       "G95                                  Wattestäbchen/Tupfer             3   \n",
       "G159                                                 Kork             3   \n",
       "Gfrags                          Fragmentierte Kunststoffe           294   \n",
       "G922                               Etiketten, Strichcodes            27   \n",
       "G923    Taschentücher, Toilettenpapier, Servietten, Pa...            26   \n",
       "G30                                    Snack-Verpackungen           253   \n",
       "G703                   Pistenmarkierungspfosten (Plastik)            21   \n",
       "G705                                 Schrauben und Bolzen           206   \n",
       "G24                   Ringe von Plastikflaschen/Behältern            20   \n",
       "G210                   Sonstiges Glas/Keramik Materialien             2   \n",
       "G28                  Stifte, Deckel, Druckbleistifte usw.             2   \n",
       "G167                        Streichhölzer oder Feuerwerke             2   \n",
       "G713                                        Skiförderband             2   \n",
       "G181            Geschirr aus Metall, Tassen, Besteck usw.             2   \n",
       "G90                               Blumentöpfe aus Plastik             2   \n",
       "G928                                 Bänder und Schleifen             2   \n",
       "G168                                          Holzbretter             2   \n",
       "G931    (Absperr)band für Absperrungen, Polizei, Baust...             2   \n",
       "G26                                             Feuerzeug             2   \n",
       "G155                Feuerwerkspapierhülsen und -fragmente             2   \n",
       "G178          Kronkorken, Lasche von Dose/Ausfreisslachen            18   \n",
       "G146                                       Papier, Karton            18   \n",
       "G934    Sandsäcke, Kunststoff für Hochwasser- und Eros...            17   \n",
       "G926                    Kaugummi, enthält oft Kunststoffe            17   \n",
       "G10     Lebensmittelbehälter zum einmaligen Gebrauch a...            17   \n",
       "G25               Tabak; Kunststoffverpackungen, Behälter            16   \n",
       "G905    Haarspangen, Haargummis, persönliche Accessoir...            16   \n",
       "G34                          Besteck, Teller und Tabletts            16   \n",
       "G710    Ski / Snowboards (Ski, Befestigungen und ander...            15   \n",
       "G936                             Folien für Gewächshäuser           132   \n",
       "G208                   Glas oder Keramikfragmente >2.5 cm            13   \n",
       "G901                    Medizinische Masken, synthetische            13   \n",
       "G707                                Skiausrüstungsetikett            13   \n",
       "Gfoam                             Expandiertes Polystyrol           121   \n",
       "G198                           Andere Metallteile < 50 cm            12   \n",
       "G709    Teller von Skistock (runder Teil aus Plastik u...            11   \n",
       "G152    Papier &Karton;Zigarettenschachteln, Papier/Ka...            11   \n",
       "G145                                     Andere Textilien            11   \n",
       "G7                             Getränkeflaschen < = 0,5 l            11   \n",
       "G170                                   Holz (verarbeitet)            11   \n",
       "G93                                           Kabelbinder           104   \n",
       "G66     Umreifungsbänder; Hartplastik für Verpackung f...            10   \n",
       "G48                                   Seile, synthetische            10   \n",
       "G21                   Getränke-Deckel, Getränkeverschluss             1   \n",
       "G917                                              Blähton             1   \n",
       "G916                           Bleistifte und Bruchstücke             1   \n",
       "G148                              Kartonkisten und Stücke             1   \n",
       "G929       Elektronik und Teile; Sensoren, Headsets usw.              1   \n",
       "G199                           Andere Metallteile > 50 cm             1   \n",
       "G195                                 Batterien (Haushalt)             1   \n",
       "G8                                Getränkeflaschen > 0,5L             1   \n",
       "G71                                       Schuhe Sandalen             1   \n",
       "G131                                          Gummibänder             1   \n",
       "G67                           Industriefolie (Kunststoff)             1   \n",
       "G96     Hygienebinden/Höscheneinlagen/Tampons und Appl...             1   \n",
       "G101    Robidog Hundekot-Säcklein, andere Hundekotsäck...             1   \n",
       "G65                                                 Eimer             1   \n",
       "G708                                            Skistöcke             1   \n",
       "G128                            Reifen und Antriebsriemen             1   \n",
       "G935    Gummi-Puffer für Geh- und Wanderstöcke und Tei...             1   \n",
       "G126                                                Bälle             0   \n",
       "G115                                     Schaumstoff <5mm             0   \n",
       "G116                                        Granulat <5mm             0   \n",
       "G904                      Feuerwerkskörper; Raketenkappen             0   \n",
       "G903    Behälter und Packungen für Handdesinfektionsmi...             0   \n",
       "G902                           Medizinische Masken, Stoff             0   \n",
       "G118                   Kleine Industrielle Kügelchen <5mm             0   \n",
       "G119                      Folienartiger Kunststoff (>1mm)             0   \n",
       "G84                                      CD oder CD-Hülle             0   \n",
       "G13     Flaschen, Behälter, Fässer zum Transportieren ...             0   \n",
       "G88                                   Telefon inkl. Teile             0   \n",
       "G900       Handschuhe Latex, persönliche Schutzausrüstung             0   \n",
       "G113                                           Fäden <5mm             0   \n",
       "G9      Flaschen und Behälter für Reinigungsmittel und...             0   \n",
       "G906                              Kaffeekapseln Aluminium             0   \n",
       "G125                   Luftballons und Luftballonstäbchen             0   \n",
       "G114                                          Folien <5mm             0   \n",
       "G161                                   Verarbeitetes Holz             0   \n",
       "G92                                         Köderbehälter             0   \n",
       "G907                         Kaffeekapseln aus Kunststoff             0   \n",
       "G942      Kunststoffspäne von Drehbänken, CNC-Bearbeitung             0   \n",
       "G937                          Pheromonköder für Weinberge             0   \n",
       "G938                    Zahnstocher, Zahnseide Kunststoff             0   \n",
       "G939                 Kunststoffblumen, Kunststoffpflanzen             0   \n",
       "G94                                             Tischtuch             0   \n",
       "G940               Schaumstoff EVA (flexibler Kunststoff)             0   \n",
       "G103                        Kunststofffragmente rund <5mm             0   \n",
       "G102                                           Flip-Flops             0   \n",
       "G908                    Klebeband; elektrisch, isolierend             0   \n",
       "G944              Pelletmasse aus dem Spritzgussverfahren             0   \n",
       "G945                                        Rasierklingen             0   \n",
       "G97                     Behälter von Toilettenerfrischer              0   \n",
       "G98                                Windeln – Feuchttücher             0   \n",
       "G99                                     Spritzen - Nadeln             0   \n",
       "G999    Keine Gegenstände bei dieser Erhebung/Studie g...             0   \n",
       "G933    Taschen, Etuis für Zubehör, Brillen, Elektroni...             0   \n",
       "G932    Bio-Beads, Mikroplastik für die Abwasserbehand...             0   \n",
       "G104    Kunststofffragmente abgerundet / rundlich  <5m...             0   \n",
       "G930                               Schaumstoff-Ohrstöpsel             0   \n",
       "G106                       Kunststofffragmente eckig <5mm             0   \n",
       "G927    Plastikschnur von Rasentrimmern, die zum Schne...             0   \n",
       "G925    Pakete: Trockenmittel/Feuchtigkeitsabsorber, m...             0   \n",
       "G107                            Zylindrische Pellets <5mm             0   \n",
       "G108                         Scheibenförmige Pellets <5mm             0   \n",
       "G109                                  Flache Pellets <5mm             0   \n",
       "G133                  Kondome einschließlich Verpackungen             0   \n",
       "G11            Kosmetika für den Strand, z.B. Sonnencreme             0   \n",
       "G111                           Kugelförmige Pellets < 5mm             0   \n",
       "G112                           Industriepellets (Nurdles)             0   \n",
       "G914    Büroklammern, Wäscheklammern, Gebrauchsgegenst...             0   \n",
       "G913                                            Schnuller             0   \n",
       "G91     Bio-Filtermaterial  / Trägermaterial aus Kunst...             0   \n",
       "G132                                   Schwimmer (Angeln)             0   \n",
       "G61                                Sonstiges Angelzubehör             0   \n",
       "G70                                 Schrotflintenpatronen             0   \n",
       "G214                                              Öl/Teer             0   \n",
       "G19                                             Autoteile             0   \n",
       "G190                Farbtöpfe, Farbbüchsen,  ( Farbeimer)             0   \n",
       "G151                                   Tetrapack, Kartons             0   \n",
       "G193                    Teile von Autos und Autobatterien             0   \n",
       "G150                              Milchkartons, Tetrapack             0   \n",
       "G196                       Grosse metallische Gegenstände             0   \n",
       "G197                                     sonstiges Metall             0   \n",
       "G2                                         Säcke, Taschen             0   \n",
       "G147                    Papiertragetaschen, (Papiertüten)             0   \n",
       "G201                       Gläser, einschliesslich Stücke             0   \n",
       "G202                                           Glühbirnen             0   \n",
       "G203    Geschirr aus Keramik oder Glas, Tassen, Teller...             0   \n",
       "G205                                    Leuchtstoffröhren             0   \n",
       "G207                       Tonkrüge zum Fangen von Kraken             0   \n",
       "G144                                              Tampons             0   \n",
       "G188                       Andere Kanister/Behälter < 4 L             0   \n",
       "G186                                     Industrieschrott             0   \n",
       "G185                            Behälter mittlerer Grösse             0   \n",
       "G173                                            Sonstiges             0   \n",
       "G165    Glacestengel (Eisstiele), Zahnstocher, Essstäb...             0   \n",
       "G166                                           Farbpinsel             0   \n",
       "G160                                             Paletten             0   \n",
       "G157                                               Papier             0   \n",
       "G17              Kartuschen von Kartuschen-spritzpistolen             0   \n",
       "G172                               Sonstiges Holz > 50 cm             0   \n",
       "G174                                    Aerosolspraydosen             0   \n",
       "G183                                 Teile von Angelhaken             0   \n",
       "G154                         Zeitungen oder Zeitschriften             0   \n",
       "G176                   Konservendosen (Lebensmitteldosen)             0   \n",
       "G153    Papierbecher, Lebensmittelverpackungen aus Pap...             0   \n",
       "G179                                         Einweg Grill             0   \n",
       "G180                                      Haushaltsgeräte             0   \n",
       "G182       Angeln; Haken, Gewichte, Köder, Senkblei, usw.             0   \n",
       "G213                                       Paraffinwachs              0   \n",
       "G22     Deckel für Chemikalien, Reinigungsmittel (Ohne...             0   \n",
       "G68                                   Fiberglas-Fragmente             0   \n",
       "G143                                  Segel und Segeltuch             0   \n",
       "G49                                           Seile > 1cm             0   \n",
       "G5                             Plastiksäcke/ Plastiktüten             0   \n",
       "G50                                          Schnur < 1cm             0   \n",
       "G51                                           Fischernetz             0   \n",
       "G52                                  Netze und Teilstücke             0   \n",
       "G53                           Netze und Teilstücke < 50cm             0   \n",
       "G55                              Angelschnur (verwickelt)             0   \n",
       "G56                                     Verhedderte Netze             0   \n",
       "G59                         Monofile Angelschnur (Angeln)             0   \n",
       "G6      Flaschen und Behälter aus Kunststoff, nicht fü...             0   \n",
       "G60                    Lichtstab, Knicklicht, Glow-sticks             0   \n",
       "G162                                               Kisten             0   \n",
       "G62                                   Schwimmer für Netze             0   \n",
       "G63                                                 Bojen             0   \n",
       "G64                                             Kotflügel             0   \n",
       "G43     Sicherheitsetiketten, Siegel für Fischerei ode...             0   \n",
       "G41                  Handschuhe Industriell/Professionell             0   \n",
       "G40                            Handschuhe Haushalt/Garten             0   \n",
       "G136                                               Schuhe             0   \n",
       "G141                                             Teppiche             0   \n",
       "G140       Sack oder Beutel (Tragetasche), Jute oder Hanf             0   \n",
       "G14                Flaschen für Motoröl (Motorölflaschen)             0   \n",
       "G29                      Kämme, Bürsten und Sonnenbrillen             0   \n",
       "G138                                  Schuhe und Sandalen             0   \n",
       "G137                      Kleidung, Handtücher und Lappen             0   \n",
       "G32                            Spielzeug und Partyartikel             0   \n",
       "G4      Kleine Plastikbeutel; Gefrierbeutel, Zippsäckc...             0   \n",
       "G135    Kleidung, Fussbekleidung, Kopfbedeckung, Hands...             0   \n",
       "G134                                      Sonstiges Gummi             0   \n",
       "G36     Säcke aus strapazierfähigem Kunststoff für 25 ...             0   \n",
       "G37                     Netzbeutel, Netztasche, Netzsäcke             0   \n",
       "G38     Abdeckungen; Kunststoffverpackungen, Folien zu...             0   \n",
       "G39                                            Handschuhe             0   \n",
       "G1                                          Sixpack-Ringe             0   \n",
       "\n",
       "        Anteil  p/100 m  Häufigkeitsrate     Material  \n",
       "code                                                   \n",
       "G702         0        0               20         Holz  \n",
       "G100         0        0               40      Plastik  \n",
       "G31          0        0               25      Plastik  \n",
       "G200         1        0               45         Glas  \n",
       "G74          0        0               10      Plastik  \n",
       "G129         0        0                5        Gummi  \n",
       "G921         0        0               10         Glas  \n",
       "G177         0        1               60       Metall  \n",
       "G124         0        0                5      Plastik  \n",
       "G20          0        0               20      Plastik  \n",
       "G156         0        0               25       Papier  \n",
       "G105         0        0                5      Plastik  \n",
       "G918         0        0               15       Metall  \n",
       "G123         0        0                5      Plastik  \n",
       "G23          0        0               10      Plastik  \n",
       "G941         0        0               30      Plastik  \n",
       "G87          0        2               60      Plastik  \n",
       "G704        66        0               10      Plastik  \n",
       "G191         0        0               20       Metall  \n",
       "G149         0        0               10       Papier  \n",
       "G706         0        0               25      Plastik  \n",
       "G204         0        0               40         Glas  \n",
       "G73          0        0                5      Plastik  \n",
       "G211         0        0               15      Plastik  \n",
       "G175         0        0               15       Metall  \n",
       "G943         0        0               10      Plastik  \n",
       "G915         0        0                5      Plastik  \n",
       "G117         0        0                5      Plastik  \n",
       "G33          0        0               40      Plastik  \n",
       "G12          0        0                5      Plastik  \n",
       "G89          0        0               15      Plastik  \n",
       "G142         0        0               40        Stoff  \n",
       "G122         0        0               30      Plastik  \n",
       "G27          4       14               95      Plastik  \n",
       "G919         0        0               20       Metall  \n",
       "G194         0        0               10       Metall  \n",
       "G711         0        0               10      Plastik  \n",
       "G712         0        0               10        Stoff  \n",
       "G35          0        0               15      Plastik  \n",
       "G171         0        0               15         Holz  \n",
       "G3           0        0               10      Plastik  \n",
       "G139         0        0                5        Stoff  \n",
       "G158         0        0               15       Papier  \n",
       "G95          0        0                5      Plastik  \n",
       "G159         0        0               10         Holz  \n",
       "Gfrags       3       12              100      Plastik  \n",
       "G922         0        0               40      Plastik  \n",
       "G923         0        0               40       Papier  \n",
       "G30          3       11               95      Plastik  \n",
       "G703         0        0               10      Plastik  \n",
       "G705         2        3               65       Metall  \n",
       "G24          0        0               35      Plastik  \n",
       "G210         0        0                5         Glas  \n",
       "G28          0        0               10      Plastik  \n",
       "G167         0        0                5         Holz  \n",
       "G713         0        0                5      Plastik  \n",
       "G181         0        0                5       Metall  \n",
       "G90          0        0               10      Plastik  \n",
       "G928         0        0                5      Plastik  \n",
       "G168         0        0                5         Holz  \n",
       "G931         0        0               10      Plastik  \n",
       "G26          0        0               10      Plastik  \n",
       "G155         0        0                5       Papier  \n",
       "G178         0        0               40       Metall  \n",
       "G146         0        0               25       Papier  \n",
       "G934         0        0               15      Plastik  \n",
       "G926         0        0               25      Plastik  \n",
       "G10          0        0               15      Plastik  \n",
       "G25          0        0               25      Plastik  \n",
       "G905         0        0               40      Plastik  \n",
       "G34          0        0               20      Plastik  \n",
       "G710         0        0               30      Plastik  \n",
       "G936         1        0               15      Plastik  \n",
       "G208         0        0               25         Glas  \n",
       "G901         0        0               20      Plastik  \n",
       "G707         0        0               25      Plastik  \n",
       "Gfoam        1        0               20      Plastik  \n",
       "G198         0        0               40       Metall  \n",
       "G709         0        0               30      Plastik  \n",
       "G152         0        0               25       Papier  \n",
       "G145         0        0               20        Stoff  \n",
       "G7           0        0               40      Plastik  \n",
       "G170         0        0               10         Holz  \n",
       "G93          1        2               60      Plastik  \n",
       "G66          0        0               10      Plastik  \n",
       "G48          0        0               20      Plastik  \n",
       "G21          0        0                5      Plastik  \n",
       "G917         0        0                5         Glas  \n",
       "G916         0        0                5         Holz  \n",
       "G148         0        0                5       Papier  \n",
       "G929         0        0                5       Metall  \n",
       "G199         0        0                5       Metall  \n",
       "G195         0        0                5       Metall  \n",
       "G8           0        0                5      Plastik  \n",
       "G71          0        0                5      Plastik  \n",
       "G131         0        0                5        Gummi  \n",
       "G67          0        0                5      Plastik  \n",
       "G96          0        0                5      Plastik  \n",
       "G101         0        0                5      Plastik  \n",
       "G65          0        0                5      Plastik  \n",
       "G708         0        0                5       Metall  \n",
       "G128         0        0                5        Gummi  \n",
       "G935         0        0                5      Plastik  \n",
       "G126         0        0                0        Gummi  \n",
       "G115         0        0                0      Plastik  \n",
       "G116         0        0                0      Plastik  \n",
       "G904         0        0                0      Plastik  \n",
       "G903         0        0                0      Plastik  \n",
       "G902         0        0                0        Stoff  \n",
       "G118         0        0                0      Plastik  \n",
       "G119         0        0                0      Plastik  \n",
       "G84          0        0                0      Plastik  \n",
       "G13          0        0                0      Plastik  \n",
       "G88          0        0                0      Plastik  \n",
       "G900         0        0                0        Gummi  \n",
       "G113         0        0                0      Plastik  \n",
       "G9           0        0                0      Plastik  \n",
       "G906         0        0                0       Metall  \n",
       "G125         0        0                0        Gummi  \n",
       "G114         0        0                0      Plastik  \n",
       "G161         0        0                0         Holz  \n",
       "G92          0        0                0      Plastik  \n",
       "G907         0        0                0      Plastik  \n",
       "G942         0        0                0      Plastik  \n",
       "G937         0        0                0      Plastik  \n",
       "G938         0        0                0      Plastik  \n",
       "G939         0        0                0      Plastik  \n",
       "G94          0        0                0      Plastik  \n",
       "G940         0        0                0      Plastik  \n",
       "G103         0        0                0      Plastik  \n",
       "G102         0        0                0      Plastik  \n",
       "G908         0        0                0      Plastik  \n",
       "G944         0        0                0      Plastik  \n",
       "G945         0        0                0       Metall  \n",
       "G97          0        0                0      Plastik  \n",
       "G98          0        0                0      Plastik  \n",
       "G99          0        0                0      Plastik  \n",
       "G999         0        0                0    Unbekannt  \n",
       "G933         0        0                0      Plastik  \n",
       "G932         0        0                0      Plastik  \n",
       "G104         0        0                0      Plastik  \n",
       "G930         0        0                0      Plastik  \n",
       "G106         0        0                0      Plastik  \n",
       "G927         0        0                0      Plastik  \n",
       "G925         0        0                0      Plastik  \n",
       "G107         0        0                0      Plastik  \n",
       "G108         0        0                0      Plastik  \n",
       "G109         0        0                0      Plastik  \n",
       "G133         0        0                0        Gummi  \n",
       "G11          0        0                0      Plastik  \n",
       "G111         0        0                0      Plastik  \n",
       "G112         0        0                0      Plastik  \n",
       "G914         0        0                0      Plastik  \n",
       "G913         0        0                0      Plastik  \n",
       "G91          0        0                0      Plastik  \n",
       "G132         0        0                0        Gummi  \n",
       "G61          0        0                0      Plastik  \n",
       "G70          0        0                0      Plastik  \n",
       "G214         0        0                0  Chemikalien  \n",
       "G19          0        0                0      Plastik  \n",
       "G190         0        0                0       Metall  \n",
       "G151         0        0                0       Papier  \n",
       "G193         0        0                0       Metall  \n",
       "G150         0        0                0       Papier  \n",
       "G196         0        0                0       Metall  \n",
       "G197         0        0                0       Metall  \n",
       "G2           0        0                0      Plastik  \n",
       "G147         0        0                0       Papier  \n",
       "G201         0        0                0         Glas  \n",
       "G202         0        0                0         Glas  \n",
       "G203         0        0                0         Glas  \n",
       "G205         0        0                0         Glas  \n",
       "G207         0        0                0         Glas  \n",
       "G144         0        0                0        Stoff  \n",
       "G188         0        0                0       Metall  \n",
       "G186         0        0                0       Metall  \n",
       "G185         0        0                0       Metall  \n",
       "G173         0        0                0         Holz  \n",
       "G165         0        0                0         Holz  \n",
       "G166         0        0                0         Holz  \n",
       "G160         0        0                0         Holz  \n",
       "G157         0        0                0       Papier  \n",
       "G17          0        0                0      Plastik  \n",
       "G172         0        0                0         Holz  \n",
       "G174         0        0                0       Metall  \n",
       "G183         0        0                0       Metall  \n",
       "G154         0        0                0       Papier  \n",
       "G176         0        0                0       Metall  \n",
       "G153         0        0                0       Papier  \n",
       "G179         0        0                0       Metall  \n",
       "G180         0        0                0       Metall  \n",
       "G182         0        0                0       Metall  \n",
       "G213         0        0                0  Chemikalien  \n",
       "G22          0        0                0      Plastik  \n",
       "G68          0        0                0      Plastik  \n",
       "G143         0        0                0        Stoff  \n",
       "G49          0        0                0      Plastik  \n",
       "G5           0        0                0      Plastik  \n",
       "G50          0        0                0      Plastik  \n",
       "G51          0        0                0      Plastik  \n",
       "G52          0        0                0      Plastik  \n",
       "G53          0        0                0      Plastik  \n",
       "G55          0        0                0      Plastik  \n",
       "G56          0        0                0      Plastik  \n",
       "G59          0        0                0      Plastik  \n",
       "G6           0        0                0      Plastik  \n",
       "G60          0        0                0      Plastik  \n",
       "G162         0        0                0         Holz  \n",
       "G62          0        0                0      Plastik  \n",
       "G63          0        0                0      Plastik  \n",
       "G64          0        0                0      Plastik  \n",
       "G43          0        0                0      Plastik  \n",
       "G41          0        0                0      Plastik  \n",
       "G40          0        0                0      Plastik  \n",
       "G136         0        0                0        Stoff  \n",
       "G141         0        0                0        Stoff  \n",
       "G140         0        0                0        Stoff  \n",
       "G14          0        0                0      Plastik  \n",
       "G29          0        0                0      Plastik  \n",
       "G138         0        0                0        Stoff  \n",
       "G137         0        0                0        Stoff  \n",
       "G32          0        0                0      Plastik  \n",
       "G4           0        0                0      Plastik  \n",
       "G135         0        0                0        Stoff  \n",
       "G134         0        0                0        Gummi  \n",
       "G36          0        0                0      Plastik  \n",
       "G37          0        0                0      Plastik  \n",
       "G38          0        0                0      Plastik  \n",
       "G39          0        0                0      Plastik  \n",
       "G1           0        0                0      Plastik  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "complete_inventory = fdx.code_summary.copy()\n",
    "complete_inventory[\"quantity\"] = complete_inventory[\"quantity\"].map(lambda x: featuredata.thousandsSeparator(x, language))\n",
    "complete_inventory[\"% of total\"] = complete_inventory[\"% of total\"].astype(int)\n",
    "complete_inventory[unit_label] = complete_inventory[unit_label].astype(int)\n",
    "complete_inventory.rename(columns=featuredata.inventory_table_de, inplace=True)\n",
    "complete_inventory.sort_values(by=\"Objekte (St.)\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "doc = SimpleDocTemplate(pdf_link, pagesize=A4, leftMargin=2.5*cm, rightMargin=2.5*cm, topMargin=2.5*cm, bottomMargin=1*cm)\n",
    "\n",
    "pageinfo= f\"IQAASL/Ergebnisse der Erhebung/{this_feature['name']}\"\n",
    "\n",
    "\n",
    "link_to_source = f'{source_prefix}{source}'\n",
    "\n",
    "def myLaterPages(canvas, doc):\n",
    "    canvas.saveState()\n",
    "    canvas.setLineWidth(.001*cm)\n",
    "    canvas.setFillAlpha(.8)\n",
    "    canvas.line(2.5*cm, 27.6*cm,  18.5*cm, 27.6*cm) \n",
    "    canvas.setFont('Times-Roman',9)\n",
    "    canvas.drawString(2.5*cm, 1*cm, link_to_source)\n",
    "    canvas.drawString(18.5*cm, 1*cm,  \"S.%d \" % (doc.page,))\n",
    "    canvas.drawString(2.5*cm, 27.7*cm, pageinfo)\n",
    "    canvas.restoreState()\n",
    "    \n",
    "doc.build(pdfcomponents,  onFirstPage=myLaterPages, onLaterPages=myLaterPages)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "toc-autonumbering": true,
  "toc-showcode": true,
  "toc-showmarkdowntxt": false,
  "toc-showtags": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}