27 Day Kp Forecast Verification for 2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45014408",
   "metadata": {},
   "source": [
    "<center><h1>27 Day Geomagnetic Kp Forecast Verification for 2020 <br> </h1></center><br>\n",
    "<center>John Mayers, Physical Scientist <br> NOAA Space Weather Prediction Center</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7d22fb",
   "metadata": {},
   "source": [
    "The Space Weather Prediction Center produces 27-day geomagnetic forecasts found in the [\"Weekly Highlights and 27-day Forecast\"](https://www.swpc.noaa.gov/products/weekly-highlights-and-27-day-forecast). These forecast are not archived in a database and can only be found in these PDF documents. In order to assess forecast skill, the forecasts have to be extracted from these PDF files into a readable format. This fist part of this program will extract the forecasts from the PDF files (found on page 4) using tabula-py then convert them into text files. The text files will then be read into Pandas dataframes for additional analysis. Once cleaned, these forecasts can be compared against observed values in order to obtain a \"skill score\". Observed data are archived in a database and have been made available in an .xlsx spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "38b6523a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tabula\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "    \n",
    "from tabula.io import read_pdf\n",
    "from tabulate import tabulate\n",
    "from tabula.io import convert_into\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime, timedelta\n",
    "from numpy import nan\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1515312b",
   "metadata": {},
   "source": [
    "<h3>Navigating to files</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881a3e55",
   "metadata": {},
   "source": [
    "**Step 1**: Change the working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "257558db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current working directory is C:\\Users\\john.mayers\\Documents\\27_Day\\Data\\2020_WeeklyPDF.\n"
     ]
    }
   ],
   "source": [
    "#change working directory to location of pdf files and set to variable\n",
    "    \n",
    "os.chdir('C:/Users/john.mayers/Documents/27_Day/Data/2020_WeeklyPDF/')\n",
    "directory = 'C:/Users/john.mayers/Documents/27_Day/Data/2020_WeeklyPDF/'\n",
    "print(f'The current working directory is {os. getcwd()}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc42c8f",
   "metadata": {},
   "source": [
    "**Step 2**: Confirm the number of PDF files in the directory to be converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d4bcb7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The are 52 PDF files in this folder.\n"
     ]
    }
   ],
   "source": [
    "pdf_ls = []\n",
    "for file in glob.glob(\"*.pdf\"):\n",
    "    pdf_ls.append(file)\n",
    "pdf_ls\n",
    "\n",
    "num_pdfs = len(pdf_ls)\n",
    "\n",
    "print(f'The are {num_pdfs} PDF files in this folder.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93a50ac",
   "metadata": {},
   "source": [
    "<h3>Preparing to use tabula-py to convert PDF to TXT</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9085df4",
   "metadata": {},
   "source": [
    "The tabula-py wrapper has a specific format *(convert_into(input_filename, output_filename, pages=all)* to read PDF files and convert to TXT. Since hundreds of PDF files will need to be processed, loops will be needed to iterate through input and output filenames."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7893e9",
   "metadata": {},
   "source": [
    "**Step 1**: Create iterable output filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bde5fc1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output file paths will look like C:/Users/john.mayers/Documents/27_Day/Data/2020_WeeklyPDF/prf2314.txt.\n"
     ]
    }
   ],
   "source": [
    "#creating a list of output_filenames from PDF filenames in dir but with .txt extension\n",
    "\n",
    "filenames = os.listdir() #list all PDF files in folder and save to variable\n",
    "filenames = [i.split('.', 1)[0] for i in filenames] #remove file extension from list of files and resave to variable\n",
    "\n",
    "ls=[]\n",
    "\n",
    "for i in range(len(filenames)):\n",
    "    w = os.path.join(directory + filenames[i]+ \".txt\") #concat directory, filenames with .txt extension\n",
    "    ls.append(w)\n",
    "    \n",
    "print(f'The output file paths will look like {ls[0]}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb535de",
   "metadata": {},
   "source": [
    "**Step 2**: Create iterable input filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ed8c863b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input file paths will look like: C:/Users/john.mayers/Documents/27_Day/Data/2020_WeeklyPDF/prf2314.pdf.\n"
     ]
    }
   ],
   "source": [
    "#creating a list of input_filenames\n",
    "\n",
    "ls1=[]\n",
    "\n",
    "for i in range(len(filenames)):\n",
    "    r = os.path.join(directory + filenames[i]+ \".pdf\") #concat directory, filenames, with .pdf extension\n",
    "    ls1.append(r)\n",
    "\n",
    "print(f'The input file paths will look like: {ls1[0]}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ede028",
   "metadata": {},
   "source": [
    "<h3>Converting PDF to TXT using tabula-py</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3befd732",
   "metadata": {},
   "source": [
    "**Step 1**: Using tabula-py, iterate through the input and output files created in the previous step to convert PDF files to TXT files. Note each PDF file can take up to 60 seconds to be converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1a61926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 58/58 [36:53<00:00, 38.16s/it]\n"
     ]
    }
   ],
   "source": [
    "#using tabula-py convert_into() function to convert PDF files in dir to TXT\n",
    "\n",
    "for pdf in tqdm(directory):\n",
    "    for i in range(len(ls)):\n",
    "        convert_into(ls1[i], ls[i], pages=4) #iterating through input and output filenames from lists above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ee288a",
   "metadata": {},
   "source": [
    "**Step 2**: Verify all PDF files were converted to TXT files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "59b0126c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All PDF files were successfully converted to TXT files\n"
     ]
    }
   ],
   "source": [
    "#verifying conversion was successful\n",
    "\n",
    "num_txt = len(glob.glob1(directory,\"*.txt\"))\n",
    "\n",
    "if num_pdfs == num_txt:\n",
    "    print(\"All PDF files were successfully converted to TXT files\")\n",
    "else: \n",
    "    print(\"Some PDF files were not successfully converted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a5d8a5",
   "metadata": {},
   "source": [
    "<h3>Converting all TXT files to Pandas Dataframe</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "619504d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "f=[]\n",
    "\n",
    "for file in os.listdir(directory):\n",
    "    if file.endswith(\".txt\"):\n",
    "        f.append(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "b8a50ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = pd.DataFrame(pd.read_csv(f[0]))\n",
    "  \n",
    "for i in range(1,len(f)):\n",
    "    df = pd.DataFrame(data)\n",
    "    main_df = pd.concat([main_df,df], axis=1)\n",
    "    main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "a1c91fdd",
   "metadata": {},
   "outputs": [
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       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    06 Jan  72  12  4  Unnamed: 4  20 Jan    70    5    2  Unnamed: 9  ...  \\\n",
       "0        7  72   5  2         NaN      21  70.0  5.0  2.0         NaN  ...   \n",
       "1        8  72   8  3         NaN      22  70.0  5.0  2.0         NaN  ...   \n",
       "2        9  72   8  3         NaN      23  70.0  5.0  2.0         NaN  ...   \n",
       "3       10  72   8  3         NaN      24  70.0  5.0  2.0         NaN  ...   \n",
       "4       11  72   5  2         NaN      25  71.0  5.0  2.0         NaN  ...   \n",
       "5       12  71   5  2         NaN      26  72.0  5.0  2.0         NaN  ...   \n",
       "6       13  70   5  2         NaN      27  72.0  5.0  2.0         NaN  ...   \n",
       "7       14  70  12  4         NaN      28  72.0  5.0  2.0         NaN  ...   \n",
       "8       15  70  12  4         NaN      29  72.0  5.0  2.0         NaN  ...   \n",
       "9       16  70   5  2         NaN      30  72.0  5.0  2.0         NaN  ...   \n",
       "10      17  70   5  2         NaN      31  72.0  5.0  2.0         NaN  ...   \n",
       "11      18  70   5  2         NaN  01 Feb  72.0  8.0  3.0         NaN  ...   \n",
       "12      19  70   5  2         NaN     NaN   NaN  NaN  NaN         NaN  ...   \n",
       "\n",
       "    16 Nov  75   5  2  Unnamed: 4  30 Nov  75.1  5.1  2.1  Unnamed: 9  \n",
       "0       17  72   5  2         NaN  01 Dec  75.0  5.0  2.0         NaN  \n",
       "1       18  72   5  2         NaN      02  75.0  5.0  2.0         NaN  \n",
       "2       19  70   5  2         NaN      03  75.0  8.0  3.0         NaN  \n",
       "3       20  70   5  2         NaN      04  75.0  8.0  3.0         NaN  \n",
       "4       21  70   8  3         NaN      05  75.0  5.0  2.0         NaN  \n",
       "5       22  70  12  4         NaN      06  75.0  5.0  2.0         NaN  \n",
       "6       23  70   8  3         NaN      07  75.0  5.0  2.0         NaN  \n",
       "7       24  70  10  4         NaN      08  75.0  5.0  2.0         NaN  \n",
       "8       25  70  12  4         NaN      09  72.0  5.0  2.0         NaN  \n",
       "9       26  72   5  2         NaN      10  72.0  5.0  2.0         NaN  \n",
       "10      27  72   5  2         NaN      11  70.0  5.0  2.0         NaN  \n",
       "11      28  75   5  2         NaN      12  70.0  5.0  2.0         NaN  \n",
       "12      29  75   5  2         NaN     NaN   NaN  NaN  NaN         NaN  \n",
       "\n",
       "[13 rows x 520 columns]"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e43cc3",
   "metadata": {},
   "source": [
    "<h3>Cleaning the Data for Analysis</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6a7568",
   "metadata": {},
   "source": [
    "<h4>Understanding the table structure</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "83c0be73",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_copy = main_df.copy() #make a copy of the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "673bd0e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This table has 13 rows and 520 columns.\n"
     ]
    }
   ],
   "source": [
    "print(f'This table has {main_copy.shape[0]} rows and {main_copy.shape[1]} columns.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066989f1",
   "metadata": {},
   "source": [
    "<h4>Looking at a subset of the table corresponding to the first forecast</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "8641d2a8",
   "metadata": {
    "scrolled": true
   },
   "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>06 Jan</th>\n",
       "      <th>72</th>\n",
       "      <th>12</th>\n",
       "      <th>4</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>20 Jan</th>\n",
       "      <th>70</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01 Feb</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    06 Jan  72  12  4  Unnamed: 4  20 Jan    70    5    2  Unnamed: 9\n",
       "0        7  72   5  2         NaN      21  70.0  5.0  2.0         NaN\n",
       "1        8  72   8  3         NaN      22  70.0  5.0  2.0         NaN\n",
       "2        9  72   8  3         NaN      23  70.0  5.0  2.0         NaN\n",
       "3       10  72   8  3         NaN      24  70.0  5.0  2.0         NaN\n",
       "4       11  72   5  2         NaN      25  71.0  5.0  2.0         NaN\n",
       "5       12  71   5  2         NaN      26  72.0  5.0  2.0         NaN\n",
       "6       13  70   5  2         NaN      27  72.0  5.0  2.0         NaN\n",
       "7       14  70  12  4         NaN      28  72.0  5.0  2.0         NaN\n",
       "8       15  70  12  4         NaN      29  72.0  5.0  2.0         NaN\n",
       "9       16  70   5  2         NaN      30  72.0  5.0  2.0         NaN\n",
       "10      17  70   5  2         NaN      31  72.0  5.0  2.0         NaN\n",
       "11      18  70   5  2         NaN  01 Feb  72.0  8.0  3.0         NaN\n",
       "12      19  70   5  2         NaN     NaN   NaN  NaN  NaN         NaN"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf1 = main_copy.iloc[:,0:10] #looking at the cols corresponding to the first PDF. Each PDF has 10 cols.\n",
    "pdf1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44a1405",
   "metadata": {},
   "source": [
    "<h4>Fixing col lables</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "b505d6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>06 Jan</th>\n",
       "      <th>72</th>\n",
       "      <th>12</th>\n",
       "      <th>4</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>20 Jan</th>\n",
       "      <th>70</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>16 Nov</th>\n",
       "      <th>75</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>30 Nov</th>\n",
       "      <th>75.1</th>\n",
       "      <th>5.1</th>\n",
       "      <th>2.1</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   06 Jan  72  12  4  Unnamed: 4 20 Jan    70    5    2  Unnamed: 9  ...  \\\n",
       "0       7  72   5  2         NaN     21  70.0  5.0  2.0         NaN  ...   \n",
       "1       8  72   8  3         NaN     22  70.0  5.0  2.0         NaN  ...   \n",
       "2       9  72   8  3         NaN     23  70.0  5.0  2.0         NaN  ...   \n",
       "3      10  72   8  3         NaN     24  70.0  5.0  2.0         NaN  ...   \n",
       "4      11  72   5  2         NaN     25  71.0  5.0  2.0         NaN  ...   \n",
       "\n",
       "   16 Nov  75  5  2  Unnamed: 4  30 Nov  75.1  5.1  2.1  Unnamed: 9  \n",
       "0      17  72  5  2         NaN  01 Dec  75.0  5.0  2.0         NaN  \n",
       "1      18  72  5  2         NaN      02  75.0  5.0  2.0         NaN  \n",
       "2      19  70  5  2         NaN      03  75.0  8.0  3.0         NaN  \n",
       "3      20  70  5  2         NaN      04  75.0  8.0  3.0         NaN  \n",
       "4      21  70  8  3         NaN      05  75.0  5.0  2.0         NaN  \n",
       "\n",
       "[5 rows x 520 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04e1cd5",
   "metadata": {},
   "source": [
    "Every 5th col needs to be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "00e4aec0",
   "metadata": {},
   "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>06 Jan</th>\n",
       "      <th>72</th>\n",
       "      <th>12</th>\n",
       "      <th>4</th>\n",
       "      <th>20 Jan</th>\n",
       "      <th>70</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>16 Nov</th>\n",
       "      <th>75</th>\n",
       "      <th>...</th>\n",
       "      <th>5.1</th>\n",
       "      <th>2.1</th>\n",
       "      <th>16 Nov</th>\n",
       "      <th>75</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>30 Nov</th>\n",
       "      <th>75.1</th>\n",
       "      <th>5.1</th>\n",
       "      <th>2.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>03</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>04</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>06</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>07</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>08</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>09</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>01 Feb</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 416 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    06 Jan  72  12  4  20 Jan    70    5    2 16 Nov  75  ...  5.1  2.1  \\\n",
       "0        7  72   5  2      21  70.0  5.0  2.0     17  72  ...  5.0  2.0   \n",
       "1        8  72   8  3      22  70.0  5.0  2.0     18  72  ...  5.0  2.0   \n",
       "2        9  72   8  3      23  70.0  5.0  2.0     19  70  ...  8.0  3.0   \n",
       "3       10  72   8  3      24  70.0  5.0  2.0     20  70  ...  8.0  3.0   \n",
       "4       11  72   5  2      25  71.0  5.0  2.0     21  70  ...  5.0  2.0   \n",
       "5       12  71   5  2      26  72.0  5.0  2.0     22  70  ...  5.0  2.0   \n",
       "6       13  70   5  2      27  72.0  5.0  2.0     23  70  ...  5.0  2.0   \n",
       "7       14  70  12  4      28  72.0  5.0  2.0     24  70  ...  5.0  2.0   \n",
       "8       15  70  12  4      29  72.0  5.0  2.0     25  70  ...  5.0  2.0   \n",
       "9       16  70   5  2      30  72.0  5.0  2.0     26  72  ...  5.0  2.0   \n",
       "10      17  70   5  2      31  72.0  5.0  2.0     27  72  ...  5.0  2.0   \n",
       "11      18  70   5  2  01 Feb  72.0  8.0  3.0     28  75  ...  5.0  2.0   \n",
       "12      19  70   5  2     NaN   NaN  NaN  NaN     29  75  ...  NaN  NaN   \n",
       "\n",
       "   16 Nov  75   5  2  30 Nov  75.1  5.1  2.1  \n",
       "0      17  72   5  2  01 Dec  75.0  5.0  2.0  \n",
       "1      18  72   5  2      02  75.0  5.0  2.0  \n",
       "2      19  70   5  2      03  75.0  8.0  3.0  \n",
       "3      20  70   5  2      04  75.0  8.0  3.0  \n",
       "4      21  70   8  3      05  75.0  5.0  2.0  \n",
       "5      22  70  12  4      06  75.0  5.0  2.0  \n",
       "6      23  70   8  3      07  75.0  5.0  2.0  \n",
       "7      24  70  10  4      08  75.0  5.0  2.0  \n",
       "8      25  70  12  4      09  72.0  5.0  2.0  \n",
       "9      26  72   5  2      10  72.0  5.0  2.0  \n",
       "10     27  72   5  2      11  70.0  5.0  2.0  \n",
       "11     28  75   5  2      12  70.0  5.0  2.0  \n",
       "12     29  75   5  2     NaN   NaN  NaN  NaN  \n",
       "\n",
       "[13 rows x 416 columns]"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main = main_copy.loc[:, (np.arange(len(main_copy.columns)) + 1) % 5 != 0]\n",
    "main"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54e25f6f",
   "metadata": {},
   "source": [
    "Creating a list of lists that repeats col labels based on the number of tables in the main df, then flattening that list of lists into a large list to pass into the df as the new col labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "9c8d715f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cols = [['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp']] \n",
    "\n",
    "k=int(len(main.columns)/8)\n",
    "\n",
    "res = [ele for ele in new_cols for i in range(k)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "6c39f1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp'],\n",
       " ['Date', 'Radio', 'Ap', 'Kp', 'Date', 'Radio', 'Ap', 'Kp']]"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res #list of lists of col headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "69875d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_list = [item for sublist in res for item in sublist] #flattening into 1 large list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "3d263c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(main.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "e20ffcf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['06 Jan', '72', '12', '4', '20 Jan', '70', '5', '2', '16 Nov', '75',\n",
       "       ...\n",
       "       '5.1', '2.1', '16 Nov', '75', '5', '2', '30 Nov', '75.1', '5.1', '2.1'],\n",
       "      dtype='object', length=416)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = main.columns # to be transferred into first row\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "c52f25a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\john.mayers\\AppData\\Local\\Temp\\2\\ipykernel_14588\\1404910668.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  main.loc[-1] = row #adding a row and setting it to row\n"
     ]
    }
   ],
   "source": [
    "main.loc[-1] = row #adding a row and setting it to row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "2cb50a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "main.index = main.index + 1 #shifting index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "2945102a",
   "metadata": {},
   "outputs": [],
   "source": [
    "main = main.sort_index() #sorting by index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "c9c8a2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "main.columns = flat_list #setting new col labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "f8270fa0",
   "metadata": {},
   "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",
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       "        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>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>...</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>20 Jan</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>16 Nov</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16 Nov</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>30 Nov</td>\n",
       "      <td>75.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>03</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>04</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>71.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
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       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>06</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>07</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>08</td>\n",
       "      <td>75.0</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>09</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>01 Feb</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 416 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Radio  Ap Kp    Date Radio   Ap   Kp    Date Radio  ...   Ap   Kp  \\\n",
       "0   06 Jan    72  12  4  20 Jan    70    5    2  16 Nov    75  ...  5.1  2.1   \n",
       "1        7    72   5  2      21  70.0  5.0  2.0      17    72  ...  5.0  2.0   \n",
       "2        8    72   8  3      22  70.0  5.0  2.0      18    72  ...  5.0  2.0   \n",
       "3        9    72   8  3      23  70.0  5.0  2.0      19    70  ...  8.0  3.0   \n",
       "4       10    72   8  3      24  70.0  5.0  2.0      20    70  ...  8.0  3.0   \n",
       "5       11    72   5  2      25  71.0  5.0  2.0      21    70  ...  5.0  2.0   \n",
       "6       12    71   5  2      26  72.0  5.0  2.0      22    70  ...  5.0  2.0   \n",
       "7       13    70   5  2      27  72.0  5.0  2.0      23    70  ...  5.0  2.0   \n",
       "8       14    70  12  4      28  72.0  5.0  2.0      24    70  ...  5.0  2.0   \n",
       "9       15    70  12  4      29  72.0  5.0  2.0      25    70  ...  5.0  2.0   \n",
       "10      16    70   5  2      30  72.0  5.0  2.0      26    72  ...  5.0  2.0   \n",
       "11      17    70   5  2      31  72.0  5.0  2.0      27    72  ...  5.0  2.0   \n",
       "12      18    70   5  2  01 Feb  72.0  8.0  3.0      28    75  ...  5.0  2.0   \n",
       "13      19    70   5  2     NaN   NaN  NaN  NaN      29    75  ...  NaN  NaN   \n",
       "\n",
       "      Date Radio  Ap Kp    Date Radio   Ap   Kp  \n",
       "0   16 Nov    75   5  2  30 Nov  75.1  5.1  2.1  \n",
       "1       17    72   5  2  01 Dec  75.0  5.0  2.0  \n",
       "2       18    72   5  2      02  75.0  5.0  2.0  \n",
       "3       19    70   5  2      03  75.0  8.0  3.0  \n",
       "4       20    70   5  2      04  75.0  8.0  3.0  \n",
       "5       21    70   8  3      05  75.0  5.0  2.0  \n",
       "6       22    70  12  4      06  75.0  5.0  2.0  \n",
       "7       23    70   8  3      07  75.0  5.0  2.0  \n",
       "8       24    70  10  4      08  75.0  5.0  2.0  \n",
       "9       25    70  12  4      09  72.0  5.0  2.0  \n",
       "10      26    72   5  2      10  72.0  5.0  2.0  \n",
       "11      27    72   5  2      11  70.0  5.0  2.0  \n",
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       "13      29    75   5  2     NaN   NaN  NaN  NaN  \n",
       "\n",
       "[14 rows x 416 columns]"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
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    "main"
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  {
   "cell_type": "markdown",
   "id": "cc7f5614",
   "metadata": {},
   "source": [
    "<h3>Inspecting individual forecasts from main df</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "c1aa41dd",
   "metadata": {},
   "outputs": [
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       "      Date Radio  Ap Kp    Date Radio   Ap   Kp\n",
       "0   06 Jan    72  12  4  20 Jan    70    5    2\n",
       "1        7    72   5  2      21  70.0  5.0  2.0\n",
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       "3        9    72   8  3      23  70.0  5.0  2.0\n",
       "4       10    72   8  3      24  70.0  5.0  2.0\n",
       "5       11    72   5  2      25  71.0  5.0  2.0\n",
       "6       12    71   5  2      26  72.0  5.0  2.0\n",
       "7       13    70   5  2      27  72.0  5.0  2.0\n",
       "8       14    70  12  4      28  72.0  5.0  2.0\n",
       "9       15    70  12  4      29  72.0  5.0  2.0\n",
       "10      16    70   5  2      30  72.0  5.0  2.0\n",
       "11      17    70   5  2      31  72.0  5.0  2.0\n",
       "12      18    70   5  2  01 Feb  72.0  8.0  3.0\n",
       "13      19    70   5  2     NaN   NaN  NaN  NaN"
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       "      Date Radio  Ap Kp    Date Radio   Ap   Kp\n",
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       "1       17    72   5  2  01 Dec  75.0  5.0  2.0\n",
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       "5       21    70   8  3      05  75.0  5.0  2.0\n",
       "6       22    70  12  4      06  75.0  5.0  2.0\n",
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       "      <td>3</td>\n",
       "      <td>07</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>08</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>09</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date Radio  Ap Kp    Date Radio   Ap   Kp\n",
       "0   16 Nov    75   5  2  30 Nov  75.1  5.1  2.1\n",
       "1       17    72   5  2  01 Dec  75.0  5.0  2.0\n",
       "2       18    72   5  2      02  75.0  5.0  2.0\n",
       "3       19    70   5  2      03  75.0  8.0  3.0\n",
       "4       20    70   5  2      04  75.0  8.0  3.0\n",
       "5       21    70   8  3      05  75.0  5.0  2.0\n",
       "6       22    70  12  4      06  75.0  5.0  2.0\n",
       "7       23    70   8  3      07  75.0  5.0  2.0\n",
       "8       24    70  10  4      08  75.0  5.0  2.0\n",
       "9       25    70  12  4      09  72.0  5.0  2.0\n",
       "10      26    72   5  2      10  72.0  5.0  2.0\n",
       "11      27    72   5  2      11  70.0  5.0  2.0\n",
       "12      28    75   5  2      12  70.0  5.0  2.0\n",
       "13      29    75   5  2     NaN   NaN  NaN  NaN"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main.iloc[:,16:24]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b958e6",
   "metadata": {},
   "source": [
    "Starting with col9, all values in row0 should be set to Nan. All these values should be replaced with NaN. They are erroneous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "ee8352f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
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       "      <th>Kp</th>\n",
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       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "      <th>Date</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Ap</th>\n",
       "      <th>Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16 Nov</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>30 Nov</td>\n",
       "      <td>75.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16 Nov</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16 Nov</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>30 Nov</td>\n",
       "      <td>75.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 408 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date Radio Ap Kp    Date Radio   Ap   Kp    Date Radio  ...   Ap   Kp  \\\n",
       "0  16 Nov    75  5  2  30 Nov  75.1  5.1  2.1  16 Nov    75  ...  5.1  2.1   \n",
       "\n",
       "     Date Radio Ap Kp    Date Radio   Ap   Kp  \n",
       "0  16 Nov    75  5  2  30 Nov  75.1  5.1  2.1  \n",
       "\n",
       "[1 rows x 408 columns]"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main.iloc[:1,8:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "61accae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "main.iloc[:1,8:] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "a54b2e4e",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>72</td>\n",
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       "      <td>21</td>\n",
       "      <td>70.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>03</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>04</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 416 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date Radio  Ap Kp    Date Radio   Ap   Kp Date Radio  ...   Ap   Kp Date  \\\n",
       "0  06 Jan    72  12  4  20 Jan    70    5    2  NaN   NaN  ...  NaN  NaN  NaN   \n",
       "1       7    72   5  2      21  70.0  5.0  2.0   17    72  ...  5.0  2.0   17   \n",
       "2       8    72   8  3      22  70.0  5.0  2.0   18    72  ...  5.0  2.0   18   \n",
       "3       9    72   8  3      23  70.0  5.0  2.0   19    70  ...  8.0  3.0   19   \n",
       "4      10    72   8  3      24  70.0  5.0  2.0   20    70  ...  8.0  3.0   20   \n",
       "\n",
       "  Radio   Ap   Kp    Date Radio   Ap   Kp  \n",
       "0   NaN  NaN  NaN     NaN   NaN  NaN  NaN  \n",
       "1    72    5    2  01 Dec  75.0  5.0  2.0  \n",
       "2    72    5    2      02  75.0  5.0  2.0  \n",
       "3    70    5    2      03  75.0  8.0  3.0  \n",
       "4    70    5    2      04  75.0  8.0  3.0  \n",
       "\n",
       "[5 rows x 416 columns]"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1652fa5e",
   "metadata": {},
   "source": [
    "<h3>Cleaning up dates</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9967c0",
   "metadata": {},
   "source": [
    "The dates present a problem, since only the first date is printed, followed by the day only, until the next col or if the month changes before then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "83ac0d30",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "      <td>17</td>\n",
       "      <td>01 Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "      <td>18</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "      <td>19</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "      <td>20</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "      <td>21</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "      <td>22</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "      <td>23</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "      <td>25</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>01 Feb</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date    Date Date    Date Date    Date Date    Date Date    Date  ...  \\\n",
       "0   06 Jan  20 Jan  NaN     NaN  NaN     NaN  NaN     NaN  NaN     NaN  ...   \n",
       "1        7      21   17  01 Dec   17  01 Dec   17  01 Dec   17  01 Dec  ...   \n",
       "2        8      22   18      02   18      02   18      02   18      02  ...   \n",
       "3        9      23   19      03   19      03   19      03   19      03  ...   \n",
       "4       10      24   20      04   20      04   20      04   20      04  ...   \n",
       "5       11      25   21      05   21      05   21      05   21      05  ...   \n",
       "6       12      26   22      06   22      06   22      06   22      06  ...   \n",
       "7       13      27   23      07   23      07   23      07   23      07  ...   \n",
       "8       14      28   24      08   24      08   24      08   24      08  ...   \n",
       "9       15      29   25      09   25      09   25      09   25      09  ...   \n",
       "10      16      30   26      10   26      10   26      10   26      10  ...   \n",
       "11      17      31   27      11   27      11   27      11   27      11  ...   \n",
       "12      18  01 Feb   28      12   28      12   28      12   28      12  ...   \n",
       "13      19     NaN   29     NaN   29     NaN   29     NaN   29     NaN  ...   \n",
       "\n",
       "   Date    Date Date    Date Date    Date Date    Date Date    Date  \n",
       "0   NaN     NaN  NaN     NaN  NaN     NaN  NaN     NaN  NaN     NaN  \n",
       "1    17  01 Dec   17  01 Dec   17  01 Dec   17  01 Dec   17  01 Dec  \n",
       "2    18      02   18      02   18      02   18      02   18      02  \n",
       "3    19      03   19      03   19      03   19      03   19      03  \n",
       "4    20      04   20      04   20      04   20      04   20      04  \n",
       "5    21      05   21      05   21      05   21      05   21      05  \n",
       "6    22      06   22      06   22      06   22      06   22      06  \n",
       "7    23      07   23      07   23      07   23      07   23      07  \n",
       "8    24      08   24      08   24      08   24      08   24      08  \n",
       "9    25      09   25      09   25      09   25      09   25      09  \n",
       "10   26      10   26      10   26      10   26      10   26      10  \n",
       "11   27      11   27      11   27      11   27      11   27      11  \n",
       "12   28      12   28      12   28      12   28      12   28      12  \n",
       "13   29     NaN   29     NaN   29     NaN   29     NaN   29     NaN  \n",
       "\n",
       "[14 rows x 104 columns]"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main['Date']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc229356",
   "metadata": {},
   "source": [
    "In the first forecast, the start date is row0, col1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "499740f9",
   "metadata": {},
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date\n",
       "0  06 Jan"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = main['Date'].iloc[0:1,:1]\n",
    "c1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d18abf2",
   "metadata": {},
   "source": [
    "In the second forecast, and all following, the start date is row1, co12, col4, col6, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "07b7c6ff",
   "metadata": {},
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date\n",
       "1   17"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = main['Date'].iloc[1:2,2:3]\n",
    "d1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cb8b6f",
   "metadata": {},
   "source": [
    "<h3>Building a new df with dates and kp forecasts</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e5b62e",
   "metadata": {},
   "source": [
    "In order to avoid some tricky programming to fill in the table, the approach will be to populate 27 days lists beginning with the start time of each forecast. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d02b012f",
   "metadata": {},
   "source": [
    "<h4>Extracting start times</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "56390550",
   "metadata": {},
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date\n",
       "0  06 Jan"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = pd.DataFrame(data=c1)\n",
    "c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "9af7006b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan']"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_ls = c2['Date'].tolist()\n",
    "c2_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "8d19aee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan', '2020']"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_ls.append('2020')\n",
    "c2_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "131d9487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'06 Jan 2020'"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = c2_ls[0] + ' ' + c2_ls[1]\n",
    "start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "5c84c2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "format ='%d %b %Y'\n",
    "dt = datetime.strptime(start, format).date() #start date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "b3a9bba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan 2020',\n",
       " '07 Jan 2020',\n",
       " '08 Jan 2020',\n",
       " '09 Jan 2020',\n",
       " '10 Jan 2020',\n",
       " '11 Jan 2020',\n",
       " '12 Jan 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020']"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "res = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (dt + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb65419",
   "metadata": {},
   "source": [
    "<h4>Using Regex to find entries with months in dataframe</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "14e1da3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "8b2a5b65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from re import match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "a14935f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['06 Jan'],\n",
       " [7],\n",
       " [8],\n",
       " [9],\n",
       " [10],\n",
       " [11],\n",
       " [12],\n",
       " [13],\n",
       " [14],\n",
       " [15],\n",
       " [16],\n",
       " [17],\n",
       " [18],\n",
       " [19]]"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = main['Date'].iloc[:,0:1].values.tolist()\n",
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "1a4d6b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'((\\\\d+) [a-zA-Z]+)'"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex = r\"((\\d+) [a-zA-Z]+)\" #matches day and 3-digit month df\n",
    "regex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "55b46aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# s = ['10', '20 Jul', '1 Oct', '2']\n",
    "# s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "b835f313",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# list(filter(lambda v: match(regex, v), s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "88c09d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_list1 = [item for sublist in ls for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "a942c57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = [item for item in flat_list1 if not(pd.isnull(item)) == True] # removing nan in list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "147ee0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan',\n",
       " '7',\n",
       " '8',\n",
       " '9',\n",
       " '10',\n",
       " '11',\n",
       " '12',\n",
       " '13',\n",
       " '14',\n",
       " '15',\n",
       " '16',\n",
       " '17',\n",
       " '18',\n",
       " '19']"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list1 = [str(i) for i in new_list] # converting each element to a string\n",
    "new_list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "6ba1e189",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan']"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e =list(filter(lambda x: match(regex, x), new_list1))\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "d29026e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan', '2020']"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e.append('2020')\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "788cadea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'06 Jan 2020'"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = e[0] + ' ' + e[1]\n",
    "start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "c889cd4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "format ='%d %b %Y'\n",
    "dt = datetime.strptime(start, format).date() #start date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7f6faa",
   "metadata": {},
   "source": [
    "<h4>With initial start date, populating 27 days total</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "ee9037cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan 2020',\n",
       " '07 Jan 2020',\n",
       " '08 Jan 2020',\n",
       " '09 Jan 2020',\n",
       " '10 Jan 2020',\n",
       " '11 Jan 2020',\n",
       " '12 Jan 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020']"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "res = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (dt + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb0119f",
   "metadata": {},
   "source": [
    "So need to extract each forecast's start date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "0aa3f763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan 2020',\n",
       " '07 Jan 2020',\n",
       " '08 Jan 2020',\n",
       " '09 Jan 2020',\n",
       " '10 Jan 2020',\n",
       " '11 Jan 2020',\n",
       " '12 Jan 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020']"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "date1 = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (dt + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    date1.append(date)\n",
    "date1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "470fa100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'06 Jan 2020'"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "begin = date1[0]\n",
    "begin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "6d74e019",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 6, 0, 0)"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate = datetime.strptime(begin, \"%d %b %Y\")\n",
    "Begindate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893cccf0",
   "metadata": {},
   "source": [
    "<h4>Manually calculating forecast start times from pattern</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88823814",
   "metadata": {},
   "source": [
    "Pattern: Each successive forecast increases by 7 days from the previous forecast or n times 7 from the first forecast where n is the number of forecasts from the first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "d95c37be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 13, 0, 0)"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate2 = Begindate + timedelta(days=7)\n",
    "Begindate2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "9f394e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 20, 0, 0)"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate3 = Begindate + timedelta(days=14)\n",
    "Begindate3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "b59b6c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 27, 0, 0)"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate4 = Begindate + timedelta(days=21)\n",
    "Begindate4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "744fa810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 2, 3, 0, 0)"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate5 = Begindate + timedelta(days=28)\n",
    "Begindate5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b70e0c",
   "metadata": {},
   "source": [
    "<h4> Automatically calculating start times for n forecasts</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "6c6ce4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-13 00:00:00\n",
      "2020-01-20 00:00:00\n",
      "2020-01-27 00:00:00\n",
      "2020-02-03 00:00:00\n",
      "2020-02-10 00:00:00\n",
      "2020-02-17 00:00:00\n",
      "2020-02-24 00:00:00\n",
      "2020-03-02 00:00:00\n",
      "2020-03-09 00:00:00\n",
      "2020-03-16 00:00:00\n",
      "2020-03-23 00:00:00\n",
      "2020-03-30 00:00:00\n",
      "2020-04-06 00:00:00\n",
      "2020-04-13 00:00:00\n",
      "2020-04-20 00:00:00\n",
      "2020-04-27 00:00:00\n",
      "2020-05-04 00:00:00\n",
      "2020-05-11 00:00:00\n",
      "2020-05-18 00:00:00\n",
      "2020-05-25 00:00:00\n",
      "2020-06-01 00:00:00\n",
      "2020-06-08 00:00:00\n",
      "2020-06-15 00:00:00\n",
      "2020-06-22 00:00:00\n",
      "2020-06-29 00:00:00\n",
      "2020-07-06 00:00:00\n",
      "2020-07-13 00:00:00\n",
      "2020-07-20 00:00:00\n",
      "2020-07-27 00:00:00\n",
      "2020-08-03 00:00:00\n",
      "2020-08-10 00:00:00\n",
      "2020-08-17 00:00:00\n",
      "2020-08-24 00:00:00\n",
      "2020-08-31 00:00:00\n",
      "2020-09-07 00:00:00\n",
      "2020-09-14 00:00:00\n",
      "2020-09-21 00:00:00\n",
      "2020-09-28 00:00:00\n",
      "2020-10-05 00:00:00\n",
      "2020-10-12 00:00:00\n",
      "2020-10-19 00:00:00\n",
      "2020-10-26 00:00:00\n",
      "2020-11-02 00:00:00\n",
      "2020-11-09 00:00:00\n",
      "2020-11-16 00:00:00\n",
      "2020-11-23 00:00:00\n",
      "2020-11-30 00:00:00\n",
      "2020-12-07 00:00:00\n",
      "2020-12-14 00:00:00\n",
      "2020-12-21 00:00:00\n",
      "2020-12-28 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#iterate the number of forecasts\n",
    "Begindate = datetime.strptime(begin, \"%d %b %Y\")\n",
    "mult = list(range(0,num_pdfs*7,7))\n",
    "mult = mult[1:]\n",
    "\n",
    "for j in mult: #multiples of 7 starting with 7\n",
    "    x = Begindate + timedelta(days=j)\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "7175ef59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2020, 1, 13, 0, 0),\n",
       " datetime.datetime(2020, 1, 20, 0, 0),\n",
       " datetime.datetime(2020, 1, 27, 0, 0),\n",
       " datetime.datetime(2020, 2, 3, 0, 0),\n",
       " datetime.datetime(2020, 2, 10, 0, 0),\n",
       " datetime.datetime(2020, 2, 17, 0, 0),\n",
       " datetime.datetime(2020, 2, 24, 0, 0),\n",
       " datetime.datetime(2020, 3, 2, 0, 0),\n",
       " datetime.datetime(2020, 3, 9, 0, 0),\n",
       " datetime.datetime(2020, 3, 16, 0, 0),\n",
       " datetime.datetime(2020, 3, 23, 0, 0),\n",
       " datetime.datetime(2020, 3, 30, 0, 0),\n",
       " datetime.datetime(2020, 4, 6, 0, 0),\n",
       " datetime.datetime(2020, 4, 13, 0, 0),\n",
       " datetime.datetime(2020, 4, 20, 0, 0),\n",
       " datetime.datetime(2020, 4, 27, 0, 0),\n",
       " datetime.datetime(2020, 5, 4, 0, 0),\n",
       " datetime.datetime(2020, 5, 11, 0, 0),\n",
       " datetime.datetime(2020, 5, 18, 0, 0),\n",
       " datetime.datetime(2020, 5, 25, 0, 0),\n",
       " datetime.datetime(2020, 6, 1, 0, 0),\n",
       " datetime.datetime(2020, 6, 8, 0, 0),\n",
       " datetime.datetime(2020, 6, 15, 0, 0),\n",
       " datetime.datetime(2020, 6, 22, 0, 0),\n",
       " datetime.datetime(2020, 6, 29, 0, 0),\n",
       " datetime.datetime(2020, 7, 6, 0, 0),\n",
       " datetime.datetime(2020, 7, 13, 0, 0),\n",
       " datetime.datetime(2020, 7, 20, 0, 0),\n",
       " datetime.datetime(2020, 7, 27, 0, 0),\n",
       " datetime.datetime(2020, 8, 3, 0, 0),\n",
       " datetime.datetime(2020, 8, 10, 0, 0),\n",
       " datetime.datetime(2020, 8, 17, 0, 0),\n",
       " datetime.datetime(2020, 8, 24, 0, 0),\n",
       " datetime.datetime(2020, 8, 31, 0, 0),\n",
       " datetime.datetime(2020, 9, 7, 0, 0),\n",
       " datetime.datetime(2020, 9, 14, 0, 0),\n",
       " datetime.datetime(2020, 9, 21, 0, 0),\n",
       " datetime.datetime(2020, 9, 28, 0, 0),\n",
       " datetime.datetime(2020, 10, 5, 0, 0),\n",
       " datetime.datetime(2020, 10, 12, 0, 0),\n",
       " datetime.datetime(2020, 10, 19, 0, 0),\n",
       " datetime.datetime(2020, 10, 26, 0, 0),\n",
       " datetime.datetime(2020, 11, 2, 0, 0),\n",
       " datetime.datetime(2020, 11, 9, 0, 0),\n",
       " datetime.datetime(2020, 11, 16, 0, 0),\n",
       " datetime.datetime(2020, 11, 23, 0, 0),\n",
       " datetime.datetime(2020, 11, 30, 0, 0),\n",
       " datetime.datetime(2020, 12, 7, 0, 0),\n",
       " datetime.datetime(2020, 12, 14, 0, 0),\n",
       " datetime.datetime(2020, 12, 21, 0, 0),\n",
       " datetime.datetime(2020, 12, 28, 0, 0)]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iterate the number of forecasts\n",
    "Begindate = datetime.strptime(begin, \"%d %b %Y\")\n",
    "mult = list(range(0,num_pdfs*7,7)) #multiples of 7 \n",
    "mult = mult[1:] #multiples of 7 starting with 7\n",
    "\n",
    "ls=[]\n",
    "\n",
    "for j in mult:\n",
    "    x = Begindate + timedelta(days=j)\n",
    "    ls.append(x)\n",
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "9f1c42c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '02 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020']"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "res = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (ls[0] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "21b5d1da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '02 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020',\n",
       " '09 Feb 2020',\n",
       " '10 Feb 2020',\n",
       " '11 Feb 2020',\n",
       " '12 Feb 2020',\n",
       " '13 Feb 2020',\n",
       " '14 Feb 2020',\n",
       " '15 Feb 2020']"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "res = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (ls[1] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e062c9",
   "metadata": {},
   "source": [
    "<h4> Manually compiling all forecast dates into a list</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "b3d0e4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan 2020',\n",
       " '07 Jan 2020',\n",
       " '08 Jan 2020',\n",
       " '09 Jan 2020',\n",
       " '10 Jan 2020',\n",
       " '11 Jan 2020',\n",
       " '12 Jan 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '02 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020',\n",
       " '20 Jan 2020',\n",
       " '21 Jan 2020',\n",
       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
       " '24 Jan 2020',\n",
       " '25 Jan 2020',\n",
       " '26 Jan 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '02 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020',\n",
       " '09 Feb 2020',\n",
       " '10 Feb 2020',\n",
       " '11 Feb 2020',\n",
       " '12 Feb 2020',\n",
       " '13 Feb 2020',\n",
       " '14 Feb 2020',\n",
       " '15 Feb 2020',\n",
       " '27 Jan 2020',\n",
       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '02 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020',\n",
       " '09 Feb 2020',\n",
       " '10 Feb 2020',\n",
       " '11 Feb 2020',\n",
       " '12 Feb 2020',\n",
       " '13 Feb 2020',\n",
       " '14 Feb 2020',\n",
       " '15 Feb 2020',\n",
       " '16 Feb 2020',\n",
       " '17 Feb 2020',\n",
       " '18 Feb 2020',\n",
       " '19 Feb 2020',\n",
       " '20 Feb 2020',\n",
       " '21 Feb 2020',\n",
       " '22 Feb 2020',\n",
       " '03 Feb 2020',\n",
       " '04 Feb 2020',\n",
       " '05 Feb 2020',\n",
       " '06 Feb 2020',\n",
       " '07 Feb 2020',\n",
       " '08 Feb 2020',\n",
       " '09 Feb 2020',\n",
       " '10 Feb 2020',\n",
       " '11 Feb 2020',\n",
       " '12 Feb 2020',\n",
       " '13 Feb 2020',\n",
       " '14 Feb 2020',\n",
       " '15 Feb 2020',\n",
       " '16 Feb 2020',\n",
       " '17 Feb 2020',\n",
       " '18 Feb 2020',\n",
       " '19 Feb 2020',\n",
       " '20 Feb 2020',\n",
       " '21 Feb 2020',\n",
       " '22 Feb 2020',\n",
       " '23 Feb 2020',\n",
       " '24 Feb 2020',\n",
       " '25 Feb 2020',\n",
       " '26 Feb 2020',\n",
       " '27 Feb 2020',\n",
       " '28 Feb 2020',\n",
       " '29 Feb 2020']"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27\n",
    " \n",
    "res = []\n",
    "\n",
    "format ='%d %b %Y'\n",
    "dt = datetime.strptime(start, format).date() \n",
    " \n",
    "for day in range(k):\n",
    "    date = (dt + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "\n",
    "res\n",
    " \n",
    "for day in range(k):\n",
    "    date = (ls[0] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res\n",
    "\n",
    "\n",
    "for day in range(k):\n",
    "    date = (ls[1] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res\n",
    "\n",
    "for day in range(k):\n",
    "    date = (ls[2] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res\n",
    "\n",
    "for day in range(k):\n",
    "    date = (ls[3] + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res.append(date)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "8c056957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41923fcb",
   "metadata": {},
   "source": [
    "<h4>Automatically compile n forecast dates into a list</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "08b686fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '06 Jul 2020',\n",
       " '07 Jul 2020',\n",
       " '08 Jul 2020',\n",
       " '09 Jul 2020',\n",
       " '10 Jul 2020',\n",
       " '11 Jul 2020',\n",
       " '22 Jun 2020',\n",
       " '23 Jun 2020',\n",
       " '24 Jun 2020',\n",
       " '25 Jun 2020',\n",
       " '26 Jun 2020',\n",
       " '27 Jun 2020',\n",
       " '28 Jun 2020',\n",
       " '29 Jun 2020',\n",
       " '30 Jun 2020',\n",
       " '01 Jul 2020',\n",
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       " '03 Jul 2020',\n",
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       " '06 Jul 2020',\n",
       " '07 Jul 2020',\n",
       " '08 Jul 2020',\n",
       " '09 Jul 2020',\n",
       " '10 Jul 2020',\n",
       " '11 Jul 2020',\n",
       " '12 Jul 2020',\n",
       " '13 Jul 2020',\n",
       " '14 Jul 2020',\n",
       " '15 Jul 2020',\n",
       " '16 Jul 2020',\n",
       " '17 Jul 2020',\n",
       " '18 Jul 2020',\n",
       " '29 Jun 2020',\n",
       " '30 Jun 2020',\n",
       " '01 Jul 2020',\n",
       " '02 Jul 2020',\n",
       " '03 Jul 2020',\n",
       " '04 Jul 2020',\n",
       " '05 Jul 2020',\n",
       " '06 Jul 2020',\n",
       " '07 Jul 2020',\n",
       " '08 Jul 2020',\n",
       " '09 Jul 2020',\n",
       " '10 Jul 2020',\n",
       " '11 Jul 2020',\n",
       " '12 Jul 2020',\n",
       " '13 Jul 2020',\n",
       " '14 Jul 2020',\n",
       " '15 Jul 2020',\n",
       " '16 Jul 2020',\n",
       " '17 Jul 2020',\n",
       " '18 Jul 2020',\n",
       " '19 Jul 2020',\n",
       " '20 Jul 2020',\n",
       " '21 Jul 2020',\n",
       " '22 Jul 2020',\n",
       " '23 Jul 2020',\n",
       " '24 Jul 2020',\n",
       " '25 Jul 2020',\n",
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       " '07 Jul 2020',\n",
       " '08 Jul 2020',\n",
       " '09 Jul 2020',\n",
       " '10 Jul 2020',\n",
       " '11 Jul 2020',\n",
       " '12 Jul 2020',\n",
       " '13 Jul 2020',\n",
       " '14 Jul 2020',\n",
       " '15 Jul 2020',\n",
       " '16 Jul 2020',\n",
       " '17 Jul 2020',\n",
       " '18 Jul 2020',\n",
       " '19 Jul 2020',\n",
       " '20 Jul 2020',\n",
       " '21 Jul 2020',\n",
       " '22 Jul 2020',\n",
       " '23 Jul 2020',\n",
       " '24 Jul 2020',\n",
       " '25 Jul 2020',\n",
       " '26 Jul 2020',\n",
       " '27 Jul 2020',\n",
       " '28 Jul 2020',\n",
       " '29 Jul 2020',\n",
       " '30 Jul 2020',\n",
       " '31 Jul 2020',\n",
       " '01 Aug 2020',\n",
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       " '16 Jul 2020',\n",
       " '17 Jul 2020',\n",
       " '18 Jul 2020',\n",
       " '19 Jul 2020',\n",
       " '20 Jul 2020',\n",
       " '21 Jul 2020',\n",
       " '22 Jul 2020',\n",
       " '23 Jul 2020',\n",
       " '24 Jul 2020',\n",
       " '25 Jul 2020',\n",
       " '26 Jul 2020',\n",
       " '27 Jul 2020',\n",
       " '28 Jul 2020',\n",
       " '29 Jul 2020',\n",
       " '30 Jul 2020',\n",
       " '31 Jul 2020',\n",
       " '01 Aug 2020',\n",
       " '02 Aug 2020',\n",
       " '03 Aug 2020',\n",
       " '04 Aug 2020',\n",
       " '05 Aug 2020',\n",
       " '06 Aug 2020',\n",
       " '07 Aug 2020',\n",
       " '08 Aug 2020',\n",
       " '20 Jul 2020',\n",
       " '21 Jul 2020',\n",
       " '22 Jul 2020',\n",
       " '23 Jul 2020',\n",
       " '24 Jul 2020',\n",
       " '25 Jul 2020',\n",
       " '26 Jul 2020',\n",
       " '27 Jul 2020',\n",
       " '28 Jul 2020',\n",
       " '29 Jul 2020',\n",
       " '30 Jul 2020',\n",
       " '31 Jul 2020',\n",
       " '01 Aug 2020',\n",
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       " '28 Jul 2020',\n",
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       " '31 Jul 2020',\n",
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       " '20 Aug 2020',\n",
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       " '22 Aug 2020',\n",
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       " '05 Aug 2020',\n",
       " '06 Aug 2020',\n",
       " '07 Aug 2020',\n",
       " '08 Aug 2020',\n",
       " '09 Aug 2020',\n",
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       " '13 Aug 2020',\n",
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       " '29 Aug 2020',\n",
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       " '13 Aug 2020',\n",
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       " '16 Aug 2020',\n",
       " '17 Aug 2020',\n",
       " '18 Aug 2020',\n",
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       " '20 Aug 2020',\n",
       " '21 Aug 2020',\n",
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       " '24 Aug 2020',\n",
       " '25 Aug 2020',\n",
       " '26 Aug 2020',\n",
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       " '28 Aug 2020',\n",
       " '29 Aug 2020',\n",
       " '30 Aug 2020',\n",
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       " '01 Sep 2020',\n",
       " '02 Sep 2020',\n",
       " '03 Sep 2020',\n",
       " '04 Sep 2020',\n",
       " '05 Sep 2020',\n",
       " '17 Aug 2020',\n",
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       " '20 Aug 2020',\n",
       " '21 Aug 2020',\n",
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       " '25 Aug 2020',\n",
       " '26 Aug 2020',\n",
       " '27 Aug 2020',\n",
       " '28 Aug 2020',\n",
       " '29 Aug 2020',\n",
       " '30 Aug 2020',\n",
       " '31 Aug 2020',\n",
       " '01 Sep 2020',\n",
       " '02 Sep 2020',\n",
       " '03 Sep 2020',\n",
       " '04 Sep 2020',\n",
       " '05 Sep 2020',\n",
       " '06 Sep 2020',\n",
       " '07 Sep 2020',\n",
       " '08 Sep 2020',\n",
       " '09 Sep 2020',\n",
       " '10 Sep 2020',\n",
       " '11 Sep 2020',\n",
       " '12 Sep 2020',\n",
       " '24 Aug 2020',\n",
       " '25 Aug 2020',\n",
       " '26 Aug 2020',\n",
       " '27 Aug 2020',\n",
       " '28 Aug 2020',\n",
       " '29 Aug 2020',\n",
       " '30 Aug 2020',\n",
       " '31 Aug 2020',\n",
       " '01 Sep 2020',\n",
       " '02 Sep 2020',\n",
       " '03 Sep 2020',\n",
       " '04 Sep 2020',\n",
       " '05 Sep 2020',\n",
       " '06 Sep 2020',\n",
       " '07 Sep 2020',\n",
       " '08 Sep 2020',\n",
       " '09 Sep 2020',\n",
       " '10 Sep 2020',\n",
       " '11 Sep 2020',\n",
       " '12 Sep 2020',\n",
       " '13 Sep 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '31 Aug 2020',\n",
       " '01 Sep 2020',\n",
       " '02 Sep 2020',\n",
       " '03 Sep 2020',\n",
       " '04 Sep 2020',\n",
       " '05 Sep 2020',\n",
       " '06 Sep 2020',\n",
       " '07 Sep 2020',\n",
       " '08 Sep 2020',\n",
       " '09 Sep 2020',\n",
       " '10 Sep 2020',\n",
       " '11 Sep 2020',\n",
       " '12 Sep 2020',\n",
       " '13 Sep 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '20 Sep 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '07 Sep 2020',\n",
       " '08 Sep 2020',\n",
       " '09 Sep 2020',\n",
       " '10 Sep 2020',\n",
       " '11 Sep 2020',\n",
       " '12 Sep 2020',\n",
       " '13 Sep 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '20 Sep 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '27 Sep 2020',\n",
       " '28 Sep 2020',\n",
       " '29 Sep 2020',\n",
       " '30 Sep 2020',\n",
       " '01 Oct 2020',\n",
       " '02 Oct 2020',\n",
       " '03 Oct 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '20 Sep 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '27 Sep 2020',\n",
       " '28 Sep 2020',\n",
       " '29 Sep 2020',\n",
       " '30 Sep 2020',\n",
       " '01 Oct 2020',\n",
       " '02 Oct 2020',\n",
       " '03 Oct 2020',\n",
       " '04 Oct 2020',\n",
       " '05 Oct 2020',\n",
       " '06 Oct 2020',\n",
       " '07 Oct 2020',\n",
       " '08 Oct 2020',\n",
       " '09 Oct 2020',\n",
       " '10 Oct 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '27 Sep 2020',\n",
       " '28 Sep 2020',\n",
       " '29 Sep 2020',\n",
       " '30 Sep 2020',\n",
       " '01 Oct 2020',\n",
       " '02 Oct 2020',\n",
       " '03 Oct 2020',\n",
       " '04 Oct 2020',\n",
       " '05 Oct 2020',\n",
       " '06 Oct 2020',\n",
       " '07 Oct 2020',\n",
       " '08 Oct 2020',\n",
       " '09 Oct 2020',\n",
       " '10 Oct 2020',\n",
       " '11 Oct 2020',\n",
       " '12 Oct 2020',\n",
       " '13 Oct 2020',\n",
       " '14 Oct 2020',\n",
       " '15 Oct 2020',\n",
       " '16 Oct 2020',\n",
       " '17 Oct 2020',\n",
       " '28 Sep 2020',\n",
       " ...]"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27 #iterates through all elements of ls to append dates in correct order\n",
    "\n",
    "res2=[]\n",
    "\n",
    "for i in range(len(ls)):\n",
    "    for day in range(k):\n",
    "        date = (ls[i] + timedelta(days = day))\n",
    "        date = date.strftime('%d %b %Y')\n",
    "        res2.append(date)\n",
    "res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "a2952d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1377 dates. Recall we are missing the first forecast.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(res2)} dates. Recall we are missing the first forecast.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351da80a",
   "metadata": {},
   "source": [
    "<h4>Adding in first forecast to list</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "29aaac4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['06 Jan 2020',\n",
       " '07 Jan 2020',\n",
       " '08 Jan 2020',\n",
       " '09 Jan 2020',\n",
       " '10 Jan 2020',\n",
       " '11 Jan 2020',\n",
       " '12 Jan 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
       " '16 Jan 2020',\n",
       " '17 Jan 2020',\n",
       " '18 Jan 2020',\n",
       " '19 Jan 2020',\n",
       " '20 Jan 2020',\n",
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       " '22 Jan 2020',\n",
       " '23 Jan 2020',\n",
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       " '28 Jan 2020',\n",
       " '29 Jan 2020',\n",
       " '30 Jan 2020',\n",
       " '31 Jan 2020',\n",
       " '01 Feb 2020',\n",
       " '13 Jan 2020',\n",
       " '14 Jan 2020',\n",
       " '15 Jan 2020',\n",
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       " '18 Jan 2020',\n",
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       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '07 Sep 2020',\n",
       " '08 Sep 2020',\n",
       " '09 Sep 2020',\n",
       " '10 Sep 2020',\n",
       " '11 Sep 2020',\n",
       " '12 Sep 2020',\n",
       " '13 Sep 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '20 Sep 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '27 Sep 2020',\n",
       " '28 Sep 2020',\n",
       " '29 Sep 2020',\n",
       " '30 Sep 2020',\n",
       " '01 Oct 2020',\n",
       " '02 Oct 2020',\n",
       " '03 Oct 2020',\n",
       " '14 Sep 2020',\n",
       " '15 Sep 2020',\n",
       " '16 Sep 2020',\n",
       " '17 Sep 2020',\n",
       " '18 Sep 2020',\n",
       " '19 Sep 2020',\n",
       " '20 Sep 2020',\n",
       " '21 Sep 2020',\n",
       " '22 Sep 2020',\n",
       " '23 Sep 2020',\n",
       " '24 Sep 2020',\n",
       " '25 Sep 2020',\n",
       " '26 Sep 2020',\n",
       " '27 Sep 2020',\n",
       " '28 Sep 2020',\n",
       " '29 Sep 2020',\n",
       " '30 Sep 2020',\n",
       " '01 Oct 2020',\n",
       " '02 Oct 2020',\n",
       " '03 Oct 2020',\n",
       " '04 Oct 2020',\n",
       " '05 Oct 2020',\n",
       " '06 Oct 2020',\n",
       " '07 Oct 2020',\n",
       " '08 Oct 2020',\n",
       " '09 Oct 2020',\n",
       " '10 Oct 2020',\n",
       " '21 Sep 2020',\n",
       " ...]"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 27 #now let's append the first forecast \n",
    "\n",
    "res11 = []\n",
    " \n",
    "for day in range(k):\n",
    "    date = (dt + timedelta(days = day))\n",
    "    date = date.strftime('%d %b %Y')\n",
    "    res11.append(date)\n",
    "\n",
    "for i in range(len(ls)):\n",
    "    for day in range(k):\n",
    "        date = (ls[i] + timedelta(days = day))\n",
    "        date = date.strftime('%d %b %Y')\n",
    "        res11.append(date)\n",
    "res11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "b7175cad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1404 dates corresponding to 1404 rows from 52 forecasts.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(res11)} dates corresponding to {num_pdfs*27} rows from {num_pdfs} forecasts.') #matches number of forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2049cbc5",
   "metadata": {},
   "source": [
    "<h4>Extracting Kp forecast values from the main df and creating a new df</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "b2e2a08e",
   "metadata": {},
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       "      <th></th>\n",
       "      <th>Kp</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kp   Kp   Kp   Kp   Kp   Kp   Kp   Kp   Kp   Kp  ...   Kp   Kp   Kp   Kp  \\\n",
       "0   4    2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN   \n",
       "1   2  2.0    2  2.0    2  2.0    2  2.0    2  2.0  ...    2  2.0    2  2.0   \n",
       "2   3  2.0    2  2.0    2  2.0    2  2.0    2  2.0  ...    2  2.0    2  2.0   \n",
       "3   3  2.0    2  3.0    2  3.0    2  3.0    2  3.0  ...    2  3.0    2  3.0   \n",
       "4   3  2.0    2  3.0    2  3.0    2  3.0    2  3.0  ...    2  3.0    2  3.0   \n",
       "5   2  2.0    3  2.0    3  2.0    3  2.0    3  2.0  ...    3  2.0    3  2.0   \n",
       "6   2  2.0    4  2.0    4  2.0    4  2.0    4  2.0  ...    4  2.0    4  2.0   \n",
       "7   2  2.0    3  2.0    3  2.0    3  2.0    3  2.0  ...    3  2.0    3  2.0   \n",
       "8   4  2.0    4  2.0    4  2.0    4  2.0    4  2.0  ...    4  2.0    4  2.0   \n",
       "9   4  2.0    4  2.0    4  2.0    4  2.0    4  2.0  ...    4  2.0    4  2.0   \n",
       "10  2  2.0    2  2.0    2  2.0    2  2.0    2  2.0  ...    2  2.0    2  2.0   \n",
       "11  2  2.0    2  2.0    2  2.0    2  2.0    2  2.0  ...    2  2.0    2  2.0   \n",
       "12  2  3.0    2  2.0    2  2.0    2  2.0    2  2.0  ...    2  2.0    2  2.0   \n",
       "13  2  NaN    2  NaN    2  NaN    2  NaN    2  NaN  ...    2  NaN    2  NaN   \n",
       "\n",
       "     Kp   Kp   Kp   Kp   Kp   Kp  \n",
       "0   NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1     2  2.0    2  2.0    2  2.0  \n",
       "2     2  2.0    2  2.0    2  2.0  \n",
       "3     2  3.0    2  3.0    2  3.0  \n",
       "4     2  3.0    2  3.0    2  3.0  \n",
       "5     3  2.0    3  2.0    3  2.0  \n",
       "6     4  2.0    4  2.0    4  2.0  \n",
       "7     3  2.0    3  2.0    3  2.0  \n",
       "8     4  2.0    4  2.0    4  2.0  \n",
       "9     4  2.0    4  2.0    4  2.0  \n",
       "10    2  2.0    2  2.0    2  2.0  \n",
       "11    2  2.0    2  2.0    2  2.0  \n",
       "12    2  2.0    2  2.0    2  2.0  \n",
       "13    2  NaN    2  NaN    2  NaN  \n",
       "\n",
       "[14 rows x 104 columns]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp_wide = main['Kp']\n",
    "kp_wide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "2d08403c",
   "metadata": {},
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kp</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kp</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kp</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kp</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kp</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Kp</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>Kp</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>Kp</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>Kp</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>Kp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1456 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     variable value\n",
       "0          Kp     4\n",
       "1          Kp     2\n",
       "2          Kp     3\n",
       "3          Kp     3\n",
       "4          Kp     3\n",
       "...       ...   ...\n",
       "1451       Kp   2.0\n",
       "1452       Kp   2.0\n",
       "1453       Kp   2.0\n",
       "1454       Kp   2.0\n",
       "1455       Kp   NaN\n",
       "\n",
       "[1456 rows x 2 columns]"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp_long_nan = kp_wide.melt()\n",
    "kp_long_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "5e88a849",
   "metadata": {},
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    {
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       "     variable value\n",
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       "     value\n",
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    "kp_long = kp_long.rename(columns={\"value\": \"Kp\"})"
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       "      index   Kp\n",
       "0         0    4\n",
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       "4         4    3\n",
       "...     ...  ...\n",
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     "execution_count": 445,
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   "source": [
    "kp_long = kp_long.reset_index()\n",
    "kp_long"
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   "cell_type": "code",
   "execution_count": 446,
   "id": "d88f9ec4",
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       "       Kp\n",
       "0       4\n",
       "1       2\n",
       "2       3\n",
       "3       3\n",
       "4       3\n",
       "...   ...\n",
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    "kp_long"
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   "id": "bd268b05",
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       " ...]"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "res11"
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  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "a6ff73f1",
   "metadata": {
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      ],
      "text/plain": [
       "              res\n",
       "0     06 Jan 2020\n",
       "1     07 Jan 2020\n",
       "2     08 Jan 2020\n",
       "3     09 Jan 2020\n",
       "4     10 Jan 2020\n",
       "...           ...\n",
       "1399  19 Jan 2021\n",
       "1400  20 Jan 2021\n",
       "1401  21 Jan 2021\n",
       "1402  22 Jan 2021\n",
       "1403  23 Jan 2021\n",
       "\n",
       "[1404 rows x 1 columns]"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame(res11, columns=['res']) #res11 (dates) needs to be transformed into a pandas df\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "bf67f8a0",
   "metadata": {},
   "outputs": [
    {
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       "             Date\n",
       "0     06 Jan 2020\n",
       "1     07 Jan 2020\n",
       "2     08 Jan 2020\n",
       "3     09 Jan 2020\n",
       "4     10 Jan 2020\n",
       "...           ...\n",
       "1399  19 Jan 2021\n",
       "1400  20 Jan 2021\n",
       "1401  21 Jan 2021\n",
       "1402  22 Jan 2021\n",
       "1403  23 Jan 2021\n",
       "\n",
       "[1404 rows x 1 columns]"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df.rename(columns={\"res\": \"Date\"})\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "68100e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Kp'] = kp_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "38d41524",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Kp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1401</th>\n",
       "      <td>21 Jan 2021</td>\n",
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       "      <th>1402</th>\n",
       "      <td>22 Jan 2021</td>\n",
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       "      <th>1403</th>\n",
       "      <td>23 Jan 2021</td>\n",
       "      <td>NaN</td>\n",
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       "             Date   Kp\n",
       "0     06 Jan 2020    4\n",
       "1     07 Jan 2020    2\n",
       "2     08 Jan 2020    3\n",
       "3     09 Jan 2020    3\n",
       "4     10 Jan 2020    3\n",
       "...           ...  ...\n",
       "1399  19 Jan 2021  NaN\n",
       "1400  20 Jan 2021  NaN\n",
       "1401  21 Jan 2021  NaN\n",
       "1402  22 Jan 2021  NaN\n",
       "1403  23 Jan 2021  NaN\n",
       "\n",
       "[1404 rows x 2 columns]"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "f866c8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_df.dropna(how=\"any\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "540fd958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', 2, 3, 4, '2', nan], dtype=object)"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Kp'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12568d7e",
   "metadata": {},
   "source": [
    "<h4>The final table that observed Kp can be added to for analysis</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "4a2fa780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_df['Kp'] = final_df['Kp'].astype('int') #convert kp to integers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1d1ada",
   "metadata": {},
   "source": [
    "Above you can see that the after Feb 1, the date does not continue with 2 Feb, but with 13 Feb, which is the starting date in the next forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c1c054",
   "metadata": {},
   "source": [
    "<h3>Importing Observed Geomagnetic (Kp) Data</h3><br/>\n",
    "Observed geomagnetic Kp data are archived locally and were made available through a SQL query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "ed9870e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        date  max_kp\n",
       "0 2014-01-01    4.00\n",
       "1 2014-01-02    4.67\n",
       "2 2014-01-03    2.67\n",
       "3 2014-01-04    2.33\n",
       "4 2014-01-05    2.00"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = pd.read_excel('C:/Users/john.mayers/Documents/27_Day/Data/maxKp.xlsx')\n",
    "obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745358d1",
   "metadata": {},
   "source": [
    "<h3>Matching observed Kp with forecast Kp</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fda4019",
   "metadata": {},
   "source": [
    "<h4>Creating a subset of obs that match up to the 135 days corresponding to the 5 forecasts</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "03d9c32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 13, 0, 0)"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Begindate + timedelta(days=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "9b00f9e0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2014-01-09</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>2.00</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <th>11</th>\n",
       "      <td>2014-01-12</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-13</td>\n",
       "      <td>3.00</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>3.67</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-24</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  max_kp\n",
       "5  2014-01-06    1.67\n",
       "6  2014-01-07    3.33\n",
       "7  2014-01-08    3.33\n",
       "8  2014-01-09    3.33\n",
       "9  2014-01-10    2.00\n",
       "10 2014-01-11    2.67\n",
       "11 2014-01-12    4.00\n",
       "12 2014-01-13    3.00\n",
       "13 2014-01-14    3.67\n",
       "14 2014-01-15    2.00\n",
       "15 2014-01-16    1.33\n",
       "16 2014-01-17    1.67\n",
       "17 2014-01-18    1.33\n",
       "18 2014-01-19    0.67\n",
       "19 2014-01-20    1.33\n",
       "20 2014-01-21    3.00\n",
       "21 2014-01-22    3.00\n",
       "22 2014-01-23    2.67\n",
       "23 2014-01-24    3.00\n",
       "24 2014-01-25    3.00\n",
       "25 2014-01-26    2.67\n",
       "26 2014-01-27    1.33\n",
       "27 2014-01-28    2.33\n",
       "28 2014-01-29    2.33\n",
       "29 2014-01-30    2.67\n",
       "30 2014-01-31    1.00\n",
       "31 2014-02-01    2.67"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.iloc[5:27+5,0:2]#obs corresponding forecast 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "6d5d7670",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max_kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-13</td>\n",
       "      <td>3.00</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>3.67</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>1.33</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-24</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2014-02-05</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014-02-06</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014-02-07</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-02-08</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  max_kp\n",
       "12 2014-01-13    3.00\n",
       "13 2014-01-14    3.67\n",
       "14 2014-01-15    2.00\n",
       "15 2014-01-16    1.33\n",
       "16 2014-01-17    1.67\n",
       "17 2014-01-18    1.33\n",
       "18 2014-01-19    0.67\n",
       "19 2014-01-20    1.33\n",
       "20 2014-01-21    3.00\n",
       "21 2014-01-22    3.00\n",
       "22 2014-01-23    2.67\n",
       "23 2014-01-24    3.00\n",
       "24 2014-01-25    3.00\n",
       "25 2014-01-26    2.67\n",
       "26 2014-01-27    1.33\n",
       "27 2014-01-28    2.33\n",
       "28 2014-01-29    2.33\n",
       "29 2014-01-30    2.67\n",
       "30 2014-01-31    1.00\n",
       "31 2014-02-01    2.67\n",
       "32 2014-02-02    2.00\n",
       "33 2014-02-03    2.33\n",
       "34 2014-02-04    2.00\n",
       "35 2014-02-05    2.00\n",
       "36 2014-02-06    3.00\n",
       "37 2014-02-07    4.00\n",
       "38 2014-02-08    4.67"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.iloc[12:12+27,0:2] #obs corresponding to forecast 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "295184cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.00</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>3.00</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>2.67</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-24</td>\n",
       "      <td>3.00</td>\n",
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       "      <th>24</th>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>3.00</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>2.67</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>1.33</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
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       "      <th>28</th>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2014-02-05</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014-02-06</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014-02-07</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-02-08</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-02-09</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014-02-11</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014-02-12</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014-02-13</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014-02-15</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  max_kp\n",
       "19 2014-01-20    1.33\n",
       "20 2014-01-21    3.00\n",
       "21 2014-01-22    3.00\n",
       "22 2014-01-23    2.67\n",
       "23 2014-01-24    3.00\n",
       "24 2014-01-25    3.00\n",
       "25 2014-01-26    2.67\n",
       "26 2014-01-27    1.33\n",
       "27 2014-01-28    2.33\n",
       "28 2014-01-29    2.33\n",
       "29 2014-01-30    2.67\n",
       "30 2014-01-31    1.00\n",
       "31 2014-02-01    2.67\n",
       "32 2014-02-02    2.00\n",
       "33 2014-02-03    2.33\n",
       "34 2014-02-04    2.00\n",
       "35 2014-02-05    2.00\n",
       "36 2014-02-06    3.00\n",
       "37 2014-02-07    4.00\n",
       "38 2014-02-08    4.67\n",
       "39 2014-02-09    4.33\n",
       "40 2014-02-10    4.00\n",
       "41 2014-02-11    3.00\n",
       "42 2014-02-12    3.33\n",
       "43 2014-02-13    1.00\n",
       "44 2014-02-14    1.67\n",
       "45 2014-02-15    4.67"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.iloc[19:19+27,0:2] #obs corresponding to forecast 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad25cea",
   "metadata": {},
   "source": [
    "<h4> Writing a loop to populate a list of obs corresponding to forecast dates </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "62c6153d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obs.iloc[5:5+27,1:2] # obs corresponding forecast 1\n",
    "# obs.iloc[12:12+27,1:2] # obs corresponding to forecast 2\n",
    "# obs.iloc[19:19+27,1:2] # obs corresponding to forecast 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e6d413",
   "metadata": {},
   "source": [
    "The pattern emerges... 7 gets added each iteration to the start and end position of the row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "32ddc8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 7,\n",
       " 14,\n",
       " 21,\n",
       " 28,\n",
       " 35,\n",
       " 42,\n",
       " 49,\n",
       " 56,\n",
       " 63,\n",
       " 70,\n",
       " 77,\n",
       " 84,\n",
       " 91,\n",
       " 98,\n",
       " 105,\n",
       " 112,\n",
       " 119,\n",
       " 126,\n",
       " 133,\n",
       " 140,\n",
       " 147,\n",
       " 154,\n",
       " 161,\n",
       " 168,\n",
       " 175,\n",
       " 182,\n",
       " 189,\n",
       " 196,\n",
       " 203,\n",
       " 210,\n",
       " 217,\n",
       " 224,\n",
       " 231,\n",
       " 238,\n",
       " 245,\n",
       " 252,\n",
       " 259,\n",
       " 266,\n",
       " 273,\n",
       " 280,\n",
       " 287,\n",
       " 294,\n",
       " 301,\n",
       " 308,\n",
       " 315,\n",
       " 322,\n",
       " 329,\n",
       " 336,\n",
       " 343,\n",
       " 350,\n",
       " 357]"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(0,num_pdfs*7,7)) #recalling multiples of 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "832d605f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[         date  max_kp\n",
       " 5  2014-01-06    1.67\n",
       " 6  2014-01-07    3.33\n",
       " 7  2014-01-08    3.33\n",
       " 8  2014-01-09    3.33\n",
       " 9  2014-01-10    2.00\n",
       " 10 2014-01-11    2.67\n",
       " 11 2014-01-12    4.00\n",
       " 12 2014-01-13    3.00\n",
       " 13 2014-01-14    3.67\n",
       " 14 2014-01-15    2.00\n",
       " 15 2014-01-16    1.33\n",
       " 16 2014-01-17    1.67\n",
       " 17 2014-01-18    1.33\n",
       " 18 2014-01-19    0.67\n",
       " 19 2014-01-20    1.33\n",
       " 20 2014-01-21    3.00\n",
       " 21 2014-01-22    3.00\n",
       " 22 2014-01-23    2.67\n",
       " 23 2014-01-24    3.00\n",
       " 24 2014-01-25    3.00\n",
       " 25 2014-01-26    2.67\n",
       " 26 2014-01-27    1.33\n",
       " 27 2014-01-28    2.33\n",
       " 28 2014-01-29    2.33\n",
       " 29 2014-01-30    2.67\n",
       " 30 2014-01-31    1.00\n",
       " 31 2014-02-01    2.67,\n",
       "          date  max_kp\n",
       " 12 2014-01-13    3.00\n",
       " 13 2014-01-14    3.67\n",
       " 14 2014-01-15    2.00\n",
       " 15 2014-01-16    1.33\n",
       " 16 2014-01-17    1.67\n",
       " 17 2014-01-18    1.33\n",
       " 18 2014-01-19    0.67\n",
       " 19 2014-01-20    1.33\n",
       " 20 2014-01-21    3.00\n",
       " 21 2014-01-22    3.00\n",
       " 22 2014-01-23    2.67\n",
       " 23 2014-01-24    3.00\n",
       " 24 2014-01-25    3.00\n",
       " 25 2014-01-26    2.67\n",
       " 26 2014-01-27    1.33\n",
       " 27 2014-01-28    2.33\n",
       " 28 2014-01-29    2.33\n",
       " 29 2014-01-30    2.67\n",
       " 30 2014-01-31    1.00\n",
       " 31 2014-02-01    2.67\n",
       " 32 2014-02-02    2.00\n",
       " 33 2014-02-03    2.33\n",
       " 34 2014-02-04    2.00\n",
       " 35 2014-02-05    2.00\n",
       " 36 2014-02-06    3.00\n",
       " 37 2014-02-07    4.00\n",
       " 38 2014-02-08    4.67,\n",
       "          date  max_kp\n",
       " 19 2014-01-20    1.33\n",
       " 20 2014-01-21    3.00\n",
       " 21 2014-01-22    3.00\n",
       " 22 2014-01-23    2.67\n",
       " 23 2014-01-24    3.00\n",
       " 24 2014-01-25    3.00\n",
       " 25 2014-01-26    2.67\n",
       " 26 2014-01-27    1.33\n",
       " 27 2014-01-28    2.33\n",
       " 28 2014-01-29    2.33\n",
       " 29 2014-01-30    2.67\n",
       " 30 2014-01-31    1.00\n",
       " 31 2014-02-01    2.67\n",
       " 32 2014-02-02    2.00\n",
       " 33 2014-02-03    2.33\n",
       " 34 2014-02-04    2.00\n",
       " 35 2014-02-05    2.00\n",
       " 36 2014-02-06    3.00\n",
       " 37 2014-02-07    4.00\n",
       " 38 2014-02-08    4.67\n",
       " 39 2014-02-09    4.33\n",
       " 40 2014-02-10    4.00\n",
       " 41 2014-02-11    3.00\n",
       " 42 2014-02-12    3.33\n",
       " 43 2014-02-13    1.00\n",
       " 44 2014-02-14    1.67\n",
       " 45 2014-02-15    4.67,\n",
       "          date  max_kp\n",
       " 26 2014-01-27    1.33\n",
       " 27 2014-01-28    2.33\n",
       " 28 2014-01-29    2.33\n",
       " 29 2014-01-30    2.67\n",
       " 30 2014-01-31    1.00\n",
       " 31 2014-02-01    2.67\n",
       " 32 2014-02-02    2.00\n",
       " 33 2014-02-03    2.33\n",
       " 34 2014-02-04    2.00\n",
       " 35 2014-02-05    2.00\n",
       " 36 2014-02-06    3.00\n",
       " 37 2014-02-07    4.00\n",
       " 38 2014-02-08    4.67\n",
       " 39 2014-02-09    4.33\n",
       " 40 2014-02-10    4.00\n",
       " 41 2014-02-11    3.00\n",
       " 42 2014-02-12    3.33\n",
       " 43 2014-02-13    1.00\n",
       " 44 2014-02-14    1.67\n",
       " 45 2014-02-15    4.67\n",
       " 46 2014-02-16    5.33\n",
       " 47 2014-02-17    2.67\n",
       " 48 2014-02-18    3.00\n",
       " 49 2014-02-19    6.33\n",
       " 50 2014-02-20    6.00\n",
       " 51 2014-02-21    3.33\n",
       " 52 2014-02-22    4.33,\n",
       "          date  max_kp\n",
       " 33 2014-02-03    2.33\n",
       " 34 2014-02-04    2.00\n",
       " 35 2014-02-05    2.00\n",
       " 36 2014-02-06    3.00\n",
       " 37 2014-02-07    4.00\n",
       " 38 2014-02-08    4.67\n",
       " 39 2014-02-09    4.33\n",
       " 40 2014-02-10    4.00\n",
       " 41 2014-02-11    3.00\n",
       " 42 2014-02-12    3.33\n",
       " 43 2014-02-13    1.00\n",
       " 44 2014-02-14    1.67\n",
       " 45 2014-02-15    4.67\n",
       " 46 2014-02-16    5.33\n",
       " 47 2014-02-17    2.67\n",
       " 48 2014-02-18    3.00\n",
       " 49 2014-02-19    6.33\n",
       " 50 2014-02-20    6.00\n",
       " 51 2014-02-21    3.33\n",
       " 52 2014-02-22    4.33\n",
       " 53 2014-02-23    4.00\n",
       " 54 2014-02-24    2.67\n",
       " 55 2014-02-25    2.00\n",
       " 56 2014-02-26    2.33\n",
       " 57 2014-02-27    6.00\n",
       " 58 2014-02-28    4.00\n",
       " 59 2014-03-01    2.67,\n",
       "          date  max_kp\n",
       " 40 2014-02-10    4.00\n",
       " 41 2014-02-11    3.00\n",
       " 42 2014-02-12    3.33\n",
       " 43 2014-02-13    1.00\n",
       " 44 2014-02-14    1.67\n",
       " 45 2014-02-15    4.67\n",
       " 46 2014-02-16    5.33\n",
       " 47 2014-02-17    2.67\n",
       " 48 2014-02-18    3.00\n",
       " 49 2014-02-19    6.33\n",
       " 50 2014-02-20    6.00\n",
       " 51 2014-02-21    3.33\n",
       " 52 2014-02-22    4.33\n",
       " 53 2014-02-23    4.00\n",
       " 54 2014-02-24    2.67\n",
       " 55 2014-02-25    2.00\n",
       " 56 2014-02-26    2.33\n",
       " 57 2014-02-27    6.00\n",
       " 58 2014-02-28    4.00\n",
       " 59 2014-03-01    2.67\n",
       " 60 2014-03-02    3.33\n",
       " 61 2014-03-03    3.00\n",
       " 62 2014-03-04    2.67\n",
       " 63 2014-03-05    2.67\n",
       " 64 2014-03-06    2.67\n",
       " 65 2014-03-07    1.33\n",
       " 66 2014-03-08    1.33,\n",
       "          date  max_kp\n",
       " 47 2014-02-17    2.67\n",
       " 48 2014-02-18    3.00\n",
       " 49 2014-02-19    6.33\n",
       " 50 2014-02-20    6.00\n",
       " 51 2014-02-21    3.33\n",
       " 52 2014-02-22    4.33\n",
       " 53 2014-02-23    4.00\n",
       " 54 2014-02-24    2.67\n",
       " 55 2014-02-25    2.00\n",
       " 56 2014-02-26    2.33\n",
       " 57 2014-02-27    6.00\n",
       " 58 2014-02-28    4.00\n",
       " 59 2014-03-01    2.67\n",
       " 60 2014-03-02    3.33\n",
       " 61 2014-03-03    3.00\n",
       " 62 2014-03-04    2.67\n",
       " 63 2014-03-05    2.67\n",
       " 64 2014-03-06    2.67\n",
       " 65 2014-03-07    1.33\n",
       " 66 2014-03-08    1.33\n",
       " 67 2014-03-09    1.33\n",
       " 68 2014-03-10    3.00\n",
       " 69 2014-03-11    1.67\n",
       " 70 2014-03-12    2.67\n",
       " 71 2014-03-13    4.67\n",
       " 72 2014-03-14    2.00\n",
       " 73 2014-03-15    2.33,\n",
       "          date  max_kp\n",
       " 54 2014-02-24    2.67\n",
       " 55 2014-02-25    2.00\n",
       " 56 2014-02-26    2.33\n",
       " 57 2014-02-27    6.00\n",
       " 58 2014-02-28    4.00\n",
       " 59 2014-03-01    2.67\n",
       " 60 2014-03-02    3.33\n",
       " 61 2014-03-03    3.00\n",
       " 62 2014-03-04    2.67\n",
       " 63 2014-03-05    2.67\n",
       " 64 2014-03-06    2.67\n",
       " 65 2014-03-07    1.33\n",
       " 66 2014-03-08    1.33\n",
       " 67 2014-03-09    1.33\n",
       " 68 2014-03-10    3.00\n",
       " 69 2014-03-11    1.67\n",
       " 70 2014-03-12    2.67\n",
       " 71 2014-03-13    4.67\n",
       " 72 2014-03-14    2.00\n",
       " 73 2014-03-15    2.33\n",
       " 74 2014-03-16    1.00\n",
       " 75 2014-03-17    1.33\n",
       " 76 2014-03-18    2.33\n",
       " 77 2014-03-19    2.00\n",
       " 78 2014-03-20    2.33\n",
       " 79 2014-03-21    3.00\n",
       " 80 2014-03-22    2.33,\n",
       "          date  max_kp\n",
       " 61 2014-03-03    3.00\n",
       " 62 2014-03-04    2.67\n",
       " 63 2014-03-05    2.67\n",
       " 64 2014-03-06    2.67\n",
       " 65 2014-03-07    1.33\n",
       " 66 2014-03-08    1.33\n",
       " 67 2014-03-09    1.33\n",
       " 68 2014-03-10    3.00\n",
       " 69 2014-03-11    1.67\n",
       " 70 2014-03-12    2.67\n",
       " 71 2014-03-13    4.67\n",
       " 72 2014-03-14    2.00\n",
       " 73 2014-03-15    2.33\n",
       " 74 2014-03-16    1.00\n",
       " 75 2014-03-17    1.33\n",
       " 76 2014-03-18    2.33\n",
       " 77 2014-03-19    2.00\n",
       " 78 2014-03-20    2.33\n",
       " 79 2014-03-21    3.00\n",
       " 80 2014-03-22    2.33\n",
       " 81 2014-03-23    2.00\n",
       " 82 2014-03-24    2.00\n",
       " 83 2014-03-25    3.33\n",
       " 84 2014-03-26    3.67\n",
       " 85 2014-03-27    2.67\n",
       " 86 2014-03-28    3.00\n",
       " 87 2014-03-29    3.33,\n",
       "          date  max_kp\n",
       " 68 2014-03-10    3.00\n",
       " 69 2014-03-11    1.67\n",
       " 70 2014-03-12    2.67\n",
       " 71 2014-03-13    4.67\n",
       " 72 2014-03-14    2.00\n",
       " 73 2014-03-15    2.33\n",
       " 74 2014-03-16    1.00\n",
       " 75 2014-03-17    1.33\n",
       " 76 2014-03-18    2.33\n",
       " 77 2014-03-19    2.00\n",
       " 78 2014-03-20    2.33\n",
       " 79 2014-03-21    3.00\n",
       " 80 2014-03-22    2.33\n",
       " 81 2014-03-23    2.00\n",
       " 82 2014-03-24    2.00\n",
       " 83 2014-03-25    3.33\n",
       " 84 2014-03-26    3.67\n",
       " 85 2014-03-27    2.67\n",
       " 86 2014-03-28    3.00\n",
       " 87 2014-03-29    3.33\n",
       " 88 2014-03-30    2.33\n",
       " 89 2014-03-31    3.00\n",
       " 90 2014-04-01    2.67\n",
       " 91 2014-04-02    2.00\n",
       " 92 2014-04-03    2.33\n",
       " 93 2014-04-04    2.00\n",
       " 94 2014-04-05    4.00,\n",
       "           date  max_kp\n",
       " 75  2014-03-17    1.33\n",
       " 76  2014-03-18    2.33\n",
       " 77  2014-03-19    2.00\n",
       " 78  2014-03-20    2.33\n",
       " 79  2014-03-21    3.00\n",
       " 80  2014-03-22    2.33\n",
       " 81  2014-03-23    2.00\n",
       " 82  2014-03-24    2.00\n",
       " 83  2014-03-25    3.33\n",
       " 84  2014-03-26    3.67\n",
       " 85  2014-03-27    2.67\n",
       " 86  2014-03-28    3.00\n",
       " 87  2014-03-29    3.33\n",
       " 88  2014-03-30    2.33\n",
       " 89  2014-03-31    3.00\n",
       " 90  2014-04-01    2.67\n",
       " 91  2014-04-02    2.00\n",
       " 92  2014-04-03    2.33\n",
       " 93  2014-04-04    2.00\n",
       " 94  2014-04-05    4.00\n",
       " 95  2014-04-06    2.67\n",
       " 96  2014-04-07    4.33\n",
       " 97  2014-04-08    2.33\n",
       " 98  2014-04-09    2.00\n",
       " 99  2014-04-10    1.33\n",
       " 100 2014-04-11    3.33\n",
       " 101 2014-04-12    5.00,\n",
       "           date  max_kp\n",
       " 82  2014-03-24    2.00\n",
       " 83  2014-03-25    3.33\n",
       " 84  2014-03-26    3.67\n",
       " 85  2014-03-27    2.67\n",
       " 86  2014-03-28    3.00\n",
       " 87  2014-03-29    3.33\n",
       " 88  2014-03-30    2.33\n",
       " 89  2014-03-31    3.00\n",
       " 90  2014-04-01    2.67\n",
       " 91  2014-04-02    2.00\n",
       " 92  2014-04-03    2.33\n",
       " 93  2014-04-04    2.00\n",
       " 94  2014-04-05    4.00\n",
       " 95  2014-04-06    2.67\n",
       " 96  2014-04-07    4.33\n",
       " 97  2014-04-08    2.33\n",
       " 98  2014-04-09    2.00\n",
       " 99  2014-04-10    1.33\n",
       " 100 2014-04-11    3.33\n",
       " 101 2014-04-12    5.00\n",
       " 102 2014-04-13    4.33\n",
       " 103 2014-04-14    3.00\n",
       " 104 2014-04-15    2.67\n",
       " 105 2014-04-16    2.33\n",
       " 106 2014-04-17    3.33\n",
       " 107 2014-04-18    3.33\n",
       " 108 2014-04-19    3.67,\n",
       "           date  max_kp\n",
       " 89  2014-03-31    3.00\n",
       " 90  2014-04-01    2.67\n",
       " 91  2014-04-02    2.00\n",
       " 92  2014-04-03    2.33\n",
       " 93  2014-04-04    2.00\n",
       " 94  2014-04-05    4.00\n",
       " 95  2014-04-06    2.67\n",
       " 96  2014-04-07    4.33\n",
       " 97  2014-04-08    2.33\n",
       " 98  2014-04-09    2.00\n",
       " 99  2014-04-10    1.33\n",
       " 100 2014-04-11    3.33\n",
       " 101 2014-04-12    5.00\n",
       " 102 2014-04-13    4.33\n",
       " 103 2014-04-14    3.00\n",
       " 104 2014-04-15    2.67\n",
       " 105 2014-04-16    2.33\n",
       " 106 2014-04-17    3.33\n",
       " 107 2014-04-18    3.33\n",
       " 108 2014-04-19    3.67\n",
       " 109 2014-04-20    5.00\n",
       " 110 2014-04-21    4.33\n",
       " 111 2014-04-22    2.00\n",
       " 112 2014-04-23    2.33\n",
       " 113 2014-04-24    3.33\n",
       " 114 2014-04-25    3.33\n",
       " 115 2014-04-26    2.67,\n",
       "           date  max_kp\n",
       " 96  2014-04-07    4.33\n",
       " 97  2014-04-08    2.33\n",
       " 98  2014-04-09    2.00\n",
       " 99  2014-04-10    1.33\n",
       " 100 2014-04-11    3.33\n",
       " 101 2014-04-12    5.00\n",
       " 102 2014-04-13    4.33\n",
       " 103 2014-04-14    3.00\n",
       " 104 2014-04-15    2.67\n",
       " 105 2014-04-16    2.33\n",
       " 106 2014-04-17    3.33\n",
       " 107 2014-04-18    3.33\n",
       " 108 2014-04-19    3.67\n",
       " 109 2014-04-20    5.00\n",
       " 110 2014-04-21    4.33\n",
       " 111 2014-04-22    2.00\n",
       " 112 2014-04-23    2.33\n",
       " 113 2014-04-24    3.33\n",
       " 114 2014-04-25    3.33\n",
       " 115 2014-04-26    2.67\n",
       " 116 2014-04-27    1.67\n",
       " 117 2014-04-28    2.67\n",
       " 118 2014-04-29    3.67\n",
       " 119 2014-04-30    4.00\n",
       " 120 2014-05-01    3.00\n",
       " 121 2014-05-02    2.33\n",
       " 122 2014-05-03    3.33,\n",
       "           date  max_kp\n",
       " 103 2014-04-14    3.00\n",
       " 104 2014-04-15    2.67\n",
       " 105 2014-04-16    2.33\n",
       " 106 2014-04-17    3.33\n",
       " 107 2014-04-18    3.33\n",
       " 108 2014-04-19    3.67\n",
       " 109 2014-04-20    5.00\n",
       " 110 2014-04-21    4.33\n",
       " 111 2014-04-22    2.00\n",
       " 112 2014-04-23    2.33\n",
       " 113 2014-04-24    3.33\n",
       " 114 2014-04-25    3.33\n",
       " 115 2014-04-26    2.67\n",
       " 116 2014-04-27    1.67\n",
       " 117 2014-04-28    2.67\n",
       " 118 2014-04-29    3.67\n",
       " 119 2014-04-30    4.00\n",
       " 120 2014-05-01    3.00\n",
       " 121 2014-05-02    2.33\n",
       " 122 2014-05-03    3.33\n",
       " 123 2014-05-04    4.33\n",
       " 124 2014-05-05    3.33\n",
       " 125 2014-05-06    1.33\n",
       " 126 2014-05-07    2.33\n",
       " 127 2014-05-08    4.33\n",
       " 128 2014-05-09    3.33\n",
       " 129 2014-05-10    3.33,\n",
       "           date  max_kp\n",
       " 110 2014-04-21    4.33\n",
       " 111 2014-04-22    2.00\n",
       " 112 2014-04-23    2.33\n",
       " 113 2014-04-24    3.33\n",
       " 114 2014-04-25    3.33\n",
       " 115 2014-04-26    2.67\n",
       " 116 2014-04-27    1.67\n",
       " 117 2014-04-28    2.67\n",
       " 118 2014-04-29    3.67\n",
       " 119 2014-04-30    4.00\n",
       " 120 2014-05-01    3.00\n",
       " 121 2014-05-02    2.33\n",
       " 122 2014-05-03    3.33\n",
       " 123 2014-05-04    4.33\n",
       " 124 2014-05-05    3.33\n",
       " 125 2014-05-06    1.33\n",
       " 126 2014-05-07    2.33\n",
       " 127 2014-05-08    4.33\n",
       " 128 2014-05-09    3.33\n",
       " 129 2014-05-10    3.33\n",
       " 130 2014-05-11    4.00\n",
       " 131 2014-05-12    2.67\n",
       " 132 2014-05-13    2.33\n",
       " 133 2014-05-14    2.33\n",
       " 134 2014-05-15    2.00\n",
       " 135 2014-05-16    2.00\n",
       " 136 2014-05-17    1.33,\n",
       "           date  max_kp\n",
       " 117 2014-04-28    2.67\n",
       " 118 2014-04-29    3.67\n",
       " 119 2014-04-30    4.00\n",
       " 120 2014-05-01    3.00\n",
       " 121 2014-05-02    2.33\n",
       " 122 2014-05-03    3.33\n",
       " 123 2014-05-04    4.33\n",
       " 124 2014-05-05    3.33\n",
       " 125 2014-05-06    1.33\n",
       " 126 2014-05-07    2.33\n",
       " 127 2014-05-08    4.33\n",
       " 128 2014-05-09    3.33\n",
       " 129 2014-05-10    3.33\n",
       " 130 2014-05-11    4.00\n",
       " 131 2014-05-12    2.67\n",
       " 132 2014-05-13    2.33\n",
       " 133 2014-05-14    2.33\n",
       " 134 2014-05-15    2.00\n",
       " 135 2014-05-16    2.00\n",
       " 136 2014-05-17    1.33\n",
       " 137 2014-05-18    2.00\n",
       " 138 2014-05-19    1.67\n",
       " 139 2014-05-20    1.67\n",
       " 140 2014-05-21    1.00\n",
       " 141 2014-05-22    3.33\n",
       " 142 2014-05-23    5.33\n",
       " 143 2014-05-24    2.33,\n",
       "           date  max_kp\n",
       " 124 2014-05-05    3.33\n",
       " 125 2014-05-06    1.33\n",
       " 126 2014-05-07    2.33\n",
       " 127 2014-05-08    4.33\n",
       " 128 2014-05-09    3.33\n",
       " 129 2014-05-10    3.33\n",
       " 130 2014-05-11    4.00\n",
       " 131 2014-05-12    2.67\n",
       " 132 2014-05-13    2.33\n",
       " 133 2014-05-14    2.33\n",
       " 134 2014-05-15    2.00\n",
       " 135 2014-05-16    2.00\n",
       " 136 2014-05-17    1.33\n",
       " 137 2014-05-18    2.00\n",
       " 138 2014-05-19    1.67\n",
       " 139 2014-05-20    1.67\n",
       " 140 2014-05-21    1.00\n",
       " 141 2014-05-22    3.33\n",
       " 142 2014-05-23    5.33\n",
       " 143 2014-05-24    2.33\n",
       " 144 2014-05-25    2.00\n",
       " 145 2014-05-26    1.67\n",
       " 146 2014-05-27    1.67\n",
       " 147 2014-05-28    1.67\n",
       " 148 2014-05-29    2.67\n",
       " 149 2014-05-30    4.33\n",
       " 150 2014-05-31    1.67,\n",
       "           date  max_kp\n",
       " 131 2014-05-12    2.67\n",
       " 132 2014-05-13    2.33\n",
       " 133 2014-05-14    2.33\n",
       " 134 2014-05-15    2.00\n",
       " 135 2014-05-16    2.00\n",
       " 136 2014-05-17    1.33\n",
       " 137 2014-05-18    2.00\n",
       " 138 2014-05-19    1.67\n",
       " 139 2014-05-20    1.67\n",
       " 140 2014-05-21    1.00\n",
       " 141 2014-05-22    3.33\n",
       " 142 2014-05-23    5.33\n",
       " 143 2014-05-24    2.33\n",
       " 144 2014-05-25    2.00\n",
       " 145 2014-05-26    1.67\n",
       " 146 2014-05-27    1.67\n",
       " 147 2014-05-28    1.67\n",
       " 148 2014-05-29    2.67\n",
       " 149 2014-05-30    4.33\n",
       " 150 2014-05-31    1.67\n",
       " 151 2014-06-01    2.00\n",
       " 152 2014-06-02    2.67\n",
       " 153 2014-06-03    3.33\n",
       " 154 2014-06-04    1.67\n",
       " 155 2014-06-05    3.00\n",
       " 156 2014-06-06    2.33\n",
       " 157 2014-06-07    4.00,\n",
       "           date  max_kp\n",
       " 138 2014-05-19    1.67\n",
       " 139 2014-05-20    1.67\n",
       " 140 2014-05-21    1.00\n",
       " 141 2014-05-22    3.33\n",
       " 142 2014-05-23    5.33\n",
       " 143 2014-05-24    2.33\n",
       " 144 2014-05-25    2.00\n",
       " 145 2014-05-26    1.67\n",
       " 146 2014-05-27    1.67\n",
       " 147 2014-05-28    1.67\n",
       " 148 2014-05-29    2.67\n",
       " 149 2014-05-30    4.33\n",
       " 150 2014-05-31    1.67\n",
       " 151 2014-06-01    2.00\n",
       " 152 2014-06-02    2.67\n",
       " 153 2014-06-03    3.33\n",
       " 154 2014-06-04    1.67\n",
       " 155 2014-06-05    3.00\n",
       " 156 2014-06-06    2.33\n",
       " 157 2014-06-07    4.00\n",
       " 158 2014-06-08    6.33\n",
       " 159 2014-06-09    2.33\n",
       " 160 2014-06-10    2.33\n",
       " 161 2014-06-11    2.67\n",
       " 162 2014-06-12    2.00\n",
       " 163 2014-06-13    2.00\n",
       " 164 2014-06-14    3.00,\n",
       "           date  max_kp\n",
       " 145 2014-05-26    1.67\n",
       " 146 2014-05-27    1.67\n",
       " 147 2014-05-28    1.67\n",
       " 148 2014-05-29    2.67\n",
       " 149 2014-05-30    4.33\n",
       " 150 2014-05-31    1.67\n",
       " 151 2014-06-01    2.00\n",
       " 152 2014-06-02    2.67\n",
       " 153 2014-06-03    3.33\n",
       " 154 2014-06-04    1.67\n",
       " 155 2014-06-05    3.00\n",
       " 156 2014-06-06    2.33\n",
       " 157 2014-06-07    4.00\n",
       " 158 2014-06-08    6.33\n",
       " 159 2014-06-09    2.33\n",
       " 160 2014-06-10    2.33\n",
       " 161 2014-06-11    2.67\n",
       " 162 2014-06-12    2.00\n",
       " 163 2014-06-13    2.00\n",
       " 164 2014-06-14    3.00\n",
       " 165 2014-06-15    2.33\n",
       " 166 2014-06-16    2.33\n",
       " 167 2014-06-17    3.00\n",
       " 168 2014-06-18    5.00\n",
       " 169 2014-06-19    3.00\n",
       " 170 2014-06-20    2.67\n",
       " 171 2014-06-21    2.33,\n",
       "           date  max_kp\n",
       " 152 2014-06-02    2.67\n",
       " 153 2014-06-03    3.33\n",
       " 154 2014-06-04    1.67\n",
       " 155 2014-06-05    3.00\n",
       " 156 2014-06-06    2.33\n",
       " 157 2014-06-07    4.00\n",
       " 158 2014-06-08    6.33\n",
       " 159 2014-06-09    2.33\n",
       " 160 2014-06-10    2.33\n",
       " 161 2014-06-11    2.67\n",
       " 162 2014-06-12    2.00\n",
       " 163 2014-06-13    2.00\n",
       " 164 2014-06-14    3.00\n",
       " 165 2014-06-15    2.33\n",
       " 166 2014-06-16    2.33\n",
       " 167 2014-06-17    3.00\n",
       " 168 2014-06-18    5.00\n",
       " 169 2014-06-19    3.00\n",
       " 170 2014-06-20    2.67\n",
       " 171 2014-06-21    2.33\n",
       " 172 2014-06-22    1.67\n",
       " 173 2014-06-23    3.33\n",
       " 174 2014-06-24    2.67\n",
       " 175 2014-06-25    2.33\n",
       " 176 2014-06-26    2.33\n",
       " 177 2014-06-27    1.67\n",
       " 178 2014-06-28    2.67,\n",
       "           date  max_kp\n",
       " 159 2014-06-09    2.33\n",
       " 160 2014-06-10    2.33\n",
       " 161 2014-06-11    2.67\n",
       " 162 2014-06-12    2.00\n",
       " 163 2014-06-13    2.00\n",
       " 164 2014-06-14    3.00\n",
       " 165 2014-06-15    2.33\n",
       " 166 2014-06-16    2.33\n",
       " 167 2014-06-17    3.00\n",
       " 168 2014-06-18    5.00\n",
       " 169 2014-06-19    3.00\n",
       " 170 2014-06-20    2.67\n",
       " 171 2014-06-21    2.33\n",
       " 172 2014-06-22    1.67\n",
       " 173 2014-06-23    3.33\n",
       " 174 2014-06-24    2.67\n",
       " 175 2014-06-25    2.33\n",
       " 176 2014-06-26    2.33\n",
       " 177 2014-06-27    1.67\n",
       " 178 2014-06-28    2.67\n",
       " 179 2014-06-29    3.33\n",
       " 180 2014-06-30    3.00\n",
       " 181 2014-07-01    2.33\n",
       " 182 2014-07-02    1.67\n",
       " 183 2014-07-03    2.67\n",
       " 184 2014-07-04    2.00\n",
       " 185 2014-07-05    1.33,\n",
       "           date  max_kp\n",
       " 166 2014-06-16    2.33\n",
       " 167 2014-06-17    3.00\n",
       " 168 2014-06-18    5.00\n",
       " 169 2014-06-19    3.00\n",
       " 170 2014-06-20    2.67\n",
       " 171 2014-06-21    2.33\n",
       " 172 2014-06-22    1.67\n",
       " 173 2014-06-23    3.33\n",
       " 174 2014-06-24    2.67\n",
       " 175 2014-06-25    2.33\n",
       " 176 2014-06-26    2.33\n",
       " 177 2014-06-27    1.67\n",
       " 178 2014-06-28    2.67\n",
       " 179 2014-06-29    3.33\n",
       " 180 2014-06-30    3.00\n",
       " 181 2014-07-01    2.33\n",
       " 182 2014-07-02    1.67\n",
       " 183 2014-07-03    2.67\n",
       " 184 2014-07-04    2.00\n",
       " 185 2014-07-05    1.33\n",
       " 186 2014-07-06    2.00\n",
       " 187 2014-07-07    2.33\n",
       " 188 2014-07-08    2.33\n",
       " 189 2014-07-09    3.00\n",
       " 190 2014-07-10    3.00\n",
       " 191 2014-07-11    2.00\n",
       " 192 2014-07-12    2.33,\n",
       "           date  max_kp\n",
       " 173 2014-06-23    3.33\n",
       " 174 2014-06-24    2.67\n",
       " 175 2014-06-25    2.33\n",
       " 176 2014-06-26    2.33\n",
       " 177 2014-06-27    1.67\n",
       " 178 2014-06-28    2.67\n",
       " 179 2014-06-29    3.33\n",
       " 180 2014-06-30    3.00\n",
       " 181 2014-07-01    2.33\n",
       " 182 2014-07-02    1.67\n",
       " 183 2014-07-03    2.67\n",
       " 184 2014-07-04    2.00\n",
       " 185 2014-07-05    1.33\n",
       " 186 2014-07-06    2.00\n",
       " 187 2014-07-07    2.33\n",
       " 188 2014-07-08    2.33\n",
       " 189 2014-07-09    3.00\n",
       " 190 2014-07-10    3.00\n",
       " 191 2014-07-11    2.00\n",
       " 192 2014-07-12    2.33\n",
       " 193 2014-07-13    2.00\n",
       " 194 2014-07-14    3.00\n",
       " 195 2014-07-15    2.33\n",
       " 196 2014-07-16    1.67\n",
       " 197 2014-07-17    1.67\n",
       " 198 2014-07-18    1.00\n",
       " 199 2014-07-19    1.00,\n",
       "           date  max_kp\n",
       " 180 2014-06-30    3.00\n",
       " 181 2014-07-01    2.33\n",
       " 182 2014-07-02    1.67\n",
       " 183 2014-07-03    2.67\n",
       " 184 2014-07-04    2.00\n",
       " 185 2014-07-05    1.33\n",
       " 186 2014-07-06    2.00\n",
       " 187 2014-07-07    2.33\n",
       " 188 2014-07-08    2.33\n",
       " 189 2014-07-09    3.00\n",
       " 190 2014-07-10    3.00\n",
       " 191 2014-07-11    2.00\n",
       " 192 2014-07-12    2.33\n",
       " 193 2014-07-13    2.00\n",
       " 194 2014-07-14    3.00\n",
       " 195 2014-07-15    2.33\n",
       " 196 2014-07-16    1.67\n",
       " 197 2014-07-17    1.67\n",
       " 198 2014-07-18    1.00\n",
       " 199 2014-07-19    1.00\n",
       " 200 2014-07-20    1.33\n",
       " 201 2014-07-21    2.00\n",
       " 202 2014-07-22    2.33\n",
       " 203 2014-07-23    2.67\n",
       " 204 2014-07-24    2.33\n",
       " 205 2014-07-25    3.00\n",
       " 206 2014-07-26    2.33,\n",
       "           date  max_kp\n",
       " 187 2014-07-07    2.33\n",
       " 188 2014-07-08    2.33\n",
       " 189 2014-07-09    3.00\n",
       " 190 2014-07-10    3.00\n",
       " 191 2014-07-11    2.00\n",
       " 192 2014-07-12    2.33\n",
       " 193 2014-07-13    2.00\n",
       " 194 2014-07-14    3.00\n",
       " 195 2014-07-15    2.33\n",
       " 196 2014-07-16    1.67\n",
       " 197 2014-07-17    1.67\n",
       " 198 2014-07-18    1.00\n",
       " 199 2014-07-19    1.00\n",
       " 200 2014-07-20    1.33\n",
       " 201 2014-07-21    2.00\n",
       " 202 2014-07-22    2.33\n",
       " 203 2014-07-23    2.67\n",
       " 204 2014-07-24    2.33\n",
       " 205 2014-07-25    3.00\n",
       " 206 2014-07-26    2.33\n",
       " 207 2014-07-27    2.00\n",
       " 208 2014-07-28    3.33\n",
       " 209 2014-07-29    1.33\n",
       " 210 2014-07-30    2.67\n",
       " 211 2014-07-31    2.00\n",
       " 212 2014-08-01    3.67\n",
       " 213 2014-08-02    3.67,\n",
       "           date  max_kp\n",
       " 194 2014-07-14    3.00\n",
       " 195 2014-07-15    2.33\n",
       " 196 2014-07-16    1.67\n",
       " 197 2014-07-17    1.67\n",
       " 198 2014-07-18    1.00\n",
       " 199 2014-07-19    1.00\n",
       " 200 2014-07-20    1.33\n",
       " 201 2014-07-21    2.00\n",
       " 202 2014-07-22    2.33\n",
       " 203 2014-07-23    2.67\n",
       " 204 2014-07-24    2.33\n",
       " 205 2014-07-25    3.00\n",
       " 206 2014-07-26    2.33\n",
       " 207 2014-07-27    2.00\n",
       " 208 2014-07-28    3.33\n",
       " 209 2014-07-29    1.33\n",
       " 210 2014-07-30    2.67\n",
       " 211 2014-07-31    2.00\n",
       " 212 2014-08-01    3.67\n",
       " 213 2014-08-02    3.67\n",
       " 214 2014-08-03    3.33\n",
       " 215 2014-08-04    3.00\n",
       " 216 2014-08-05    3.00\n",
       " 217 2014-08-06    2.67\n",
       " 218 2014-08-07    1.67\n",
       " 219 2014-08-08    3.00\n",
       " 220 2014-08-09    2.00,\n",
       "           date  max_kp\n",
       " 201 2014-07-21    2.00\n",
       " 202 2014-07-22    2.33\n",
       " 203 2014-07-23    2.67\n",
       " 204 2014-07-24    2.33\n",
       " 205 2014-07-25    3.00\n",
       " 206 2014-07-26    2.33\n",
       " 207 2014-07-27    2.00\n",
       " 208 2014-07-28    3.33\n",
       " 209 2014-07-29    1.33\n",
       " 210 2014-07-30    2.67\n",
       " 211 2014-07-31    2.00\n",
       " 212 2014-08-01    3.67\n",
       " 213 2014-08-02    3.67\n",
       " 214 2014-08-03    3.33\n",
       " 215 2014-08-04    3.00\n",
       " 216 2014-08-05    3.00\n",
       " 217 2014-08-06    2.67\n",
       " 218 2014-08-07    1.67\n",
       " 219 2014-08-08    3.00\n",
       " 220 2014-08-09    2.00\n",
       " 221 2014-08-10    4.00\n",
       " 222 2014-08-11    3.33\n",
       " 223 2014-08-12    4.00\n",
       " 224 2014-08-13    2.33\n",
       " 225 2014-08-14    1.67\n",
       " 226 2014-08-15    2.33\n",
       " 227 2014-08-16    1.00,\n",
       "           date  max_kp\n",
       " 208 2014-07-28    3.33\n",
       " 209 2014-07-29    1.33\n",
       " 210 2014-07-30    2.67\n",
       " 211 2014-07-31    2.00\n",
       " 212 2014-08-01    3.67\n",
       " 213 2014-08-02    3.67\n",
       " 214 2014-08-03    3.33\n",
       " 215 2014-08-04    3.00\n",
       " 216 2014-08-05    3.00\n",
       " 217 2014-08-06    2.67\n",
       " 218 2014-08-07    1.67\n",
       " 219 2014-08-08    3.00\n",
       " 220 2014-08-09    2.00\n",
       " 221 2014-08-10    4.00\n",
       " 222 2014-08-11    3.33\n",
       " 223 2014-08-12    4.00\n",
       " 224 2014-08-13    2.33\n",
       " 225 2014-08-14    1.67\n",
       " 226 2014-08-15    2.33\n",
       " 227 2014-08-16    1.00\n",
       " 228 2014-08-17    3.00\n",
       " 229 2014-08-18    2.00\n",
       " 230 2014-08-19    5.67\n",
       " 231 2014-08-20    2.33\n",
       " 232 2014-08-21    3.00\n",
       " 233 2014-08-22    2.67\n",
       " 234 2014-08-23    2.33,\n",
       "           date  max_kp\n",
       " 215 2014-08-04    3.00\n",
       " 216 2014-08-05    3.00\n",
       " 217 2014-08-06    2.67\n",
       " 218 2014-08-07    1.67\n",
       " 219 2014-08-08    3.00\n",
       " 220 2014-08-09    2.00\n",
       " 221 2014-08-10    4.00\n",
       " 222 2014-08-11    3.33\n",
       " 223 2014-08-12    4.00\n",
       " 224 2014-08-13    2.33\n",
       " 225 2014-08-14    1.67\n",
       " 226 2014-08-15    2.33\n",
       " 227 2014-08-16    1.00\n",
       " 228 2014-08-17    3.00\n",
       " 229 2014-08-18    2.00\n",
       " 230 2014-08-19    5.67\n",
       " 231 2014-08-20    2.33\n",
       " 232 2014-08-21    3.00\n",
       " 233 2014-08-22    2.67\n",
       " 234 2014-08-23    2.33\n",
       " 235 2014-08-24    1.67\n",
       " 236 2014-08-25    1.00\n",
       " 237 2014-08-26    1.67\n",
       " 238 2014-08-27    4.33\n",
       " 239 2014-08-28    5.00\n",
       " 240 2014-08-29    4.67\n",
       " 241 2014-08-30    3.67,\n",
       "           date  max_kp\n",
       " 222 2014-08-11    3.33\n",
       " 223 2014-08-12    4.00\n",
       " 224 2014-08-13    2.33\n",
       " 225 2014-08-14    1.67\n",
       " 226 2014-08-15    2.33\n",
       " 227 2014-08-16    1.00\n",
       " 228 2014-08-17    3.00\n",
       " 229 2014-08-18    2.00\n",
       " 230 2014-08-19    5.67\n",
       " 231 2014-08-20    2.33\n",
       " 232 2014-08-21    3.00\n",
       " 233 2014-08-22    2.67\n",
       " 234 2014-08-23    2.33\n",
       " 235 2014-08-24    1.67\n",
       " 236 2014-08-25    1.00\n",
       " 237 2014-08-26    1.67\n",
       " 238 2014-08-27    4.33\n",
       " 239 2014-08-28    5.00\n",
       " 240 2014-08-29    4.67\n",
       " 241 2014-08-30    3.67\n",
       " 242 2014-08-31    4.00\n",
       " 243 2014-09-01    2.67\n",
       " 244 2014-09-02    3.00\n",
       " 245 2014-09-03    3.33\n",
       " 246 2014-09-04    2.67\n",
       " 247 2014-09-05    2.33\n",
       " 248 2014-09-06    3.00,\n",
       "           date  max_kp\n",
       " 229 2014-08-18    2.00\n",
       " 230 2014-08-19    5.67\n",
       " 231 2014-08-20    2.33\n",
       " 232 2014-08-21    3.00\n",
       " 233 2014-08-22    2.67\n",
       " 234 2014-08-23    2.33\n",
       " 235 2014-08-24    1.67\n",
       " 236 2014-08-25    1.00\n",
       " 237 2014-08-26    1.67\n",
       " 238 2014-08-27    4.33\n",
       " 239 2014-08-28    5.00\n",
       " 240 2014-08-29    4.67\n",
       " 241 2014-08-30    3.67\n",
       " 242 2014-08-31    4.00\n",
       " 243 2014-09-01    2.67\n",
       " 244 2014-09-02    3.00\n",
       " 245 2014-09-03    3.33\n",
       " 246 2014-09-04    2.67\n",
       " 247 2014-09-05    2.33\n",
       " 248 2014-09-06    3.00\n",
       " 249 2014-09-07    3.33\n",
       " 250 2014-09-08    2.33\n",
       " 251 2014-09-09    2.67\n",
       " 252 2014-09-10    3.33\n",
       " 253 2014-09-11    3.67\n",
       " 254 2014-09-12    6.67\n",
       " 255 2014-09-13    4.33,\n",
       "           date  max_kp\n",
       " 236 2014-08-25    1.00\n",
       " 237 2014-08-26    1.67\n",
       " 238 2014-08-27    4.33\n",
       " 239 2014-08-28    5.00\n",
       " 240 2014-08-29    4.67\n",
       " 241 2014-08-30    3.67\n",
       " 242 2014-08-31    4.00\n",
       " 243 2014-09-01    2.67\n",
       " 244 2014-09-02    3.00\n",
       " 245 2014-09-03    3.33\n",
       " 246 2014-09-04    2.67\n",
       " 247 2014-09-05    2.33\n",
       " 248 2014-09-06    3.00\n",
       " 249 2014-09-07    3.33\n",
       " 250 2014-09-08    2.33\n",
       " 251 2014-09-09    2.67\n",
       " 252 2014-09-10    3.33\n",
       " 253 2014-09-11    3.67\n",
       " 254 2014-09-12    6.67\n",
       " 255 2014-09-13    4.33\n",
       " 256 2014-09-14    1.33\n",
       " 257 2014-09-15    1.00\n",
       " 258 2014-09-16    2.33\n",
       " 259 2014-09-17    2.67\n",
       " 260 2014-09-18    3.33\n",
       " 261 2014-09-19    5.00\n",
       " 262 2014-09-20    2.33,\n",
       "           date  max_kp\n",
       " 243 2014-09-01    2.67\n",
       " 244 2014-09-02    3.00\n",
       " 245 2014-09-03    3.33\n",
       " 246 2014-09-04    2.67\n",
       " 247 2014-09-05    2.33\n",
       " 248 2014-09-06    3.00\n",
       " 249 2014-09-07    3.33\n",
       " 250 2014-09-08    2.33\n",
       " 251 2014-09-09    2.67\n",
       " 252 2014-09-10    3.33\n",
       " 253 2014-09-11    3.67\n",
       " 254 2014-09-12    6.67\n",
       " 255 2014-09-13    4.33\n",
       " 256 2014-09-14    1.33\n",
       " 257 2014-09-15    1.00\n",
       " 258 2014-09-16    2.33\n",
       " 259 2014-09-17    2.67\n",
       " 260 2014-09-18    3.33\n",
       " 261 2014-09-19    5.00\n",
       " 262 2014-09-20    2.33\n",
       " 263 2014-09-21    3.33\n",
       " 264 2014-09-22    3.00\n",
       " 265 2014-09-23    3.00\n",
       " 266 2014-09-24    4.33\n",
       " 267 2014-09-25    4.00\n",
       " 268 2014-09-26    3.33\n",
       " 269 2014-09-27    4.33,\n",
       "           date  max_kp\n",
       " 250 2014-09-08    2.33\n",
       " 251 2014-09-09    2.67\n",
       " 252 2014-09-10    3.33\n",
       " 253 2014-09-11    3.67\n",
       " 254 2014-09-12    6.67\n",
       " 255 2014-09-13    4.33\n",
       " 256 2014-09-14    1.33\n",
       " 257 2014-09-15    1.00\n",
       " 258 2014-09-16    2.33\n",
       " 259 2014-09-17    2.67\n",
       " 260 2014-09-18    3.33\n",
       " 261 2014-09-19    5.00\n",
       " 262 2014-09-20    2.33\n",
       " 263 2014-09-21    3.33\n",
       " 264 2014-09-22    3.00\n",
       " 265 2014-09-23    3.00\n",
       " 266 2014-09-24    4.33\n",
       " 267 2014-09-25    4.00\n",
       " 268 2014-09-26    3.33\n",
       " 269 2014-09-27    4.33\n",
       " 270 2014-09-28    2.67\n",
       " 271 2014-09-29    3.67\n",
       " 272 2014-09-30    4.00\n",
       " 273 2014-10-01    3.67\n",
       " 274 2014-10-02    2.67\n",
       " 275 2014-10-03    2.33\n",
       " 276 2014-10-04    2.33,\n",
       "           date  max_kp\n",
       " 257 2014-09-15    1.00\n",
       " 258 2014-09-16    2.33\n",
       " 259 2014-09-17    2.67\n",
       " 260 2014-09-18    3.33\n",
       " 261 2014-09-19    5.00\n",
       " 262 2014-09-20    2.33\n",
       " 263 2014-09-21    3.33\n",
       " 264 2014-09-22    3.00\n",
       " 265 2014-09-23    3.00\n",
       " 266 2014-09-24    4.33\n",
       " 267 2014-09-25    4.00\n",
       " 268 2014-09-26    3.33\n",
       " 269 2014-09-27    4.33\n",
       " 270 2014-09-28    2.67\n",
       " 271 2014-09-29    3.67\n",
       " 272 2014-09-30    4.00\n",
       " 273 2014-10-01    3.67\n",
       " 274 2014-10-02    2.67\n",
       " 275 2014-10-03    2.33\n",
       " 276 2014-10-04    2.33\n",
       " 277 2014-10-05    2.00\n",
       " 278 2014-10-06    2.67\n",
       " 279 2014-10-07    3.33\n",
       " 280 2014-10-08    3.00\n",
       " 281 2014-10-09    4.00\n",
       " 282 2014-10-10    3.33\n",
       " 283 2014-10-11    3.00,\n",
       "           date  max_kp\n",
       " 264 2014-09-22    3.00\n",
       " 265 2014-09-23    3.00\n",
       " 266 2014-09-24    4.33\n",
       " 267 2014-09-25    4.00\n",
       " 268 2014-09-26    3.33\n",
       " 269 2014-09-27    4.33\n",
       " 270 2014-09-28    2.67\n",
       " 271 2014-09-29    3.67\n",
       " 272 2014-09-30    4.00\n",
       " 273 2014-10-01    3.67\n",
       " 274 2014-10-02    2.67\n",
       " 275 2014-10-03    2.33\n",
       " 276 2014-10-04    2.33\n",
       " 277 2014-10-05    2.00\n",
       " 278 2014-10-06    2.67\n",
       " 279 2014-10-07    3.33\n",
       " 280 2014-10-08    3.00\n",
       " 281 2014-10-09    4.00\n",
       " 282 2014-10-10    3.33\n",
       " 283 2014-10-11    3.00\n",
       " 284 2014-10-12    2.00\n",
       " 285 2014-10-13    2.67\n",
       " 286 2014-10-14    4.67\n",
       " 287 2014-10-15    4.67\n",
       " 288 2014-10-16    3.33\n",
       " 289 2014-10-17    3.00\n",
       " 290 2014-10-18    3.67,\n",
       "           date  max_kp\n",
       " 271 2014-09-29    3.67\n",
       " 272 2014-09-30    4.00\n",
       " 273 2014-10-01    3.67\n",
       " 274 2014-10-02    2.67\n",
       " 275 2014-10-03    2.33\n",
       " 276 2014-10-04    2.33\n",
       " 277 2014-10-05    2.00\n",
       " 278 2014-10-06    2.67\n",
       " 279 2014-10-07    3.33\n",
       " 280 2014-10-08    3.00\n",
       " 281 2014-10-09    4.00\n",
       " 282 2014-10-10    3.33\n",
       " 283 2014-10-11    3.00\n",
       " 284 2014-10-12    2.00\n",
       " 285 2014-10-13    2.67\n",
       " 286 2014-10-14    4.67\n",
       " 287 2014-10-15    4.67\n",
       " 288 2014-10-16    3.33\n",
       " 289 2014-10-17    3.00\n",
       " 290 2014-10-18    3.67\n",
       " 291 2014-10-19    3.33\n",
       " 292 2014-10-20    5.00\n",
       " 293 2014-10-21    3.67\n",
       " 294 2014-10-22    3.67\n",
       " 295 2014-10-23    3.67\n",
       " 296 2014-10-24    3.67\n",
       " 297 2014-10-25    3.33,\n",
       "           date  max_kp\n",
       " 278 2014-10-06    2.67\n",
       " 279 2014-10-07    3.33\n",
       " 280 2014-10-08    3.00\n",
       " 281 2014-10-09    4.00\n",
       " 282 2014-10-10    3.33\n",
       " 283 2014-10-11    3.00\n",
       " 284 2014-10-12    2.00\n",
       " 285 2014-10-13    2.67\n",
       " 286 2014-10-14    4.67\n",
       " 287 2014-10-15    4.67\n",
       " 288 2014-10-16    3.33\n",
       " 289 2014-10-17    3.00\n",
       " 290 2014-10-18    3.67\n",
       " 291 2014-10-19    3.33\n",
       " 292 2014-10-20    5.00\n",
       " 293 2014-10-21    3.67\n",
       " 294 2014-10-22    3.67\n",
       " 295 2014-10-23    3.67\n",
       " 296 2014-10-24    3.67\n",
       " 297 2014-10-25    3.33\n",
       " 298 2014-10-26    3.33\n",
       " 299 2014-10-27    4.33\n",
       " 300 2014-10-28    4.33\n",
       " 301 2014-10-29    3.67\n",
       " 302 2014-10-30    2.00\n",
       " 303 2014-10-31    2.33\n",
       " 304 2014-11-01    2.67,\n",
       "           date  max_kp\n",
       " 285 2014-10-13    2.67\n",
       " 286 2014-10-14    4.67\n",
       " 287 2014-10-15    4.67\n",
       " 288 2014-10-16    3.33\n",
       " 289 2014-10-17    3.00\n",
       " 290 2014-10-18    3.67\n",
       " 291 2014-10-19    3.33\n",
       " 292 2014-10-20    5.00\n",
       " 293 2014-10-21    3.67\n",
       " 294 2014-10-22    3.67\n",
       " 295 2014-10-23    3.67\n",
       " 296 2014-10-24    3.67\n",
       " 297 2014-10-25    3.33\n",
       " 298 2014-10-26    3.33\n",
       " 299 2014-10-27    4.33\n",
       " 300 2014-10-28    4.33\n",
       " 301 2014-10-29    3.67\n",
       " 302 2014-10-30    2.00\n",
       " 303 2014-10-31    2.33\n",
       " 304 2014-11-01    2.67\n",
       " 305 2014-11-02    3.33\n",
       " 306 2014-11-03    2.67\n",
       " 307 2014-11-04    4.33\n",
       " 308 2014-11-05    3.67\n",
       " 309 2014-11-06    3.33\n",
       " 310 2014-11-07    3.33\n",
       " 311 2014-11-08    3.33,\n",
       "           date  max_kp\n",
       " 292 2014-10-20    5.00\n",
       " 293 2014-10-21    3.67\n",
       " 294 2014-10-22    3.67\n",
       " 295 2014-10-23    3.67\n",
       " 296 2014-10-24    3.67\n",
       " 297 2014-10-25    3.33\n",
       " 298 2014-10-26    3.33\n",
       " 299 2014-10-27    4.33\n",
       " 300 2014-10-28    4.33\n",
       " 301 2014-10-29    3.67\n",
       " 302 2014-10-30    2.00\n",
       " 303 2014-10-31    2.33\n",
       " 304 2014-11-01    2.67\n",
       " 305 2014-11-02    3.33\n",
       " 306 2014-11-03    2.67\n",
       " 307 2014-11-04    4.33\n",
       " 308 2014-11-05    3.67\n",
       " 309 2014-11-06    3.33\n",
       " 310 2014-11-07    3.33\n",
       " 311 2014-11-08    3.33\n",
       " 312 2014-11-09    3.33\n",
       " 313 2014-11-10    4.67\n",
       " 314 2014-11-11    4.67\n",
       " 315 2014-11-12    3.67\n",
       " 316 2014-11-13    2.00\n",
       " 317 2014-11-14    3.00\n",
       " 318 2014-11-15    4.33,\n",
       "           date  max_kp\n",
       " 299 2014-10-27    4.33\n",
       " 300 2014-10-28    4.33\n",
       " 301 2014-10-29    3.67\n",
       " 302 2014-10-30    2.00\n",
       " 303 2014-10-31    2.33\n",
       " 304 2014-11-01    2.67\n",
       " 305 2014-11-02    3.33\n",
       " 306 2014-11-03    2.67\n",
       " 307 2014-11-04    4.33\n",
       " 308 2014-11-05    3.67\n",
       " 309 2014-11-06    3.33\n",
       " 310 2014-11-07    3.33\n",
       " 311 2014-11-08    3.33\n",
       " 312 2014-11-09    3.33\n",
       " 313 2014-11-10    4.67\n",
       " 314 2014-11-11    4.67\n",
       " 315 2014-11-12    3.67\n",
       " 316 2014-11-13    2.00\n",
       " 317 2014-11-14    3.00\n",
       " 318 2014-11-15    4.33\n",
       " 319 2014-11-16    4.33\n",
       " 320 2014-11-17    3.33\n",
       " 321 2014-11-18    3.33\n",
       " 322 2014-11-19    3.67\n",
       " 323 2014-11-20    3.67\n",
       " 324 2014-11-21    4.00\n",
       " 325 2014-11-22    3.67,\n",
       "           date  max_kp\n",
       " 306 2014-11-03    2.67\n",
       " 307 2014-11-04    4.33\n",
       " 308 2014-11-05    3.67\n",
       " 309 2014-11-06    3.33\n",
       " 310 2014-11-07    3.33\n",
       " 311 2014-11-08    3.33\n",
       " 312 2014-11-09    3.33\n",
       " 313 2014-11-10    4.67\n",
       " 314 2014-11-11    4.67\n",
       " 315 2014-11-12    3.67\n",
       " 316 2014-11-13    2.00\n",
       " 317 2014-11-14    3.00\n",
       " 318 2014-11-15    4.33\n",
       " 319 2014-11-16    4.33\n",
       " 320 2014-11-17    3.33\n",
       " 321 2014-11-18    3.33\n",
       " 322 2014-11-19    3.67\n",
       " 323 2014-11-20    3.67\n",
       " 324 2014-11-21    4.00\n",
       " 325 2014-11-22    3.67\n",
       " 326 2014-11-23    3.00\n",
       " 327 2014-11-24    2.67\n",
       " 328 2014-11-25    2.00\n",
       " 329 2014-11-26    2.00\n",
       " 330 2014-11-27    3.00\n",
       " 331 2014-11-28    2.67\n",
       " 332 2014-11-29    1.67,\n",
       "           date  max_kp\n",
       " 313 2014-11-10    4.67\n",
       " 314 2014-11-11    4.67\n",
       " 315 2014-11-12    3.67\n",
       " 316 2014-11-13    2.00\n",
       " 317 2014-11-14    3.00\n",
       " 318 2014-11-15    4.33\n",
       " 319 2014-11-16    4.33\n",
       " 320 2014-11-17    3.33\n",
       " 321 2014-11-18    3.33\n",
       " 322 2014-11-19    3.67\n",
       " 323 2014-11-20    3.67\n",
       " 324 2014-11-21    4.00\n",
       " 325 2014-11-22    3.67\n",
       " 326 2014-11-23    3.00\n",
       " 327 2014-11-24    2.67\n",
       " 328 2014-11-25    2.00\n",
       " 329 2014-11-26    2.00\n",
       " 330 2014-11-27    3.00\n",
       " 331 2014-11-28    2.67\n",
       " 332 2014-11-29    1.67\n",
       " 333 2014-11-30    3.00\n",
       " 334 2014-12-01    4.00\n",
       " 335 2014-12-02    3.33\n",
       " 336 2014-12-03    3.00\n",
       " 337 2014-12-04    4.00\n",
       " 338 2014-12-05    3.33\n",
       " 339 2014-12-06    4.00,\n",
       "           date  max_kp\n",
       " 320 2014-11-17    3.33\n",
       " 321 2014-11-18    3.33\n",
       " 322 2014-11-19    3.67\n",
       " 323 2014-11-20    3.67\n",
       " 324 2014-11-21    4.00\n",
       " 325 2014-11-22    3.67\n",
       " 326 2014-11-23    3.00\n",
       " 327 2014-11-24    2.67\n",
       " 328 2014-11-25    2.00\n",
       " 329 2014-11-26    2.00\n",
       " 330 2014-11-27    3.00\n",
       " 331 2014-11-28    2.67\n",
       " 332 2014-11-29    1.67\n",
       " 333 2014-11-30    3.00\n",
       " 334 2014-12-01    4.00\n",
       " 335 2014-12-02    3.33\n",
       " 336 2014-12-03    3.00\n",
       " 337 2014-12-04    4.00\n",
       " 338 2014-12-05    3.33\n",
       " 339 2014-12-06    4.00\n",
       " 340 2014-12-07    5.00\n",
       " 341 2014-12-08    4.00\n",
       " 342 2014-12-09    4.00\n",
       " 343 2014-12-10    3.00\n",
       " 344 2014-12-11    2.67\n",
       " 345 2014-12-12    4.67\n",
       " 346 2014-12-13    3.33,\n",
       "           date  max_kp\n",
       " 327 2014-11-24    2.67\n",
       " 328 2014-11-25    2.00\n",
       " 329 2014-11-26    2.00\n",
       " 330 2014-11-27    3.00\n",
       " 331 2014-11-28    2.67\n",
       " 332 2014-11-29    1.67\n",
       " 333 2014-11-30    3.00\n",
       " 334 2014-12-01    4.00\n",
       " 335 2014-12-02    3.33\n",
       " 336 2014-12-03    3.00\n",
       " 337 2014-12-04    4.00\n",
       " 338 2014-12-05    3.33\n",
       " 339 2014-12-06    4.00\n",
       " 340 2014-12-07    5.00\n",
       " 341 2014-12-08    4.00\n",
       " 342 2014-12-09    4.00\n",
       " 343 2014-12-10    3.00\n",
       " 344 2014-12-11    2.67\n",
       " 345 2014-12-12    4.67\n",
       " 346 2014-12-13    3.33\n",
       " 347 2014-12-14    3.33\n",
       " 348 2014-12-15    3.33\n",
       " 349 2014-12-16    3.00\n",
       " 350 2014-12-17    3.00\n",
       " 351 2014-12-18    2.00\n",
       " 352 2014-12-19    3.00\n",
       " 353 2014-12-20    3.33,\n",
       "           date  max_kp\n",
       " 334 2014-12-01    4.00\n",
       " 335 2014-12-02    3.33\n",
       " 336 2014-12-03    3.00\n",
       " 337 2014-12-04    4.00\n",
       " 338 2014-12-05    3.33\n",
       " 339 2014-12-06    4.00\n",
       " 340 2014-12-07    5.00\n",
       " 341 2014-12-08    4.00\n",
       " 342 2014-12-09    4.00\n",
       " 343 2014-12-10    3.00\n",
       " 344 2014-12-11    2.67\n",
       " 345 2014-12-12    4.67\n",
       " 346 2014-12-13    3.33\n",
       " 347 2014-12-14    3.33\n",
       " 348 2014-12-15    3.33\n",
       " 349 2014-12-16    3.00\n",
       " 350 2014-12-17    3.00\n",
       " 351 2014-12-18    2.00\n",
       " 352 2014-12-19    3.00\n",
       " 353 2014-12-20    3.33\n",
       " 354 2014-12-21    3.67\n",
       " 355 2014-12-22    5.00\n",
       " 356 2014-12-23    4.33\n",
       " 357 2014-12-24    4.33\n",
       " 358 2014-12-25    4.00\n",
       " 359 2014-12-26    4.33\n",
       " 360 2014-12-27    3.33,\n",
       "           date  max_kp\n",
       " 341 2014-12-08    4.00\n",
       " 342 2014-12-09    4.00\n",
       " 343 2014-12-10    3.00\n",
       " 344 2014-12-11    2.67\n",
       " 345 2014-12-12    4.67\n",
       " 346 2014-12-13    3.33\n",
       " 347 2014-12-14    3.33\n",
       " 348 2014-12-15    3.33\n",
       " 349 2014-12-16    3.00\n",
       " 350 2014-12-17    3.00\n",
       " 351 2014-12-18    2.00\n",
       " 352 2014-12-19    3.00\n",
       " 353 2014-12-20    3.33\n",
       " 354 2014-12-21    3.67\n",
       " 355 2014-12-22    5.00\n",
       " 356 2014-12-23    4.33\n",
       " 357 2014-12-24    4.33\n",
       " 358 2014-12-25    4.00\n",
       " 359 2014-12-26    4.33\n",
       " 360 2014-12-27    3.33\n",
       " 361 2014-12-28    4.00\n",
       " 362 2014-12-29    5.00\n",
       " 363 2014-12-30    4.33\n",
       " 364 2014-12-31    3.33\n",
       " 365 2015-01-01    2.67\n",
       " 366 2015-01-02    4.00\n",
       " 367 2015-01-03    5.00,\n",
       "           date  max_kp\n",
       " 348 2014-12-15    3.33\n",
       " 349 2014-12-16    3.00\n",
       " 350 2014-12-17    3.00\n",
       " 351 2014-12-18    2.00\n",
       " 352 2014-12-19    3.00\n",
       " 353 2014-12-20    3.33\n",
       " 354 2014-12-21    3.67\n",
       " 355 2014-12-22    5.00\n",
       " 356 2014-12-23    4.33\n",
       " 357 2014-12-24    4.33\n",
       " 358 2014-12-25    4.00\n",
       " 359 2014-12-26    4.33\n",
       " 360 2014-12-27    3.33\n",
       " 361 2014-12-28    4.00\n",
       " 362 2014-12-29    5.00\n",
       " 363 2014-12-30    4.33\n",
       " 364 2014-12-31    3.33\n",
       " 365 2015-01-01    2.67\n",
       " 366 2015-01-02    4.00\n",
       " 367 2015-01-03    5.00\n",
       " 368 2015-01-04    4.67\n",
       " 369 2015-01-05    5.33\n",
       " 370 2015-01-06    3.67\n",
       " 371 2015-01-07    6.67\n",
       " 372 2015-01-08    4.33\n",
       " 373 2015-01-09    2.67\n",
       " 374 2015-01-10    3.33,\n",
       "           date  max_kp\n",
       " 355 2014-12-22    5.00\n",
       " 356 2014-12-23    4.33\n",
       " 357 2014-12-24    4.33\n",
       " 358 2014-12-25    4.00\n",
       " 359 2014-12-26    4.33\n",
       " 360 2014-12-27    3.33\n",
       " 361 2014-12-28    4.00\n",
       " 362 2014-12-29    5.00\n",
       " 363 2014-12-30    4.33\n",
       " 364 2014-12-31    3.33\n",
       " 365 2015-01-01    2.67\n",
       " 366 2015-01-02    4.00\n",
       " 367 2015-01-03    5.00\n",
       " 368 2015-01-04    4.67\n",
       " 369 2015-01-05    5.33\n",
       " 370 2015-01-06    3.67\n",
       " 371 2015-01-07    6.67\n",
       " 372 2015-01-08    4.33\n",
       " 373 2015-01-09    2.67\n",
       " 374 2015-01-10    3.33\n",
       " 375 2015-01-11    3.33\n",
       " 376 2015-01-12    3.00\n",
       " 377 2015-01-13    2.67\n",
       " 378 2015-01-14    3.00\n",
       " 379 2015-01-15    3.00\n",
       " 380 2015-01-16    3.00\n",
       " 381 2015-01-17    3.00,\n",
       "           date  max_kp\n",
       " 362 2014-12-29    5.00\n",
       " 363 2014-12-30    4.33\n",
       " 364 2014-12-31    3.33\n",
       " 365 2015-01-01    2.67\n",
       " 366 2015-01-02    4.00\n",
       " 367 2015-01-03    5.00\n",
       " 368 2015-01-04    4.67\n",
       " 369 2015-01-05    5.33\n",
       " 370 2015-01-06    3.67\n",
       " 371 2015-01-07    6.67\n",
       " 372 2015-01-08    4.33\n",
       " 373 2015-01-09    2.67\n",
       " 374 2015-01-10    3.33\n",
       " 375 2015-01-11    3.33\n",
       " 376 2015-01-12    3.00\n",
       " 377 2015-01-13    2.67\n",
       " 378 2015-01-14    3.00\n",
       " 379 2015-01-15    3.00\n",
       " 380 2015-01-16    3.00\n",
       " 381 2015-01-17    3.00\n",
       " 382 2015-01-18    2.33\n",
       " 383 2015-01-19    2.67\n",
       " 384 2015-01-20    1.67\n",
       " 385 2015-01-21    3.67\n",
       " 386 2015-01-22    3.67\n",
       " 387 2015-01-23    3.00\n",
       " 388 2015-01-24    2.33]"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates =[]\n",
    "\n",
    "for i in range(0,num_pdfs*7,7):\n",
    "    r = obs.iloc[5 + (i):5 +(i) +27,0:2]\n",
    "    dates.append(r)\n",
    "dates\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "844ef2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[    max_kp\n",
       " 5     1.67\n",
       " 6     3.33\n",
       " 7     3.33\n",
       " 8     3.33\n",
       " 9     2.00\n",
       " 10    2.67\n",
       " 11    4.00\n",
       " 12    3.00\n",
       " 13    3.67\n",
       " 14    2.00\n",
       " 15    1.33\n",
       " 16    1.67\n",
       " 17    1.33\n",
       " 18    0.67\n",
       " 19    1.33\n",
       " 20    3.00\n",
       " 21    3.00\n",
       " 22    2.67\n",
       " 23    3.00\n",
       " 24    3.00\n",
       " 25    2.67\n",
       " 26    1.33\n",
       " 27    2.33\n",
       " 28    2.33\n",
       " 29    2.67\n",
       " 30    1.00\n",
       " 31    2.67,\n",
       "     max_kp\n",
       " 12    3.00\n",
       " 13    3.67\n",
       " 14    2.00\n",
       " 15    1.33\n",
       " 16    1.67\n",
       " 17    1.33\n",
       " 18    0.67\n",
       " 19    1.33\n",
       " 20    3.00\n",
       " 21    3.00\n",
       " 22    2.67\n",
       " 23    3.00\n",
       " 24    3.00\n",
       " 25    2.67\n",
       " 26    1.33\n",
       " 27    2.33\n",
       " 28    2.33\n",
       " 29    2.67\n",
       " 30    1.00\n",
       " 31    2.67\n",
       " 32    2.00\n",
       " 33    2.33\n",
       " 34    2.00\n",
       " 35    2.00\n",
       " 36    3.00\n",
       " 37    4.00\n",
       " 38    4.67,\n",
       "     max_kp\n",
       " 19    1.33\n",
       " 20    3.00\n",
       " 21    3.00\n",
       " 22    2.67\n",
       " 23    3.00\n",
       " 24    3.00\n",
       " 25    2.67\n",
       " 26    1.33\n",
       " 27    2.33\n",
       " 28    2.33\n",
       " 29    2.67\n",
       " 30    1.00\n",
       " 31    2.67\n",
       " 32    2.00\n",
       " 33    2.33\n",
       " 34    2.00\n",
       " 35    2.00\n",
       " 36    3.00\n",
       " 37    4.00\n",
       " 38    4.67\n",
       " 39    4.33\n",
       " 40    4.00\n",
       " 41    3.00\n",
       " 42    3.33\n",
       " 43    1.00\n",
       " 44    1.67\n",
       " 45    4.67,\n",
       "     max_kp\n",
       " 26    1.33\n",
       " 27    2.33\n",
       " 28    2.33\n",
       " 29    2.67\n",
       " 30    1.00\n",
       " 31    2.67\n",
       " 32    2.00\n",
       " 33    2.33\n",
       " 34    2.00\n",
       " 35    2.00\n",
       " 36    3.00\n",
       " 37    4.00\n",
       " 38    4.67\n",
       " 39    4.33\n",
       " 40    4.00\n",
       " 41    3.00\n",
       " 42    3.33\n",
       " 43    1.00\n",
       " 44    1.67\n",
       " 45    4.67\n",
       " 46    5.33\n",
       " 47    2.67\n",
       " 48    3.00\n",
       " 49    6.33\n",
       " 50    6.00\n",
       " 51    3.33\n",
       " 52    4.33,\n",
       "     max_kp\n",
       " 33    2.33\n",
       " 34    2.00\n",
       " 35    2.00\n",
       " 36    3.00\n",
       " 37    4.00\n",
       " 38    4.67\n",
       " 39    4.33\n",
       " 40    4.00\n",
       " 41    3.00\n",
       " 42    3.33\n",
       " 43    1.00\n",
       " 44    1.67\n",
       " 45    4.67\n",
       " 46    5.33\n",
       " 47    2.67\n",
       " 48    3.00\n",
       " 49    6.33\n",
       " 50    6.00\n",
       " 51    3.33\n",
       " 52    4.33\n",
       " 53    4.00\n",
       " 54    2.67\n",
       " 55    2.00\n",
       " 56    2.33\n",
       " 57    6.00\n",
       " 58    4.00\n",
       " 59    2.67,\n",
       "     max_kp\n",
       " 40    4.00\n",
       " 41    3.00\n",
       " 42    3.33\n",
       " 43    1.00\n",
       " 44    1.67\n",
       " 45    4.67\n",
       " 46    5.33\n",
       " 47    2.67\n",
       " 48    3.00\n",
       " 49    6.33\n",
       " 50    6.00\n",
       " 51    3.33\n",
       " 52    4.33\n",
       " 53    4.00\n",
       " 54    2.67\n",
       " 55    2.00\n",
       " 56    2.33\n",
       " 57    6.00\n",
       " 58    4.00\n",
       " 59    2.67\n",
       " 60    3.33\n",
       " 61    3.00\n",
       " 62    2.67\n",
       " 63    2.67\n",
       " 64    2.67\n",
       " 65    1.33\n",
       " 66    1.33,\n",
       "     max_kp\n",
       " 47    2.67\n",
       " 48    3.00\n",
       " 49    6.33\n",
       " 50    6.00\n",
       " 51    3.33\n",
       " 52    4.33\n",
       " 53    4.00\n",
       " 54    2.67\n",
       " 55    2.00\n",
       " 56    2.33\n",
       " 57    6.00\n",
       " 58    4.00\n",
       " 59    2.67\n",
       " 60    3.33\n",
       " 61    3.00\n",
       " 62    2.67\n",
       " 63    2.67\n",
       " 64    2.67\n",
       " 65    1.33\n",
       " 66    1.33\n",
       " 67    1.33\n",
       " 68    3.00\n",
       " 69    1.67\n",
       " 70    2.67\n",
       " 71    4.67\n",
       " 72    2.00\n",
       " 73    2.33,\n",
       "     max_kp\n",
       " 54    2.67\n",
       " 55    2.00\n",
       " 56    2.33\n",
       " 57    6.00\n",
       " 58    4.00\n",
       " 59    2.67\n",
       " 60    3.33\n",
       " 61    3.00\n",
       " 62    2.67\n",
       " 63    2.67\n",
       " 64    2.67\n",
       " 65    1.33\n",
       " 66    1.33\n",
       " 67    1.33\n",
       " 68    3.00\n",
       " 69    1.67\n",
       " 70    2.67\n",
       " 71    4.67\n",
       " 72    2.00\n",
       " 73    2.33\n",
       " 74    1.00\n",
       " 75    1.33\n",
       " 76    2.33\n",
       " 77    2.00\n",
       " 78    2.33\n",
       " 79    3.00\n",
       " 80    2.33,\n",
       "     max_kp\n",
       " 61    3.00\n",
       " 62    2.67\n",
       " 63    2.67\n",
       " 64    2.67\n",
       " 65    1.33\n",
       " 66    1.33\n",
       " 67    1.33\n",
       " 68    3.00\n",
       " 69    1.67\n",
       " 70    2.67\n",
       " 71    4.67\n",
       " 72    2.00\n",
       " 73    2.33\n",
       " 74    1.00\n",
       " 75    1.33\n",
       " 76    2.33\n",
       " 77    2.00\n",
       " 78    2.33\n",
       " 79    3.00\n",
       " 80    2.33\n",
       " 81    2.00\n",
       " 82    2.00\n",
       " 83    3.33\n",
       " 84    3.67\n",
       " 85    2.67\n",
       " 86    3.00\n",
       " 87    3.33,\n",
       "     max_kp\n",
       " 68    3.00\n",
       " 69    1.67\n",
       " 70    2.67\n",
       " 71    4.67\n",
       " 72    2.00\n",
       " 73    2.33\n",
       " 74    1.00\n",
       " 75    1.33\n",
       " 76    2.33\n",
       " 77    2.00\n",
       " 78    2.33\n",
       " 79    3.00\n",
       " 80    2.33\n",
       " 81    2.00\n",
       " 82    2.00\n",
       " 83    3.33\n",
       " 84    3.67\n",
       " 85    2.67\n",
       " 86    3.00\n",
       " 87    3.33\n",
       " 88    2.33\n",
       " 89    3.00\n",
       " 90    2.67\n",
       " 91    2.00\n",
       " 92    2.33\n",
       " 93    2.00\n",
       " 94    4.00,\n",
       "      max_kp\n",
       " 75     1.33\n",
       " 76     2.33\n",
       " 77     2.00\n",
       " 78     2.33\n",
       " 79     3.00\n",
       " 80     2.33\n",
       " 81     2.00\n",
       " 82     2.00\n",
       " 83     3.33\n",
       " 84     3.67\n",
       " 85     2.67\n",
       " 86     3.00\n",
       " 87     3.33\n",
       " 88     2.33\n",
       " 89     3.00\n",
       " 90     2.67\n",
       " 91     2.00\n",
       " 92     2.33\n",
       " 93     2.00\n",
       " 94     4.00\n",
       " 95     2.67\n",
       " 96     4.33\n",
       " 97     2.33\n",
       " 98     2.00\n",
       " 99     1.33\n",
       " 100    3.33\n",
       " 101    5.00,\n",
       "      max_kp\n",
       " 82     2.00\n",
       " 83     3.33\n",
       " 84     3.67\n",
       " 85     2.67\n",
       " 86     3.00\n",
       " 87     3.33\n",
       " 88     2.33\n",
       " 89     3.00\n",
       " 90     2.67\n",
       " 91     2.00\n",
       " 92     2.33\n",
       " 93     2.00\n",
       " 94     4.00\n",
       " 95     2.67\n",
       " 96     4.33\n",
       " 97     2.33\n",
       " 98     2.00\n",
       " 99     1.33\n",
       " 100    3.33\n",
       " 101    5.00\n",
       " 102    4.33\n",
       " 103    3.00\n",
       " 104    2.67\n",
       " 105    2.33\n",
       " 106    3.33\n",
       " 107    3.33\n",
       " 108    3.67,\n",
       "      max_kp\n",
       " 89     3.00\n",
       " 90     2.67\n",
       " 91     2.00\n",
       " 92     2.33\n",
       " 93     2.00\n",
       " 94     4.00\n",
       " 95     2.67\n",
       " 96     4.33\n",
       " 97     2.33\n",
       " 98     2.00\n",
       " 99     1.33\n",
       " 100    3.33\n",
       " 101    5.00\n",
       " 102    4.33\n",
       " 103    3.00\n",
       " 104    2.67\n",
       " 105    2.33\n",
       " 106    3.33\n",
       " 107    3.33\n",
       " 108    3.67\n",
       " 109    5.00\n",
       " 110    4.33\n",
       " 111    2.00\n",
       " 112    2.33\n",
       " 113    3.33\n",
       " 114    3.33\n",
       " 115    2.67,\n",
       "      max_kp\n",
       " 96     4.33\n",
       " 97     2.33\n",
       " 98     2.00\n",
       " 99     1.33\n",
       " 100    3.33\n",
       " 101    5.00\n",
       " 102    4.33\n",
       " 103    3.00\n",
       " 104    2.67\n",
       " 105    2.33\n",
       " 106    3.33\n",
       " 107    3.33\n",
       " 108    3.67\n",
       " 109    5.00\n",
       " 110    4.33\n",
       " 111    2.00\n",
       " 112    2.33\n",
       " 113    3.33\n",
       " 114    3.33\n",
       " 115    2.67\n",
       " 116    1.67\n",
       " 117    2.67\n",
       " 118    3.67\n",
       " 119    4.00\n",
       " 120    3.00\n",
       " 121    2.33\n",
       " 122    3.33,\n",
       "      max_kp\n",
       " 103    3.00\n",
       " 104    2.67\n",
       " 105    2.33\n",
       " 106    3.33\n",
       " 107    3.33\n",
       " 108    3.67\n",
       " 109    5.00\n",
       " 110    4.33\n",
       " 111    2.00\n",
       " 112    2.33\n",
       " 113    3.33\n",
       " 114    3.33\n",
       " 115    2.67\n",
       " 116    1.67\n",
       " 117    2.67\n",
       " 118    3.67\n",
       " 119    4.00\n",
       " 120    3.00\n",
       " 121    2.33\n",
       " 122    3.33\n",
       " 123    4.33\n",
       " 124    3.33\n",
       " 125    1.33\n",
       " 126    2.33\n",
       " 127    4.33\n",
       " 128    3.33\n",
       " 129    3.33,\n",
       "      max_kp\n",
       " 110    4.33\n",
       " 111    2.00\n",
       " 112    2.33\n",
       " 113    3.33\n",
       " 114    3.33\n",
       " 115    2.67\n",
       " 116    1.67\n",
       " 117    2.67\n",
       " 118    3.67\n",
       " 119    4.00\n",
       " 120    3.00\n",
       " 121    2.33\n",
       " 122    3.33\n",
       " 123    4.33\n",
       " 124    3.33\n",
       " 125    1.33\n",
       " 126    2.33\n",
       " 127    4.33\n",
       " 128    3.33\n",
       " 129    3.33\n",
       " 130    4.00\n",
       " 131    2.67\n",
       " 132    2.33\n",
       " 133    2.33\n",
       " 134    2.00\n",
       " 135    2.00\n",
       " 136    1.33,\n",
       "      max_kp\n",
       " 117    2.67\n",
       " 118    3.67\n",
       " 119    4.00\n",
       " 120    3.00\n",
       " 121    2.33\n",
       " 122    3.33\n",
       " 123    4.33\n",
       " 124    3.33\n",
       " 125    1.33\n",
       " 126    2.33\n",
       " 127    4.33\n",
       " 128    3.33\n",
       " 129    3.33\n",
       " 130    4.00\n",
       " 131    2.67\n",
       " 132    2.33\n",
       " 133    2.33\n",
       " 134    2.00\n",
       " 135    2.00\n",
       " 136    1.33\n",
       " 137    2.00\n",
       " 138    1.67\n",
       " 139    1.67\n",
       " 140    1.00\n",
       " 141    3.33\n",
       " 142    5.33\n",
       " 143    2.33,\n",
       "      max_kp\n",
       " 124    3.33\n",
       " 125    1.33\n",
       " 126    2.33\n",
       " 127    4.33\n",
       " 128    3.33\n",
       " 129    3.33\n",
       " 130    4.00\n",
       " 131    2.67\n",
       " 132    2.33\n",
       " 133    2.33\n",
       " 134    2.00\n",
       " 135    2.00\n",
       " 136    1.33\n",
       " 137    2.00\n",
       " 138    1.67\n",
       " 139    1.67\n",
       " 140    1.00\n",
       " 141    3.33\n",
       " 142    5.33\n",
       " 143    2.33\n",
       " 144    2.00\n",
       " 145    1.67\n",
       " 146    1.67\n",
       " 147    1.67\n",
       " 148    2.67\n",
       " 149    4.33\n",
       " 150    1.67,\n",
       "      max_kp\n",
       " 131    2.67\n",
       " 132    2.33\n",
       " 133    2.33\n",
       " 134    2.00\n",
       " 135    2.00\n",
       " 136    1.33\n",
       " 137    2.00\n",
       " 138    1.67\n",
       " 139    1.67\n",
       " 140    1.00\n",
       " 141    3.33\n",
       " 142    5.33\n",
       " 143    2.33\n",
       " 144    2.00\n",
       " 145    1.67\n",
       " 146    1.67\n",
       " 147    1.67\n",
       " 148    2.67\n",
       " 149    4.33\n",
       " 150    1.67\n",
       " 151    2.00\n",
       " 152    2.67\n",
       " 153    3.33\n",
       " 154    1.67\n",
       " 155    3.00\n",
       " 156    2.33\n",
       " 157    4.00,\n",
       "      max_kp\n",
       " 138    1.67\n",
       " 139    1.67\n",
       " 140    1.00\n",
       " 141    3.33\n",
       " 142    5.33\n",
       " 143    2.33\n",
       " 144    2.00\n",
       " 145    1.67\n",
       " 146    1.67\n",
       " 147    1.67\n",
       " 148    2.67\n",
       " 149    4.33\n",
       " 150    1.67\n",
       " 151    2.00\n",
       " 152    2.67\n",
       " 153    3.33\n",
       " 154    1.67\n",
       " 155    3.00\n",
       " 156    2.33\n",
       " 157    4.00\n",
       " 158    6.33\n",
       " 159    2.33\n",
       " 160    2.33\n",
       " 161    2.67\n",
       " 162    2.00\n",
       " 163    2.00\n",
       " 164    3.00,\n",
       "      max_kp\n",
       " 145    1.67\n",
       " 146    1.67\n",
       " 147    1.67\n",
       " 148    2.67\n",
       " 149    4.33\n",
       " 150    1.67\n",
       " 151    2.00\n",
       " 152    2.67\n",
       " 153    3.33\n",
       " 154    1.67\n",
       " 155    3.00\n",
       " 156    2.33\n",
       " 157    4.00\n",
       " 158    6.33\n",
       " 159    2.33\n",
       " 160    2.33\n",
       " 161    2.67\n",
       " 162    2.00\n",
       " 163    2.00\n",
       " 164    3.00\n",
       " 165    2.33\n",
       " 166    2.33\n",
       " 167    3.00\n",
       " 168    5.00\n",
       " 169    3.00\n",
       " 170    2.67\n",
       " 171    2.33,\n",
       "      max_kp\n",
       " 152    2.67\n",
       " 153    3.33\n",
       " 154    1.67\n",
       " 155    3.00\n",
       " 156    2.33\n",
       " 157    4.00\n",
       " 158    6.33\n",
       " 159    2.33\n",
       " 160    2.33\n",
       " 161    2.67\n",
       " 162    2.00\n",
       " 163    2.00\n",
       " 164    3.00\n",
       " 165    2.33\n",
       " 166    2.33\n",
       " 167    3.00\n",
       " 168    5.00\n",
       " 169    3.00\n",
       " 170    2.67\n",
       " 171    2.33\n",
       " 172    1.67\n",
       " 173    3.33\n",
       " 174    2.67\n",
       " 175    2.33\n",
       " 176    2.33\n",
       " 177    1.67\n",
       " 178    2.67,\n",
       "      max_kp\n",
       " 159    2.33\n",
       " 160    2.33\n",
       " 161    2.67\n",
       " 162    2.00\n",
       " 163    2.00\n",
       " 164    3.00\n",
       " 165    2.33\n",
       " 166    2.33\n",
       " 167    3.00\n",
       " 168    5.00\n",
       " 169    3.00\n",
       " 170    2.67\n",
       " 171    2.33\n",
       " 172    1.67\n",
       " 173    3.33\n",
       " 174    2.67\n",
       " 175    2.33\n",
       " 176    2.33\n",
       " 177    1.67\n",
       " 178    2.67\n",
       " 179    3.33\n",
       " 180    3.00\n",
       " 181    2.33\n",
       " 182    1.67\n",
       " 183    2.67\n",
       " 184    2.00\n",
       " 185    1.33,\n",
       "      max_kp\n",
       " 166    2.33\n",
       " 167    3.00\n",
       " 168    5.00\n",
       " 169    3.00\n",
       " 170    2.67\n",
       " 171    2.33\n",
       " 172    1.67\n",
       " 173    3.33\n",
       " 174    2.67\n",
       " 175    2.33\n",
       " 176    2.33\n",
       " 177    1.67\n",
       " 178    2.67\n",
       " 179    3.33\n",
       " 180    3.00\n",
       " 181    2.33\n",
       " 182    1.67\n",
       " 183    2.67\n",
       " 184    2.00\n",
       " 185    1.33\n",
       " 186    2.00\n",
       " 187    2.33\n",
       " 188    2.33\n",
       " 189    3.00\n",
       " 190    3.00\n",
       " 191    2.00\n",
       " 192    2.33,\n",
       "      max_kp\n",
       " 173    3.33\n",
       " 174    2.67\n",
       " 175    2.33\n",
       " 176    2.33\n",
       " 177    1.67\n",
       " 178    2.67\n",
       " 179    3.33\n",
       " 180    3.00\n",
       " 181    2.33\n",
       " 182    1.67\n",
       " 183    2.67\n",
       " 184    2.00\n",
       " 185    1.33\n",
       " 186    2.00\n",
       " 187    2.33\n",
       " 188    2.33\n",
       " 189    3.00\n",
       " 190    3.00\n",
       " 191    2.00\n",
       " 192    2.33\n",
       " 193    2.00\n",
       " 194    3.00\n",
       " 195    2.33\n",
       " 196    1.67\n",
       " 197    1.67\n",
       " 198    1.00\n",
       " 199    1.00,\n",
       "      max_kp\n",
       " 180    3.00\n",
       " 181    2.33\n",
       " 182    1.67\n",
       " 183    2.67\n",
       " 184    2.00\n",
       " 185    1.33\n",
       " 186    2.00\n",
       " 187    2.33\n",
       " 188    2.33\n",
       " 189    3.00\n",
       " 190    3.00\n",
       " 191    2.00\n",
       " 192    2.33\n",
       " 193    2.00\n",
       " 194    3.00\n",
       " 195    2.33\n",
       " 196    1.67\n",
       " 197    1.67\n",
       " 198    1.00\n",
       " 199    1.00\n",
       " 200    1.33\n",
       " 201    2.00\n",
       " 202    2.33\n",
       " 203    2.67\n",
       " 204    2.33\n",
       " 205    3.00\n",
       " 206    2.33,\n",
       "      max_kp\n",
       " 187    2.33\n",
       " 188    2.33\n",
       " 189    3.00\n",
       " 190    3.00\n",
       " 191    2.00\n",
       " 192    2.33\n",
       " 193    2.00\n",
       " 194    3.00\n",
       " 195    2.33\n",
       " 196    1.67\n",
       " 197    1.67\n",
       " 198    1.00\n",
       " 199    1.00\n",
       " 200    1.33\n",
       " 201    2.00\n",
       " 202    2.33\n",
       " 203    2.67\n",
       " 204    2.33\n",
       " 205    3.00\n",
       " 206    2.33\n",
       " 207    2.00\n",
       " 208    3.33\n",
       " 209    1.33\n",
       " 210    2.67\n",
       " 211    2.00\n",
       " 212    3.67\n",
       " 213    3.67,\n",
       "      max_kp\n",
       " 194    3.00\n",
       " 195    2.33\n",
       " 196    1.67\n",
       " 197    1.67\n",
       " 198    1.00\n",
       " 199    1.00\n",
       " 200    1.33\n",
       " 201    2.00\n",
       " 202    2.33\n",
       " 203    2.67\n",
       " 204    2.33\n",
       " 205    3.00\n",
       " 206    2.33\n",
       " 207    2.00\n",
       " 208    3.33\n",
       " 209    1.33\n",
       " 210    2.67\n",
       " 211    2.00\n",
       " 212    3.67\n",
       " 213    3.67\n",
       " 214    3.33\n",
       " 215    3.00\n",
       " 216    3.00\n",
       " 217    2.67\n",
       " 218    1.67\n",
       " 219    3.00\n",
       " 220    2.00,\n",
       "      max_kp\n",
       " 201    2.00\n",
       " 202    2.33\n",
       " 203    2.67\n",
       " 204    2.33\n",
       " 205    3.00\n",
       " 206    2.33\n",
       " 207    2.00\n",
       " 208    3.33\n",
       " 209    1.33\n",
       " 210    2.67\n",
       " 211    2.00\n",
       " 212    3.67\n",
       " 213    3.67\n",
       " 214    3.33\n",
       " 215    3.00\n",
       " 216    3.00\n",
       " 217    2.67\n",
       " 218    1.67\n",
       " 219    3.00\n",
       " 220    2.00\n",
       " 221    4.00\n",
       " 222    3.33\n",
       " 223    4.00\n",
       " 224    2.33\n",
       " 225    1.67\n",
       " 226    2.33\n",
       " 227    1.00,\n",
       "      max_kp\n",
       " 208    3.33\n",
       " 209    1.33\n",
       " 210    2.67\n",
       " 211    2.00\n",
       " 212    3.67\n",
       " 213    3.67\n",
       " 214    3.33\n",
       " 215    3.00\n",
       " 216    3.00\n",
       " 217    2.67\n",
       " 218    1.67\n",
       " 219    3.00\n",
       " 220    2.00\n",
       " 221    4.00\n",
       " 222    3.33\n",
       " 223    4.00\n",
       " 224    2.33\n",
       " 225    1.67\n",
       " 226    2.33\n",
       " 227    1.00\n",
       " 228    3.00\n",
       " 229    2.00\n",
       " 230    5.67\n",
       " 231    2.33\n",
       " 232    3.00\n",
       " 233    2.67\n",
       " 234    2.33,\n",
       "      max_kp\n",
       " 215    3.00\n",
       " 216    3.00\n",
       " 217    2.67\n",
       " 218    1.67\n",
       " 219    3.00\n",
       " 220    2.00\n",
       " 221    4.00\n",
       " 222    3.33\n",
       " 223    4.00\n",
       " 224    2.33\n",
       " 225    1.67\n",
       " 226    2.33\n",
       " 227    1.00\n",
       " 228    3.00\n",
       " 229    2.00\n",
       " 230    5.67\n",
       " 231    2.33\n",
       " 232    3.00\n",
       " 233    2.67\n",
       " 234    2.33\n",
       " 235    1.67\n",
       " 236    1.00\n",
       " 237    1.67\n",
       " 238    4.33\n",
       " 239    5.00\n",
       " 240    4.67\n",
       " 241    3.67,\n",
       "      max_kp\n",
       " 222    3.33\n",
       " 223    4.00\n",
       " 224    2.33\n",
       " 225    1.67\n",
       " 226    2.33\n",
       " 227    1.00\n",
       " 228    3.00\n",
       " 229    2.00\n",
       " 230    5.67\n",
       " 231    2.33\n",
       " 232    3.00\n",
       " 233    2.67\n",
       " 234    2.33\n",
       " 235    1.67\n",
       " 236    1.00\n",
       " 237    1.67\n",
       " 238    4.33\n",
       " 239    5.00\n",
       " 240    4.67\n",
       " 241    3.67\n",
       " 242    4.00\n",
       " 243    2.67\n",
       " 244    3.00\n",
       " 245    3.33\n",
       " 246    2.67\n",
       " 247    2.33\n",
       " 248    3.00,\n",
       "      max_kp\n",
       " 229    2.00\n",
       " 230    5.67\n",
       " 231    2.33\n",
       " 232    3.00\n",
       " 233    2.67\n",
       " 234    2.33\n",
       " 235    1.67\n",
       " 236    1.00\n",
       " 237    1.67\n",
       " 238    4.33\n",
       " 239    5.00\n",
       " 240    4.67\n",
       " 241    3.67\n",
       " 242    4.00\n",
       " 243    2.67\n",
       " 244    3.00\n",
       " 245    3.33\n",
       " 246    2.67\n",
       " 247    2.33\n",
       " 248    3.00\n",
       " 249    3.33\n",
       " 250    2.33\n",
       " 251    2.67\n",
       " 252    3.33\n",
       " 253    3.67\n",
       " 254    6.67\n",
       " 255    4.33,\n",
       "      max_kp\n",
       " 236    1.00\n",
       " 237    1.67\n",
       " 238    4.33\n",
       " 239    5.00\n",
       " 240    4.67\n",
       " 241    3.67\n",
       " 242    4.00\n",
       " 243    2.67\n",
       " 244    3.00\n",
       " 245    3.33\n",
       " 246    2.67\n",
       " 247    2.33\n",
       " 248    3.00\n",
       " 249    3.33\n",
       " 250    2.33\n",
       " 251    2.67\n",
       " 252    3.33\n",
       " 253    3.67\n",
       " 254    6.67\n",
       " 255    4.33\n",
       " 256    1.33\n",
       " 257    1.00\n",
       " 258    2.33\n",
       " 259    2.67\n",
       " 260    3.33\n",
       " 261    5.00\n",
       " 262    2.33,\n",
       "      max_kp\n",
       " 243    2.67\n",
       " 244    3.00\n",
       " 245    3.33\n",
       " 246    2.67\n",
       " 247    2.33\n",
       " 248    3.00\n",
       " 249    3.33\n",
       " 250    2.33\n",
       " 251    2.67\n",
       " 252    3.33\n",
       " 253    3.67\n",
       " 254    6.67\n",
       " 255    4.33\n",
       " 256    1.33\n",
       " 257    1.00\n",
       " 258    2.33\n",
       " 259    2.67\n",
       " 260    3.33\n",
       " 261    5.00\n",
       " 262    2.33\n",
       " 263    3.33\n",
       " 264    3.00\n",
       " 265    3.00\n",
       " 266    4.33\n",
       " 267    4.00\n",
       " 268    3.33\n",
       " 269    4.33,\n",
       "      max_kp\n",
       " 250    2.33\n",
       " 251    2.67\n",
       " 252    3.33\n",
       " 253    3.67\n",
       " 254    6.67\n",
       " 255    4.33\n",
       " 256    1.33\n",
       " 257    1.00\n",
       " 258    2.33\n",
       " 259    2.67\n",
       " 260    3.33\n",
       " 261    5.00\n",
       " 262    2.33\n",
       " 263    3.33\n",
       " 264    3.00\n",
       " 265    3.00\n",
       " 266    4.33\n",
       " 267    4.00\n",
       " 268    3.33\n",
       " 269    4.33\n",
       " 270    2.67\n",
       " 271    3.67\n",
       " 272    4.00\n",
       " 273    3.67\n",
       " 274    2.67\n",
       " 275    2.33\n",
       " 276    2.33,\n",
       "      max_kp\n",
       " 257    1.00\n",
       " 258    2.33\n",
       " 259    2.67\n",
       " 260    3.33\n",
       " 261    5.00\n",
       " 262    2.33\n",
       " 263    3.33\n",
       " 264    3.00\n",
       " 265    3.00\n",
       " 266    4.33\n",
       " 267    4.00\n",
       " 268    3.33\n",
       " 269    4.33\n",
       " 270    2.67\n",
       " 271    3.67\n",
       " 272    4.00\n",
       " 273    3.67\n",
       " 274    2.67\n",
       " 275    2.33\n",
       " 276    2.33\n",
       " 277    2.00\n",
       " 278    2.67\n",
       " 279    3.33\n",
       " 280    3.00\n",
       " 281    4.00\n",
       " 282    3.33\n",
       " 283    3.00,\n",
       "      max_kp\n",
       " 264    3.00\n",
       " 265    3.00\n",
       " 266    4.33\n",
       " 267    4.00\n",
       " 268    3.33\n",
       " 269    4.33\n",
       " 270    2.67\n",
       " 271    3.67\n",
       " 272    4.00\n",
       " 273    3.67\n",
       " 274    2.67\n",
       " 275    2.33\n",
       " 276    2.33\n",
       " 277    2.00\n",
       " 278    2.67\n",
       " 279    3.33\n",
       " 280    3.00\n",
       " 281    4.00\n",
       " 282    3.33\n",
       " 283    3.00\n",
       " 284    2.00\n",
       " 285    2.67\n",
       " 286    4.67\n",
       " 287    4.67\n",
       " 288    3.33\n",
       " 289    3.00\n",
       " 290    3.67,\n",
       "      max_kp\n",
       " 271    3.67\n",
       " 272    4.00\n",
       " 273    3.67\n",
       " 274    2.67\n",
       " 275    2.33\n",
       " 276    2.33\n",
       " 277    2.00\n",
       " 278    2.67\n",
       " 279    3.33\n",
       " 280    3.00\n",
       " 281    4.00\n",
       " 282    3.33\n",
       " 283    3.00\n",
       " 284    2.00\n",
       " 285    2.67\n",
       " 286    4.67\n",
       " 287    4.67\n",
       " 288    3.33\n",
       " 289    3.00\n",
       " 290    3.67\n",
       " 291    3.33\n",
       " 292    5.00\n",
       " 293    3.67\n",
       " 294    3.67\n",
       " 295    3.67\n",
       " 296    3.67\n",
       " 297    3.33,\n",
       "      max_kp\n",
       " 278    2.67\n",
       " 279    3.33\n",
       " 280    3.00\n",
       " 281    4.00\n",
       " 282    3.33\n",
       " 283    3.00\n",
       " 284    2.00\n",
       " 285    2.67\n",
       " 286    4.67\n",
       " 287    4.67\n",
       " 288    3.33\n",
       " 289    3.00\n",
       " 290    3.67\n",
       " 291    3.33\n",
       " 292    5.00\n",
       " 293    3.67\n",
       " 294    3.67\n",
       " 295    3.67\n",
       " 296    3.67\n",
       " 297    3.33\n",
       " 298    3.33\n",
       " 299    4.33\n",
       " 300    4.33\n",
       " 301    3.67\n",
       " 302    2.00\n",
       " 303    2.33\n",
       " 304    2.67,\n",
       "      max_kp\n",
       " 285    2.67\n",
       " 286    4.67\n",
       " 287    4.67\n",
       " 288    3.33\n",
       " 289    3.00\n",
       " 290    3.67\n",
       " 291    3.33\n",
       " 292    5.00\n",
       " 293    3.67\n",
       " 294    3.67\n",
       " 295    3.67\n",
       " 296    3.67\n",
       " 297    3.33\n",
       " 298    3.33\n",
       " 299    4.33\n",
       " 300    4.33\n",
       " 301    3.67\n",
       " 302    2.00\n",
       " 303    2.33\n",
       " 304    2.67\n",
       " 305    3.33\n",
       " 306    2.67\n",
       " 307    4.33\n",
       " 308    3.67\n",
       " 309    3.33\n",
       " 310    3.33\n",
       " 311    3.33,\n",
       "      max_kp\n",
       " 292    5.00\n",
       " 293    3.67\n",
       " 294    3.67\n",
       " 295    3.67\n",
       " 296    3.67\n",
       " 297    3.33\n",
       " 298    3.33\n",
       " 299    4.33\n",
       " 300    4.33\n",
       " 301    3.67\n",
       " 302    2.00\n",
       " 303    2.33\n",
       " 304    2.67\n",
       " 305    3.33\n",
       " 306    2.67\n",
       " 307    4.33\n",
       " 308    3.67\n",
       " 309    3.33\n",
       " 310    3.33\n",
       " 311    3.33\n",
       " 312    3.33\n",
       " 313    4.67\n",
       " 314    4.67\n",
       " 315    3.67\n",
       " 316    2.00\n",
       " 317    3.00\n",
       " 318    4.33,\n",
       "      max_kp\n",
       " 299    4.33\n",
       " 300    4.33\n",
       " 301    3.67\n",
       " 302    2.00\n",
       " 303    2.33\n",
       " 304    2.67\n",
       " 305    3.33\n",
       " 306    2.67\n",
       " 307    4.33\n",
       " 308    3.67\n",
       " 309    3.33\n",
       " 310    3.33\n",
       " 311    3.33\n",
       " 312    3.33\n",
       " 313    4.67\n",
       " 314    4.67\n",
       " 315    3.67\n",
       " 316    2.00\n",
       " 317    3.00\n",
       " 318    4.33\n",
       " 319    4.33\n",
       " 320    3.33\n",
       " 321    3.33\n",
       " 322    3.67\n",
       " 323    3.67\n",
       " 324    4.00\n",
       " 325    3.67,\n",
       "      max_kp\n",
       " 306    2.67\n",
       " 307    4.33\n",
       " 308    3.67\n",
       " 309    3.33\n",
       " 310    3.33\n",
       " 311    3.33\n",
       " 312    3.33\n",
       " 313    4.67\n",
       " 314    4.67\n",
       " 315    3.67\n",
       " 316    2.00\n",
       " 317    3.00\n",
       " 318    4.33\n",
       " 319    4.33\n",
       " 320    3.33\n",
       " 321    3.33\n",
       " 322    3.67\n",
       " 323    3.67\n",
       " 324    4.00\n",
       " 325    3.67\n",
       " 326    3.00\n",
       " 327    2.67\n",
       " 328    2.00\n",
       " 329    2.00\n",
       " 330    3.00\n",
       " 331    2.67\n",
       " 332    1.67,\n",
       "      max_kp\n",
       " 313    4.67\n",
       " 314    4.67\n",
       " 315    3.67\n",
       " 316    2.00\n",
       " 317    3.00\n",
       " 318    4.33\n",
       " 319    4.33\n",
       " 320    3.33\n",
       " 321    3.33\n",
       " 322    3.67\n",
       " 323    3.67\n",
       " 324    4.00\n",
       " 325    3.67\n",
       " 326    3.00\n",
       " 327    2.67\n",
       " 328    2.00\n",
       " 329    2.00\n",
       " 330    3.00\n",
       " 331    2.67\n",
       " 332    1.67\n",
       " 333    3.00\n",
       " 334    4.00\n",
       " 335    3.33\n",
       " 336    3.00\n",
       " 337    4.00\n",
       " 338    3.33\n",
       " 339    4.00,\n",
       "      max_kp\n",
       " 320    3.33\n",
       " 321    3.33\n",
       " 322    3.67\n",
       " 323    3.67\n",
       " 324    4.00\n",
       " 325    3.67\n",
       " 326    3.00\n",
       " 327    2.67\n",
       " 328    2.00\n",
       " 329    2.00\n",
       " 330    3.00\n",
       " 331    2.67\n",
       " 332    1.67\n",
       " 333    3.00\n",
       " 334    4.00\n",
       " 335    3.33\n",
       " 336    3.00\n",
       " 337    4.00\n",
       " 338    3.33\n",
       " 339    4.00\n",
       " 340    5.00\n",
       " 341    4.00\n",
       " 342    4.00\n",
       " 343    3.00\n",
       " 344    2.67\n",
       " 345    4.67\n",
       " 346    3.33,\n",
       "      max_kp\n",
       " 327    2.67\n",
       " 328    2.00\n",
       " 329    2.00\n",
       " 330    3.00\n",
       " 331    2.67\n",
       " 332    1.67\n",
       " 333    3.00\n",
       " 334    4.00\n",
       " 335    3.33\n",
       " 336    3.00\n",
       " 337    4.00\n",
       " 338    3.33\n",
       " 339    4.00\n",
       " 340    5.00\n",
       " 341    4.00\n",
       " 342    4.00\n",
       " 343    3.00\n",
       " 344    2.67\n",
       " 345    4.67\n",
       " 346    3.33\n",
       " 347    3.33\n",
       " 348    3.33\n",
       " 349    3.00\n",
       " 350    3.00\n",
       " 351    2.00\n",
       " 352    3.00\n",
       " 353    3.33,\n",
       "      max_kp\n",
       " 334    4.00\n",
       " 335    3.33\n",
       " 336    3.00\n",
       " 337    4.00\n",
       " 338    3.33\n",
       " 339    4.00\n",
       " 340    5.00\n",
       " 341    4.00\n",
       " 342    4.00\n",
       " 343    3.00\n",
       " 344    2.67\n",
       " 345    4.67\n",
       " 346    3.33\n",
       " 347    3.33\n",
       " 348    3.33\n",
       " 349    3.00\n",
       " 350    3.00\n",
       " 351    2.00\n",
       " 352    3.00\n",
       " 353    3.33\n",
       " 354    3.67\n",
       " 355    5.00\n",
       " 356    4.33\n",
       " 357    4.33\n",
       " 358    4.00\n",
       " 359    4.33\n",
       " 360    3.33,\n",
       "      max_kp\n",
       " 341    4.00\n",
       " 342    4.00\n",
       " 343    3.00\n",
       " 344    2.67\n",
       " 345    4.67\n",
       " 346    3.33\n",
       " 347    3.33\n",
       " 348    3.33\n",
       " 349    3.00\n",
       " 350    3.00\n",
       " 351    2.00\n",
       " 352    3.00\n",
       " 353    3.33\n",
       " 354    3.67\n",
       " 355    5.00\n",
       " 356    4.33\n",
       " 357    4.33\n",
       " 358    4.00\n",
       " 359    4.33\n",
       " 360    3.33\n",
       " 361    4.00\n",
       " 362    5.00\n",
       " 363    4.33\n",
       " 364    3.33\n",
       " 365    2.67\n",
       " 366    4.00\n",
       " 367    5.00,\n",
       "      max_kp\n",
       " 348    3.33\n",
       " 349    3.00\n",
       " 350    3.00\n",
       " 351    2.00\n",
       " 352    3.00\n",
       " 353    3.33\n",
       " 354    3.67\n",
       " 355    5.00\n",
       " 356    4.33\n",
       " 357    4.33\n",
       " 358    4.00\n",
       " 359    4.33\n",
       " 360    3.33\n",
       " 361    4.00\n",
       " 362    5.00\n",
       " 363    4.33\n",
       " 364    3.33\n",
       " 365    2.67\n",
       " 366    4.00\n",
       " 367    5.00\n",
       " 368    4.67\n",
       " 369    5.33\n",
       " 370    3.67\n",
       " 371    6.67\n",
       " 372    4.33\n",
       " 373    2.67\n",
       " 374    3.33,\n",
       "      max_kp\n",
       " 355    5.00\n",
       " 356    4.33\n",
       " 357    4.33\n",
       " 358    4.00\n",
       " 359    4.33\n",
       " 360    3.33\n",
       " 361    4.00\n",
       " 362    5.00\n",
       " 363    4.33\n",
       " 364    3.33\n",
       " 365    2.67\n",
       " 366    4.00\n",
       " 367    5.00\n",
       " 368    4.67\n",
       " 369    5.33\n",
       " 370    3.67\n",
       " 371    6.67\n",
       " 372    4.33\n",
       " 373    2.67\n",
       " 374    3.33\n",
       " 375    3.33\n",
       " 376    3.00\n",
       " 377    2.67\n",
       " 378    3.00\n",
       " 379    3.00\n",
       " 380    3.00\n",
       " 381    3.00,\n",
       "      max_kp\n",
       " 362    5.00\n",
       " 363    4.33\n",
       " 364    3.33\n",
       " 365    2.67\n",
       " 366    4.00\n",
       " 367    5.00\n",
       " 368    4.67\n",
       " 369    5.33\n",
       " 370    3.67\n",
       " 371    6.67\n",
       " 372    4.33\n",
       " 373    2.67\n",
       " 374    3.33\n",
       " 375    3.33\n",
       " 376    3.00\n",
       " 377    2.67\n",
       " 378    3.00\n",
       " 379    3.00\n",
       " 380    3.00\n",
       " 381    3.00\n",
       " 382    2.33\n",
       " 383    2.67\n",
       " 384    1.67\n",
       " 385    3.67\n",
       " 386    3.67\n",
       " 387    3.00\n",
       " 388    2.33]"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates =[] \n",
    "\n",
    "for i in range(0,num_pdfs*7,7):\n",
    "    r = obs.iloc[5 + (i):5 +(i) +27,1:2] # just the values without dates\n",
    "    dates.append(r)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "37a3e700",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This \"pandas list\" has 52 elements but we need 1404, so we will flatten the list.\n"
     ]
    }
   ],
   "source": [
    "print(f'This \"pandas list\" has {len(dates)} elements but we need {num_pdfs *27 }, so we will flatten the list.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "cde3ba07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.67],\n",
       " [3.33],\n",
       " [3.33],\n",
       " [3.33],\n",
       " [2.0],\n",
       " [2.67],\n",
       " [4.0],\n",
       " [3.0],\n",
       " [3.67],\n",
       " [2.0],\n",
       " [1.33],\n",
       " [1.67],\n",
       " [1.33],\n",
       " [0.67],\n",
       " [1.33],\n",
       " [3.0],\n",
       " [3.0],\n",
       " [2.67],\n",
       " [3.0],\n",
       " [3.0],\n",
       " [2.67],\n",
       " [1.33],\n",
       " [2.33],\n",
       " [2.33],\n",
       " [2.67],\n",
       " [1.0],\n",
       " [2.67]]"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates[0].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf561f13",
   "metadata": {},
   "source": [
    "Interating through each pandas list to convert to a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "15e4b326",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  [3.0],\n",
       "  [2.33]]]"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll=[]\n",
    "\n",
    "for l in range(num_pdfs):\n",
    "    a = dates[l].values.tolist()\n",
    "    ll.append(a)\n",
    "ll\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160b0fae",
   "metadata": {},
   "source": [
    "Flattening the list twice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "a05ab1c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [3.0],\n",
       " [3.0],\n",
       " ...]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat=[]\n",
    "\n",
    "for sublist in ll:\n",
    "    for element in sublist:\n",
    "        flat.append(element)\n",
    "flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "6814e064",
   "metadata": {},
   "outputs": [
    {
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       " 3.67,\n",
       " 6.67,\n",
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       " 1.0,\n",
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       " 5.0,\n",
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       " 2.67,\n",
       " 3.0,\n",
       " 3.33,\n",
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       " 3.0,\n",
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       " 2.67,\n",
       " 3.33,\n",
       " 3.67,\n",
       " 6.67,\n",
       " 4.33,\n",
       " 1.33,\n",
       " 1.0,\n",
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       " 2.67,\n",
       " 3.33,\n",
       " 5.0,\n",
       " 2.33,\n",
       " 3.33,\n",
       " 3.0,\n",
       " 3.0,\n",
       " 4.33,\n",
       " 4.0,\n",
       " 3.33,\n",
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       " 2.33,\n",
       " 2.67,\n",
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       " 3.67,\n",
       " 6.67,\n",
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       " 1.33,\n",
       " 1.0,\n",
       " 2.33,\n",
       " 2.67,\n",
       " 3.33,\n",
       " 5.0,\n",
       " 2.33,\n",
       " 3.33,\n",
       " 3.0,\n",
       " 3.0,\n",
       " 4.33,\n",
       " 4.0,\n",
       " 3.33,\n",
       " 4.33,\n",
       " 2.67,\n",
       " 3.67,\n",
       " 4.0,\n",
       " 3.67,\n",
       " 2.67,\n",
       " 2.33,\n",
       " 2.33,\n",
       " 1.0,\n",
       " 2.33,\n",
       " 2.67,\n",
       " 3.33,\n",
       " 5.0,\n",
       " 2.33,\n",
       " 3.33,\n",
       " 3.0,\n",
       " 3.0,\n",
       " 4.33,\n",
       " 4.0,\n",
       " 3.33,\n",
       " 4.33,\n",
       " 2.67,\n",
       " 3.67,\n",
       " 4.0,\n",
       " 3.67,\n",
       " 2.67,\n",
       " 2.33,\n",
       " 2.33,\n",
       " 2.0,\n",
       " 2.67,\n",
       " 3.33,\n",
       " 3.0,\n",
       " 4.0,\n",
       " 3.33,\n",
       " 3.0,\n",
       " 3.0,\n",
       " ...]"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat2=[]\n",
    "\n",
    "for sublist in flat:\n",
    "    for element in sublist:\n",
    "        flat2.append(element)\n",
    "flat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "75ece2d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are now 1404 observed values corresponding to 1404 forecasts in the correct order which can now be merged into 1 df.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are now {len(flat2)} observed values corresponding to {num_pdfs*27} forecasts in the correct order which can now be merged into 1 df.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a619b6",
   "metadata": {},
   "source": [
    "<h4>Final Merge</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "cbcf24be",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_df = final_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "780aa981",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Forecast Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan 2020</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07 Jan 2020</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Forecast Kp\n",
       "0  06 Jan 2020           4\n",
       "1  07 Jan 2020           2\n",
       "2  08 Jan 2020           3\n",
       "3  09 Jan 2020           3\n",
       "4  10 Jan 2020           3"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_df = complete_df.rename(columns={\"Kp\": \"Forecast Kp\"})\n",
    "complete_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "4e2da720",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<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>Date</th>\n",
       "      <th>Forecast Kp</th>\n",
       "      <th>Observed Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan 2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07 Jan 2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Forecast Kp  Observed Kp\n",
       "0  06 Jan 2020           4         1.67\n",
       "1  07 Jan 2020           2         3.33\n",
       "2  08 Jan 2020           3         3.33\n",
       "3  09 Jan 2020           3         3.33\n",
       "4  10 Jan 2020           3         2.00"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_df['Observed Kp'] = flat2\n",
    "complete_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc79226f",
   "metadata": {},
   "source": [
    "<h4>Converting Kp to whole integer</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "e0395aff",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>Forecast Kp</th>\n",
       "      <th>Observed Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan 2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07 Jan 2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Forecast Kp  Observed Kp\n",
       "0  06 Jan 2020           4         1.67\n",
       "1  07 Jan 2020           2         3.33\n",
       "2  08 Jan 2020           3         3.33\n",
       "3  09 Jan 2020           3         3.33\n",
       "4  10 Jan 2020           3         2.00"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete = complete_df.copy()\n",
    "complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "8ec88e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete[\"Observed Kp\"]= complete[\"Observed Kp\"].replace(0.00, 0)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(0.33, 0)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(0.67, 1)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(1.00, 1)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(1.33, 1)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(1.67, 2)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(2.00, 2)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(2.33, 2)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(2.67, 3)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(3.00, 3)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(3.33, 3)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(3.67, 4)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(4.00, 4)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(4.33, 4)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(4.67, 5)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(5.00, 5)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(5.33, 5)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(5.67, 6)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(6.00, 6)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(6.33, 6)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(6.67, 7)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(7.00, 7)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(7.33, 7)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(7.67, 8)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(8.00, 8)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(8.33, 8)\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(8.67, 8)\n",
    "\n",
    "complete[\"Observed Kp\"]=complete[\"Observed Kp\"].replace(9.00, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09041973",
   "metadata": {},
   "source": [
    "<h3>Final Table for Analysis</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "715d5872",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>Forecast Kp</th>\n",
       "      <th>Observed Kp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan 2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07 Jan 2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Forecast Kp  Observed Kp\n",
       "0  06 Jan 2020           4            2\n",
       "1  07 Jan 2020           2            3\n",
       "2  08 Jan 2020           3            3\n",
       "3  09 Jan 2020           3            3\n",
       "4  10 Jan 2020           3            2"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete['Observed Kp']= complete['Observed Kp'].astype('int')\n",
    "complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "87e9ee9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete.dropna(how=\"any\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "56116ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete['Forecast Kp'] = complete['Forecast Kp'].astype('int') #convert kp to integers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae144c3e",
   "metadata": {},
   "source": [
    "<h3>Forecast Performance</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "c8454c2f",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>Forecast Kp</th>\n",
       "      <th>Observed Kp</th>\n",
       "      <th>Forecast Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06 Jan 2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07 Jan 2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 Jan 2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Forecast Kp  Observed Kp  Forecast Error\n",
       "0  06 Jan 2020            4            2               2\n",
       "1  07 Jan 2020            2            3              -1\n",
       "2  08 Jan 2020            3            3               0\n",
       "3  09 Jan 2020            3            3               0\n",
       "4  10 Jan 2020            3            2               1"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete[\"Forecast Error\"] = complete[\"Forecast Kp\"] - complete[\"Observed Kp\"] \n",
    "complete.head()\n",
    "\n",
    "# negative under forecast\n",
    "# positive over forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "a6f2fda9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the 52 forecasts, beginning on 06 Jan 2020 and ending on 23 Jan 2021, the average forecast error was -0.5038402457757296.\n"
     ]
    }
   ],
   "source": [
    "last = final_df['Date'].iloc[-1]\n",
    "\n",
    "print(f'For the {num_pdfs} forecasts, beginning on {begin} and ending on {last}, the average forecast error was {sum(complete[\"Forecast Error\"])/len(complete)}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "fdb45bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The forecast was perfect 389 times or 29.877112135176652 percent of the time.\n"
     ]
    }
   ],
   "source": [
    "perfect = complete['Forecast Error'].value_counts()[0]\n",
    "print(f'The forecast was perfect {perfect} times or {perfect/len(complete) * 100} percent of the time.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42556eab",
   "metadata": {},
   "source": [
    "<h4> Fitting a Regression </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "7d52e0bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "74e4fa37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=complete['Date']\n",
    "X_axis=np.arange(len(X))\n",
    "lr.fit(X_axis.reshape(-1,1), complete['Forecast Error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "e4af06f4",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 7000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(70, 40), dpi=100)\n",
    "\n",
    "plt.bar(X_axis, complete['Forecast Error'])\n",
    "plt.plot(X_axis, lr.coef_*X_axis+lr.intercept_, color='red', linewidth=30)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"Kp Error\")\n",
    "plt.title(\"2020 Kp Forecast Errors\")\n",
    "plt.legend(['Regression'])\n",
    "\n",
    "plt.rc('axes', titlesize=100)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=50)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=50)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=50)    # fontsize of the tick labels\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fb5634",
   "metadata": {},
   "source": [
    "<h3>Conclusions</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8364ed",
   "metadata": {},
   "source": [
    "These forecasts show a tendency to underforecast Kp but overall are only about half a Kp underforecast. Errors increased through the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10667b6a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}