biodiversity_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Biodiversity Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction\n",
    "On this project, we're going to do some exploratory data analysis and statistical analysis about species at various national parks.\n",
    "\n",
    "The data that we'll be working with for this project is *inspired* by real data, but is mostly fictional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Par 1: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.stats import chi2_contingency\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset about different species in the National Parks and inspect it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "species = pd.read_csv('species_info.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  category                scientific_name  \\\n",
      "0   Mammal  Clethrionomys gapperi gapperi   \n",
      "1   Mammal                      Bos bison   \n",
      "2   Mammal                     Bos taurus   \n",
      "3   Mammal                     Ovis aries   \n",
      "4   Mammal                 Cervus elaphus   \n",
      "\n",
      "                                        common_names conservation_status  \n",
      "0                           Gapper's Red-Backed Vole                 NaN  \n",
      "1                              American Bison, Bison                 NaN  \n",
      "2  Aurochs, Aurochs, Domestic Cattle (Feral), Dom...                 NaN  \n",
      "3  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)                 NaN  \n",
      "4                                      Wapiti Or Elk                 NaN  \n"
     ]
    }
   ],
   "source": [
    "print(species.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's learning a bit more about our data by answer each of the following questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5824\n"
     ]
    }
   ],
   "source": [
    "num_species = len(species)\n",
    "print(num_species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the different values of `category` in `species`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 7different categories\n",
      "          \n",
      "Vascular Plant       4470\n",
      "Bird                  521\n",
      "Nonvascular Plant     333\n",
      "Mammal                214\n",
      "Fish                  127\n",
      "Amphibian              80\n",
      "Reptile                79\n",
      "Name: category, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "categ_num = species.category.nunique()\n",
    "print (\"There are \" + str(categ_num) + \"different categories\")\n",
    "print(\"          \")\n",
    "categ_values = species['category'].value_counts()\n",
    "\n",
    "print(categ_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the different values of `conservation_status`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The values of conservation status are: [nan 'Species of Concern' 'Endangered' 'Threatened' 'In Recovery']\n"
     ]
    }
   ],
   "source": [
    "#conserv_categ = species['conservation_status'].value_counts()\n",
    "conserv_categ = species['conservation_status'].unique()\n",
    "print('The values of conservation status are: ' + str(conserv_categ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The column `conservation_status` has several possible values:\n",
    "- `Species of Concern`: declining or appear to be in need of conservation\n",
    "- `Threatened`: vulnerable to endangerment in the near future\n",
    "- `Endangered`: seriously at risk of extinction\n",
    "- `In Recovery`: formerly `Endangered`, but currnetly neither in danger of extinction throughout all or a significant portion of its range\n",
    "\n",
    "We'd like to count up how many species meet each of these criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  conservation_status  scientific_name\n",
      "0          Endangered               16\n",
      "1         In Recovery                4\n",
      "2  Species of Concern              161\n",
      "3          Threatened               10\n"
     ]
    }
   ],
   "source": [
    "status_count = species.groupby('conservation_status').scientific_name.count().reset_index()\n",
    "print(status_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw before, there are far more than 200 species in the `species` table.  Clearly, only a small number of them are categorized as needing some sort of protection.  The rest have `conservation_status` equal to `None`.  Because `groupby` does not include `None`, we will need to fill in the null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "species.fillna('No Intervention', inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how many species require `No Intervention`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  conservation_status  scientific_name\n",
      "0          Endangered               16\n",
      "1         In Recovery                4\n",
      "2     No Intervention             5633\n",
      "3  Species of Concern              161\n",
      "4          Threatened               10\n"
     ]
    }
   ],
   "source": [
    "status_count = species.groupby('conservation_status').scientific_name.count().reset_index()\n",
    "print(status_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we'll create a bar chart but first, let's sort the columns by how many species are in each categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  conservation_status  scientific_name\n",
      "1         In Recovery                4\n",
      "4          Threatened               10\n",
      "0          Endangered               15\n",
      "3  Species of Concern              151\n",
      "2     No Intervention             5363\n"
     ]
    }
   ],
   "source": [
    "protection_counts = species.groupby('conservation_status')\\\n",
    "    .scientific_name.nunique().reset_index()\\\n",
    "    .sort_values(by='scientific_name')\n",
    "\n",
    "print(protection_counts.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a bar chart to show conservation status by species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "ax = plt.subplot()\n",
    "plt.bar(range(len(protection_counts)),\n",
    "        protection_counts.scientific_name.values)\n",
    "ax.set_xticks(range(len(protection_counts)))\n",
    "ax.set_xticklabels(protection_counts.conservation_status.values)\n",
    "plt.ylabel('Number of Species')\n",
    "plt.title('Conservation Status by Species')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Are certain types of species more likely to be endangered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#species['is_protected'] = species.conservation_status.apply(lambda x: \n",
    "   #                                                         True if species.conservation_status != 'No Intervention' \n",
    "    #                                                        else False)\n",
    "\n",
    "species['is_protected'] = species.conservation_status != 'No Intervention'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_counts = species.groupby(['category', 'is_protected']).scientific_name.nunique().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    category  is_protected  scientific_name\n",
      "0  Amphibian         False               72\n",
      "1  Amphibian          True                7\n",
      "2       Bird         False              413\n",
      "3       Bird          True               75\n",
      "4       Fish         False              115\n"
     ]
    }
   ],
   "source": [
    "print(category_counts.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's going to be easier to view this data if we pivot it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_pivot = category_counts.pivot(columns='is_protected',\n",
    "                                      index='category',\n",
    "                                      values='scientific_name')\\\n",
    "                                .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_protected           category  False  True\n",
      "0                     Amphibian     72     7\n",
      "1                          Bird    413    75\n",
      "2                          Fish    115    11\n",
      "3                        Mammal    146    30\n",
      "4             Nonvascular Plant    328     5\n",
      "5                       Reptile     73     5\n",
      "6                Vascular Plant   4216    46\n"
     ]
    }
   ],
   "source": [
    "print(category_pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_pivot.columns = ['category', 'not_protected', 'protected']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new column of `category_pivot` called `percent_protected`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_pivot['percent_protcted'] = category_pivot.protected/(category_pivot.protected+category_pivot.not_protected) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            category  not_protected  protected  percent_protcted\n",
      "0          Amphibian             72          7          8.860759\n",
      "1               Bird            413         75         15.368852\n",
      "2               Fish            115         11          8.730159\n",
      "3             Mammal            146         30         17.045455\n",
      "4  Nonvascular Plant            328          5          1.501502\n",
      "5            Reptile             73          5          6.410256\n",
      "6     Vascular Plant           4216         46          1.079305\n"
     ]
    }
   ],
   "source": [
    "print(category_pivot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Statistical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like species in category `Mammal` are more likely to be endangered than species in `Bird`.  We're going to do a significance test, a Chi Square test to see if this statement is true. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "contingency = [[30, 146], [75, 413]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1617014831654557,\n",
       " 0.6875948096661336,\n",
       " 1,\n",
       " array([[ 27.8313253, 148.1686747],\n",
       "        [ 77.1686747, 410.8313253]]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(contingency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is 0.69 so this difference isn't significant!\n",
    "\n",
    "Let's test the difference between `Reptile` and `Mammal`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.289183096203645,\n",
       " 0.03835559022969898,\n",
       " 1,\n",
       " array([[ 10.7480315,  67.2519685],\n",
       "        [ 24.2519685, 151.7480315]]))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont = [[5, 73], [30,146]]\n",
    "chi2_contingency(cont)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is 0.03 so there is a significant difference between `Reptile` and `Mammal`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next step we will need the file observations which includes sightings of different species at several national parks for the past 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>scientific_name</th>\n",
       "      <th>park_name</th>\n",
       "      <th>observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vicia benghalensis</td>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Neovison vison</td>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prunus subcordata</td>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abutilon theophrasti</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Githopsis specularioides</td>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            scientific_name                            park_name  observations\n",
       "0        Vicia benghalensis  Great Smoky Mountains National Park            68\n",
       "1            Neovison vison  Great Smoky Mountains National Park            77\n",
       "2         Prunus subcordata               Yosemite National Park           138\n",
       "3      Abutilon theophrasti                  Bryce National Park            84\n",
       "4  Githopsis specularioides  Great Smoky Mountains National Park            85"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observations = pd.read_csv('observations.csv')\n",
    "observations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There have been some studies of the number of sheep sightings at different national parks.  There are several different scientific names for different types of sheep.  We'd like to know which rows of `species` are referring to sheep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>category</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>is_sheep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Clethrionomys gapperi gapperi</td>\n",
       "      <td>Gapper's Red-Backed Vole</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Bos bison</td>\n",
       "      <td>American Bison, Bison</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Bos taurus</td>\n",
       "      <td>Aurochs, Aurochs, Domestic Cattle (Feral), Dom...</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Cervus elaphus</td>\n",
       "      <td>Wapiti Or Elk</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                scientific_name  \\\n",
       "0   Mammal  Clethrionomys gapperi gapperi   \n",
       "1   Mammal                      Bos bison   \n",
       "2   Mammal                     Bos taurus   \n",
       "3   Mammal                     Ovis aries   \n",
       "4   Mammal                 Cervus elaphus   \n",
       "\n",
       "                                        common_names conservation_status  \\\n",
       "0                           Gapper's Red-Backed Vole     No Intervention   \n",
       "1                              American Bison, Bison     No Intervention   \n",
       "2  Aurochs, Aurochs, Domestic Cattle (Feral), Dom...     No Intervention   \n",
       "3  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "4                                      Wapiti Or Elk     No Intervention   \n",
       "\n",
       "   is_protected  is_sheep  \n",
       "0         False     False  \n",
       "1         False     False  \n",
       "2         False     False  \n",
       "3         False      True  \n",
       "4         False     False  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species['is_sheep'] = species.common_names.apply(lambda x: True if 'Sheep' in x \n",
    "                                                else False)\n",
    "species.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the rows of `species` where `is_sheep` is `True` and examine the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>category</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>is_sheep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Rumex acetosella</td>\n",
       "      <td>Sheep Sorrel, Sheep Sorrell</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Festuca filiformis</td>\n",
       "      <td>Fineleaf Sheep Fescue</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis canadensis</td>\n",
       "      <td>Bighorn Sheep, Bighorn Sheep</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3758</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Rumex acetosella</td>\n",
       "      <td>Common Sheep Sorrel, Field Sorrel, Red Sorrel,...</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3761</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Rumex paucifolius</td>\n",
       "      <td>Alpine Sheep Sorrel, Fewleaved Dock, Meadow Dock</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4091</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Carex illota</td>\n",
       "      <td>Sheep Sedge, Smallhead Sedge</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4383</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Potentilla ovina var. ovina</td>\n",
       "      <td>Sheep Cinquefoil</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4446</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis canadensis sierrae</td>\n",
       "      <td>Sierra Nevada Bighorn Sheep</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category              scientific_name  \\\n",
       "3             Mammal                   Ovis aries   \n",
       "1139  Vascular Plant             Rumex acetosella   \n",
       "2233  Vascular Plant           Festuca filiformis   \n",
       "3014          Mammal              Ovis canadensis   \n",
       "3758  Vascular Plant             Rumex acetosella   \n",
       "3761  Vascular Plant            Rumex paucifolius   \n",
       "4091  Vascular Plant                 Carex illota   \n",
       "4383  Vascular Plant  Potentilla ovina var. ovina   \n",
       "4446          Mammal      Ovis canadensis sierrae   \n",
       "\n",
       "                                           common_names conservation_status  \\\n",
       "3     Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "1139                        Sheep Sorrel, Sheep Sorrell     No Intervention   \n",
       "2233                              Fineleaf Sheep Fescue     No Intervention   \n",
       "3014                       Bighorn Sheep, Bighorn Sheep  Species of Concern   \n",
       "3758  Common Sheep Sorrel, Field Sorrel, Red Sorrel,...     No Intervention   \n",
       "3761   Alpine Sheep Sorrel, Fewleaved Dock, Meadow Dock     No Intervention   \n",
       "4091                       Sheep Sedge, Smallhead Sedge     No Intervention   \n",
       "4383                                   Sheep Cinquefoil     No Intervention   \n",
       "4446                        Sierra Nevada Bighorn Sheep          Endangered   \n",
       "\n",
       "      is_protected  is_sheep  \n",
       "3            False      True  \n",
       "1139         False      True  \n",
       "2233         False      True  \n",
       "3014          True      True  \n",
       "3758         False      True  \n",
       "3761         False      True  \n",
       "4091         False      True  \n",
       "4383         False      True  \n",
       "4446          True      True  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species[species.is_sheep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>category</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>is_sheep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis canadensis</td>\n",
       "      <td>Bighorn Sheep, Bighorn Sheep</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4446</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis canadensis sierrae</td>\n",
       "      <td>Sierra Nevada Bighorn Sheep</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     category          scientific_name  \\\n",
       "3      Mammal               Ovis aries   \n",
       "3014   Mammal          Ovis canadensis   \n",
       "4446   Mammal  Ovis canadensis sierrae   \n",
       "\n",
       "                                           common_names conservation_status  \\\n",
       "3     Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "3014                       Bighorn Sheep, Bighorn Sheep  Species of Concern   \n",
       "4446                        Sierra Nevada Bighorn Sheep          Endangered   \n",
       "\n",
       "      is_protected  is_sheep  \n",
       "3            False      True  \n",
       "3014          True      True  \n",
       "4446          True      True  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheep_species = species[(species.is_sheep == True) & (species.category == 'Mammal')]\n",
    "sheep_species.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>category</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>is_sheep</th>\n",
       "      <th>park_name</th>\n",
       "      <th>observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis canadensis</td>\n",
       "      <td>Bighorn Sheep, Bighorn Sheep</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  scientific_name  \\\n",
       "0   Mammal       Ovis aries   \n",
       "1   Mammal       Ovis aries   \n",
       "2   Mammal       Ovis aries   \n",
       "3   Mammal       Ovis aries   \n",
       "4   Mammal  Ovis canadensis   \n",
       "\n",
       "                                        common_names conservation_status  \\\n",
       "0  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "1  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "2  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "3  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)     No Intervention   \n",
       "4                       Bighorn Sheep, Bighorn Sheep  Species of Concern   \n",
       "\n",
       "   is_protected  is_sheep                            park_name  observations  \n",
       "0         False      True               Yosemite National Park           126  \n",
       "1         False      True  Great Smoky Mountains National Park            76  \n",
       "2         False      True                  Bryce National Park           119  \n",
       "3         False      True            Yellowstone National Park           221  \n",
       "4          True      True            Yellowstone National Park           219  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheep_observations = sheep_species.merge(observations)\n",
    "sheep_observations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>park_name</th>\n",
       "      <th>observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             park_name  observations\n",
       "0                  Bryce National Park           250\n",
       "1  Great Smoky Mountains National Park           149\n",
       "2            Yellowstone National Park           507\n",
       "3               Yosemite National Park           282"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_by_park = sheep_observations.groupby('park_name').observations.sum().reset_index()\n",
    "obs_by_park"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to create a bar chart to show the different number of observations per week at each park."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "ax=plt.subplot()\n",
    "plt.bar(range(len(obs_by_park)), obs_by_park.observations.values)\n",
    "ax.set_xticks(range(len(obs_by_park)))\n",
    "ax.set_xticklabels(obs_by_park.park_name.values)\n",
    "plt.ylabel('Number of Observations')\n",
    "plt.title('Observations of Sheep per Week')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scientists know that 15% of sheep at Bryce National Park have foot and mouth disease.  Park rangers at Yellowstone National Park have been running a program to reduce the rate of foot and mouth disease at that park.  The scientists want to test whether or not this program is working.  They want to be able to detect reductions of at least 5 percentage points.\n",
    "Therefore we need to calculate the number of sheep that they would need to observe from each park with the level of significance (90%).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.333333333333336"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimum_detectable_effect = 100 * 0.05 / 0.15\n",
    "minimum_detectable_effect\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = 15\n",
    "sample_size_per_variant = 870"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of sample needed to be observed would be 870 sheeps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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