biodiversity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "The goal of this project is to analyze biodiversity data from the National Parks Service, with special emphasis on the various species observed in the different national Park locations.\n",
    "\n",
    "This project will scope, analyze, prepare, plot data, and seek to explain the findings from the analysis\n",
    "\n",
    "Here are a few questions that this project intends to answer\n",
    "- What is the distribution of conservation status for species?\n",
    "- Are certain species more likely to be endangered?\n",
    "- Which animal is most common and what is their distribution amongst the parks?\n",
    "- Are the difference between species and their conservation status significant?\n",
    "\n",
    "**Data Sources:**\n",
    "Both `Observations.csv` and `Species_info.csv` was provided by Codecademy\n",
    "\n",
    "*Note*: The data within this project is inspired by real data and doesn't 100 accurately describe the real data in the real world\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scoping\n",
    "### Project Goals\n",
    "\n",
    "Within this project, the main goal is to understand the characteristics of the species within the national parks and their conservations status, and the relationship between these species and the national parks.\n",
    "\n",
    "### Data\n",
    "\n",
    "Within this project, there are two data sets we be working with. The first file, `Observations.csv`, has information about each species in the nation parks. The second file, `Species_info.csv`, has information about each species in general, like their conservation status.\n",
    "\n",
    "### Analysis\n",
    "\n",
    "Through the use of descriptive statistics techniques, data visualization techniques, and statistical inference, the following will be found:\n",
    "\n",
    "- Distributions\n",
    "- Counts\n",
    "- Relationship between species\n",
    "- Conservation status of species\n",
    "- Observations of species in parks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Python Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import  pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (23296, 3)\n",
      "       observations\n",
      "count  23296.000000\n",
      "mean     142.287904\n",
      "std       69.890532\n",
      "min        9.000000\n",
      "25%       86.000000\n",
      "50%      124.000000\n",
      "75%      195.000000\n",
      "max      321.000000\n"
     ]
    },
    {
     "data": {
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       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observations = pd.read_csv('observations.csv')\n",
    "print(f\"Shape: {observations.shape}\")\n",
    "print(observations.describe())\n",
    "observations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columns in observations\n",
    "- **scientific_name** - the scientific name of animal\n",
    "- **park_name** - the name of the national park the record was made in\n",
    "- **observations** - the number of observations in the past 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (5824, 4)\n",
      "              category    scientific_name        common_names  \\\n",
      "count             5824               5824                5824   \n",
      "unique               7               5541                5504   \n",
      "top     Vascular Plant  Castor canadensis  Brachythecium Moss   \n",
      "freq              4470                  3                   7   \n",
      "\n",
      "       conservation_status  \n",
      "count                  191  \n",
      "unique                   4  \n",
      "top     Species of Concern  \n",
      "freq                   161  \n"
     ]
    },
    {
     "data": {
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       "<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",
       "    </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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "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                 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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species = pd.read_csv('species_info.csv')\n",
    "print(f\"Shape: {species.shape}\")\n",
    "print(species.describe())\n",
    "species.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columns in species\n",
    "- **category** - the category of taxonomy for each specie\n",
    "- **scientific_name** - the scientific name of each specie\n",
    "- **common_name** - the common name of specie\n",
    "- **conservation_status** - the specie conservation status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "\n",
    "#### **species**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of species:5541\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of species:{species.scientific_name.nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nnumber of categories:7\n",
      "categories:['Mammal' 'Bird' 'Reptile' 'Amphibian' 'Fish' 'Vascular Plant'\n",
      " 'Nonvascular Plant']\n"
     ]
    }
   ],
   "source": [
    "print(f\"nnumber of categories:{species.category.nunique()}\")\n",
    "print(f\"categories:{species.category.unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Amphibian              80\n",
       "Bird                  521\n",
       "Fish                  127\n",
       "Mammal                214\n",
       "Nonvascular Plant     333\n",
       "Reptile                79\n",
       "Vascular Plant       4470\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.groupby(\"category\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of conservation statuses:4\n",
      "unique conservation statuses:[nan 'Species of Concern' 'Endangered' 'Threatened' 'In Recovery']\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of conservation statuses:{species.conservation_status.nunique()}\")\n",
    "print(f\"unique conservation statuses:{species.conservation_status.unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan values:5633\n",
      "conservation_status\n",
      "Endangered             16\n",
      "In Recovery             4\n",
      "Species of Concern    161\n",
      "Threatened             10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"nan values:{species.conservation_status.isna().sum()}\")\n",
    "\n",
    "print(species.groupby(\"conservation_status\").size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of parks:4\n",
      "unique parks:['Great Smoky Mountains National Park' 'Yosemite National Park'\n",
      " 'Bryce National Park' 'Yellowstone National Park']\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of parks:{observations.park_name.nunique()}\")\n",
    "print(f\"unique parks:{observations.park_name.unique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of observations: 3314739\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of observations: {observations.observations.sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most of the conservation_status column is empty because of species not having being any level of endangerment, Structurally Missing Data, we are going to create a new category as species having no level of endangerment may be important in our analysis Another thing that may be important is if species is protected or not protected as this information can be used in the future to see where the resources going."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "conservation_status\n",
       "Endangered              16\n",
       "In Recovery              4\n",
       "Normal                5633\n",
       "Species of Concern     161\n",
       "Threatened              10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.fillna('Normal', inplace=True)\n",
    "species.groupby(\"conservation_status\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "species['isProtected'] = species.conservation_status != 'Normal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>conservation_status</th>\n",
       "      <th>In Recovery</th>\n",
       "      <th>Species of Concern</th>\n",
       "      <th>Threatened</th>\n",
       "      <th>Endangered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amphibian</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bird</th>\n",
       "      <td>3.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mammal</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonvascular Plant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reptile</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vascular Plant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "conservation_status  In Recovery  Species of Concern  Threatened  Endangered\n",
       "category                                                                    \n",
       "Amphibian                    NaN                 4.0         2.0         1.0\n",
       "Bird                         3.0                72.0         NaN         4.0\n",
       "Fish                         NaN                 4.0         4.0         3.0\n",
       "Mammal                       1.0                28.0         2.0         7.0\n",
       "Nonvascular Plant            NaN                 5.0         NaN         NaN\n",
       "Reptile                      NaN                 5.0         NaN         NaN\n",
       "Vascular Plant               NaN                43.0         2.0         1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#? What is the distribution of conservation status for species?\n",
    "conservationCategory = species[species.conservation_status != 'Normal']\\\n",
    "    .groupby(['category','conservation_status'])['scientific_name']\\\n",
    "    .count().unstack().reindex(columns=['In Recovery','Species of Concern','Threatened','Endangered'])\n",
    "conservationCategory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Conservation Status of different Species')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = conservationCategory.plot(kind = 'bar', figsize = (8,8),\\\n",
    "stacked = True, color=['green', 'salmon', 'orangered', 'darkred'])\n",
    "ax.set_xlabel(\"Conservation Status\")\n",
    "ax.set_ylabel(\"Number of Species\")\n",
    "ax.set_title('Conservation Status of different Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>not_protected</th>\n",
       "      <th>protected</th>\n",
       "      <th>percent_protected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amphibian</td>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "      <td>8.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bird</td>\n",
       "      <td>413</td>\n",
       "      <td>75</td>\n",
       "      <td>15.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fish</td>\n",
       "      <td>115</td>\n",
       "      <td>11</td>\n",
       "      <td>8.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "      <td>17.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nonvascular Plant</td>\n",
       "      <td>328</td>\n",
       "      <td>5</td>\n",
       "      <td>1.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reptile</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>6.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>4216</td>\n",
       "      <td>46</td>\n",
       "      <td>1.079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category  not_protected  protected  percent_protected\n",
       "0          Amphibian             72          7              8.861\n",
       "1               Bird            413         75             15.369\n",
       "2               Fish            115         11              8.730\n",
       "3             Mammal            146         30             17.045\n",
       "4  Nonvascular Plant            328          5              1.502\n",
       "5            Reptile             73          5              6.410\n",
       "6     Vascular Plant           4216         46              1.079"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_counts = species.groupby(['category','isProtected'])\\\n",
    "    .scientific_name.nunique().reset_index()\\\n",
    "    .pivot(columns='isProtected',index='category',values='scientific_name')\\\n",
    "    .reset_index()\n",
    "\n",
    "category_counts.columns = ['category', 'not_protected', 'protected']\n",
    "\n",
    "category_counts['percent_protected'] = \\\n",
    "    round(category_counts.protected / \\\n",
    "    (category_counts.protected + category_counts.not_protected) * 100,3)\n",
    "\n",
    "category_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why does the bird and mammal categories having a higher percentage of protected? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No relationship\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6875948096661336"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "level_of_significance = 0.05\n",
    "\n",
    "#? Is there a relationship between birds and mammals?\n",
    "contingency1 = [[30,146],[75,413]]\n",
    "p_value1 = chi2_contingency(contingency1)[1]\n",
    "\n",
    "if(p_value1 > level_of_significance):\n",
    "    print(\"No relationship\")\n",
    "else:\n",
    "    print(\"There is a relationship\")\n",
    "\n",
    "p_value1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why does the Nonvascular Plant and Vascular Plant categories having a higher percentage of protected? Is it just because they are plants?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a relationship\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.03835559022969898"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency2 = [[30, 146],\n",
    "               [5, 73]]\n",
    "p_value2 = chi2_contingency(contingency2)[1]\n",
    "\n",
    "if p_value2 > level_of_significance:\n",
    "    print(\"No relationship\")\n",
    "else:\n",
    "    print(\"There is a relationship\")\n",
    "\n",
    "p_value2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories_values = []\n",
    "for i in range(category_counts.shape[0]):\n",
    "    row = category_counts.iloc[i]\n",
    "    category = row[0]\n",
    "    not_p = row[1]\n",
    "    yes_p = row[2]\n",
    "    info = [category,yes_p,not_p]\n",
    "    categories_values.append(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there more relationships between categories of species?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amphibian ~ Nonvascular Plant: 0.001778554820715609\n",
      "Amphibian ~ Vascular Plant: 1.0441355146657018e-08\n",
      "Bird ~ Nonvascular Plant: 1.0546306904975004e-10\n",
      "Bird ~ Vascular Plant: 4.612268031690143e-79\n",
      "Fish ~ Nonvascular Plant: 0.0004960633228752838\n",
      "Fish ~ Vascular Plant: 1.486846949188912e-12\n",
      "Mammal ~ Nonvascular Plant: 1.481868915759314e-10\n",
      "Mammal ~ Reptile: 0.03835559022969898\n",
      "Mammal ~ Vascular Plant: 1.4405067309854046e-55\n",
      "Nonvascular Plant ~ Amphibian: 0.001778554820715609\n",
      "Nonvascular Plant ~ Bird: 1.0546306904974968e-10\n",
      "Nonvascular Plant ~ Fish: 0.0004960633228752838\n",
      "Nonvascular Plant ~ Mammal: 1.481868915759314e-10\n",
      "Nonvascular Plant ~ Reptile: 0.03362698310726171\n",
      "Reptile ~ Mammal: 0.03835559022969898\n",
      "Reptile ~ Nonvascular Plant: 0.0336269831072617\n",
      "Reptile ~ Vascular Plant: 0.00014505215494675828\n",
      "Vascular Plant ~ Amphibian: 1.044135514665698e-08\n",
      "Vascular Plant ~ Bird: 4.612268031690143e-79\n",
      "Vascular Plant ~ Fish: 1.4868469491889182e-12\n",
      "Vascular Plant ~ Mammal: 1.4405067309854046e-55\n",
      "Vascular Plant ~ Reptile: 0.00014505215494675828\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(categories_values)):\n",
    "    for j in range(len(categories_values)):\n",
    "        if i != j:\n",
    "            a = categories_values[i][1:]\n",
    "            b = categories_values[j][1:]\n",
    "            c = [a,b]\n",
    "            p = chi2_contingency(c)[1]\n",
    "            if p < level_of_significance:\n",
    "                print(f\"{categories_values[i][0]} ~ {categories_values[j][0]}: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which animal is most common and what is their distribution amongst the parks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Gappers', 'RedBacked', 'Vole'],\n",
       " ['American', 'Bison'],\n",
       " ['Aurochs', 'Domestic', 'Cattle', 'Feral', 'Domesticated'],\n",
       " ['Domestic', 'Sheep', 'Mouflon', 'Red', 'Feral'],\n",
       " ['Wapiti', 'Or', 'Elk'],\n",
       " ['WhiteTailed', 'Deer']]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "import string\n",
    "\n",
    "def remove_punctuations(text):\n",
    "    for punctuation in string.punctuation:\n",
    "        text = text.replace(punctuation, '')\n",
    "    return text\n",
    "\n",
    "common_Names = species[species.category == \"Mammal\"]\\\n",
    "    .common_names\\\n",
    "    .apply(remove_punctuations)\\\n",
    "    .str.split().tolist()\n",
    "\n",
    "cleanRows = []\n",
    "\n",
    "for item in common_Names:\n",
    "    item = list(dict.fromkeys(item))\n",
    "    cleanRows.append(item)\n",
    "    \n",
    "cleanRows[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gappers', 'RedBacked', 'Vole', 'American', 'Bison', 'Aurochs']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = list(chain.from_iterable(i if isinstance(i, list) else [i] for i in cleanRows))\n",
    "res[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Word</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Bat</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Shrew</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Myotis</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Mouse</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Chipmunk</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>American</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Squirrel</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Vole</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Western</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Word  Count\n",
       "38        Bat     23\n",
       "85      Shrew     18\n",
       "138    Myotis     17\n",
       "168     Mouse     16\n",
       "186  Chipmunk     13\n",
       "64   American     12\n",
       "46   Squirrel     12\n",
       "14       Vole     11\n",
       "185   Eastern     11\n",
       "124   Western     10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_counted = []\n",
    "\n",
    "for i in res:\n",
    "    x = res.count(i)\n",
    "    words_counted.append((i,x))\n",
    "\n",
    "pd.DataFrame(set(words_counted), columns =['Word', 'Count']).sort_values(\"Count\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "species['isBat'] = species.common_names.str.contains(r\"\\bBat\\b\", regex = True)\n",
    "species[species.isBat]\n",
    "bat_observations = observations.merge(species[species.isBat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>3433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>8362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>4786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             park_name  observations\n",
       "0                  Bryce National Park          3433\n",
       "1  Great Smoky Mountains National Park          2411\n",
       "2            Yellowstone National Park          8362\n",
       "3               Yosemite National Park          4786"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_observations.groupby('park_name').observations.sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>isProtected</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>False</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>1299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>4044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>4318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>2345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>2441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             park_name  isProtected  observations\n",
       "0                  Bryce National Park        False          1596\n",
       "1                  Bryce National Park         True          1837\n",
       "2  Great Smoky Mountains National Park        False          1299\n",
       "3  Great Smoky Mountains National Park         True          1112\n",
       "4            Yellowstone National Park        False          4044\n",
       "5            Yellowstone National Park         True          4318\n",
       "6               Yosemite National Park        False          2345\n",
       "7               Yosemite National Park         True          2441"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_by_park = bat_observations.groupby(['park_name', 'isProtected']).observations.sum().reset_index()\n",
    "obs_by_park"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4))\n",
    "sns.barplot(x=obs_by_park.park_name, y= obs_by_park.observations, hue=obs_by_park.isProtected)\n",
    "plt.xlabel('National Parks')\n",
    "plt.ylabel('Number of Observations')\n",
    "plt.title('Observations of Bats per Week')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "Within this project, several data visualizations and inferences about the various in four of the National Parks presented in this data set\n",
    "\n",
    "Some of the questions that were able to be answered now:\n",
    "\n",
    "- What is the distribution of conservation status for species?\n",
    "\n",
    "    > The major of species without data set where not part of conservation: 191 were and 5,633 weren't.\n",
    "\n",
    "- Are certain species more likely to be endangered?\n",
    "\n",
    "    > Birds and Mammals had the highest percentage of being in protection.\n",
    "\n",
    "- Which animal is most common and what is their distribution amongst the parks?\n",
    "\n",
    "    > Through analysis, bats were founded to be that most common animal which the majority of bats in our data set residing in Yellowstone National Park.\n",
    "    \n",
    "- Are the difference between species and their conservation status significant?\n",
    "    > There are some cases such as for mammals and reptiles and Reptile Nonvascular Plant"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}