Appendix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60082490",
   "metadata": {},
   "source": [
    "## Importing the libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0324cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0ecef2",
   "metadata": {},
   "source": [
    "## Loading the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d85c1fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = pd.read_csv('observations.csv')\n",
    "species = pd.read_csv('species_info.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37189886",
   "metadata": {},
   "source": [
    "## Exploring both datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e579c9",
   "metadata": {},
   "source": [
    "### Species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "382aa0bc",
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da914e47",
   "metadata": {},
   "source": [
    "#### The `species_info.csv` contains information on the different species in the National Parks. \n",
    "#### The columns in the data set include:\n",
    "#### `category` - The category of taxonomy for each species;\n",
    "#### `scientific_name` - The scientific name of each species;\n",
    "#### `common_names` - The common names of each species;\n",
    "#### `conservation_status` - The species conservation status."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308b2c29",
   "metadata": {},
   "source": [
    "### Observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fa2cde0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            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\n"
     ]
    }
   ],
   "source": [
    "print(observations.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2cf39b",
   "metadata": {},
   "source": [
    "#### The `Observations.csv` contains information from recorded sightings of different species throughout the national parks in the past 7 days. \n",
    "#### The columns included are:\n",
    "#### `scientific_name` - The scientific name of each species;\n",
    "#### `park_name`- The name of the national park;\n",
    "#### `observations` - The number of observations in the past 7 days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c0cc16",
   "metadata": {},
   "source": [
    "### Checking datasets dimentions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afe5862e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "species shape: (5824, 4)\n",
      "observations shape: (23296, 3)\n"
     ]
    }
   ],
   "source": [
    "print(f'species shape: {species.shape}')\n",
    "print(f'observations shape: {observations.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee41f36",
   "metadata": {},
   "source": [
    "#### The `species` dataset has 5,824 rows, and 4 columns, while `observations` has 23,2296 rows, and 3 columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799ca661",
   "metadata": {},
   "source": [
    "## Exploring ```species``` dataset more in depth."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85b00c1",
   "metadata": {},
   "source": [
    "### Dataset structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb4b2dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of species: 5541\n",
      "Total number of category: 7\n",
      "Names of categories: ['Mammal' 'Bird' 'Reptile' 'Amphibian' 'Fish' 'Vascular Plant'\n",
      " 'Nonvascular Plant']\n"
     ]
    }
   ],
   "source": [
    "print(f'Total number of species: {species.scientific_name.nunique()}')\n",
    "print(f'Total number of category: {species.category.nunique()}')\n",
    "print(f'Names of categories: {species.category.unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2c583f",
   "metadata": {},
   "source": [
    "#### The `species` dataset contains 5541 different species, distribuited in 7 types of categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2730b32",
   "metadata": {},
   "source": [
    "### Exploring the ```category``` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3bc535e",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.groupby('category').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc996bd",
   "metadata": {},
   "source": [
    "#### We can see that by far `Vascular Plant` is the most commun one and `Reptile` is the most rare."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d8e921",
   "metadata": {},
   "source": [
    "### Exploring the ```conservation_status``` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "023e92b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of conservation statuses: 4\n",
      "Conservation statuses names: [nan 'Species of Concern' 'Endangered' 'Threatened' 'In Recovery']\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of conservation statuses: {species.conservation_status.nunique()}')\n",
    "print(f'Conservation statuses names: {species.conservation_status.unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399f08c7",
   "metadata": {},
   "source": [
    "#### The `conservation_status` column shows 4 different types of status and a 'nan' value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77e6b01e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conservation_status\n",
      "Endangered             16\n",
      "In Recovery             4\n",
      "Species of Concern    161\n",
      "Threatened             10\n",
      "dtype: int64\n",
      "The data shows 5633 NaNs values\n"
     ]
    }
   ],
   "source": [
    "print(species.groupby('conservation_status').size())\n",
    "print(f'The data shows {species.conservation_status.isna().sum()} NaNs values')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d01b29d4",
   "metadata": {},
   "source": [
    "#### There are 5,633 NaNs values which, in this case, means that they are species without concerns. \n",
    "#### On the other hand there are 161 species of concern, 16 endangered, 10 threatened, and 4 in recovery."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da75986e",
   "metadata": {},
   "source": [
    "### Checking for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db2dd9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(species.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c589c265",
   "metadata": {},
   "source": [
    "#### There is no duplicated values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96edab9b",
   "metadata": {},
   "source": [
    "## Looking at the `observations` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "407514a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data shows a total of 4 parks\n",
      "The parks names are the following: ['Great Smoky Mountains National Park' 'Yosemite National Park'\n",
      " 'Bryce National Park' 'Yellowstone National Park']\n"
     ]
    }
   ],
   "source": [
    "print(f'The data shows a total of {observations.park_name.nunique()} parks')\n",
    "print(f'The parks names are the following: {observations.park_name.unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6214eb8a",
   "metadata": {},
   "source": [
    "#### We can see that the data was captured on four different parks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04e76c3",
   "metadata": {},
   "source": [
    "### Checking the total number of observations recored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e00e8c29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 3314739\n"
     ]
    }
   ],
   "source": [
    "print(f'Observations: {observations.observations.sum()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4626ad3e",
   "metadata": {},
   "source": [
    "#### The dataset has a total of 3314739 observations divided amoung four parks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5429919",
   "metadata": {},
   "source": [
    "### Taking a closer look at the `scientific_name` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b330653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scientific names: 5541\n"
     ]
    }
   ],
   "source": [
    "print(f'Scientific names: {observations.scientific_name.nunique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3391879d",
   "metadata": {},
   "source": [
    "#### The `observations` dataset shows a total of 5541 different scientific names registered. The same as the `species` dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8460f238",
   "metadata": {},
   "source": [
    "### Checking for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fcddc546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(observations.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51cdd48",
   "metadata": {},
   "source": [
    "#### There is 15 duplicated rows in the `observations` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b606d8d",
   "metadata": {},
   "source": [
    "### Deleting the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d186eede",
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = observations.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd0ec62",
   "metadata": {},
   "source": [
    "### Checking for NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d54b67e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scientific_name    0\n",
      "park_name          0\n",
      "observations       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(observations.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3c8a8c",
   "metadata": {},
   "source": [
    "#### There is no NaNs values in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b25ed92",
   "metadata": {},
   "source": [
    "### Filling the NaNs in the `species` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a573ff8e",
   "metadata": {},
   "source": [
    "#### The possible values for this column are the following: \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 currently neither in danger of extinction throughout all or a significant portion of its range\n",
    "\n",
    "#### In the exploration, a lot of `nan` values were detected. These values will need to be converted to `No Intervention` as this species are not in concern "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3915a2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "conservation_status\n",
       "Endangered              16\n",
       "In Recovery              4\n",
       "No Intervention       5633\n",
       "Species of Concern     161\n",
       "Threatened              10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.fillna('No Intervention', inplace = True)\n",
    "species.groupby('conservation_status').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115e03d1",
   "metadata": {},
   "source": [
    "## Analyzing the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9063fef4",
   "metadata": {},
   "source": [
    "### Taking a look at how the species are distributed on the 'conservation_status' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84cf973d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category             Amphibian  Bird  Fish  Mammal  Nonvascular Plant  \\\n",
      "conservation_status                                                     \n",
      "Endangered                 1.0   4.0   3.0     7.0                NaN   \n",
      "In Recovery                NaN   3.0   NaN     1.0                NaN   \n",
      "Species of Concern         4.0  72.0   4.0    28.0                5.0   \n",
      "Threatened                 2.0   NaN   4.0     2.0                NaN   \n",
      "\n",
      "category             Reptile  Vascular Plant  \n",
      "conservation_status                           \n",
      "Endangered               NaN             1.0  \n",
      "In Recovery              NaN             NaN  \n",
      "Species of Concern       5.0            43.0  \n",
      "Threatened               NaN             2.0  \n"
     ]
    }
   ],
   "source": [
    "# Grouping the data to get a better understand of distribution of species among the conservation_status column.\n",
    "conservationCategory = species[species.conservation_status != 'No Intervention'].groupby(['conservation_status', 'category'])['scientific_name'].count().unstack()\n",
    "print(conservationCategory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "664a8760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a bar chart to visualize the distribution of species among the conservation_status column.\n",
    "ax = conservationCategory.plot(kind = 'bar', figsize=(8,6), stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a64112",
   "metadata": {},
   "source": [
    "#### It is possible to see that the biggest number of species are classified as `Species of Concern`. Having that the `Bird` category is the one with the biggest volume of species in this classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08d0089",
   "metadata": {},
   "source": [
    "### Analyzing the volume and distribution of each animal categories amoung the parks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c369c5e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Joining the observations with the species dataset.\n",
    "outer = observations.merge(species, on = 'scientific_name', how = 'outer')\n",
    "\n",
    "# Dorpping the plants categories from the dataset\n",
    "criteria1 = outer['category'] == 'Vascular Plant'\n",
    "outer = outer.drop(outer[criteria1].index) \n",
    "\n",
    "criteria2 = outer['category'] == 'Nonvascular Plant'\n",
    "outer = outer.drop(outer[criteria2].index) \n",
    "\n",
    "# Grouping the data in the necessary order. \n",
    "outer_cate = outer.groupby(['park_name','category'])['observations'].sum().unstack()\n",
    "\n",
    "# Plotting a bar char that shows the distribution and volume of each category among the parks.\n",
    "ax = outer_cate.plot(kind = 'bar', figsize = (14,10))\n",
    "plt.title('Volume and Distribution of Animals Categories Among the Parks')\n",
    "plt.ylabel('Number of Observations')\n",
    "plt.xlabel(\"Park Name\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b30f2a",
   "metadata": {},
   "source": [
    "#### It is possible to conclude that `Yellowstone National Park` has the biggest volume of animals for every category, and specially `birds`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5546778a",
   "metadata": {},
   "source": [
    "## Checking the mammal population density among the parks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaebf170",
   "metadata": {},
   "source": [
    "### Creating a dataset with each park the total area (km²)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5179196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             park_name  area_(km²)\n",
      "0  Great Smoky Mountains National Park     2144.10\n",
      "1               Yosemite National Park     3027.81\n",
      "2                  Bryce National Park     1342.57\n",
      "3            Yellowstone National Park     8983.18\n"
     ]
    }
   ],
   "source": [
    "# Creating a new dataset.\n",
    "data = [('Great Smoky Mountains National Park', 2144.10), ('Yosemite National Park', 3027.81),('Bryce National Park', 1342.57),('Yellowstone National Park', 8983.18)]\n",
    "park_area = pd.DataFrame(data, columns=[\"park_name\", \"area_(km²)\"])\n",
    "\n",
    "print(park_area.head())\n",
    "\n",
    "# The data was provided by ChatGPT."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0406524",
   "metadata": {},
   "source": [
    "### Getting the total area of the parks combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d397a66c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total area: 15497.66km²\n"
     ]
    }
   ],
   "source": [
    "# Summing the total area of the four parks\n",
    "total_area = park_area['area_(km²)'].sum()\n",
    "print(f'Total area: {total_area}km²')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc76e76",
   "metadata": {},
   "source": [
    "#### The four parks have a total area of 15497.66km²."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7538889",
   "metadata": {},
   "source": [
    "### Counting the total mammal observations among the four pakrs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57716c80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total observations: 162608\n"
     ]
    }
   ],
   "source": [
    "# Grouping the data to get the sum of mammal observations.\n",
    "total_mammal_obs = outer[outer.category == 'Mammal'].groupby('category')['observations'].sum().reset_index()\n",
    "\n",
    "# Transforming the total_mammal_obs in a df. \n",
    "m_obs_result = pd.DataFrame(total_mammal_obs)\n",
    "\n",
    "print(f'Total observations: {m_obs_result.iloc[0,1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745d4e9b",
   "metadata": {},
   "source": [
    "#### Was recored 162608 observations of mammals in the four parks combined."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3345b1c2",
   "metadata": {},
   "source": [
    "### Encountering the general mammal population density among the four parks combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0577d108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General mammal density: 10.49\n"
     ]
    }
   ],
   "source": [
    "# Isoleting the sum of observations from total_mammal_obs.\n",
    "t_m_obs =  m_obs_result.iloc[0,1]\n",
    "\n",
    "# Calculating the general mammal density.\n",
    "general_m_density = round((t_m_obs / total_area),2)\n",
    "print(f'General mammal density: {general_m_density}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a796c4",
   "metadata": {},
   "source": [
    "#### The General mammal population density is 10 mammals per each km²."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde09f55",
   "metadata": {},
   "source": [
    "### Joining the `park_area` dataset with the `outer` dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3617595a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    scientific_name                            park_name  observations  \\\n",
      "0    Neovison vison  Great Smoky Mountains National Park            77   \n",
      "1  Spizella pusilla  Great Smoky Mountains National Park            72   \n",
      "\n",
      "  category   common_names conservation_status  area_(km²)  \n",
      "0   Mammal  American Mink     No Intervention      2144.1  \n",
      "1     Bird  Field Sparrow     No Intervention      2144.1  \n"
     ]
    }
   ],
   "source": [
    "# Joining the tables.\n",
    "outer_area = outer.merge(park_area, on ='park_name', how = 'outer')\n",
    "print(outer_area.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43044fd5",
   "metadata": {},
   "source": [
    "#### Now each park has the indication of its area in km²."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a15b02",
   "metadata": {},
   "source": [
    "### Counting the total number of mammals on each park and checking the density of its population by km². "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c7f6ca2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping the data to show the total population of mammals in each park and its total area.\n",
    "outer_density = outer_area[outer_area.category == 'Mammal'].groupby(['park_name','area_(km²)', 'category'])['observations'].sum().reset_index().rename(columns ={'observations':'total_observations'})\n",
    "\n",
    "# Chenging the outer_density to a df. \n",
    "outer_density = pd.DataFrame(outer_density)\n",
    "\n",
    "# Finding the mammal population density for each park.\n",
    "outer_density['population_density'] = outer_density['total_observations'] / outer_density['area_(km²)']\n",
    "# print(outer_density)\n",
    "\n",
    "# Plotting a bar chart to analyze the mammal population density by each park. \n",
    "ax = outer_density['population_density'].plot(kind = 'bar', figsize = (14,10))\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(outer_density['park_name'])\n",
    "plt.xlabel('Park Name')\n",
    "plt.ylabel('Number of Mammals per km²')\n",
    "plt.title('Mammal Population Density per km² Among U.S. National Parks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c8c489",
   "metadata": {},
   "source": [
    "#### Though the Yellowstone national park is the one with the biggest number mammals observations registered the Bryce National is the US national park with the higher probability of observing mammals as it is the park with a higher mammal population density."
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
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