Mammal Population Density.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "636d4ad9",
   "metadata": {},
   "source": [
    "### Importing the libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "204c2ae3",
   "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": "468bd87a",
   "metadata": {},
   "source": [
    "### Loading the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "365bf96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = pd.read_csv('observations.csv')\n",
    "species = pd.read_csv('species_info.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73585f3",
   "metadata": {},
   "source": [
    "### Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0375616",
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = observations.drop_duplicates()\n",
    "species = species.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335daaad",
   "metadata": {},
   "source": [
    "### Creating a dataset with each park the total area (km²)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4f8539c",
   "metadata": {},
   "outputs": [],
   "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",
    "# The data was provided by ChatGPT."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b375b237",
   "metadata": {},
   "source": [
    "### Getting the total area of the parks combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d740ef11",
   "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": "21906d64",
   "metadata": {},
   "source": [
    "#### The four parks have a total area of 15497.66km²."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a1a1ac",
   "metadata": {},
   "source": [
    "### Joining the observations with the species dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdc499fd",
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db61403",
   "metadata": {},
   "source": [
    "### Counting the total mammal observations among the four pakrs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e455e79b",
   "metadata": {},
   "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": "87e18237",
   "metadata": {},
   "source": [
    "#### Was recored 162608 observations of mammals in the four parks combined."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcde025",
   "metadata": {},
   "source": [
    "### Encountering the general mammal population density among the four parks combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "badff320",
   "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": "ffb9afd7",
   "metadata": {},
   "source": [
    "### Joining the park_area dataset with the outer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a979ebf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining the tables.\n",
    "outer_area = outer.merge(park_area, on ='park_name', how = 'outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1119c19",
   "metadata": {},
   "source": [
    "#### Now each park has the indication of its area in km²."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f901350a",
   "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": 16,
   "id": "1c841b35",
   "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": "57262bef",
   "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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