biodiversity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Introduction\n",
    "\n",
    "The data in this project focuses on numbers of species across 4 major national parks in the United States.\n",
    "\n",
    "This project will attempt to answer the following questions from the data provided by National Parks Service.\n",
    "\n",
    "1. Which species appear in which park the most?\n",
    "2. Is there a significant difference between species and their conservation status?\n",
    "3. Are certain types of species more likely to be endangered? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries, Loading Data, Getting Familiar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the data from the provided 'species_info' and 'observation' csv files\n",
    "species = pd.read_csv('species_info.csv')\n",
    "observations = pd.read_csv('observations.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['category', 'scientific_name', 'common_names', 'conservation_status'], dtype='object')\n",
      "Index(['scientific_name', 'park_name', 'observations'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(species.columns)\n",
    "print(observations.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23296 entries, 0 to 23295\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   scientific_name  23296 non-null  object\n",
      " 1   park_name        23296 non-null  object\n",
      " 2   observations     23296 non-null  int64 \n",
      "dtypes: int64(1), object(2)None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5824 entries, 0 to 5823\n",
      "Data columns (total 4 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   category             5824 non-null   object\n",
      " 1   scientific_name      5824 non-null   object\n",
      " 2   common_names         5824 non-null   object\n",
      " 3   conservation_status  191 non-null    object\n",
      "dtypes: object(4)None\n"
     ]
    }
   ],
   "source": [
    "# check how many null values exist: \n",
    "# what are different data types in the data set?\n",
    "\n",
    "print(observations.info(memory_usage= False))\n",
    "print('\\n')\n",
    "print(species.info(memory_usage= False))\n",
    "\n",
    "# 5824-191 = 5633: There are 5633 missing(null) rows in the conversation status.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            category                   scientific_name  \\\n",
      "7             Mammal                     Canis latrans   \n",
      "8             Mammal                       Canis lupus   \n",
      "9             Mammal                       Canis rufus   \n",
      "29            Mammal                  Eptesicus fuscus   \n",
      "30            Mammal         Lasionycteris noctivagans   \n",
      "...              ...                               ...   \n",
      "5302  Vascular Plant             Callitriche marginata   \n",
      "5399  Vascular Plant  Camissonia sierrae ssp. alticola   \n",
      "5426  Vascular Plant                Epilobium oreganum   \n",
      "5436  Vascular Plant              Botrychium ascendens   \n",
      "5676  Vascular Plant                  Romneya coulteri   \n",
      "\n",
      "                                           common_names conservation_status  \n",
      "7                                                Coyote  Species of Concern  \n",
      "8                                             Gray Wolf          Endangered  \n",
      "9                                              Red Wolf          Endangered  \n",
      "29                                        Big Brown Bat  Species of Concern  \n",
      "30                                    Silver-Haired Bat  Species of Concern  \n",
      "...                                                 ...                 ...  \n",
      "5302                              Winged Water-Starwort  Species of Concern  \n",
      "5399  Mono Hot Springs Evening Primrose, Mono Hot Sp...  Species of Concern  \n",
      "5426            Grants Pass Willowherb, Oregon Fireweed  Species of Concern  \n",
      "5436            Trianglelobe Moonwort, Upswept Moonwort  Species of Concern  \n",
      "5676                           Coulter's Matilija Poppy  Species of Concern  \n",
      "\n",
      "[191 rows x 4 columns]\n",
      "            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",
      "5819  Vascular Plant               Solanum parishii   \n",
      "5820  Vascular Plant                  Solanum xanti   \n",
      "5821  Vascular Plant         Parthenocissus vitacea   \n",
      "5822  Vascular Plant              Vitis californica   \n",
      "5823  Vascular Plant            Tribulus terrestris   \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",
      "...                                                 ...                 ...  \n",
      "5819                                Parish's Nightshade                 NaN  \n",
      "5820            Chaparral Nightshade, Purple Nightshade                 NaN  \n",
      "5821        Thicket Creeper, Virginia Creeper, Woodbine                 NaN  \n",
      "5822            California Grape, California Wild Grape                 NaN  \n",
      "5823  Bullhead, Caltrop, Goathead, Mexican Sandbur, ...                 NaN  \n",
      "\n",
      "[5633 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NaN                   5633\n",
       "Species of Concern     161\n",
       "Endangered              16\n",
       "Threatened              10\n",
       "In Recovery              4\n",
       "Name: conservation_status, dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at if missing data is systematic or if it's random before we try to deal with it.\n",
    "print(species[species.conservation_status.notnull()])\n",
    "print(species[species.conservation_status.isnull()])\n",
    "species.conservation_status.value_counts(dropna=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Applicable        5633\n",
      "Species of Concern     161\n",
      "Endangered              16\n",
      "Threatened              10\n",
      "In Recovery              4\n",
      "Name: conservation_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# It appears that the missing rows are structurally missing data.\n",
    "# The reason why the data is missing in the conservation status column is presumably,\n",
    "# because the species in question is not under any conservation\n",
    "species.conservation_status.fillna('Not Applicable',inplace=True)\n",
    "print(species.conservation_status.value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Castor canadensis            3\n",
      "Canis lupus                  3\n",
      "Hypochaeris radicata         3\n",
      "Columba livia                3\n",
      "Puma concolor                3\n",
      "                            ..\n",
      "Carex                        1\n",
      "Hexastylis shuttleworthii    1\n",
      "Hexastylis heterophylla      1\n",
      "Hexastylis arifolia          1\n",
      "Tribulus terrestris          1\n",
      "Name: scientific_name, Length: 5541, dtype: int64\n",
      "5824\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>dup_scientific_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Cervus elaphus</td>\n",
       "      <td>Wapiti Or Elk</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Odocoileus virginianus</td>\n",
       "      <td>White-Tailed Deer</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Sus scrofa</td>\n",
       "      <td>Feral Hog, Wild Pig</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Canis lupus</td>\n",
       "      <td>Gray Wolf</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Urocyon cinereoargenteus</td>\n",
       "      <td>Common Gray Fox, Gray Fox</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5619</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Panicum rigidulum var. rigidulum</td>\n",
       "      <td>Redtop Panicgrass, Redtop Panicum</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5638</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Setaria pumila</td>\n",
       "      <td>Cattail Grass, Yellow Bristle Grass, Yellow Br...</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5640</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Vulpia bromoides</td>\n",
       "      <td>Brome Fescue, Brome Six-Weeks Grass, Desert Fe...</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5643</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Vulpia myuros</td>\n",
       "      <td>Foxtail Fescue, Rattail Fescue, Rat-Tail Fescu...</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>Rosa eglanteria</td>\n",
       "      <td>Sweetbriar Rose</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>557 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            category                   scientific_name  \\\n",
       "4             Mammal                    Cervus elaphus   \n",
       "5             Mammal            Odocoileus virginianus   \n",
       "6             Mammal                        Sus scrofa   \n",
       "8             Mammal                       Canis lupus   \n",
       "10            Mammal          Urocyon cinereoargenteus   \n",
       "...              ...                               ...   \n",
       "5619  Vascular Plant  Panicum rigidulum var. rigidulum   \n",
       "5638  Vascular Plant                    Setaria pumila   \n",
       "5640  Vascular Plant                  Vulpia bromoides   \n",
       "5643  Vascular Plant                     Vulpia myuros   \n",
       "5750  Vascular Plant                   Rosa eglanteria   \n",
       "\n",
       "                                           common_names conservation_status  \\\n",
       "4                                         Wapiti Or Elk      Not Applicable   \n",
       "5                                     White-Tailed Deer      Not Applicable   \n",
       "6                                   Feral Hog, Wild Pig      Not Applicable   \n",
       "8                                             Gray Wolf          Endangered   \n",
       "10                            Common Gray Fox, Gray Fox      Not Applicable   \n",
       "...                                                 ...                 ...   \n",
       "5619                  Redtop Panicgrass, Redtop Panicum      Not Applicable   \n",
       "5638  Cattail Grass, Yellow Bristle Grass, Yellow Br...      Not Applicable   \n",
       "5640  Brome Fescue, Brome Six-Weeks Grass, Desert Fe...      Not Applicable   \n",
       "5643  Foxtail Fescue, Rattail Fescue, Rat-Tail Fescu...      Not Applicable   \n",
       "5750                                    Sweetbriar Rose      Not Applicable   \n",
       "\n",
       "      dup_scientific_name  \n",
       "4                    True  \n",
       "5                    True  \n",
       "6                    True  \n",
       "8                    True  \n",
       "10                   True  \n",
       "...                   ...  \n",
       "5619                 True  \n",
       "5638                 True  \n",
       "5640                 True  \n",
       "5643                 True  \n",
       "5750                 True  \n",
       "\n",
       "[557 rows x 5 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It appears that scientific name is common between two csv files/datasets. Let's count how many unique scientific names are in species dataframe\n",
    "# and see if it matches total count of rows. If it does, it probably indicates that the species_info file is a database of all distinct species names. \n",
    "\n",
    "print(species.scientific_name.value_counts(dropna=False))\n",
    "print(species.scientific_name.count()) # This is the total count of scientific_names \n",
    "species[species.scientific_name=='Castor canadensis'] # This is an example of a duplicate\n",
    "# explore duplicated values \n",
    "species['dup_scientific_name']= species.scientific_name.duplicated(False)\n",
    "species[species.dup_scientific_name == True]\n",
    "#This showed that scientific name is duplicated when multiple common names exist for the same species\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brachythecium Moss                                                            7\n",
      "Dicranum Moss                                                                 7\n",
      "Panic Grass                                                                   6\n",
      "Bryum Moss                                                                    6\n",
      "Sphagnum                                                                      6\n",
      "                                                                             ..\n",
      "Pine                                                                          1\n",
      "Red Spruce                                                                    1\n",
      "Norway Spruce                                                                 1\n",
      "Fraser Fir                                                                    1\n",
      "Bullhead, Caltrop, Goathead, Mexican Sandbur, Puncture Vine, Texas Sandbur    1\n",
      "Name: common_names, Length: 5504, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Now let's look at the duplicates in the common_names column...\n",
    "\n",
    "species['dup_common_name'] =species.common_names.duplicated(False)\n",
    "print(species.common_names.value_counts(dropna=False)) \n",
    "# There are duplicates in the common_names as well. \n",
    "# Let's see the data to understand if they're typos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicates_common_name = species[(species.dup_common_name == True) ]\n",
    "duplicates_scientific_name = species[(species.dup_scientific_name == True) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning/Wranglig Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "['Not Applicable' 'Species of Concern' 'Endangered' 'Threatened'\n",
      " 'In Recovery']\n",
      "\n",
      "\n",
      "7\n",
      "['Mammal' 'Bird' 'Reptile' 'Amphibian' 'Fish' 'Vascular Plant'\n",
      " 'Nonvascular Plant']\n",
      "\n",
      "\n",
      "category               category\n",
      "scientific_name          object\n",
      "common_names             object\n",
      "conservation_status    category\n",
      "dup_scientific_name        bool\n",
      "dup_common_name            bool\n",
      "dtype: object\n",
      "\n",
      "\n",
      "scientific_name    object\n",
      "park_name          object\n",
      "observations        int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "84     220\n",
       "85     210\n",
       "91     206\n",
       "92     203\n",
       "95     202\n",
       "      ... \n",
       "9        1\n",
       "321      1\n",
       "303      1\n",
       "314      1\n",
       "310      1\n",
       "Name: observations, Length: 304, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category, and conservation_status columns are categorical data so let's dig into them and see if there are any categorical names that has a typo\n",
    "# and if we can quickly clean that up. First let's count unique/distinct values\n",
    "print(species.conservation_status.nunique()) # There are 4 distinct statuses\n",
    "print(species.conservation_status.unique())\n",
    "\n",
    "print('\\n');print(species.category.nunique())# There are 7 distinct categories\n",
    "print(species.category.unique())\n",
    "\n",
    "#need to associate a ranking to differnet levels of conservation status\n",
    "# per https://www.iucnredlist.org/ and ranking is as follows: \n",
    "# Not Evaluated, Data Deficient, Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered, Extinct in the Wild and Extinct.\n",
    "# Dataset shall have the following order: Species of Concern <Threatened < Endangered < In Recovery\n",
    "#CategoricalDtype() # Another way to convert type to categorical, but prefer using pandas method Categorical in this project to prevent unnecessary library import\n",
    "species['conservation_status'] = pd.Categorical(values= species.conservation_status, categories=['Not Applicable','Species of Concern', 'Threatened', 'Endangered','In Recovery'], ordered = True)\n",
    "species['category'] =pd.Categorical(values= species.category,categories=['Mammal', 'Bird', 'Reptile', 'Amphibian', 'Fish' ,'Vascular Plant','Nonvascular Plant'])\n",
    "\n",
    "print('\\n'); print(species.dtypes)\n",
    "\n",
    "# observation seems to be a numerical value, so let's convert that to an integer, since it's a count.\n",
    "observations.observations.astype('int')\n",
    "print('\\n'); print(observations.dtypes)\n",
    "observations.observations.value_counts() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5541\n",
      "5541\n"
     ]
    }
   ],
   "source": [
    "print(observations.scientific_name.nunique())\n",
    "# There are 5541 unique species in the observations dataset\n",
    "print(species.scientific_name.nunique())\n",
    "# There are 5541 unique species in the species dataset\n",
    "\n",
    "# This means that it will likely be a 1:1 match once we merge two datasets together. \n",
    "# Left merge is used on the scientific_name column as that's the only shared column between the tables.\n",
    "df=pd.merge(observations,species,how='left',on='scientific_name')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's clean the df dataframe\n",
    "df.drop(columns=['dup_common_name','dup_scientific_name','common_names'],inplace=True)\n",
    "df.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics\n",
    "Let's gather some quick stats\n",
    "1. How many different species in each park?\n",
    "2. How many species on average in each park?\n",
    "3. What is the top 10 most observed species across all national parks?\n",
    "4. Which national park has the most endangered species?\n",
    "5. Which category, on average, has the most endangered species?\n",
    "5. For each conservation status, what is the ratio of each species category? (pie chart) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "park_name\n",
       "Bryce National Park                    5828\n",
       "Great Smoky Mountains National Park    5823\n",
       "Yellowstone National Park              5824\n",
       "Yosemite National Park                 5826\n",
       "dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. How many different species in each park? There are more than 5820 species in each park.\n",
    "df_size= df.groupby('park_name').size()\n",
    "#df_size= df_size[df_size.observations != 0]\n",
    "df_size\n",
    "#sns.countplot(x='',y='',data=df_grouped,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Yellowstone has the highest number of species averaging around 250 different kinds.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>park_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bryce National Park</th>\n",
       "      <td>9</td>\n",
       "      <td>176</td>\n",
       "      <td>98.866678</td>\n",
       "      <td>576195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Smoky Mountains National Park</th>\n",
       "      <td>10</td>\n",
       "      <td>147</td>\n",
       "      <td>74.109050</td>\n",
       "      <td>431537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellowstone National Park</th>\n",
       "      <td>57</td>\n",
       "      <td>321</td>\n",
       "      <td>247.770604</td>\n",
       "      <td>1443016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yosemite National Park</th>\n",
       "      <td>31</td>\n",
       "      <td>223</td>\n",
       "      <td>148.164950</td>\n",
       "      <td>863209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     min  max        mean      sum\n",
       "park_name                                                         \n",
       "Bryce National Park                    9  176   98.866678   576195\n",
       "Great Smoky Mountains National Park   10  147   74.109050   431537\n",
       "Yellowstone National Park             57  321  247.770604  1443016\n",
       "Yosemite National Park                31  223  148.164950   863209"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. How many species on average in each park? \n",
    "\n",
    "print('\\nYellowstone has the highest number of species averaging around 250 different kinds.\\n')\n",
    "df_group_1 = df.groupby('park_name').observations.aggregate(['min', 'max','mean','sum'])\n",
    "df_group_1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 Species Names by Total Count\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientific_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Holcus lanatus</th>\n",
       "      <td>1780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Castor canadensis</th>\n",
       "      <td>1725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hypochaeris radicata</th>\n",
       "      <td>1720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puma concolor</th>\n",
       "      <td>1711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Procyon lotor</th>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columba livia</th>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oncorhynchus mykiss</th>\n",
       "      <td>1626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myotis lucifugus</th>\n",
       "      <td>1547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streptopelia decaocto</th>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canis lupus</th>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        sum\n",
       "scientific_name            \n",
       "Holcus lanatus         1780\n",
       "Castor canadensis      1725\n",
       "Hypochaeris radicata   1720\n",
       "Puma concolor          1711\n",
       "Procyon lotor          1692\n",
       "Columba livia          1653\n",
       "Oncorhynchus mykiss    1626\n",
       "Myotis lucifugus       1547\n",
       "Streptopelia decaocto  1530\n",
       "Canis lupus            1430"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. What is the top 10 most observed species across all national parks?\n",
    "\n",
    "print('\\nTop 10 Species Names by Total Count\\n')\n",
    "df_group_2= df.groupby(['scientific_name']).observations.aggregate(['sum'])\n",
    "df_group_2.sort_values('sum',ascending=False,inplace=True)\n",
    "df_group_2 = df_group_2.iloc[:10]\n",
    "df_group_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean Observation of Species by National Park and Category\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>category</th>\n",
       "      <th>Mammal</th>\n",
       "      <th>Bird</th>\n",
       "      <th>Reptile</th>\n",
       "      <th>Amphibian</th>\n",
       "      <th>Fish</th>\n",
       "      <th>Vascular Plant</th>\n",
       "      <th>Nonvascular Plant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>park_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bryce National Park</th>\n",
       "      <td>20680</td>\n",
       "      <td>51647</td>\n",
       "      <td>7950</td>\n",
       "      <td>7380</td>\n",
       "      <td>12587</td>\n",
       "      <td>442959</td>\n",
       "      <td>32992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Smoky Mountains National Park</th>\n",
       "      <td>15071</td>\n",
       "      <td>37728</td>\n",
       "      <td>5691</td>\n",
       "      <td>5707</td>\n",
       "      <td>9306</td>\n",
       "      <td>333177</td>\n",
       "      <td>24857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellowstone National Park</th>\n",
       "      <td>50869</td>\n",
       "      <td>126996</td>\n",
       "      <td>19554</td>\n",
       "      <td>19440</td>\n",
       "      <td>30946</td>\n",
       "      <td>1112190</td>\n",
       "      <td>83021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yosemite National Park</th>\n",
       "      <td>30535</td>\n",
       "      <td>76207</td>\n",
       "      <td>11483</td>\n",
       "      <td>11444</td>\n",
       "      <td>18792</td>\n",
       "      <td>664965</td>\n",
       "      <td>49783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category                             Mammal    Bird  Reptile  Amphibian  \\\n",
       "park_name                                                                 \n",
       "Bryce National Park                   20680   51647     7950       7380   \n",
       "Great Smoky Mountains National Park   15071   37728     5691       5707   \n",
       "Yellowstone National Park             50869  126996    19554      19440   \n",
       "Yosemite National Park                30535   76207    11483      11444   \n",
       "\n",
       "category                              Fish  Vascular Plant  Nonvascular Plant  \n",
       "park_name                                                                      \n",
       "Bryce National Park                  12587          442959              32992  \n",
       "Great Smoky Mountains National Park   9306          333177              24857  \n",
       "Yellowstone National Park            30946         1112190              83021  \n",
       "Yosemite National Park               18792          664965              49783  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4a. Total endangered species count per national park and category\n",
    "# This would be cool to show on a US map with different size circles.\n",
    "\n",
    "print('\\nMean Observation of Species by National Park and Category\\n')\n",
    "df_group_3_sum=df.pivot_table(index=['park_name'],columns='category',values='observations',aggfunc=['sum'])\n",
    "df_group_3_sum = df_group_3_sum.droplevel(0,axis=1)\n",
    "df_group_3_sum\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean Observation of Species by National Park and Category\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>category</th>\n",
       "      <th>Mammal</th>\n",
       "      <th>Bird</th>\n",
       "      <th>Reptile</th>\n",
       "      <th>Amphibian</th>\n",
       "      <th>Fish</th>\n",
       "      <th>Vascular Plant</th>\n",
       "      <th>Nonvascular Plant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>park_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bryce National Park</th>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>101</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Smoky Mountains National Park</th>\n",
       "      <td>69</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellowstone National Park</th>\n",
       "      <td>236</td>\n",
       "      <td>244</td>\n",
       "      <td>248</td>\n",
       "      <td>243</td>\n",
       "      <td>240</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yosemite National Park</th>\n",
       "      <td>141</td>\n",
       "      <td>146</td>\n",
       "      <td>145</td>\n",
       "      <td>143</td>\n",
       "      <td>146</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category                             Mammal  Bird  Reptile  Amphibian  Fish  \\\n",
       "park_name                                                                     \n",
       "Bryce National Park                      95    99      101         92    98   \n",
       "Great Smoky Mountains National Park      69    72       72         71    72   \n",
       "Yellowstone National Park               236   244      248        243   240   \n",
       "Yosemite National Park                  141   146      145        143   146   \n",
       "\n",
       "category                             Vascular Plant  Nonvascular Plant  \n",
       "park_name                                                               \n",
       "Bryce National Park                              99                 99  \n",
       "Great Smoky Mountains National Park              75                 75  \n",
       "Yellowstone National Park                       249                249  \n",
       "Yosemite National Park                          149                149  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4b. Average endangered species count per national park and category\n",
    "\n",
    "print('\\nMean Observation of Species by National Park and Category\\n')\n",
    "df_group_3_mean= df.pivot_table(index=['park_name'],columns='category',values='observations',aggfunc=['mean']).round().astype(int)\n",
    "df_group_3_mean = df_group_3_mean.droplevel(0,axis=1)\n",
    "df_group_3_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean Observation of Species by Category and Conservation Status\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>conservation_status</th>\n",
       "      <th>Not Applicable</th>\n",
       "      <th>Species of Concern</th>\n",
       "      <th>Threatened</th>\n",
       "      <th>Endangered</th>\n",
       "      <th>In Recovery</th>\n",
       "      <th>Grand Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mammal</th>\n",
       "      <td>142</td>\n",
       "      <td>129</td>\n",
       "      <td>72</td>\n",
       "      <td>46</td>\n",
       "      <td>60</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bird</th>\n",
       "      <td>144</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>116</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reptile</th>\n",
       "      <td>142</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amphibian</th>\n",
       "      <td>141</td>\n",
       "      <td>128</td>\n",
       "      <td>70</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fish</th>\n",
       "      <td>144</td>\n",
       "      <td>132</td>\n",
       "      <td>82</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vascular Plant</th>\n",
       "      <td>143</td>\n",
       "      <td>129</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonvascular Plant</th>\n",
       "      <td>143</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>999</td>\n",
       "      <td>907</td>\n",
       "      <td>293</td>\n",
       "      <td>196</td>\n",
       "      <td>176</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "conservation_status  Not Applicable  Species of Concern  Threatened  \\\n",
       "category                                                              \n",
       "Mammal                          142                 129          72   \n",
       "Bird                            144                 127           0   \n",
       "Reptile                         142                 127           0   \n",
       "Amphibian                       141                 128          70   \n",
       "Fish                            144                 132          82   \n",
       "Vascular Plant                  143                 129          69   \n",
       "Nonvascular Plant               143                 135           0   \n",
       "Total                           999                 907         293   \n",
       "\n",
       "conservation_status  Endangered  In Recovery  Grand Total  \n",
       "category                                                   \n",
       "Mammal                       46           60          449  \n",
       "Bird                         36          116          423  \n",
       "Reptile                       0            0          269  \n",
       "Amphibian                    36            0          375  \n",
       "Fish                         38            0          396  \n",
       "Vascular Plant               40            0          381  \n",
       "Nonvascular Plant             0            0          278  \n",
       "Total                       196          176         2571  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Which category has the most endangered species on average?\n",
    "#df_group_4= df.groupby(['conservation_status','category']).observations.aggregate(['sum'])\n",
    "\n",
    "# null values in the conservation status column likely represent species not categorized for conservation, i.e not under conservation.\n",
    "print('\\nMean Observation of Species by Category and Conservation Status\\n')\n",
    "df_group_4=df.pivot_table(index='category',columns='conservation_status',values='observations',aggfunc=['mean'],fill_value=(0)).round().astype(int)\n",
    "df_group_4.loc['Total']=df_group_4.sum()\n",
    "df_group_4['mean','Grand Total']=df_group_4.sum(axis=1) #adds a total line to the right of pivot table\n",
    "df_group_4= df_group_4.droplevel(0,axis=1)\n",
    "df_group_4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "1. Average species count\n",
    "2. Average species count per category and national park\n",
    "3. 'Endangered' species count per national park\n",
    "4. 'Endangered' species count per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total species count per national park\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "#observations.groupby('park_name').mean()\n",
    "sns.barplot(x='park_name', y='observations',data=observations) #creates plot\n",
    "#inserts new line in between each work in x tick labels.\n",
    "#from here\n",
    "xlabels_new=[]\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    xlabels_new.append(label.get_text().replace(' ','\\n'))\n",
    "ax.set_xticklabels(xlabels_new) \n",
    "# up to here\n",
    "ax.bar_label(ax.containers[-1], fmt='%d', label_type='center') #annotates in the center of bars \n",
    "#ax.tick_params(axis='x', labelrotation =10) # not needed because there is a break in between each word in x tick labels\n",
    "\n",
    "plt.title('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of species per category and national park\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "sns.barplot(x='category', y='observations',hue='park_name',data=df)\n",
    "ax.tick_params(axis='x', labelrotation =10)\n",
    "ax.grid(axis='y')\n",
    "plt.title('Average Species Count by National Park')\n",
    "plt.show()\n",
    "plt.clf()\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#stacked bar plot view\n",
    "\n",
    "ax= df_group_3_mean.plot(kind='bar',stacked=True,figsize=(8,6))\n",
    "\n",
    "xlabels_new=[]\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    xlabels_new.append(label.get_text().replace(' ','\\n'))\n",
    "ax.set_xticklabels(xlabels_new) \n",
    "ax.tick_params(axis='x', labelrotation =10)\n",
    "ax.grid(axis='y')\n",
    "plt.title('Average Species Count by Category')\n",
    "plt.show()\n",
    "plt.clf()\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of endangered species for each national park\n",
    "df_group_4=df.pivot_table(index='category',columns='conservation_status',values='observations',aggfunc=['mean'],fill_value=(0)).round().astype(int)\n",
    "df_group_4= df_group_4.droplevel(0,axis=1)\n",
    "df_group_4.plot(kind='barh', stacked=True, figsize=(10,6))\n",
    "plt.title('Average Species Count by Category and Conservation Status')\n",
    "plt.show()\n",
    "plt.clf()\n",
    "%matplotlib inline\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('machine-learning-env')",
   "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": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "73377df2183f369987af4d0c78b7a8bf94e96ddaeb54f8a6c12d927b854c75ae"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}