EDA_for_DA_Capstone_Project.ipynb

{
  "cells": [
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      "source": [
        "# Data Analytics Capstone Project Title: Austin Animal Center\n",
        "> Predicting Animal Adoption Rates in Austin, Texas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SHz1lpPVtoN1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqR_jBac4wC5"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OPlzIT5ptXRw"
      },
      "outputs": [],
      "source": [
        "data1 = pd.read_csv(\"Austin_Animal_Center_Intakes.csv\")\n",
        "data2 = pd.read_csv(\"Austin_Animal_Center_Outcomes.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 414
        },
        "id": "ocK9bCBEvBb1",
        "outputId": "90ca400d-912e-47ee-d67a-182c426e80f1"
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      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Animal ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>DateTime</th>\n",
              "      <th>MonthYear</th>\n",
              "      <th>Found Location</th>\n",
              "      <th>Intake Type</th>\n",
              "      <th>Intake Condition</th>\n",
              "      <th>Animal Type</th>\n",
              "      <th>Sex upon Intake</th>\n",
              "      <th>Age upon Intake</th>\n",
              "      <th>Breed</th>\n",
              "      <th>Color</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A786884</td>\n",
              "      <td>*Brock</td>\n",
              "      <td>01/03/2019 04:19:00 PM</td>\n",
              "      <td>January 2019</td>\n",
              "      <td>2501 Magin Meadow Dr in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Beagle Mix</td>\n",
              "      <td>Tricolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A706918</td>\n",
              "      <td>Belle</td>\n",
              "      <td>07/05/2015 12:59:00 PM</td>\n",
              "      <td>July 2015</td>\n",
              "      <td>9409 Bluegrass Dr in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Spayed Female</td>\n",
              "      <td>8 years</td>\n",
              "      <td>English Springer Spaniel</td>\n",
              "      <td>White/Liver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A724273</td>\n",
              "      <td>Runster</td>\n",
              "      <td>04/14/2016 06:43:00 PM</td>\n",
              "      <td>April 2016</td>\n",
              "      <td>2818 Palomino Trail in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Intact Male</td>\n",
              "      <td>11 months</td>\n",
              "      <td>Basenji Mix</td>\n",
              "      <td>Sable/White</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A857105</td>\n",
              "      <td>Johnny Ringo</td>\n",
              "      <td>05/12/2022 12:23:00 AM</td>\n",
              "      <td>May 2022</td>\n",
              "      <td>4404 Sarasota Drive in Austin (TX)</td>\n",
              "      <td>Public Assist</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Cat</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Domestic Shorthair</td>\n",
              "      <td>Orange Tabby</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A682524</td>\n",
              "      <td>Rio</td>\n",
              "      <td>06/29/2014 10:38:00 AM</td>\n",
              "      <td>June 2014</td>\n",
              "      <td>800 Grove Blvd in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>4 years</td>\n",
              "      <td>Doberman Pinsch/Australian Cattle Dog</td>\n",
              "      <td>Tan/Gray</td>\n",
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            "text/plain": [
              "  Animal ID          Name                DateTime     MonthYear  \\\n",
              "0   A786884        *Brock  01/03/2019 04:19:00 PM  January 2019   \n",
              "1   A706918         Belle  07/05/2015 12:59:00 PM     July 2015   \n",
              "2   A724273       Runster  04/14/2016 06:43:00 PM    April 2016   \n",
              "3   A857105  Johnny Ringo  05/12/2022 12:23:00 AM      May 2022   \n",
              "4   A682524           Rio  06/29/2014 10:38:00 AM     June 2014   \n",
              "\n",
              "                        Found Location    Intake Type Intake Condition  \\\n",
              "0  2501 Magin Meadow Dr in Austin (TX)          Stray           Normal   \n",
              "1     9409 Bluegrass Dr in Austin (TX)          Stray           Normal   \n",
              "2   2818 Palomino Trail in Austin (TX)          Stray           Normal   \n",
              "3   4404 Sarasota Drive in Austin (TX)  Public Assist           Normal   \n",
              "4        800 Grove Blvd in Austin (TX)          Stray           Normal   \n",
              "\n",
              "  Animal Type Sex upon Intake Age upon Intake  \\\n",
              "0         Dog   Neutered Male         2 years   \n",
              "1         Dog   Spayed Female         8 years   \n",
              "2         Dog     Intact Male       11 months   \n",
              "3         Cat   Neutered Male         2 years   \n",
              "4         Dog   Neutered Male         4 years   \n",
              "\n",
              "                                   Breed         Color  \n",
              "0                             Beagle Mix      Tricolor  \n",
              "1               English Springer Spaniel   White/Liver  \n",
              "2                            Basenji Mix   Sable/White  \n",
              "3                     Domestic Shorthair  Orange Tabby  \n",
              "4  Doberman Pinsch/Australian Cattle Dog      Tan/Gray  "
            ]
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          "metadata": {},
          "output_type": "execute_result"
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        "data1.head()"
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              "      <th>0</th>\n",
              "      <td>A794011</td>\n",
              "      <td>Chunk</td>\n",
              "      <td>05/08/2019 06:20:00 PM</td>\n",
              "      <td>May 2019</td>\n",
              "      <td>05/02/2017</td>\n",
              "      <td>Rto-Adopt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cat</td>\n",
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              "      <th>1</th>\n",
              "      <td>A776359</td>\n",
              "      <td>Gizmo</td>\n",
              "      <td>07/18/2018 04:02:00 PM</td>\n",
              "      <td>Jul 2018</td>\n",
              "      <td>07/12/2017</td>\n",
              "      <td>Adoption</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
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              "      <td>White/Brown</td>\n",
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              "      <th>2</th>\n",
              "      <td>A821648</td>\n",
              "      <td>NaN</td>\n",
              "      <td>08/16/2020 11:38:00 AM</td>\n",
              "      <td>Aug 2020</td>\n",
              "      <td>08/16/2019</td>\n",
              "      <td>Euthanasia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Other</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1 year</td>\n",
              "      <td>Raccoon</td>\n",
              "      <td>Gray</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A720371</td>\n",
              "      <td>Moose</td>\n",
              "      <td>02/13/2016 05:59:00 PM</td>\n",
              "      <td>Feb 2016</td>\n",
              "      <td>10/08/2015</td>\n",
              "      <td>Adoption</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>4 months</td>\n",
              "      <td>Anatol Shepherd/Labrador Retriever</td>\n",
              "      <td>Buff</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A674754</td>\n",
              "      <td>NaN</td>\n",
              "      <td>03/18/2014 11:47:00 AM</td>\n",
              "      <td>Mar 2014</td>\n",
              "      <td>03/12/2014</td>\n",
              "      <td>Transfer</td>\n",
              "      <td>Partner</td>\n",
              "      <td>Cat</td>\n",
              "      <td>Intact Male</td>\n",
              "      <td>6 days</td>\n",
              "      <td>Domestic Shorthair Mix</td>\n",
              "      <td>Orange Tabby</td>\n",
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              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  Animal ID   Name                DateTime MonthYear Date of Birth  \\\n",
              "0   A794011  Chunk  05/08/2019 06:20:00 PM  May 2019    05/02/2017   \n",
              "1   A776359  Gizmo  07/18/2018 04:02:00 PM  Jul 2018    07/12/2017   \n",
              "2   A821648    NaN  08/16/2020 11:38:00 AM  Aug 2020    08/16/2019   \n",
              "3   A720371  Moose  02/13/2016 05:59:00 PM  Feb 2016    10/08/2015   \n",
              "4   A674754    NaN  03/18/2014 11:47:00 AM  Mar 2014    03/12/2014   \n",
              "\n",
              "  Outcome Type Outcome Subtype Animal Type Sex upon Outcome Age upon Outcome  \\\n",
              "0    Rto-Adopt             NaN         Cat    Neutered Male          2 years   \n",
              "1     Adoption             NaN         Dog    Neutered Male           1 year   \n",
              "2   Euthanasia             NaN       Other          Unknown           1 year   \n",
              "3     Adoption             NaN         Dog    Neutered Male         4 months   \n",
              "4     Transfer         Partner         Cat      Intact Male           6 days   \n",
              "\n",
              "                                Breed              Color  \n",
              "0              Domestic Shorthair Mix  Brown Tabby/White  \n",
              "1             Chihuahua Shorthair Mix        White/Brown  \n",
              "2                             Raccoon               Gray  \n",
              "3  Anatol Shepherd/Labrador Retriever               Buff  \n",
              "4              Domestic Shorthair Mix       Orange Tabby  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tlyj4-u4vvQc",
        "outputId": "57bb77d5-39b0-43e9-f79b-1bf0a857c10f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(154997, 12)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6szPnmLvsDt",
        "outputId": "9f0bfb19-deac-4d23-db2c-1a4fb0849c6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(154993, 12)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TABWbqYwzXfb",
        "outputId": "8134ef8c-5667-4a3c-d258-2eb87ffafe68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 154997 entries, 0 to 154996\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   Animal ID         154997 non-null  object\n",
            " 1   Name              110218 non-null  object\n",
            " 2   DateTime          154997 non-null  object\n",
            " 3   MonthYear         154997 non-null  object\n",
            " 4   Found Location    154997 non-null  object\n",
            " 5   Intake Type       154997 non-null  object\n",
            " 6   Intake Condition  154997 non-null  object\n",
            " 7   Animal Type       154997 non-null  object\n",
            " 8   Sex upon Intake   154995 non-null  object\n",
            " 9   Age upon Intake   154996 non-null  object\n",
            " 10  Breed             154997 non-null  object\n",
            " 11  Color             154997 non-null  object\n",
            "dtypes: object(12)\n",
            "memory usage: 14.2+ MB\n"
          ]
        }
      ],
      "source": [
        "data1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsCUppmqzbu1",
        "outputId": "19a5a24e-1de4-48f2-b32b-04e21172b751"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 154993 entries, 0 to 154992\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   Animal ID         154993 non-null  object\n",
            " 1   Name              110331 non-null  object\n",
            " 2   DateTime          154993 non-null  object\n",
            " 3   MonthYear         154993 non-null  object\n",
            " 4   Date of Birth     154993 non-null  object\n",
            " 5   Outcome Type      154964 non-null  object\n",
            " 6   Outcome Subtype   71468 non-null   object\n",
            " 7   Animal Type       154993 non-null  object\n",
            " 8   Sex upon Outcome  154991 non-null  object\n",
            " 9   Age upon Outcome  154953 non-null  object\n",
            " 10  Breed             154993 non-null  object\n",
            " 11  Color             154993 non-null  object\n",
            "dtypes: object(12)\n",
            "memory usage: 14.2+ MB\n"
          ]
        }
      ],
      "source": [
        "data2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g8Xv674U3SES"
      },
      "outputs": [],
      "source": [
        "data2 = data2.drop(['Outcome Subtype'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZvSeNtv0oQt",
        "outputId": "ac44d1e1-b148-4a2f-d3d9-4f2fe478bbfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Common Columns: ['Animal ID', 'Name', 'DateTime', 'MonthYear', 'Animal Type', 'Breed', 'Color']\n"
          ]
        }
      ],
      "source": [
        "# Find common column names\n",
        "common_columns = data1.columns.intersection(data2.columns)\n",
        "\n",
        "print(\"Common Columns:\", list(common_columns))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_2aelfOgu7ra"
      },
      "outputs": [],
      "source": [
        "# Merging the dataset\n",
        "dataset = pd.merge(data1, data2, on=['Animal ID', 'Name', 'Animal Type', 'Breed', 'Color'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-lWRG_pUc1B",
        "outputId": "4c5207ca-3013-43dd-da5c-870bbf35c2b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(198622, 18)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GjbvdT57UGdx"
      },
      "outputs": [],
      "source": [
        "# To remove rows that are exact duplicates of each other\n",
        "dataset = dataset.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0SPpEpyUfq3",
        "outputId": "2ba7ea52-f650-4dc3-96c6-7dad7188c780"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(198525, 18)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bk1D2eCM958R",
        "outputId": "6f452991-da41-4759-af40-50e8d6278f5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "138163\n"
          ]
        }
      ],
      "source": [
        "#finding out the number of unique animals.\n",
        "unique_animal_id = dataset['Animal ID'].nunique()\n",
        "print(unique_animal_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "pgVxz1f_4Z3j",
        "outputId": "cddbe458-8a5c-4a42-b2d9-ec1ac90df362"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-f0b086f6-7e4a-4810-9a55-d1fc424e4523\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Animal ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>DateTime_x</th>\n",
              "      <th>MonthYear_x</th>\n",
              "      <th>Found Location</th>\n",
              "      <th>Intake Type</th>\n",
              "      <th>Intake Condition</th>\n",
              "      <th>Animal Type</th>\n",
              "      <th>Sex upon Intake</th>\n",
              "      <th>Age upon Intake</th>\n",
              "      <th>Breed</th>\n",
              "      <th>Color</th>\n",
              "      <th>DateTime_y</th>\n",
              "      <th>MonthYear_y</th>\n",
              "      <th>Date of Birth</th>\n",
              "      <th>Outcome Type</th>\n",
              "      <th>Sex upon Outcome</th>\n",
              "      <th>Age upon Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A786884</td>\n",
              "      <td>*Brock</td>\n",
              "      <td>01/03/2019 04:19:00 PM</td>\n",
              "      <td>January 2019</td>\n",
              "      <td>2501 Magin Meadow Dr in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Beagle Mix</td>\n",
              "      <td>Tricolor</td>\n",
              "      <td>01/08/2019 03:11:00 PM</td>\n",
              "      <td>Jan 2019</td>\n",
              "      <td>01/03/2017</td>\n",
              "      <td>Transfer</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A706918</td>\n",
              "      <td>Belle</td>\n",
              "      <td>07/05/2015 12:59:00 PM</td>\n",
              "      <td>July 2015</td>\n",
              "      <td>9409 Bluegrass Dr in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Spayed Female</td>\n",
              "      <td>8 years</td>\n",
              "      <td>English Springer Spaniel</td>\n",
              "      <td>White/Liver</td>\n",
              "      <td>07/05/2015 03:13:00 PM</td>\n",
              "      <td>Jul 2015</td>\n",
              "      <td>07/05/2007</td>\n",
              "      <td>Return to Owner</td>\n",
              "      <td>Spayed Female</td>\n",
              "      <td>8 years</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A724273</td>\n",
              "      <td>Runster</td>\n",
              "      <td>04/14/2016 06:43:00 PM</td>\n",
              "      <td>April 2016</td>\n",
              "      <td>2818 Palomino Trail in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Intact Male</td>\n",
              "      <td>11 months</td>\n",
              "      <td>Basenji Mix</td>\n",
              "      <td>Sable/White</td>\n",
              "      <td>04/21/2016 05:17:00 PM</td>\n",
              "      <td>Apr 2016</td>\n",
              "      <td>04/17/2015</td>\n",
              "      <td>Return to Owner</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>1 year</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A857105</td>\n",
              "      <td>Johnny Ringo</td>\n",
              "      <td>05/12/2022 12:23:00 AM</td>\n",
              "      <td>May 2022</td>\n",
              "      <td>4404 Sarasota Drive in Austin (TX)</td>\n",
              "      <td>Public Assist</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Cat</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "      <td>Domestic Shorthair</td>\n",
              "      <td>Orange Tabby</td>\n",
              "      <td>05/12/2022 02:35:00 PM</td>\n",
              "      <td>May 2022</td>\n",
              "      <td>05/12/2020</td>\n",
              "      <td>Transfer</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>2 years</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A682524</td>\n",
              "      <td>Rio</td>\n",
              "      <td>06/29/2014 10:38:00 AM</td>\n",
              "      <td>June 2014</td>\n",
              "      <td>800 Grove Blvd in Austin (TX)</td>\n",
              "      <td>Stray</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Dog</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>4 years</td>\n",
              "      <td>Doberman Pinsch/Australian Cattle Dog</td>\n",
              "      <td>Tan/Gray</td>\n",
              "      <td>07/02/2014 02:16:00 PM</td>\n",
              "      <td>Jul 2014</td>\n",
              "      <td>06/29/2010</td>\n",
              "      <td>Return to Owner</td>\n",
              "      <td>Neutered Male</td>\n",
              "      <td>4 years</td>\n",
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            "text/plain": [
              "  Animal ID          Name              DateTime_x   MonthYear_x  \\\n",
              "0   A786884        *Brock  01/03/2019 04:19:00 PM  January 2019   \n",
              "1   A706918         Belle  07/05/2015 12:59:00 PM     July 2015   \n",
              "2   A724273       Runster  04/14/2016 06:43:00 PM    April 2016   \n",
              "3   A857105  Johnny Ringo  05/12/2022 12:23:00 AM      May 2022   \n",
              "4   A682524           Rio  06/29/2014 10:38:00 AM     June 2014   \n",
              "\n",
              "                        Found Location    Intake Type Intake Condition  \\\n",
              "0  2501 Magin Meadow Dr in Austin (TX)          Stray           Normal   \n",
              "1     9409 Bluegrass Dr in Austin (TX)          Stray           Normal   \n",
              "2   2818 Palomino Trail in Austin (TX)          Stray           Normal   \n",
              "3   4404 Sarasota Drive in Austin (TX)  Public Assist           Normal   \n",
              "4        800 Grove Blvd in Austin (TX)          Stray           Normal   \n",
              "\n",
              "  Animal Type Sex upon Intake Age upon Intake  \\\n",
              "0         Dog   Neutered Male         2 years   \n",
              "1         Dog   Spayed Female         8 years   \n",
              "2         Dog     Intact Male       11 months   \n",
              "3         Cat   Neutered Male         2 years   \n",
              "4         Dog   Neutered Male         4 years   \n",
              "\n",
              "                                   Breed         Color  \\\n",
              "0                             Beagle Mix      Tricolor   \n",
              "1               English Springer Spaniel   White/Liver   \n",
              "2                            Basenji Mix   Sable/White   \n",
              "3                     Domestic Shorthair  Orange Tabby   \n",
              "4  Doberman Pinsch/Australian Cattle Dog      Tan/Gray   \n",
              "\n",
              "               DateTime_y MonthYear_y Date of Birth     Outcome Type  \\\n",
              "0  01/08/2019 03:11:00 PM    Jan 2019    01/03/2017         Transfer   \n",
              "1  07/05/2015 03:13:00 PM    Jul 2015    07/05/2007  Return to Owner   \n",
              "2  04/21/2016 05:17:00 PM    Apr 2016    04/17/2015  Return to Owner   \n",
              "3  05/12/2022 02:35:00 PM    May 2022    05/12/2020         Transfer   \n",
              "4  07/02/2014 02:16:00 PM    Jul 2014    06/29/2010  Return to Owner   \n",
              "\n",
              "  Sex upon Outcome Age upon Outcome  \n",
              "0    Neutered Male          2 years  \n",
              "1    Spayed Female          8 years  \n",
              "2    Neutered Male           1 year  \n",
              "3    Neutered Male          2 years  \n",
              "4    Neutered Male          4 years  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "2c7sJx2Dxg95",
        "outputId": "ec8b9e6d-da00-49d7-a64e-f54e2a93b229"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 198525 entries, 0 to 198621\n",
            "Data columns (total 18 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   Animal ID         198525 non-null  object\n",
            " 1   Name              153041 non-null  object\n",
            " 2   DateTime_x        198525 non-null  object\n",
            " 3   MonthYear_x       198525 non-null  object\n",
            " 4   Found Location    198525 non-null  object\n",
            " 5   Intake Type       198525 non-null  object\n",
            " 6   Intake Condition  198525 non-null  object\n",
            " 7   Animal Type       198525 non-null  object\n",
            " 8   Sex upon Intake   198523 non-null  object\n",
            " 9   Age upon Intake   198524 non-null  object\n",
            " 10  Breed             198525 non-null  object\n",
            " 11  Color             198525 non-null  object\n",
            " 12  DateTime_y        198525 non-null  object\n",
            " 13  MonthYear_y       198525 non-null  object\n",
            " 14  Date of Birth     198525 non-null  object\n",
            " 15  Outcome Type      198484 non-null  object\n",
            " 16  Sex upon Outcome  198523 non-null  object\n",
            " 17  Age upon Outcome  198485 non-null  object\n",
            "dtypes: object(18)\n",
            "memory usage: 28.8+ MB\n"
          ]
        }
      ],
      "source": [
        "dataset.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "Ygrnqrgez2bP",
        "outputId": "f92f2c66-576c-4b78-b586-659f11015225"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Animal ID</th>\n",
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              "      <td>A721033</td>\n",
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              "      <td>09/23/2016 12:00:00 PM</td>\n",
              "      <td>June 2015</td>\n",
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              "      <td>1 year</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5814f068-4655-466f-abd2-4fc184762201')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5814f068-4655-466f-abd2-4fc184762201 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5814f068-4655-466f-abd2-4fc184762201');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05fd68f3-4488-4331-b033-210fd8711efd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05fd68f3-4488-4331-b033-210fd8711efd')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05fd68f3-4488-4331-b033-210fd8711efd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Animal ID    Name              DateTime_x MonthYear_x Found Location  \\\n",
              "count     198525  153041                  198525      198525         198525   \n",
              "unique    138163   26967                  107197         120          63540   \n",
              "top      A721033     Max  09/23/2016 12:00:00 PM   June 2015    Austin (TX)   \n",
              "freq        1089    1147                      71        2712          41232   \n",
              "\n",
              "       Intake Type Intake Condition Animal Type Sex upon Intake  \\\n",
              "count       198525           198525      198525          198523   \n",
              "unique           6               20           5               5   \n",
              "top          Stray           Normal         Dog     Intact Male   \n",
              "freq        129145           174038      123900           60022   \n",
              "\n",
              "       Age upon Intake                   Breed        Color  \\\n",
              "count           198524                  198525       198525   \n",
              "unique              54                    2861          629   \n",
              "top             1 year  Domestic Shorthair Mix  Black/White   \n",
              "freq             34716                   36925        21603   \n",
              "\n",
              "                    DateTime_y MonthYear_y Date of Birth Outcome Type  \\\n",
              "count                   198525      198525        198525       198484   \n",
              "unique                  128310         120          8074           10   \n",
              "top     04/18/2016 12:00:00 AM    Jun 2019    05/20/2015     Adoption   \n",
              "freq                        57        2815          1133        94532   \n",
              "\n",
              "       Sex upon Outcome Age upon Outcome  \n",
              "count            198523           198485  \n",
              "unique                5               54  \n",
              "top       Neutered Male           1 year  \n",
              "freq              76975            35143  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data Overview\n",
        "dataset.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uby3GuFy2MhI",
        "outputId": "6ee7ff99-5fc7-444f-bc1e-43e90bf0af46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dog          123900\n",
            "Cat           65599\n",
            "Other          8228\n",
            "Bird            772\n",
            "Livestock        26\n",
            "Name: Animal Type, dtype: int64\n",
            "\n",
            "Stray                 129145\n",
            "Owner Surrender        47082\n",
            "Public Assist          14685\n",
            "Wildlife                5924\n",
            "Abandoned               1405\n",
            "Euthanasia Request       284\n",
            "Name: Intake Type, dtype: int64\n",
            "\n",
            "Adoption           94532\n",
            "Transfer           48600\n",
            "Return to Owner    40734\n",
            "Euthanasia         10308\n",
            "Rto-Adopt           1925\n",
            "Died                1497\n",
            "Disposal             740\n",
            "Missing              110\n",
            "Relocate              26\n",
            "Stolen                12\n",
            "Name: Outcome Type, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Count unique values in categorical columns\n",
        "print(dataset['Animal Type'].value_counts())\n",
        "print()\n",
        "print(dataset['Intake Type'].value_counts())\n",
        "print()\n",
        "print(dataset['Outcome Type'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "OVIDouyu7Q6E",
        "outputId": "306eaae9-e160-4758-b0ff-e7889ea4cd0c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Assuming 'dataset' is your DataFrame\n",
        "animal_type_counts = dataset['Animal Type'].value_counts()\n",
        "intake_type_counts = dataset['Intake Type'].value_counts()\n",
        "outcome_type_counts = dataset['Outcome Type'].value_counts()\n",
        "\n",
        "# Create a figure with subplots to display the counts of each column\n",
        "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))\n",
        "\n",
        "# Plot the counts of 'Animal Type'\n",
        "sns.barplot(x=animal_type_counts.index, y=animal_type_counts.values, ax=axes[0])\n",
        "axes[0].set_title('Animal Type')\n",
        "axes[0].set_xticklabels(axes[0].get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "\n",
        "# Plot the counts of 'Intake Type'\n",
        "sns.barplot(x=intake_type_counts.index, y=intake_type_counts.values, ax=axes[1])\n",
        "axes[1].set_title('Intake Type')\n",
        "axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "\n",
        "# Plot the counts of 'Outcome Type'\n",
        "sns.barplot(x=outcome_type_counts.index, y=outcome_type_counts.values, ax=axes[2])\n",
        "axes[2].set_title('Outcome Type')\n",
        "axes[2].set_xticklabels(axes[2].get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMpH91pe2o6o",
        "outputId": "45434107-5c24-419b-f4df-2b9f9cb570da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Dog' 'Cat' 'Other' 'Bird' 'Livestock']\n",
            "\n",
            "The number of unique values in Breed column 2861\n"
          ]
        }
      ],
      "source": [
        "# Animal Types and Breeds\n",
        "print(dataset['Animal Type'].unique())\n",
        "print()\n",
        "print(\"The number of unique values in Breed column\", dataset['Breed'].nunique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7BxHSEWV2a7c"
      },
      "outputs": [],
      "source": [
        "# # Check for missing values\n",
        "# missing_values = dataset.isnull().sum()\n",
        "# print(\"Missing Values:\")\n",
        "# print(missing_values)\n",
        "\n",
        "# # Remove rows with missing values in any column\n",
        "# dataset.dropna(axis=0, how='any', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "gRKjlgfwOdW5",
        "outputId": "644eee60-33d9-4c7b-b0ae-123224ad9ede"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Intake Patterns\n",
        "# Count plot for Intake Type\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(x='Intake Type', data=dataset, palette='Set1')\n",
        "plt.title('Distribution of Intake Types')\n",
        "plt.xlabel('Intake Type')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yG1Dgp4TAf3F",
        "outputId": "bbbcbf56-c72b-4320-8c44-99c2d612871b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Outcome Patterns\n",
        "# Count plot for Outcome Type\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(x='Outcome Type', data=dataset, palette='Set2')\n",
        "plt.title('Distribution of Outcome Types')\n",
        "plt.xlabel('Outcome Type')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrmAtnqU2kJY",
        "outputId": "8a642948-82ae-4892-b5da-c427a86ed70f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DateTime_x\n",
            "2013-10-31    1833\n",
            "2013-11-30    1602\n",
            "2013-12-31    1511\n",
            "2014-01-31    1595\n",
            "2014-02-28    1480\n",
            "              ... \n",
            "2023-05-31    1458\n",
            "2023-06-30    1122\n",
            "2023-07-31     798\n",
            "2023-08-31     649\n",
            "2023-09-30      13\n",
            "Freq: M, Length: 120, dtype: int64\n",
            "\n",
            "MonthYear_x\n",
            "2013-10-31    1833\n",
            "2013-11-30    1602\n",
            "2013-12-31    1511\n",
            "2014-01-31    1595\n",
            "2014-02-28    1480\n",
            "              ... \n",
            "2023-05-31    1458\n",
            "2023-06-30    1122\n",
            "2023-07-31     798\n",
            "2023-08-31     649\n",
            "2023-09-30      13\n",
            "Freq: M, Length: 120, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Time Analysis\n",
        "dataset['DateTime_x'] = pd.to_datetime(dataset['DateTime_x'])\n",
        "dataset['MonthYear_x'] = pd.to_datetime(dataset['MonthYear_x'])\n",
        "print(dataset.resample('M', on='DateTime_x').size())  # Monthly intakes\n",
        "print()\n",
        "print(dataset.resample('M', on='MonthYear_x').size())  # Monthly outcomes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "DDkREpxRBgYM",
        "outputId": "400150ed-f978-4e96-f9b9-b45cc3bf44a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Resampling by month for intakes\n",
        "monthly_intakes = dataset.resample('M', on='DateTime_x').size()\n",
        "\n",
        "# Plotting the monthly trends for intakes\n",
        "plt.figure(figsize=(10, 5))\n",
        "monthly_intakes.plot(kind='line', marker='o', color='blue', label='Monthly Intakes')\n",
        "plt.title('Monthly Intakes Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Count')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "QBllkRSxlwlU",
        "outputId": "d3e7cb58-e540-421f-e91f-346483ea130a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Resampling by month for outcomes\n",
        "monthly_outcomes = dataset.resample('M', on='MonthYear_x').size()\n",
        "\n",
        "# Plotting the monthly trends for outcomes\n",
        "plt.figure(figsize=(10, 5))\n",
        "monthly_outcomes.plot(kind='line', marker='o', color='green', label='Monthly Outcomes')\n",
        "plt.title('Monthly Outcomes Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Count')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QURPP6iJ5cvG",
        "outputId": "63cdbb9f-c97b-454b-87c4-61b2aa4ec468"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "count    126875.000000\n",
            "mean          3.076029\n",
            "std           3.152647\n",
            "min          -3.000000\n",
            "25%           0.583333\n",
            "50%           2.000000\n",
            "75%           4.000000\n",
            "max          30.000000\n",
            "Name: Age upon Intake, dtype: float64\n",
            "\n",
            "count    144433.000000\n",
            "mean          2.765272\n",
            "std           3.118675\n",
            "min          -3.000000\n",
            "25%           0.333333\n",
            "50%           2.000000\n",
            "75%           4.000000\n",
            "max          30.000000\n",
            "Name: Age upon Outcome, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Age Distribution\n",
        "# Calculate age in years\n",
        "def age_in_years(age_string):\n",
        "    try:\n",
        "        # If the input is already a float (e.g., 3.5 years), return it as is\n",
        "        return float(age_string)\n",
        "    except ValueError:\n",
        "        age_parts = age_string.split()\n",
        "        if len(age_parts) == 2:\n",
        "            if age_parts[1] == 'years':\n",
        "                return float(age_parts[0])\n",
        "            elif age_parts[1] == 'months':\n",
        "                return float(age_parts[0]) / 12\n",
        "    return None\n",
        "\n",
        "\n",
        "dataset['Age upon Intake'] = dataset['Age upon Intake'].apply(age_in_years)\n",
        "dataset['Age upon Outcome'] = dataset['Age upon Outcome'].apply(age_in_years)\n",
        "\n",
        "print(dataset['Age upon Intake'].describe())\n",
        "print()\n",
        "print(dataset['Age upon Outcome'].describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "VcMIXTdyK1tD",
        "outputId": "b4a4d210-b74b-4ca2-8481-b9ebde600651"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Filter out NaN values before plotting\n",
        "age_upon_intake = dataset['Age upon Intake'].dropna()\n",
        "\n",
        "# Plotting the age distribution for intake\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.hist(age_upon_intake, bins=30, color='skyblue')\n",
        "plt.title('Age Distribution upon Intake')\n",
        "plt.xlabel('Age (years)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "XcrdnCUKNGEg",
        "outputId": "7ceba8ea-f35d-49ad-9a79-10de23f99c90"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Filter out NaN values before plotting\n",
        "age_upon_outcome = dataset['Age upon Outcome'].dropna()\n",
        "\n",
        "# Plotting the age distribution for outcome\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.hist(age_upon_outcome, bins=30, color='salmon')\n",
        "plt.title('Age Distribution upon Outcome')\n",
        "plt.xlabel('Age (years)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "SYw2pZObwfgw",
        "outputId": "f5e9b6dc-d998-42b3-f7f0-ca32587d4219"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculate mean age upon intake and outcome\n",
        "mean_age_intake = dataset['Age upon Intake'].mean()\n",
        "mean_age_outcome = dataset['Age upon Outcome'].mean()\n",
        "\n",
        "# Create a bar graph for mean ages\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(['Age upon Intake', 'Age upon Outcome'], [mean_age_intake, mean_age_outcome], color=['skyblue', 'salmon'])\n",
        "plt.title('Mean Age upon Intake vs Outcome')\n",
        "plt.xlabel('Age Type')\n",
        "plt.ylabel('Mean Age')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d90Oc6082tGq",
        "outputId": "6ca0de5d-ee0f-492d-8f24-7a820c07b435"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Intact Male      60022\n",
            "Intact Female    55399\n",
            "Neutered Male    39681\n",
            "Spayed Female    31313\n",
            "Unknown          12108\n",
            "Name: Sex upon Intake, dtype: int64\n",
            "Neutered Male    76975\n",
            "Spayed Female    65500\n",
            "Intact Male      22728\n",
            "Intact Female    21212\n",
            "Unknown          12108\n",
            "Name: Sex upon Outcome, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Sex and Gender Analysis\n",
        "print(dataset['Sex upon Intake'].value_counts())\n",
        "print(dataset['Sex upon Outcome'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "uFkBrVBVxR2P",
        "outputId": "d05b3c05-2062-4cec-eac0-7273310dff38"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data\n",
        "sex_upon_intake = dataset['Sex upon Intake'].value_counts()\n",
        "sex_upon_outcome = dataset['Sex upon Outcome'].value_counts()\n",
        "\n",
        "# Create subplots for visual comparison\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "# Bar plot for Sex upon Intake\n",
        "sns.barplot(x=sex_upon_intake.index, y=sex_upon_intake.values, ax=ax[0], palette='pastel')\n",
        "ax[0].set_title('Sex upon Intake')\n",
        "ax[0].set_xlabel('Sex')\n",
        "ax[0].set_ylabel('Count')\n",
        "\n",
        "# Bar plot for Sex upon Outcome\n",
        "sns.barplot(x=sex_upon_outcome.index, y=sex_upon_outcome.values, ax=ax[1], palette='mako')\n",
        "ax[1].set_title('Sex upon Outcome')\n",
        "ax[1].set_xlabel('Sex')\n",
        "ax[1].set_ylabel('Count')\n",
        "\n",
        "# Rotate x-axis labels for better readability\n",
        "for a in ax:\n",
        "    a.set_xticklabels(a.get_xticklabels(), rotation=45, ha='right')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "kUpxzb56tzEq",
        "outputId": "ba9c2963-148c-4f61-a422-76be5e754a31"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data\n",
        "sex_upon_intake = dataset['Sex upon Intake'].value_counts()\n",
        "\n",
        "# Create figure and subplot for Sex upon Intake\n",
        "fig, ax1 = plt.subplots(1, 1, figsize=(5, 5))\n",
        "\n",
        "# Plot for Sex upon Intake\n",
        "wedges1, texts1, autotexts1 = ax1.pie(sex_upon_intake, autopct='%1.1f%%', startangle=90)\n",
        "ax1.set_title('Sex upon Intake')\n",
        "\n",
        "# Draw a circle at the center to make it a donut chart\n",
        "centre_circle1 = plt.Circle((0,0), 0.75, fc='white')\n",
        "ax1.add_artist(centre_circle1)\n",
        "\n",
        "# Adding legend for Sex upon Intake\n",
        "ax1.legend(wedges1, sex_upon_intake.index, title='Sex upon Intake', loc='center left', bbox_to_anchor=(1, 0, 0.5, 1))\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "8w5z3qa1t5fJ",
        "outputId": "e306c57f-8965-412c-a5e1-f47220c71ecc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data\n",
        "sex_upon_outcome = dataset['Sex upon Outcome'].value_counts()\n",
        "\n",
        "# Create figure and subplot for Sex upon Outcome\n",
        "fig, ax2 = plt.subplots(1, 1, figsize=(5, 5))\n",
        "\n",
        "# Plot for Sex upon Outcome\n",
        "wedges2, texts2, autotexts2 = ax2.pie(sex_upon_outcome, autopct='%1.1f%%', startangle=90)\n",
        "ax2.set_title('Sex upon Outcome')\n",
        "\n",
        "# Draw a circle at the center to make it a donut chart\n",
        "centre_circle2 = plt.Circle((0,0), 0.75, fc='white')\n",
        "ax2.add_artist(centre_circle2)\n",
        "\n",
        "# Adding legend for Sex upon Outcome\n",
        "ax2.legend(wedges2, sex_upon_outcome.index, title='Sex upon Outcome', loc='center left', bbox_to_anchor=(1, 0, 0.5, 1))\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSRneZ1C2vCF",
        "outputId": "aa9b43ac-c8d3-4fab-b531-fdcc0485cf2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Black/White            21603\n",
            "Black                  15274\n",
            "Brown Tabby             9863\n",
            "Brown                   7498\n",
            "Brown/White             7374\n",
            "                       ...  \n",
            "Calico/Gray Tabby          1\n",
            "Lynx Point/Blue            1\n",
            "Orange/Gray                1\n",
            "Lilac Point/Gray           1\n",
            "Red Merle/Red Merle        1\n",
            "Name: Color, Length: 629, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Color Analysis\n",
        "print(dataset['Color'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "Aq37njyIulIG",
        "outputId": "2572291d-3044-4293-b951-b79671b0240d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "top_colors = dataset['Color'].value_counts().head(10)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "top_colors.plot(kind='bar', color='skyblue')\n",
        "plt.title('Top 10 Colors')\n",
        "plt.xlabel('Color')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zdfj6Wg_2w5w",
        "outputId": "b9015e88-340c-44c5-f1c4-a78e8e24c1a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "153041\n"
          ]
        }
      ],
      "source": [
        "# Names Analysis\n",
        "print(dataset['Name'].count())  # Count of named animals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJCf6dMO2x28",
        "outputId": "55690c9e-7845-44bb-bcda-a2dfa16358e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Austin (TX)                                        41232\n",
            "Travis (TX)                                         4086\n",
            "Outside Jurisdiction                                3672\n",
            "7201 Levander Loop in Austin (TX)                   1575\n",
            "Pflugerville (TX)                                   1031\n",
            "                                                   ...  \n",
            "Stradford Drive in Austin (TX)                         1\n",
            "11300 Robert Wooding in Austin (TX)                    1\n",
            "13619 S Turnersville Rd in Travis (TX)                 1\n",
            "10806 Hard Rock Road in Austin (TX)                    1\n",
            "Thomaswood Lane And Lenape Trail in Austin (TX)        1\n",
            "Name: Found Location, Length: 63540, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Location Analysis\n",
        "print(dataset['Found Location'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "1Pt933UvvWEy",
        "outputId": "d5f25958-d846-4fde-be33-41dd3ac4ff85"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-41-db605fb44243>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = dataset.corr()\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "correlation_matrix = dataset.corr()\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [
        "u7metjoT3CMs"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}