UBER_DATA_ANALYSIS.ipynb

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    "colab": {
      "name": "UBER DATA ANALYSIS.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5jGv8Sg0o1T"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 206
        },
        "id": "_18rm0Bh1Eq4",
        "outputId": "7f0dc121-79c7-45cb-c10d-d0b1e96f19b7"
      },
      "source": [
        "df=pd.read_csv('/content/My Uber Drives - 2016.csv')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>START_DATE*</th>\n",
              "      <th>END_DATE*</th>\n",
              "      <th>CATEGORY*</th>\n",
              "      <th>START*</th>\n",
              "      <th>STOP*</th>\n",
              "      <th>MILES*</th>\n",
              "      <th>PURPOSE*</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/1/2016 21:11</td>\n",
              "      <td>1/1/2016 21:17</td>\n",
              "      <td>Business</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>5.1</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/2/2016 1:25</td>\n",
              "      <td>1/2/2016 1:37</td>\n",
              "      <td>Business</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/2/2016 20:25</td>\n",
              "      <td>1/2/2016 20:38</td>\n",
              "      <td>Business</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Errand/Supplies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/5/2016 17:31</td>\n",
              "      <td>1/5/2016 17:45</td>\n",
              "      <td>Business</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>4.7</td>\n",
              "      <td>Meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/6/2016 14:42</td>\n",
              "      <td>1/6/2016 15:49</td>\n",
              "      <td>Business</td>\n",
              "      <td>Fort Pierce</td>\n",
              "      <td>West Palm Beach</td>\n",
              "      <td>63.7</td>\n",
              "      <td>Customer Visit</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      START_DATE*       END_DATE*  ... MILES*         PURPOSE*\n",
              "0  1/1/2016 21:11  1/1/2016 21:17  ...    5.1   Meal/Entertain\n",
              "1   1/2/2016 1:25   1/2/2016 1:37  ...    5.0              NaN\n",
              "2  1/2/2016 20:25  1/2/2016 20:38  ...    4.8  Errand/Supplies\n",
              "3  1/5/2016 17:31  1/5/2016 17:45  ...    4.7          Meeting\n",
              "4  1/6/2016 14:42  1/6/2016 15:49  ...   63.7   Customer Visit\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
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      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1156, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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        "outputId": "e3a8aa90-47d6-4e51-bc9f-0e431982d0b1"
      },
      "source": [
        "df.size"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8092"
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          "metadata": {},
          "execution_count": 5
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        "outputId": "7de2ebf4-c3d3-4d27-c45d-93ea7f7ba8b2"
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      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1156 entries, 0 to 1155\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   START_DATE*  1156 non-null   object \n",
            " 1   END_DATE*    1155 non-null   object \n",
            " 2   CATEGORY*    1155 non-null   object \n",
            " 3   START*       1155 non-null   object \n",
            " 4   STOP*        1155 non-null   object \n",
            " 5   MILES*       1156 non-null   float64\n",
            " 6   PURPOSE*     653 non-null    object \n",
            "dtypes: float64(1), object(6)\n",
            "memory usage: 63.3+ KB\n"
          ]
        }
      ]
    },
    {
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        "id": "yfKbr9zn2LRh",
        "outputId": "48c10715-40c7-4ae2-f240-4ca066f7edbf"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "START_DATE*      0\n",
              "END_DATE*        1\n",
              "CATEGORY*        1\n",
              "START*           1\n",
              "STOP*            1\n",
              "MILES*           0\n",
              "PURPOSE*       503\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "_A4ReK9W2Tei",
        "outputId": "f24b5e0e-eb1c-41bb-f182-94738bf93024"
      },
      "source": [
        "df1=df.dropna()\n",
        "df1.isnull().values.any()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
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        "id": "WIGZcKDG46ZL",
        "outputId": "3caa7e0d-1af4-49ee-8572-ddea4542a714"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MILES*</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>653.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.196325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>22.986429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>310.300000</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "           MILES*\n",
              "count  653.000000\n",
              "mean    11.196325\n",
              "std     22.986429\n",
              "min      0.500000\n",
              "25%      3.200000\n",
              "50%      6.400000\n",
              "75%     10.400000\n",
              "max    310.300000"
            ]
          },
          "metadata": {},
          "execution_count": 14
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        "outputId": "ee7e6288-051d-4cfa-bbce-9657671f77e3"
      },
      "source": [
        "start= df['START*'].dropna()\n",
        "un_start=set(start)\n",
        "un_start"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Apex',\n",
              " 'Arabi',\n",
              " 'Asheville',\n",
              " 'Austin',\n",
              " 'Banner Elk',\n",
              " 'Bellevue',\n",
              " 'Berkeley',\n",
              " 'Boone',\n",
              " 'Briar Meadow',\n",
              " 'Burtrose',\n",
              " 'CBD',\n",
              " 'Capitol One',\n",
              " 'Cary',\n",
              " 'Chalmette',\n",
              " 'Chapel Hill',\n",
              " 'Chessington',\n",
              " 'Colombo',\n",
              " 'Daytona Beach',\n",
              " 'Downtown',\n",
              " 'Durham',\n",
              " 'Eagan Park',\n",
              " 'East Austin',\n",
              " 'East Elmhurst',\n",
              " 'East Harlem',\n",
              " 'Eastgate',\n",
              " 'Edgehill Farms',\n",
              " 'El Cerrito',\n",
              " 'Elmhurst',\n",
              " 'Emeryville',\n",
              " 'Fairmont',\n",
              " 'Farmington Woods',\n",
              " 'Fayetteville Street',\n",
              " 'Financial District',\n",
              " 'Flatiron District',\n",
              " 'Florence',\n",
              " 'Fort Pierce',\n",
              " 'Fuquay-Varina',\n",
              " 'Galveston',\n",
              " 'Gampaha',\n",
              " 'Georgian Acres',\n",
              " 'Gulfton',\n",
              " 'Hazelwood',\n",
              " \"Hell's Kitchen\",\n",
              " 'Heritage Pines',\n",
              " 'Holly Springs',\n",
              " 'Houston',\n",
              " 'Hudson Square',\n",
              " 'Huntington Woods',\n",
              " 'Ingleside',\n",
              " 'Islamabad',\n",
              " 'Jacksonville',\n",
              " 'Jamaica',\n",
              " 'Kar?chi',\n",
              " 'Katunayaka',\n",
              " 'Katunayake',\n",
              " 'Katy',\n",
              " 'Kenner',\n",
              " 'Kilarney Woods',\n",
              " 'Kissimmee',\n",
              " 'Krendle Woods',\n",
              " 'Lahore',\n",
              " 'Lake Reams',\n",
              " 'Lake Wellingborough',\n",
              " 'Latta',\n",
              " 'Lexington Park at Amberly',\n",
              " 'Long Island City',\n",
              " 'Lower Garden District',\n",
              " 'Lower Manhattan',\n",
              " 'Mcvan',\n",
              " 'Medical Centre',\n",
              " 'Menlo Park',\n",
              " 'Meredith',\n",
              " 'Meredith Townes',\n",
              " 'Metairie',\n",
              " 'Midtown',\n",
              " 'Midtown East',\n",
              " 'Morrisville',\n",
              " 'Mountain View',\n",
              " 'New Orleans',\n",
              " 'New York',\n",
              " 'Newark',\n",
              " 'Noorpur Shahan',\n",
              " 'North Austin',\n",
              " 'Northwoods',\n",
              " 'Nugegoda',\n",
              " 'Oakland',\n",
              " 'Old City',\n",
              " 'Orlando',\n",
              " 'Palo Alto',\n",
              " 'Parkway',\n",
              " 'Parkwood',\n",
              " 'Pontchartrain Shores',\n",
              " 'Port Bolivar',\n",
              " 'Preston',\n",
              " 'R?walpindi',\n",
              " 'Raleigh',\n",
              " 'Rawalpindi',\n",
              " 'Redmond',\n",
              " 'Renaissance',\n",
              " 'Ridgeland',\n",
              " 'Rose Hill',\n",
              " 'San Francisco',\n",
              " 'Santa Clara',\n",
              " 'Savon Height',\n",
              " 'Seaport',\n",
              " 'Seattle',\n",
              " 'Sharpstown',\n",
              " 'Soho',\n",
              " 'South Congress',\n",
              " 'Stonewater',\n",
              " 'Storyville',\n",
              " 'Sugar Land',\n",
              " 'Summerwinds',\n",
              " 'Sunnyvale',\n",
              " 'Tanglewood',\n",
              " 'The Drag',\n",
              " 'Topton',\n",
              " 'Townes at Everett Crossing',\n",
              " 'Tribeca',\n",
              " 'University District',\n",
              " 'Unknown Location',\n",
              " 'Washington Avenue',\n",
              " 'Waverly Place',\n",
              " 'Wayne Ridge',\n",
              " 'West Berkeley',\n",
              " 'West Palm Beach',\n",
              " 'West University',\n",
              " 'Weston',\n",
              " 'Westpark Place',\n",
              " 'Whitebridge',\n",
              " 'Winston Salem'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
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        },
        "id": "hsBzGPQX-ugM",
        "outputId": "aede85bd-44c0-4025-d848-7e32df814222"
      },
      "source": [
        "df['START*'].value_counts()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cary                   161\n",
              "Unknown Location        55\n",
              "Morrisville             54\n",
              "Whitebridge             36\n",
              "Durham                  30\n",
              "                      ... \n",
              "Lower Manhattan          1\n",
              "Mcvan                    1\n",
              "Lake Wellingborough      1\n",
              "Renaissance              1\n",
              "Austin                   1\n",
              "Name: START*, Length: 131, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
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        "id": "QnFvbQsz9mCt",
        "outputId": "2df398c7-a141-432d-9ed5-6f32a1b61b7e"
      },
      "source": [
        "len(df['START*'].unique())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "k6E3Ohxb9yqN",
        "outputId": "7a3b5453-e3a0-4500-eba7-b41c0e06542b"
      },
      "source": [
        "df['STOP*'].value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cary                155\n",
              "Morrisville          60\n",
              "Unknown Location     56\n",
              "Whitebridge          37\n",
              "Durham               30\n",
              "                   ... \n",
              "South Congress        1\n",
              "Chapel Hill           1\n",
              "Faubourg Marigny      1\n",
              "Fuquay-Varina         1\n",
              "Northwoods            1\n",
              "Name: STOP*, Length: 137, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 423
        },
        "id": "Gwg3MH9H-Gfx",
        "outputId": "0cd954fa-d9cb-4ed3-a0d6-ae850c89cb38"
      },
      "source": [
        "df[df['START*']=='Cary']"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>START_DATE*</th>\n",
              "      <th>END_DATE*</th>\n",
              "      <th>CATEGORY*</th>\n",
              "      <th>START*</th>\n",
              "      <th>STOP*</th>\n",
              "      <th>MILES*</th>\n",
              "      <th>PURPOSE*</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1/7/2016 13:27</td>\n",
              "      <td>1/7/2016 13:33</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Cary</td>\n",
              "      <td>0.8</td>\n",
              "      <td>Meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1/10/2016 8:05</td>\n",
              "      <td>1/10/2016 8:25</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Morrisville</td>\n",
              "      <td>8.3</td>\n",
              "      <td>Meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1/15/2016 11:43</td>\n",
              "      <td>1/15/2016 12:03</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Durham</td>\n",
              "      <td>10.4</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1/18/2016 14:55</td>\n",
              "      <td>1/18/2016 15:06</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Cary</td>\n",
              "      <td>4.8</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1/20/2016 10:36</td>\n",
              "      <td>1/20/2016 11:11</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Raleigh</td>\n",
              "      <td>17.1</td>\n",
              "      <td>Meeting</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1046</th>\n",
              "      <td>12/12/2016 17:51</td>\n",
              "      <td>12/12/2016 18:01</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Morrisville</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048</th>\n",
              "      <td>12/13/2016 18:19</td>\n",
              "      <td>12/13/2016 18:29</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Cary</td>\n",
              "      <td>4.2</td>\n",
              "      <td>Errand/Supplies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1049</th>\n",
              "      <td>12/13/2016 20:20</td>\n",
              "      <td>12/13/2016 20:29</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Cary</td>\n",
              "      <td>4.1</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052</th>\n",
              "      <td>12/14/2016 17:50</td>\n",
              "      <td>12/14/2016 18:00</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Morrisville</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Meal/Entertain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1054</th>\n",
              "      <td>12/15/2016 14:20</td>\n",
              "      <td>12/15/2016 14:54</td>\n",
              "      <td>Business</td>\n",
              "      <td>Cary</td>\n",
              "      <td>Morrisville</td>\n",
              "      <td>10.6</td>\n",
              "      <td>Meeting</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>161 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           START_DATE*         END_DATE*  ... MILES*         PURPOSE*\n",
              "7       1/7/2016 13:27    1/7/2016 13:33  ...    0.8          Meeting\n",
              "8       1/10/2016 8:05    1/10/2016 8:25  ...    8.3          Meeting\n",
              "28     1/15/2016 11:43   1/15/2016 12:03  ...   10.4   Meal/Entertain\n",
              "30     1/18/2016 14:55   1/18/2016 15:06  ...    4.8   Meal/Entertain\n",
              "34     1/20/2016 10:36   1/20/2016 11:11  ...   17.1          Meeting\n",
              "...                ...               ...  ...    ...              ...\n",
              "1046  12/12/2016 17:51  12/12/2016 18:01  ...    3.0   Meal/Entertain\n",
              "1048  12/13/2016 18:19  12/13/2016 18:29  ...    4.2  Errand/Supplies\n",
              "1049  12/13/2016 20:20  12/13/2016 20:29  ...    4.1   Meal/Entertain\n",
              "1052  12/14/2016 17:50  12/14/2016 18:00  ...    3.0   Meal/Entertain\n",
              "1054  12/15/2016 14:20  12/15/2016 14:54  ...   10.6          Meeting\n",
              "\n",
              "[161 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e4HHc8BE_vnE",
        "outputId": "5b71c7ec-7533-4958-e106-82d01759315a"
      },
      "source": [
        "df2=pd.DataFrame(df['START*'].value_counts())\n",
        "df2=df2.reset_index()\n",
        "df2=df2.rename(columns={'index':'Start Point','START*':'Count'})\n",
        "df2"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start Point</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cary</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Unknown Location</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Morrisville</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Whitebridge</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Durham</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Lower Manhattan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Mcvan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Lake Wellingborough</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Renaissance</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Austin</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Start Point  Count\n",
              "0                   Cary    161\n",
              "1       Unknown Location     55\n",
              "2            Morrisville     54\n",
              "3            Whitebridge     36\n",
              "4                 Durham     30\n",
              "..                   ...    ...\n",
              "126      Lower Manhattan      1\n",
              "127                Mcvan      1\n",
              "128  Lake Wellingborough      1\n",
              "129          Renaissance      1\n",
              "130               Austin      1\n",
              "\n",
              "[131 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ItYY-36YBXId",
        "outputId": "35f3ea58-014a-4d63-f275-7412a2df4dae"
      },
      "source": [
        "df3=pd.DataFrame(df['STOP*'].value_counts())\n",
        "df3=df3.reset_index()\n",
        "df3=df3.rename(columns={'index':'END Point','STOP*':'Count'})\n",
        "df3"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>END Point</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Cary</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Morrisville</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Unknown Location</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Whitebridge</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Durham</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>South Congress</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Chapel Hill</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Faubourg Marigny</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Fuquay-Varina</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Northwoods</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            END Point  Count\n",
              "0                Cary    155\n",
              "1         Morrisville     60\n",
              "2    Unknown Location     56\n",
              "3         Whitebridge     37\n",
              "4              Durham     30\n",
              "..                ...    ...\n",
              "132    South Congress      1\n",
              "133       Chapel Hill      1\n",
              "134  Faubourg Marigny      1\n",
              "135     Fuquay-Varina      1\n",
              "136        Northwoods      1\n",
              "\n",
              "[137 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGReW9NaCFSe"
      },
      "source": [
        ""
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CpOiicfCou1",
        "outputId": "fe4c6b00-581d-40b2-c358-9993b7976549"
      },
      "source": [
        "df['MILES*'].groupby(df['PURPOSE*']).sum()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PURPOSE*\n",
              "Airport/Travel       16.5\n",
              "Between Offices     197.0\n",
              "Charity ($)          15.1\n",
              "Commute             180.2\n",
              "Customer Visit     2089.5\n",
              "Errand/Supplies     508.0\n",
              "Meal/Entertain      911.7\n",
              "Meeting            2851.3\n",
              "Moving               18.2\n",
              "Temporary Site      523.7\n",
              "Name: MILES*, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "PC0S1DUJEFk-",
        "outputId": "a0a9e7e9-ff10-4e87-9dc5-9ca3437ac511"
      },
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(df['MILES*'])\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IO6g3IG4GhoDHgV8GdgG/t8Br1gKHqp4fDtdF7uPuRWAU6DezHuC3gXlvVGRmN5rZHjPbMzQ0VOcfJR6Ts6U5xx8gCIiyw3RB8zGJSHuo9yymspl9AfiCuy/HN/FHgNvd/cR8s5+6+x3AHQBbt25t6X/Np2ZLc44/APToYjkRaTPzBkTY3fPfgZsIWxtmVgL+wt1vXeC9jwDrq56vC9dF7XM4HNfoA4aBy4GrzeyPgZVA2cym3f1jdf2pWmBytsSqrtOn+q7QxXIi0m4W6mL6TYKzl37Q3Ve7+2qCL+8fMbPfXOC1DwMXmNkmM8sB1wI7a/bZCVwfLl8NPOSBN7n7RnffCPwp8D+SHA4AU4USnRFXUVdUZnTVmUwi0i4WCoifB65z92cqK9z9APBzwPvme2E4pnAT8ADwFPAZd99nZreaWWWKjjsJxhwGgQ8SjHW0pblmcq2ojE9MacI+EWkTC41BZN39xdqV7j5kZnP3p5zabxfBgHb1uluqlqeBaxZ4j48s9DmtNl0oUSj5vIPUXeEUHJMKCBFpEwu1IGYb3PayMjpVAOa+SA4gl0mRsqArSkSkHSzUgrjYzMYi1hvQEUM9ben4ZBAQURP1VZgZnbmMWhAi0jbmDQh314R8dTg+GTSmoqb6rtaVTasFISJto94L5WQex8MupvnGICrbp3QWk4i0CQVEE4yGXUxd83QxQTBGobOYRKRdKCCaYGw6CIiOBQKiM5s+eec5EZGkU0A0wdh00G2Uz85/ONWCEJF2ooBogvHpAvlMitQ880ZBMAYxUyxTKGnCPhFJPgVEE4xPFxfsXgJOTsVRuW5CRCTJFBBNUGlBLKQyiF25bkJEJMkUEE1Qfwsi2Gd0Shehi0jyKSCaIAiIOloQObUgRKR9KCCaYHy6UF8LQl1MItJGFBBNMD5dpCOzcEBUpuI4rkFqEWkDCogmqLeLKZ9NYcDopMYgRCT5FBBLNF0oMVsq19XFlDKjI5tWC0JE2oICYonGT15FXd/Et125tMYgRKQtKCCWaLwyD1Md10FAcKqrWhAi0g4UEEtUaUHU08UEQQtCYxAi0g4UEEs0XudEfRWdGoMQkTahgFiiU11M9bUgOnMZjUGISFtQQCxRI11MY9MFSmWPsywRkSVTQCzRqZsF1d/F5H6q5SEiklQKiCU6OQZRZxeT5mMSkXahgFii8ekiXbk06dT8NwuqqMzoqoFqEUk6BcQSjU8X6O3I1L3/qXtC6FRXEUk2BcQSjU8X6e3I1r2/7ionIu1CAbFE4zOLa0F0agxCRNpErAFhZlea2X4zGzSzmyO2583svnD7bjPbGK7fZmZ7w59Hzexdcda5FItuQeieECLSJmILCDNLAzuAq4AtwHVmtqVmtxuAEXffDNwO3BaufwLY6u6XAFcCHzez+v+bvoyCgKi/tHTK6M1nOK7bjopIwsXZgtgGDLr7AXefBe4Fttfssx24O1y+H7jCzMzdJ929GK7vABJ7Vdn4dIEViwgIgL6uLKNqQYhIwsUZEGuBQ1XPD4frIvcJA2EU6Acws8vNbB/wOPCBqsBIlLFFdjEBrOzK6jRXEUm8xA5Su/tud78I+EHgw2bWUbuPmd1oZnvMbM/Q0NCy1zhTLDFbLNObX1wLYmVnTqe5ikjixRkQR4D1Vc/Xhesi9wnHGPqA4eod3P0p4ATwmtoPcPc73H2ru28dGBhoYun1qVxFvZgxCIC+TrUgRCT54gyIh4ELzGyTmeWAa4GdNfvsBK4Pl68GHnJ3D1+TATCz84BXA8/GWGtDTgXE4rqYNAYhIu0gtjOD3L1oZjcBDwBp4C5332dmtwJ73H0ncCdwj5kNAscIQgTgjcDNZlYAysCvuvuLcdXaqMqEe70dGWZO1N9ltDJsQbg7ZvVN0SEistxiPXXU3XcBu2rW3VK1PA1cE/G6e4B74qytGapbEC8uJiC6spTKzsRsiZ5Fjl+IiCyXxA5St4PqFsRirOzMAZqPSUSSTQGxBGNhC2JFA2MQoKupRSTZFBBL0OhZTCs7g4DQhH0ikmQKiCWodDH1LDYguipdTAoIEUkuBcQSjE8X6cymyaYXdxhXVrqYNB+TiCSYAmIJFnuzoIq+To1BiEjyKSCWYLEzuVZ0ZNN0ZFMagxCRRFNALMFi7wVRTfMxiUjSKSCWoNEuJghndFUXk4gkmAJiCcani4u+BqJCE/aJSNIpIJZgrMExCAhaEJqwT0SSTAGxBEvqYurM6TRXEUk0BUSDZotlZorlxgepNQYhIgmngGhQoxP1VfR1ZZkplpkulJpZlohI0yggGtTozYIqTs3oqlaEiCSTAqJBjU7UV6HpNkQk6RQQDVpqF9NKTbchIgmngGhQo/eCqNA9IUQk6RQQDVpyCyKc8ntUXUwiklAKiAYtfZBaLQgRSTYFRIOWOkjdlUuTTZum2xCRxFJANGhsuhB+yTd2CM2Mvs6cWhAiklgKiAaNTRUaHqCuWNmV1RiEiCSWAqJBY9MFVnQ21r1UsbJT022ISHIpIBo0NtX4VN8Vmo9JRJJMAdGg8ZkCKzqXFhB9uquciCSYAqJBQQtiaV1Mq7uzHFNAiEhCKSAaFIxBLK0Fsbo7z3ShzORssUlViYg0jwKiAe7elLOYVncHrx8+oVaEiCRPrAFhZlea2X4zGzSzmyO2583svnD7bjPbGK5/m5l908weDx9/NM46F2titkTZWfJZTKu78wCMqJtJRBIotoAwszSwA7gK2AJcZ2Zbana7ARhx983A7cBt4foXgZ9y99cC1wP3xFVnI8bCq5+X3oII5mManlBAiEjyxNmC2AYMuvsBd58F7gW21+yzHbg7XL4fuMLMzN0fcffvhev3AZ1mlo+x1kUZCyfqW+oYRH8YEMfUxSQiCRRnQKwFDlU9Pxyui9zH3YvAKNBfs8/PAN9y95mY6ly0samlTfVdsaoSEGpBiEgCLa0TPWZmdhFBt9Pb59h+I3AjwIYNG5atrpNdTEscg1jRkSGbNp3qKiKJFGcL4giwvur5unBd5D5mlgH6gOHw+Trg88D73P27UR/g7ne4+1Z33zowMNDk8ud2sotpiS0IM2NVV05dTCKSSHEGxMPABWa2ycxywLXAzpp9dhIMQgNcDTzk7m5mK4F/AG5293+LscaGVFoQjU71XW11d06D1CKSSLEFRDimcBPwAPAU8Bl332dmt5rZT4e73Qn0m9kg8EGgcirsTcBm4BYz2xv+rImr1sVa6s2CqvX35Dg2kZjhFRGRk2Idg3D3XcCumnW3VC1PA9dEvO4PgD+Is7alGJsu0JlNk8ssPV9XdeU4MjLahKpERJpLV1I3YGyquOQB6or+7pzOYhKRRFJANGBseunTbFSs7s4zNl2kUCo35f1ERJpFAdGAZkzUV7G6J7gWYkStCBFJGAVEA5ox1XfF6i5NtyEiyaSAaEBTWxC6mlpEEkoB0YBmTPVd0d+jgBCRZFJALJK7Mzbd3LOYAIZP6FoIEUkWBcQijU0XKZWdVeHYwVKt6sqRThlHxxUQIpIsCohFOh5OrLeySQGRShln9eQUECKSOAqIRRqZDOZhWtXVnDEIgDW9HQoIEUkcBcQijTS5BQGwpjfPkAJCRBJGAbFIlS6mprYgVuQZGp9u2vuJiDRDom8YlEQjE5UupsZbEJ/a/dxLnr8wNsPwxCzFUplMWpktIsmgb6NFOj45i9nS70ddrbcjgzu8qBsHiUiCKCAW6fhUgb7OLOmUNe09e/NB2BxVN5OIJIgCYpFGJgtNuwaionJnuqNjGqgWkeRQQCzS8clZVjZxgBqqAkJnMolIgiggFmlkcrbpLYieMCB0qquIJIkCYpFGJgpNb0FkUilWdWU1BiEiiaKAWKTjMbQgQFdTi0jyKCAWYbZYZmK21NSL5CrWrMgrIEQkURQQi9DsifqqDfTmGRpTF5OIJIcCYhFOTdTX/IA4Z0XQxVQqe9PfW0SkEQqIRRiJYR6mirWrOimWnRfUihCRhFBALEKcXUxrV3YCcOT4VNPfW0SkEQqIRTjZxdTd/BbEulVhQIwoIEQkGRQQi3CqiymOFkQXAIdHJpv+3iIijVBALMKL47N05dJ0ZNNNf+/OXJr+7py6mEQkMRQQizB0YoY1vfnY3n/tqk4Oq4tJRBIi1oAwsyvNbL+ZDZrZzRHb82Z2X7h9t5ltDNf3m9mXzeyEmX0szhoX4+jYNGt6O2J7/7UrO9WCEJHEiC0gzCwN7ACuArYA15nZlprdbgBG3H0zcDtwW7h+GvhvwIfiqq8RQ+MzDKyIsQWxspMjI1O461oIEWm9OFsQ24BBdz/g7rPAvcD2mn22A3eHy/cDV5iZufuEu3+NICgS4+h4vF1M61Z1MlMs685yIpIIcQbEWuBQ1fPD4brIfdy9CIwC/fV+gJndaGZ7zGzP0NDQEsud3+RskRMzRQZiHYMIzmRSN5OIJEFbD1K7+x3uvtXdtw4MDMT6WZV7NcQ9BgG6FkJEkiHOgDgCrK96vi5cF7mPmWWAPmA4xpoadvRkQMTYxbQ6CIhDuhZCRBIgzoB4GLjAzDaZWQ64FthZs89O4Ppw+WrgIU/oCG3lftFrYhykXtGR5ayeHAeGTsT2GSIi9crE9cbuXjSzm4AHgDRwl7vvM7NbgT3uvhO4E7jHzAaBYwQhAoCZPQusAHJm9k7g7e7+ZFz1LqRyt7c4u5gAzh/oYfCoAkJEWi+2gABw913Arpp1t1QtTwPXzPHajXHWtlhHx2fIpIyVnc2fh6na+Wt6+OKj38PdMbNYP0tEZD5tPUi9nIbGZxjozZNKxfulvXmgh7HpIkMndHc5EWktBUSd4r4GomLzmh4Avnt0IvbPEhGZjwKiTkfHphmIefwBTgXEoAaqRaTFFBB1GhqfifUMpopz+zrozqX5rgaqRaTFFBB1KJTKHJucZaAn/oAwM85fozOZRKT1FBB1CCbQO3XXt7ht1qmuIpIACog6HDwWXNl8Xn/3snzehef08v2xaY5NaNI+EWkdBUQdDg4HZxRt7O9als+7eN1KAB49dHxZPk9EJIoCog4HhyfpzKZjncm12uvW9ZEyeEQBISItpICow8HhSTas7lq2K5u78xkuPLuXR54bWZbPExGJooCow3PHJjhvmbqXKi7dsIpHDx2nXE7k3IUi8jKggFhAuewcHJ5c/oBYv5Kx6SIHXtQV1SLSGgqIBRwdn2GmWGbDMp3BVHHphmCgeq/GIUSkRRQQC6icwXTe6uVtQZw/0ENfZ5avH0jk/ZNE5GVAAbGAg8PBNRAbl7kFkUoZb7lwgC9/+ygljUOISAsoIBZw8NgEmZTxipXxT9RX68e2nM3wxCx7D+lsJhFZfgqIBTxxZIzNa3rIpJf/UL3lwgEyKePBp44u+2eLiMR6R7l25+48evg4V150Tuyf9andz0Wu37ZpNQ8++QK/feWrY69BRKSaWhDzeHZ4kuOTBS5Zv7JlNbx9y9k8ffQETxwZbVkNIvLypICYR2UupItbGBDvumwdXbk0d/3bMy2rQURenhQQ89h76DhduTQXnt3bshr6OrNc8/p1/P2j3+Po+HTL6hCRlx8FxDweOXSc16ztI51anjmY5vL+H9lEsezc+a9qRYjI8lFAzGGmWOKp741xaQu7lyo2ndXNf7x0HX/9tWc0FiEiy0YBMYev7h9itlTmDef3t7oUAG75yS30d+f40GcfZXK22OpyRORlQAExhy/sPUJ/d443bj6r1aUA0NeV5bafeR3feWGc9935DcamC60uSUTOcAqICGPTBR586ig/dfEryLbgArm5/IdXr+EvrruMvYeOc9Wf/itfeOQIM8VSq8sSkTOULpSL8E+Pf5/ZYpl3Xrq21aWc5h2vO5ezV+T57zv38Rv37eV3P5/m0g2r2HhWFxv7uzmvv5vz+rvYsLqLjmy61eWKSBtTQNSYmi2x4yuDbF7Tw8Xr+lpdzpxXWF+3bQNPv3CCJ58f45kXJ9hz8BjThfJL9lndneOKV6/h8lf288Pn9/OKlZ3LUbKInCFiDQgzuxL4MyAN/LW7/1HN9jzwCeD1wDDwbnd/Ntz2YeAGoAT8Z3d/IM5aKz76pf0cHJ7k07/0hmW7xWgjUma86pxeXnVOcI2GuzM5W+LFDYUAAAohSURBVOLYxCzDE7Mcm5jhe8en+eJjz/PZbx4G4JwVHfzAub38+o9dyOvW9pFq8em7IpJssQWEmaWBHcDbgMPAw2a2092frNrtBmDE3Teb2bXAbcC7zWwLcC1wEfAK4EEzu9DdY+twd3fu/Noz3Pm1Z3jP5Rv4oYScvVQvM6M7n6E7n2F91b0ryu4cHZvh6aPjPPX8OF/9zhBf3j/EQG+ebRtXc/H6Pi5et5JNA92c1Z1XaMyhXHZmS2VmS2Vy6RT5TCrR/4EQaYY4WxDbgEF3PwBgZvcC24HqgNgOfCRcvh/4mAX/6rYD97r7DPCMmQ2G7/fvzS7yxEyRXY89z2f2HGLPwRF+/KKz+b13/ECzP6ZlUmac09fBOX0dvOmCASZni5zVk+ehbx/lkUMj/MPjz5/cN5Myzl7RwcquLN25DF35dPCYS9Odz5DLpDAL3jNtRsqg5E6x5BTLzpPfG6PsHvyUg3AqubN+VRdldzLpFNm0kUunyKSNbDoV/gRftMWyUy4H71UKH8tVjyUP1pfKTipl9OYz9HZk6Mln6enI0JvPBI8dGXryGXo7svR2ZEinDPfgPwEOFEplxqaKjE4VGJ2aZXSqwLGJAsMnZthzcISJmSInZopMzBSZKpQoO6fdkyOdspN/llwmTWc2RWcuzavPWcGKzgx9nVl6O7JkUkY6/EmZkUnZ6SFcc7uP4JiWmS0Fj4Wa5UIpOAbZdIpcJgir6sdcOkU+myKXTgfPw3W5TIp0ysL3Cd6jUC5TLAV/Z/lMinwmTT6bOrVc9fps5TFtZ1w4unvVcvg41/aa/YJ1p78egt/pQjH4j8Vs9WMx/HsN1xVKTiZlJ/89ZNKVYx6sy1X9Wzn195CK/SLeOANiLXCo6vlh4PK59nH3opmNAv3h+q/XvDaWEeP93x/ntz73GJvO6ubW7Rfx828474z75a/WlcvwzkvXnhyAHz4xw2NHRjl8bJIHnzrK2FSBqUKJF0/MMDtaZib8ZZ4pliiVPfyiBSwIgLRZ+GWZCr64LbjZUSoMkJQZk7MljPAfS/glNz5dDAIk/MIHyKRTJ79QM1VfqjPF8sn3svB9y+7MFMvMFIJ/YM3QkU3RmU3Tkw++4Neu7KQzm+bi9StPfhln00E9U7MlvvXcSHhsykwXSkzOlnjs8HFGpwqMTRebfqOnyrFOpYJjUaoK0+WWTRtG1b+TiEV7ybpTT6L+eZ36Uo7+ovXTFqL3XcyX+pkgZZBNp3jH687loz97SdPfv60Hqc3sRuDG8OkJM9vf6HsdBL4CXN94OWcBLzb+8uXz3jaqNdTSej+5+Jfo+Marnepdllq/A9z+7oZfft5cG+IMiCPA+qrn68J1UfscNrMM0EcwWF3Pa3H3O4A7mlhzw8xsj7tvbXUd9WinWkH1xk31xqedao0S51VgDwMXmNkmM8sRDDrvrNlnJ6f+03418JAH7cKdwLVmljezTcAFwDdirFVERGrE1oIIxxRuAh4gOM31LnffZ2a3AnvcfSdwJ3BPOAh9jCBECPf7DMGAdhH4tTjPYBIRkdPFOgbh7ruAXTXrbqlangaumeO1fwj8YZz1NVkiurrq1E61guqNm+qNTzvVehrzM21YX0REmiI5M9GJiEiiKCCWyMyuNLP9ZjZoZje3up4oZvasmT1uZnvNbE+4brWZfcnMng4fV7WwvrvM7KiZPVG1LrI+C/x5eLwfM7PLElLvR8zsSHiM95rZT1Rt+3BY734z+/FlrnW9mX3ZzJ40s31m9uvh+kQe33nqTerx7TCzb5jZo2G9vx+u32Rmu8O67gtP1CE88ea+cP1uM9u4nPUumrvrp8EfgsH37wKvBHLAo8CWVtcVUeezwFk16/4YuDlcvhm4rYX1vRm4DHhiofqAnwD+keB6rDcAuxNS70eAD0XsuyX8vcgDm8Lfl/Qy1noucFm43EtwyvyWpB7feepN6vE1oCdczgK7w+P2GeDacP1fAr8SLv8q8Jfh8rXAfcv9+7uYH7UglubkdCLuPgtUphNpB9uBu8Plu4F3tqoQd/8XgrPYqs1V33bgEx74OrDSzM5dnkoDc9Q7l5PTxrj7M0Bl2phl4e7Pu/u3wuVx4CmCWQkSeXznqXcurT6+7u4nwqfZ8MeBHyWYPghOP76V434/cIUleOoGBcTSRE0nkrybSAS/sP/XzL4ZXn0OcLa7VyZi+j5wdmtKm9Nc9SX5mN8UdsvcVdVll5h6w+6MSwn+l5v441tTLyT0+JpZ2sz2AkeBLxG0Yo67e+XewNU1vWR6IaAyvVAiKSBeHt7o7pcBVwG/ZmZvrt7oQXs3saezJb2+0P8GzgcuAZ4H/mdry3kpM+sBPgf8hruPVW9L4vGNqDexx9fdS+5+CcGMD9uAV7e4pKZRQCxNXVOCtJq7HwkfjwKfJ/glfqHSdRA+Hm1dhZHmqi+Rx9zdXwi/KMrAX3Gqm6Pl9ZpZluDL9m/d/e/C1Yk9vlH1Jvn4Vrj7ceDLwA8RdM1VrjOrrulkvfbS6YUSSQGxNPVMJ9JSZtZtZr2VZeDtwBO8dJqT64H/05oK5zRXfTuB94Vn27wBGK3qKmmZmn76dxEcY2jxtDFh//adwFPu/tGqTYk8vnPVm+DjO2BmK8PlToL73zxFEBRXh7vVHt+o6YWSqdWj5O3+Q3DWx3cI+h1/t9X1RNT3SoKzPB4F9lVqJOj3/GfgaeBBYHULa/w0QbdBgaC/9oa56iM4a2RHeLwfB7YmpN57wnoeI/gSOLdq/98N690PXLXMtb6RoPvoMWBv+PMTST2+89Sb1OP7OuCRsK4ngFvC9a8kCKpB4LNAPlzfET4fDLe/crl/fxfzoyupRUQkkrqYREQkkgJCREQiKSBERCSSAkJERCIpIEREJJICQmQBZuZm9smq5xkzGzKzL4bP329mHwuXP2JmH4p4j1LVTKR7LZz518x+0sweCWcDfdLMfjlcb2b21vAnsXP1yJkt1jvKiZwhJoDXmFmnu08RXAy12Kt1pzyYjuGk8IrhO4Bt7n7YzPLAxvCCq78kOE/egF8wsw+Eny2ybBQQIvXZBbyDYAbO6wgulnvTEt+zl+Df4DCAu88QXOyFmf0K8K/hfm9SOEgrqItJpD73Ekzp0EFw9ezuBfav1VnTxfRudz9GcFXwQTP7tJm918xSYQtiB/A34c+OcJ3IslILQqQO7v5YOP30dQSticU6rYspfN9fNLPXAj8GfAh4m7u/38z+E/CWcLcdrikPpAUUECL12wn8CfBWmjiHv7s/DjxuZvcAzwDvDwPhK836DJFGqItJpH53Ab8ffqEvmZn1mNlbq1ZdAhxsxnuLNINaECJ1cvfDwJ/XsevvmdlvVL1uHeEYRNU+/wT8IfBbZvZxYIrgbKn3N69ikaXRbK4iIhJJXUwiIhJJASEiIpEUECIiEkkBISIikRQQIiISSQEhIiKRFBAiIhJJASEiIpH+P5XpyasQjdnpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "OUJb5P8WFwj-",
        "outputId": "aa299a37-1d03-48bf-9997-ccb2e5afcdf1"
      },
      "source": [
        "plt.figure(figsize=(20,6))\n",
        "sns.countplot(df['PURPOSE*'])\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "HmjsW7GoGAuy",
        "outputId": "e12fffbf-943a-440f-8f21-0971096e2f2e"
      },
      "source": [
        "df=df.reset_index()\n",
        "sns.barplot(x=df['MILES*'],y=df['PURPOSE*'])"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f532fd16d90>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "wnsrGPBiG0bi",
        "outputId": "29c6d8d3-fea5-4c87-98b3-7e2605670286"
      },
      "source": [
        "df5=pd.DataFrame(df['CATEGORY*'].value_counts())\n",
        "df5.plot(kind='bar', color='crimson')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f532f398210>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}