Uber Ride E Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "587eec09",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "- To fetch insights from the behavoir of a common Uber customer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481482ae",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2609233e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import calendar\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dcfd96",
   "metadata": {},
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1791bc5a",
   "metadata": {},
   "outputs": [
    {
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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* CATEGORY*       START*            STOP*  \\\n",
       "0  1/1/2016 21:11  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1   1/2/2016 1:25   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2  1/2/2016 20:25  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3  1/5/2016 17:31  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4  1/6/2016 14:42  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "   MILES*         PURPOSE*  \n",
       "0     5.1   Meal/Entertain  \n",
       "1     5.0              NaN  \n",
       "2     4.8  Errand/Supplies  \n",
       "3     4.7          Meeting  \n",
       "4    63.7   Customer Visit  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/My Uber Drives - 2016.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "e674924e",
   "metadata": {},
   "outputs": [
    {
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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>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Kar?chi</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>Totals</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12204.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Kar?chi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "1155            Totals               NaN       NaN               NaN   \n",
       "\n",
       "                 STOP*   MILES*        PURPOSE*  \n",
       "1151  Unknown Location      3.9  Temporary Site  \n",
       "1152  Unknown Location     16.2         Meeting  \n",
       "1153           Gampaha      6.4  Temporary Site  \n",
       "1154         Ilukwatta     48.2  Temporary Site  \n",
       "1155               NaN  12204.7             NaN  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ab05c5b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1156, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "START_DATE*     object\n",
       "END_DATE*       object\n",
       "CATEGORY*       object\n",
       "START*          object\n",
       "STOP*           object\n",
       "MILES*         float64\n",
       "PURPOSE*        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77086cac",
   "metadata": {},
   "source": [
    "- There are 6 categorical vars and I numeric vaiable. Here START_DATE and END_DATE are in oject type. We need to convert them back into datetime variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030d4112",
   "metadata": {},
   "source": [
    "### Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d1657b42",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a7f3c670",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1155</th>\n",
       "      <td>Totals</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12204.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     START_DATE* END_DATE* CATEGORY* START* STOP*   MILES* PURPOSE*\n",
       "1155      Totals       NaN       NaN    NaN   NaN  12204.7      NaN"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['END_DATE*'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b261e601",
   "metadata": {},
   "source": [
    "- As we can see this row contains wrong data for the most of the columns. We will delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6d599a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping row containing null values\n",
    "df.drop(df[df['END_DATE*'].isna()].index, axis=0, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "43e70631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*      0\n",
       "END_DATE*        0\n",
       "CATEGORY*        0\n",
       "START*           0\n",
       "STOP*            0\n",
       "MILES*           0\n",
       "PURPOSE*       502\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "2c864d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1155 entries, 0 to 1154\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   START_DATE*  1155 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*       1155 non-null   float64\n",
      " 6   PURPOSE*     653 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 63.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af92d8fa",
   "metadata": {},
   "source": [
    "- Now we have null data only in purpose column. As we have more than 55% data missing. So I am dropping this columns and excluding this from the analysis. You may also delete the null value rows and include this column in the analysis.\n",
    "sns.countplot(df['PURPOSE*'], order=df['PURPOSE*'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "afd12b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping purpose\n",
    "\n",
    "df.drop(['PURPOSE*'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c9a52f40",
   "metadata": {},
   "outputs": [
    {
     "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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      START_DATE*       END_DATE* CATEGORY*       START*            STOP*  \\\n",
       "0  1/1/2016 21:11  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1   1/2/2016 1:25   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2  1/2/2016 20:25  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3  1/5/2016 17:31  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4  1/6/2016 14:42  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "   MILES*  \n",
       "0     5.1  \n",
       "1     5.0  \n",
       "2     4.8  \n",
       "3     4.7  \n",
       "4    63.7  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b027c53",
   "metadata": {},
   "source": [
    "## Checking for duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "bc3a7d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
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       "      <th>492</th>\n",
       "      <td>6/28/2016 23:34</td>\n",
       "      <td>6/28/2016 23:59</td>\n",
       "      <td>Business</td>\n",
       "      <td>Durham</td>\n",
       "      <td>Cary</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         START_DATE*        END_DATE* CATEGORY*  START* STOP*  MILES*\n",
       "492  6/28/2016 23:34  6/28/2016 23:59  Business  Durham  Cary     9.9"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693d5475",
   "metadata": {},
   "source": [
    "### We will remover this duplicated row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "59a84e56",
   "metadata": {},
   "outputs": [
    {
     "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_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [START_DATE*, END_DATE*, CATEGORY*, START*, STOP*, MILES*]\n",
       "Index: []"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df.duplicated()].index, axis=0, inplace=True)\n",
    "\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3599979",
   "metadata": {},
   "source": [
    "### Converting start_date & end_date cols into datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "21ab34e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*    datetime64[ns]\n",
       "END_DATE*      datetime64[ns]\n",
       "CATEGORY*              object\n",
       "START*                 object\n",
       "STOP*                  object\n",
       "MILES*                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['START_DATE*'] = pd.to_datetime(df['START_DATE*'], format='%m/%d/%Y %H:%M')\n",
    "df['END_DATE*'] = pd.to_datetime(df['END_DATE*'], format='%m/%d/%Y %H:%M')\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4637f7cd",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "- univariate summarize one variable at a time.\n",
    "\n",
    "\n",
    "- category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "dfae211c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Business', 'Personal'], dtype=object)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CATEGORY*'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cffa17c",
   "metadata": {},
   "source": [
    "- There are 2 ride-categories.. Business for work related & Personal for personal travel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "7d81a01f",
   "metadata": {},
   "outputs": [
    {
     "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>tot_miles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>11477.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal</th>\n",
       "      <td>717.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tot_miles\n",
       "CATEGORY*           \n",
       "Business     11477.1\n",
       "Personal       717.7"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['CATEGORY*', 'MILES*']].groupby(['CATEGORY*']).agg(tot_miles=('MILES*', 'sum'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "d4cd38fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Miles Per Category')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 2))\n",
    "df[['CATEGORY*','MILES*']].groupby(['CATEGORY*']).agg(tot_miles=('MILES*','sum')).plot(kind='bar')\n",
    "plt.xlabel('category')\n",
    "plt.ylabel('Total Miles')\n",
    "plt.title('Total Miles Per Category')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6faba90",
   "metadata": {},
   "source": [
    "- Users mainly use Uber cabs for their Business purposes. Around 94% miles was consumed during Business trips. * Only 6% was consumed for personal trips."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11b1a2e",
   "metadata": {},
   "source": [
    "### START*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "701acacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['START*'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1768b3ca",
   "metadata": {},
   "source": [
    "- There are 177 unique starting points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "6d781628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START*\n",
       "Cary                201\n",
       "Unknown Location    148\n",
       "Morrisville          85\n",
       "Whitebridge          68\n",
       "Islamabad            57\n",
       "Lahore               36\n",
       "Durham               36\n",
       "Raleigh              28\n",
       "Kar?chi              27\n",
       "Westpark Place       17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 Start places\n",
    "\n",
    "df['START*'].value_counts(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d207cd3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Top 10 Pickup Places'}, xlabel='Pickup Count', ylabel='Places'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "df['START*'].value_counts(ascending=False)[:10].plot(kind='barh',\n",
    "                                                   ylabel='Places',\n",
    "                                                   xlabel='Pickup Count',\n",
    "                                                   title='Top 10 Pickup Places')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b555836",
   "metadata": {},
   "source": [
    "-- Cary is the most popular Starting point for this user."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022a191f",
   "metadata": {},
   "source": [
    "## STOP*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "1c17f014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['STOP*'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12aab1df",
   "metadata": {},
   "source": [
    "- There are 188 unique Drop Points (DESTINATION)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c1c6353f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STOP*\n",
       "Cary                202\n",
       "Unknown Location    149\n",
       "Morrisville          84\n",
       "Whitebridge          65\n",
       "Islamabad            58\n",
       "Durham               36\n",
       "Lahore               36\n",
       "Raleigh              29\n",
       "Kar?chi              26\n",
       "Apex                 17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['STOP*'].value_counts(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "707636b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Top 10 Drop Places'}, xlabel='Drop Count', ylabel='Places'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['STOP*'].value_counts(ascending=False)[:10].plot(kind='barh',\n",
    "                                                   ylabel='Places',\n",
    "                                                   xlabel='Drop Count',\n",
    "                                                   title='Top 10 Drop Places')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14947de",
   "metadata": {},
   "source": [
    "- Cary is the most popular Stop place for this user. Maybe his home is in Cary (as mostly start & Stop are from here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "052b7864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START*\n",
       "Unknown Location    148\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['START*']=='Unknown Location']['START*'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "c6e4fff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STOP*\n",
       "Unknown Location    149\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['STOP*']=='Unknown Location']['STOP*'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f1f6a7",
   "metadata": {},
   "source": [
    "### MILES*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "de45b672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='MILES*', ylabel='Count'>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vY9XX1+vuu+9WQUGB8vPzg0Z1WnPllVcqJiZGBw8e7KEKAQBAJIvokZ3moHPw4EFt2rRJAwcO7PA1Bw4cUH19vTIyMnqgQgAAEOm6NLJz0UUXqbS0tMX28vJyXXTRRZ0+TlVVlfbu3au9e/dKkgoKCrR3714dO3ZMDQ0N+t73vqddu3Zp1apV8vl8Ki4uVnFxsbxeryTpyy+/1BNPPKFdu3bpyJEjWrdunb7//e/riiuu0HXXXdeVjwYAACymSyM7R44ckc/na7Hd4/GoqKio08fZtWuXJk6cGFhvnjQ8Y8YMLVq0SG+//bYk6Rvf+EbQ6zZt2qQJEyYoNjZW77//vp599llVVVUpKytLt99+ux5//HFFRUV14ZMBAACrOa+w0xw+JGn9+vVKTk4OrPt8Pr3//vsaNmxYp483YcIEmabZ5v729klSVlaWtmzZ0un3AwAAfc95hZ277rpLkmQYhmbMmBG0LyYmRsOGDdPSpUtDVhwAAMCFOq+w4/f7JUk5OTnauXOnUlNTu6UoAACAUOnSnJ2CgoJQ1wEAANAtuvzV8/fff1/vv/++SkpKAiM+zV555ZULLgwAACAUuhR2fv/73+uJJ57Q2LFjlZGRIcMwQl0XAABASHQp7Pzxj3/UypUr9aMf/SjU9QAAAIRUl24q6PV6NX78+FDXAgAAEHJdCjsPPPCA3njjjVDXAgAAEHJduoxVV1enP//5z9qwYYO+/vWvKyYmJmj/smXLQlIcAADAhepS2Pnkk08Cj3DYv39/0D4mKwMAgEjSpbCzadOmUNcBAADQLbo0ZwcAAKC36NLIzsSJE9u9XLVx48YuFwQAABBKXQo7zfN1mtXX12vv3r3av39/iweEAgAAhFOXws7TTz/d6vZFixapqqrqggoCAAAIpZDO2fnhD3/Ic7EAAEBECWnY+fDDDxUXFxfKQwIAAFyQLl3Gmjp1atC6aZpyuVzatWuXfve734WkMAAAgFDoUthJTk4OWrfZbLr00kv1xBNPaNKkSSEpDAAAIBS6FHZWrFgR6joAAAC6RZfCTrPdu3frs88+k2EYuvzyy3XFFVeEqi4AAICQ6FLYKSkp0fTp07V582b1799fpmmqoqJCEydO1OrVqzVo0KBQ1wkAANAlXfo21pw5c+R2u3XgwAGdOXNGZWVl2r9/v9xut37+85+HukYAAIAu69LIzrvvvqsNGzboa1/7WmDb5Zdfrueff54Jyh0w/X65XK6mFVPiKfEAAHSrLoUdv9+vmJiYFttjYmLk9/svuCgrqywv1Usbj8qsr1NS2hAl9nOEuyQAACytS5exbrrpJv3iF7/QiRMnAtuKior0r//6r7r55ptDVpxVOQakKrF/arjLAACgT+hS2Fm+fLkqKys1bNgwXXzxxbrkkkuUk5OjyspKPffcc6GuEQAAoMu6dBkrKytLe/bsUX5+vj7//HOZpqnLL79ct9xyS6jrAwAAuCDnNbKzceNGXX755XK73ZKkW2+9VXPmzNHPf/5zXXXVVRo5cqQ++OCDbikUAACgK84r7DzzzDOaNWuWkpKSWuxLTk7WT3/6Uy1btixkxQEAAFyo8wo7H3/8sSZPntzm/kmTJmn37t2dPt7WrVs1ZcoUZWZmyjAMrV27Nmi/aZpatGiRMjMzFR8frwkTJujAgQNBbTwej+bMmaPU1FQlJibqzjvv1PHjx8/nYwEAAAs7r7Bz8uTJVr9y3iw6OlqnTp3q9PGqq6s1evRoLV++vNX9Tz31lJYtW6bly5dr586dcjqduvXWW1VZWRloM3fuXL311ltavXq1tm3bpqqqKt1xxx3y+Xyd/2AAAMCyzmuC8uDBg7Vv3z5dcsklre7/5JNPlJGR0enj5eXlKS8vr9V9pmnqmWee0aOPPqqpU6dKkl599VWlp6frjTfe0E9/+lNVVFTo5Zdf1l/+8pfA5OjXX39dWVlZ2rBhg2677bbz+XgAAMCCzmtk51vf+pYee+wx1dXVtdhXW1urxx9/XHfccUdICisoKFBxcXHQHZntdrtuvPFGbd++XVLjg0jr6+uD2mRmZio3NzfQpjUej0dutztoAQAA1nReIzu//e1vtWbNGo0YMUKzZ8/WpZdeKsMw9Nlnn+n555+Xz+fTo48+GpLCiouLJUnp6elB29PT03X06NFAm9jYWA0YMKBFm+bXt2bJkiX6/e9/H5I6AQBAZDuvsJOenq7t27frZz/7mRYuXCjTNCVJhmHotttu0wsvvNAinFwo45xnR5mm2WLbuTpqs3DhQs2bNy+w7na7lZWVdWGFAgCAiHTeNxXMzs7WunXrVFZWpkOHDsk0TQ0fPrzF6MqFcjqdkhpHb86eB1RSUhIIVE6nU16vV2VlZUHvX1JSovHjx7d5bLvdLrvdHtJ6AQBAZOrS4yIkacCAAbrqqqt09dVXhzzoSFJOTo6cTqfy8/MD27xer7Zs2RIIMmPGjFFMTExQG5fLpf3797cbdgAAQN/RpcdFhEpVVZUOHToUWC8oKNDevXuVkpKioUOHau7cuVq8eLGGDx+u4cOHa/HixUpISNA999wjqfFGhvfff7/mz5+vgQMHKiUlRQsWLNCoUaN4dAUAAJAU5rCza9cuTZw4MbDePI9mxowZWrlypR555BHV1tbqoYceUllZmcaNG6f33ntPDocj8Jqnn35a0dHRuvvuu1VbW6ubb75ZK1euVFRUVI9/HgAAEHkMs3mWcR/mdruVnJysioqKVh+F0VVFRUX689bD6p+armP/3K+o2Dj5vHVB/03s59C/3HCRBg8eHLL3BQCgL+js3++wjuzgK36/Xy6XS5KUkZEhm63L06kAAMBZ+IsaIVwul5au3aGla3cEQg8AALhwjOxEEEdKWrhLAADAchjZAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYCZN6v1Th7fOPJQMAoNtxB+UwME1T20/F6IxXul7e4H08IwsAgJDiL2kYFNdIZ7yNXb+jqE4FZ+oC+yrLS/XSxk95RhYAACFC2Olhpil9Vtb4c5QhNfil364rUG29L9DGMSCV52QBABAihJ0edspj6IxHshmmJg6W4qMNHSnz6K8fnQp3aQAAWBJhp4d97m6cJjUs0af+dkPfcNolSTuPVYazLAAALIuw04NqG0yVehq7fISj8bKVs19j+PnsZI0a/Hw7CwCAUCPs9KByT+N/k2Kk+KbvwTliDQ1MjFa939TpGl/bLwYAAF1C2OlBFU3fMk+2f7XNMAyNzugnSTpZTdgBACDUCDs9qHlkp39s8PZvDE6UJJ2saujhigAAsD7CTg9qbWRHkkZnNo7snKrxiWk7AACEFmGnhzT4pcr6xp/PHdm5aGCcHPYoNfilCq/R88UBAGBhhJ0e4q5vDDF2m6m46OBAYzMMfT2z8VLWaQ+/EgAAQom/rD2koinsJMe2fp2q+VLWaQ8jOwAAhBJhp4dUND0LKznG3+r+Uc4ESVJ5Pb8SAABCib+sPSQwshPT+sjOsJQ4SVKdz1A9s5QBAAgZwk4PME2zw8tYSXHRim+ay+P29lhpAABYHmGnB5TX1stnGrIZUr/otkdt+sc1/joqCTsAAIQMYacHlFY1ppfkWMnWzvzjZHvjr4ORHQAAQoew0wPctY032OkX03675Lioxvb13V0RAAB9R8SHnWHDhskwjBbLww8/LEmaOXNmi33XXHNNmKsO5q5rTC8J0e234zIWAACh18Gf3/DbuXOnfL6vHpC5f/9+3Xrrrfr+978f2DZ58mStWLEisB4be84tisPMXdf4zKvOhp3qBqnexzeyAAAIhYgPO4MGDQpaf/LJJ3XxxRfrxhtvDGyz2+1yOp2dPqbH45HH4wmsu93uCy+0Hc0jO4kdXMaKi7Yp1mbK6zfk9rR+Px4AAHB+Iv4y1tm8Xq9ef/113XfffTKMr2b6bt68WWlpaRoxYoRmzZqlkpKSdo+zZMkSJScnB5asrKxuq9k0TVXWdm5kR5IcTffhKff4OmgJAAA6o1eFnbVr16q8vFwzZ84MbMvLy9OqVau0ceNGLV26VDt37tRNN90UNHJzroULF6qioiKwFBYWdlvNXp/k9TWO0nQq7DR9Nb2ijpEdAABCIeIvY53t5ZdfVl5enjIzMwPbpk2bFvg5NzdXY8eOVXZ2tt555x1NnTq11ePY7XbZ7fZur1eSqusbQ4vdZiraZlNH4zVJzSM7hB0AAEKi14Sdo0ePasOGDVqzZk277TIyMpSdna2DBw/2UGXtq/I2j+p0bsKxo+nZWRXM2QEAICR6zWWsFStWKC0tTbfffnu77UpLS1VYWKiMjIweqqx9gbAT1cmw0xSKKj3+wOUvAADQdb0i7Pj9fq1YsUIzZsxQdPRXg1FVVVVasGCBPvzwQx05ckSbN2/WlClTlJqaqu985zthrPgrVd7G8BLfyZGduCgp2pBMSScquOEOAAAXqldcxtqwYYOOHTum++67L2h7VFSU9u3bp9dee03l5eXKyMjQxIkT9eabb8rhcISp2mDNIzuJbYzsmH6/XC5X04opw2i803K5VyqqaHuSNQAA6JxeEXYmTZok02wZFuLj47V+/fowVNR51d72v4lVWV6qlzYelVlfp6S0IZIa78dT7pWOE3YAALhgvSLs9GZV9R3P2XEMSJXPWxdYb36GVhGXsQAAuGC9Ys5Ob1Xt9cnb9F3zzs7Zkb6603JROSM7AABcKMJONyp2N47MxEXbFHMePR0Y2XEzsgMAwIUi7HQjV9Pjy5PiO3go1jn6NV1cPOH2qIGvnwMAcEEIO93I1TQy44g7v6lR8dGSzZB8fslVUdfxCwAAQJsIO93oZPPITtz5jewYhiFHbOOv5khpdcjrAgCgLyHsdKPKpieXn+/IjiQ57I2/mqOlNSGtCQCAvoavnnejhTcPVf+oeiUNTJbry6Lzem3zyM5RRnYAALggjOx0syiboZio8+9mRnYAAAgNwk6EcsQakgg7AABcKMJOhEpqGtk5dqam1UdlAACAziHsRKjEGJtshlRb79OpSu6kDABAVxF2IlSUzZDTEStJOsKlLAAAuoywE8EykxvDDt/IAgCg6wg7EWxIsl0Sk5QBALgQhJ0INrg57Jwh7AAA0FWEnQg2hMtYAABcMMJOBBvMZSwAAC4YYSeCNU9QrqitV3mNN8zVAADQOxF2Ilh8TJTSHIzuAABwIQg7EW7YwERJ0hHm7QAA0CWEnQg3dGCCJOkYIzsAAHQJYSfCDWsKO9xFGQCAriHsRLisAfGSpIOuMvn9/jBXAwBA70PYiXCJZq0k6YuTVXK5XGGuBgCA3oewE+EGN3393OM3VFvvC3M1AAD0PoSdCJcUFy17lCFJKqrgXjsAAJwvwk4v0C+2Oex4wlwJAAC9D2GnF0iyN/6ajjOyAwDAeSPs9AKO2MZfEyM7AACcv4gOO4sWLZJhGEGL0+kM7DdNU4sWLVJmZqbi4+M1YcIEHThwIIwVdw9H08hOUTlhBwCA8xXRYUeSRo4cKZfLFVj27dsX2PfUU09p2bJlWr58uXbu3Cmn06lbb71VlZWVYaw49AIjO24uYwEAcL4iPuxER0fL6XQGlkGDBklqHNV55pln9Oijj2rq1KnKzc3Vq6++qpqaGr3xxhthrjq0mufsnKz0ytvAjQUBADgfER92Dh48qMzMTOXk5Gj69Ok6fPiwJKmgoEDFxcWaNGlSoK3dbteNN96o7du3t3tMj8cjt9sdtESyuGhD0TbJb0rHy3hsBAAA5yOiw864ceP02muvaf369XrppZdUXFys8ePHq7S0VMXFxZKk9PT0oNekp6cH9rVlyZIlSk5ODixZWVnd9hlCwTCMwKWso2cIOwAAnI+IDjt5eXn67ne/q1GjRumWW27RO++8I0l69dVXA20Mwwh6jWmaLbada+HChaqoqAgshYWFoS8+xJonKR89XR3mSgAA6F0iOuycKzExUaNGjdLBgwcD38o6dxSnpKSkxWjPuex2u5KSkoKWSMfIDgAAXdOrwo7H49Fnn32mjIwM5eTkyOl0Kj8/P7Df6/Vqy5YtGj9+fBir7B6BkZ1Swg4AAOcjOtwFtGfBggWaMmWKhg4dqpKSEv3bv/2b3G63ZsyYIcMwNHfuXC1evFjDhw/X8OHDtXjxYiUkJOiee+4Jd+khl9Q8slPKZSwAAM5HRIed48eP6wc/+IFOnz6tQYMG6ZprrtGOHTuUnZ0tSXrkkUdUW1urhx56SGVlZRo3bpzee+89ORyOMFcees0jO4VnauXzm4qytT8vCQAANIrosLN69ep29xuGoUWLFmnRokU9U1AYJcQYirYZ8vr8KnbXaXD/+HCXBABAr9Cr5uz0ZTbDUEZSrCS+kQUAwPkg7PQiQ5LtkvhGFgAA5yOiL2P1ZabfL5fL1bRiSoahwcmNIztHmKQMAECnEXYiVGV5qV7aeFRmfZ2S0oYosZ9DQ/o3juwc4+vnAAB0GmEngjkGpMrnrQusD05qHtkh7AAA0FnM2elFmkd2jpyult9vhrkaAAB6B8JOL5KZbFdMlKHaep9c7rqOXwAAAAg7vUm0zdCwgYmSpC9LqsJcDQAAvQNhp5e5eFA/SdIhwg4AAJ1C2OllLk5rGtk5RdgBAKAzCDu9TPPIDmEHAIDOIez0Ml+FHW4sCABAZxB2epmL0xrDzqlKjypq68NcDQAAkY+w08v0s0fLmRQniUtZAAB0BmGnFwpMUuYbWQAAdIiw0wsxbwcAgM4j7PRC3GsHAIDOI+z0Qpc0TVI+zJwdAAA6RNjphZpHdo6eqZG3wR/magAAiGyEnV4oPcmufvZo+fymCk4zbwcAgPYQdnohwzB0qdMhSfq82B3magAAiGyEnV7qsqaw86mLsAMAQHsIO73U1zKSJEmfuSrDXAkAAJGNsNNLNYedzxnZAQCgXYSdXuoyp0OGIZVUelRa5Ql3OQAARCzCTi+VaI9WdkqCJC5lAQDQHsJOL/bVvB0uZQEA0BbCTi9h+v1yuVwqKiqS3994I0HCDgAAHSPs9BKV5aV6aeOnWrp2h1wul6Svwg5fPwcAoG3R4S4AnecYkKrEfo7AevO9dr48VSVvg1+x0WRXAADOxV/HXmzIgHg54qJV7zP1JQ8FBQCgVREddpYsWaKrrrpKDodDaWlpuuuuu/TFF18EtZk5c6YMwwharrnmmjBV3LMMw9DXnE2Xsk5wKQsAgNZEdNjZsmWLHn74Ye3YsUP5+flqaGjQpEmTVF0d/PDLyZMny+VyBZZ169aFqeLud+5E5dzByZKkj4+Xh7cwAAAiVETP2Xn33XeD1lesWKG0tDTt3r1bN9xwQ2C73W6X0+ns9HE9Ho88nq9uxOd2955RkcaJykeVmHhU8++6Rldm99cr/yd9dKw83KUBABCRInpk51wVFRWSpJSUlKDtmzdvVlpamkaMGKFZs2appKSk3eMsWbJEycnJgSUrK6vbau4OjgGpcqSkSZKuGDpAUuPXz2u9vnCWBQBAROo1Ycc0Tc2bN0/XX3+9cnNzA9vz8vK0atUqbdy4UUuXLtXOnTt10003BY3cnGvhwoWqqKgILIWFhT3xEUKq+XKWWVWq9CS7GvymPuFSFgAALUT0ZayzzZ49W5988om2bdsWtH3atGmBn3NzczV27FhlZ2frnXfe0dSpU1s9lt1ul91u79Z6u9vZl7O+NihdJ90efVRYrnEXDQx3aQAARJReMbIzZ84cvf3229q0aZOGDBnSbtuMjAxlZ2fr4MGDPVRd+DRfzhrpTJQk7TlaFuaKAACIPBE9smOapubMmaO33npLmzdvVk5OToevKS0tVWFhoTIyMnqgwsiQ62x8IOhHheUyTVOGYYS5IgAAIkdEj+w8/PDDev311/XGG2/I4XCouLhYxcXFqq2tlSRVVVVpwYIF+vDDD3XkyBFt3rxZU6ZMUWpqqr7zne+Eufqec2lagqJthk5VenS8rDbc5QAAEFEiOuy8+OKLqqio0IQJE5SRkRFY3nzzTUlSVFSU9u3bp29/+9saMWKEZsyYoREjRujDDz+Uw+Ho4OjWYY+2aWRm480FPyosD28xAABEmIi/jNWe+Ph4rV+/voeqiWxXDB2gj49XaM/RMt05OjPc5QAAEDEiemQHnTd2WOP9dnYcLg1zJQAARBbCTi/XfL+dixLqZRjS58WVOumuC3dZAABEDMJOL9d4v51P9V/rd+myQY3fytr6z1NhrgoAgMhB2LGA5vvtXJ3dOCn7g4Onw1wRAACRg7BjIVdnNYadbYdOy+9vf3I3AAB9BWHHQnKdiepnj9aZaq/2n6gIdzkAAEQEwo6FREcZuvbixmdjcSkLAIBGhB2LuWHEIEnSFiYpAwAgibBjOROaws7uo2U6XeUJczUAAIQfYcdislISNHpIsnx+U//z8YlwlwMAQNgRdizo298YLElau5ewAwAAYceCpozOVJTN0N7CchWcrg53OQAAhBVhx4IGOey67pJUSdLf9xaFuRoAAMKLsGNRd32j8cnnf997osOnxwMAYGWEHYu6baRT8TFRKjhdrX8UnAl3OQAAhA1hx6IS7dH6zpWNE5Vf3PJlmKsBACB8CDsW9uANF8tmSJu/OKX9RTw+AgDQNxF2LGzowATdObpx7s6LmxndAQD0TYQdi/vZhEskSev2u3SopCrM1QAA0PMIOxbm9/vVz+fW9TlJMk3p9//fAb6ZBQDocwg7FuZyubR07Q7F155SbJShDw6e1n/vPh7usgAA6FGEHYsw/X65XC4VFRWpqKhIfr9fkuRISVNm+iA9cE2GJOn/+Z9PddJdF85SAQDoUYQdi6gsL9VLGz/Vn7ce1tK1O+RyuYL2T/vGII0ekqzKugb9YvVH8jT4wlQpAAA9i7BjIY4Bqeqfmi5HSlqLfdE2Q3/4/mglxkZpx+Ez+sVf98rnZ/4OAMD6CDsW1HxJy+VySWdNSL5kUKIWf2uYYmyG3j1QrEf+34915Fhh0GWvUPH7/S0uqQEAEA7R4S4Aodd4SeuozPo6JaUNUWI/h6TGCctbdh/QlQNs2nkmVn/bU6SN+wt19cB6Pfa9cRo8eHDIamieHC1J8++6JqTHBgDgfDCyY1GOAalK7J/acntKmi4bkqonb8+Rwx6lMq9NG0/a9dquk6rxNoS2hpS0Vi+pAQDQkwg7fdR1Ocla+YNLNSghSvV+6U8funTDU5u19L0vdPgUNx8EAFgHl7H6gOY5PI0rpmQYkiSnI1aTL0lQwRmvDpY16GSVR89tPKTnNh5STkqcxmb104SRWRo1uL+yUuJlNL0OQGTxn/W/8YyMDNls/P9Y4GyEnT6grTk8kmQzDA0y3IqPrVJ5bD/57A7tPelVwZk6FZyp039/fFqSFB9j06DEGKUmxijNEaOc9AEa2M+uxNgo1ddWKiHapkS7Tf3s0coZkqFqj09+05RhmkH/CEviH2V0Cn/AO485ckD7LBN2XnjhBf3hD3+Qy+XSyJEj9cwzz+ib3/xmuMuKGI4BqfJ5276ZYHJKqvp561RT7dINCV7V9cvQmYYYGbZoHS6tU229X8fKPTpW7ml8wedl7bzbF4Gfog1Tb+6vUFy0S6OzBigjKVafHzmhxGhTcyZ/XWMuy1FsNH/E0BJ/wM8P8+OAtlki7Lz55puaO3euXnjhBV133XX605/+pLy8PH366acaOnRouMuLOC0ua53FMSBVCd46RcUaurRfvP7lhovU4DP19MZDOl1aJo8RqwYjVs5+UfJFx+mMu1qFbp/qvPVqMG1qMA35TKmh6R4+DaahBkXJUy9tPVzR9C4xkqTtr38um/G5BveP17DURA0bmKjsgQnKHpioQQ67BibGKjk+Wu7SksAltPb+H35bIwHN25u/Am+z2do8TkdtOxpt6KiG9j5DOI/dnc6n/88Vqj/g535+KfQjjF39/fUERskQDpF03lki7Cxbtkz333+/HnjgAUnSM888o/Xr1+vFF1/UkiVLwlxd5Dn3slZHoqMMJdujZMSZioo15PNWqqasShlZOdKJAg1zDpHPW6+o2Dgl9nPoX264SF6fX3/cclhnzpTKH2VXTV29yms8qqk3VR/rUJXXr+p6Qz7TUGFZrQrLavXBwdOtvr9NpmKjbTJMvwb2+1QJcbGKibLJNCW/acrnN+UzTXm99SqrqpMpQ/3iD8iw2eQ3pYYGn2o89fKbfkmGJEP2mE8kwybTNOVvOo4pye835fP7m1o1Tm+Kj92n6CibomyGTNOvmrp6GZL6J36q2Jho2WyGogxDNsNQQ0O9TrlrZEgalJyg2JhYGYbUUF+vk+XVkiTngETZY2Mlwwi8hyGpvr5eJ0orJUlDUh2Ks9tlGI2XGg1D8ng8OnaqMTBelN5f8XFxshmG6jx1OuwqkwxpeEaKEhPim47Z+DrDMFRXW6vPj5+WIWn0sEFKTuqnaJtNMVGGopr+G22zKTrKULTNUHTUV9tshuQzzaa+MeUzm/qpqe+bf673+VXvM+Vt8KvB71d9Q+O2iqpqfXa8VF6vV6YRJSMqShkpSTKiogNZu3k6WGBWmGGo3utVSaVHhqSPSg4qLu6YogxDUTajqc8b+6a5/6NsjZ83qmn9bDU1Nfq08LRMSZdnNX5L8dPCxvPt8qxUJSYkNL7OFny8wDajsV9sNkM2Q/KbkprPGdOUaUruykr9458nZEoae0mGEhP7qamZTJmqrKzSnsPFkqTROelKTExs+sxGoA+aqz57fpzX51d9g7/xvz6/vE396m3wN/W5X9W1Hp2q8spnmlp36ID8+lRen1+mKcXFRCnWZqqqpk5RNmnYIIeS+yUoPiZK8TFRssdEyR5tU2x04+88Nioq8HPz9mibrelcUqDms7vYMIyzam/9M52rtVuatvacYrPVlm21ba1d526e2laz1t6/1ffuZD1t1dRq2072R9u1t/beoT+mmv8taPq3tPHfZam8okIfHDgmU9I1lw7R1HHDNWpIcusH7ma9Pux4vV7t3r1bv/71r4O2T5o0Sdu3b2/1NR6PRx6PJ7BeUdH4B8Ttdoe0tsrKSp0uOiJPTbXKigtlxMTJrK/r1v9G22zn9V6lrsI299cmJOjgwcbHSpwuKlR1xZmg/Z6aannraoOOcfZrqk4Wqr7pNfb6Ojlj4mSqToZZIVN1UnycouITdPXFTtVFJaqowqMT7nq5Kj2qqG1QeZ1P3gZTfkkNTb+u6praTvV9VQftPOfxeLC2rv519B5lVa2/8Ewb2892qrL9NsUVJ1vdXlTuanX72Y6UhfNhsA2SGlTiPnVerzpZVhmyCo6WFbW7HgpfnD7a7v5DpYUhf89m5/4rdu56aQfnFtAdDpwq0OAB8cpOCu0XXZr/bncUant92Dl9+rR8Pp/S09ODtqenp6u4uLjV1yxZskS///3vW2zPysrqlhp7sxd64DWvdOE9AAC9y6xnpFnddOzKykolJ7c9atTrw06zc78WbZpmm1+VXrhwoebNmxdY9/v9OnPmjAYOHBjSr1e73W5lZWWpsLBQSUlJITuuVdA/HaOPOkYfdYw+6hh91L5I7R/TNFVZWanMzMx22/X6sJOamqqoqKgWozglJSUtRnua2e122e32oG39+/fvrhKVlJQUUSdHpKF/OkYfdYw+6hh91DH6qH2R2D/tjeg06/VT8mNjYzVmzBjl5+cHbc/Pz9f48ePDVBUAAIgUvX5kR5LmzZunH/3oRxo7dqyuvfZa/fnPf9axY8f04IMPhrs0AAAQZpYIO9OmTVNpaameeOIJuVwu5ebmat26dcrOzg5rXXa7XY8//niLS2ZoRP90jD7qGH3UMfqoY/RR+3p7/xhmZ29CAAAA0Av1+jk7AAAA7SHsAAAASyPsAAAASyPsAAAASyPsdJMXXnhBOTk5iouL05gxY/TBBx+Eu6SwWbRoUeODAs9anE5nYL9pmlq0aJEyMzMVHx+vCRMm6MCBA2GsuPtt3bpVU6ZMUWZmpgzD0Nq1a4P2d6ZPPB6P5syZo9TUVCUmJurOO+/U8ePhfO5V6HTUPzNnzmxxTl1zzTVBbazcP1LjY2+uuuoqORwOpaWl6a677tIXX3wR1KYvn0ed6Z++fh69+OKL+vrXvx64UeC1116r//3f/w3st9L5Q9jpBm+++abmzp2rRx99VB999JG++c1vKi8vT8eOHQt3aWEzcuRIuVyuwLJv377AvqeeekrLli3T8uXLtXPnTjmdTt16662qrAzdwx8jTXV1tUaPHq3ly5e3ur8zfTJ37ly99dZbWr16tbZt26aqqirdcccd8vl8PfUxuk1H/SNJkydPDjqn1q1bF7Tfyv0jSVu2bNHDDz+sHTt2KD8/Xw0NDZo0aZKqq6sDbfryedSZ/pH69nk0ZMgQPfnkk9q1a5d27dqlm266Sd/+9rcDgcZS54+JkLv66qvNBx98MGjbZZddZv76178OU0Xh9fjjj5ujR49udZ/f7zedTqf55JNPBrbV1dWZycnJ5h//+MceqjC8JJlvvfVWYL0zfVJeXm7GxMSYq1evDrQpKioybTab+e677/ZY7T3h3P4xTdOcMWOG+e1vf7vN1/Sl/mlWUlJiSjK3bNlimibn0bnO7R/T5DxqzYABA8z/+q//stz5w8hOiHm9Xu3evVuTJk0K2j5p0iRt3749TFWF38GDB5WZmamcnBxNnz5dhw8fliQVFBSouLg4qL/sdrtuvPHGPttfnemT3bt3q76+PqhNZmamcnNz+0y/bd68WWlpaRoxYoRmzZqlkpKSwL6+2D8VFRWSpJSUFEmcR+c6t3+acR418vl8Wr16taqrq3Xttdda7vwh7ITY6dOn5fP5WjyEND09vcXDSvuKcePG6bXXXtP69ev10ksvqbi4WOPHj1dpaWmgT+ivr3SmT4qLixUbG6sBAwa02cbK8vLytGrVKm3cuFFLly7Vzp07ddNNN8nj8Ujqe/1jmqbmzZun66+/Xrm5uZI4j87WWv9InEeStG/fPvXr1092u10PPvig3nrrLV1++eWWO38s8biISGQYRtC6aZottvUVeXl5gZ9HjRqla6+9VhdffLFeffXVwGRA+qulrvRJX+m3adOmBX7Ozc3V2LFjlZ2drXfeeUdTp05t83VW7Z/Zs2frk08+0bZt21rs4zxqu384j6RLL71Ue/fuVXl5uf72t79pxowZ2rJlS2C/Vc4fRnZCLDU1VVFRUS1SbUlJSYuE3FclJiZq1KhROnjwYOBbWfTXVzrTJ06nU16vV2VlZW226UsyMjKUnZ2tgwcPSupb/TNnzhy9/fbb2rRpk4YMGRLYznnUqK3+aU1fPI9iY2N1ySWXaOzYsVqyZIlGjx6tZ5991nLnD2EnxGJjYzVmzBjl5+cHbc/Pz9f48ePDVFVk8Xg8+uyzz5SRkaGcnBw5nc6g/vJ6vdqyZUuf7a/O9MmYMWMUExMT1Mblcmn//v19st9KS0tVWFiojIwMSX2jf0zT1OzZs7VmzRpt3LhROTk5Qfv7+nnUUf+0pi+eR+cyTVMej8d6508YJkVb3urVq82YmBjz5ZdfNj/99FNz7ty5ZmJionnkyJFwlxYW8+fPNzdv3mwePnzY3LFjh3nHHXeYDocj0B9PPvmkmZycbK5Zs8bct2+f+YMf/MDMyMgw3W53mCvvPpWVleZHH31kfvTRR6Ykc9myZeZHH31kHj161DTNzvXJgw8+aA4ZMsTcsGGDuWfPHvOmm24yR48ebTY0NITrY4VMe/1TWVlpzp8/39y+fbtZUFBgbtq0ybz22mvNwYMH95n+MU3T/NnPfmYmJyebmzdvNl0uV2CpqakJtOnL51FH/cN5ZJoLFy40t27dahYUFJiffPKJ+Zvf/Ma02Wzme++9Z5qmtc4fwk43ef75583s7GwzNjbWvPLKK4O+7tjXTJs2zczIyDBjYmLMzMxMc+rUqeaBAwcC+/1+v/n444+bTqfTtNvt5g033GDu27cvjBV3v02bNpmSWiwzZswwTbNzfVJbW2vOnj3bTElJMePj48077rjDPHbsWBg+Tei11z81NTXmpEmTzEGDBpkxMTHm0KFDzRkzZrT47FbuH9M0W+0fSeaKFSsCbfryedRR/3AemeZ9990X+Ds1aNAg8+abbw4EHdO01vljmKZp9tw4EgAAQM9izg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg6AiDBz5kwZhqEHH3ywxb6HHnpIhmFo5syZgbZ33XVX0GvPXj/XsGHDZBhGi+XJJ58MtPnb3/6mcePGKTk5WQ6HQyNHjtT8+fNbrfPIkSNd/ZgAwoCwAyBiZGVlafXq1aqtrQ1sq6ur01//+lcNHTr0go79xBNPyOVyBS1z5syRJG3YsEHTp0/X9773Pf3jH//Q7t279e///u/yer2SpDNnzuj555/X2U/X+fLLL/X6669fUE0AekZ0uAsAgGZXXnmlDh8+rDVr1ujee++VJK1Zs0ZZWVm66KKLLujYDodDTqez1X3/8z//o+uvv16//OUvA9tGjBgRGC2Ki4tTUVGRJk+eLJ/Ppz/+8Y/avn27/vCHP1xQTQB6BiM7ACLKT37yE61YsSKw/sorr+i+++7r1vd0Op06cOCA9u/f3+r+hIQELV68WL/4xS+0efNm7dixQ++//77GjRvXrXUBCA3CDoCI8qMf/Ujbtm3TkSNHdPToUf3f//2ffvjDH17wcX/1q1+pX79+QcvmzZslSXPmzNFVV12lUaNGadiwYZo+fbpeeeUVeTweSY2X0h577DE9++yzmjBhgq655hrdcsst+sc//nHBdQHofoQdABElNTVVt99+u1599VWtWLFCt99+u1JTUy/4uL/85S+1d+/eoKV5ZCYxMVHvvPOODh06pN/+9rfq16+f5s+fr6uvvlo1NTWqqalRenq63n33XQ0ZMkQPPvigXnnlFf3zn/+84LoAdD/m7ACIOPfdd59mz54tSXr++edDcszU1FRdcskl7ba5+OKLdfHFF+uBBx7Qo48+qhEjRujNN9/UT37yEz388MOttgUQ+Qg7ACLO5MmTA9+Euu2228JSw7Bhw5SQkKDq6uqg7StXrgxLPQC6jrADIOJERUXps88+C/zcGRUVFdq7d2/QtpSUlMBX1isrK1VcXBy0PyEhQUlJSVq0aJFqamr0rW99S9nZ2SovL9d//ud/qr6+XrfeeuuFfyAAYcWcHQARKSkpSUlJSZ1uv3nzZl1xxRVBy2OPPRbY/9hjjykjIyNoeeSRRyRJN954ow4fPqwf//jHuuyyy5SXl6fi4mK99957uvTSS0P+2QD0LMM8+y5ZAAAAFsPIDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsLT/HyKnNb9zLVl1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['MILES*'], kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47efb296",
   "metadata": {},
   "source": [
    "- Miles data is Rightly Skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "0b27313b",
   "metadata": {},
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>START_DATE*</th>\n",
       "      <td>1154</td>\n",
       "      <td>2016-07-17 00:07:01.611784960</td>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-03-31 20:23:30</td>\n",
       "      <td>2016-07-21 17:29:30</td>\n",
       "      <td>2016-10-27 21:16:15</td>\n",
       "      <td>2016-12-31 22:08:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>END_DATE*</th>\n",
       "      <td>1154</td>\n",
       "      <td>2016-07-17 00:30:16.117850880</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>2016-03-31 20:52:00</td>\n",
       "      <td>2016-07-21 17:37:00</td>\n",
       "      <td>2016-10-27 21:34:30</td>\n",
       "      <td>2016-12-31 23:51:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MILES*</th>\n",
       "      <td>1154.0</td>\n",
       "      <td>10.567418</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>310.3</td>\n",
       "      <td>21.588452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count                           mean                  min  \\\n",
       "START_DATE*    1154  2016-07-17 00:07:01.611784960  2016-01-01 21:11:00   \n",
       "END_DATE*      1154  2016-07-17 00:30:16.117850880  2016-01-01 21:17:00   \n",
       "MILES*       1154.0                      10.567418                  0.5   \n",
       "\n",
       "                             25%                  50%                  75%  \\\n",
       "START_DATE*  2016-03-31 20:23:30  2016-07-21 17:29:30  2016-10-27 21:16:15   \n",
       "END_DATE*    2016-03-31 20:52:00  2016-07-21 17:37:00  2016-10-27 21:34:30   \n",
       "MILES*                       2.9                  6.0                 10.4   \n",
       "\n",
       "                             max        std  \n",
       "START_DATE*  2016-12-31 22:08:00        NaN  \n",
       "END_DATE*    2016-12-31 23:51:00        NaN  \n",
       "MILES*                     310.3  21.588452  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8acf96c",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "89cdf4b4",
   "metadata": {},
   "outputs": [
    {
     "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_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES*  \n",
       "0      Fort Pierce     5.1  \n",
       "1      Fort Pierce     5.0  \n",
       "2      Fort Pierce     4.8  \n",
       "3      Fort Pierce     4.7  \n",
       "4  West Palm Beach    63.7  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "388490db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "910    2.2\n",
      "Name: (Agnew, Agnew), dtype: float64\n",
      "906    4.3\n",
      "Name: (Agnew, Cory), dtype: float64\n",
      "908    2.2\n",
      "911    2.4\n",
      "Name: (Agnew, Renaissance), dtype: float64\n",
      "879    15.2\n",
      "Name: (Almond, Bryson City), dtype: float64\n",
      "646    1.0\n",
      "825    3.3\n",
      "Name: (Apex, Apex), dtype: float64\n",
      "58      5.5\n",
      "60      5.7\n",
      "80      5.7\n",
      "173     5.6\n",
      "410     7.2\n",
      "565     5.5\n",
      "616     4.6\n",
      "647     6.0\n",
      "826     5.3\n",
      "978     5.4\n",
      "1014    5.3\n",
      "1033    8.8\n",
      "1045    4.7\n",
      "Name: (Apex, Cary), dtype: float64\n",
      "855    2.2\n",
      "Name: (Apex, Eagle Rock), dtype: float64\n",
      "1001    9.0\n",
      "Name: (Apex, Holly Springs), dtype: float64\n",
      "469    17.0\n",
      "Name: (Arabi, Metairie), dtype: float64\n",
      "621    4.9\n",
      "Name: (Arlington, Washington), dtype: float64\n",
      "613    1.3\n",
      "Name: (Arlington Park at Amberly, Lexington Park at Amberly), dtype: float64\n",
      "881    195.9\n",
      "Name: (Asheville, Mebane), dtype: float64\n",
      "871    91.8\n",
      "Name: (Asheville, Topton), dtype: float64\n",
      "232    136.0\n",
      "Name: (Austin, Katy), dtype: float64\n",
      "547     8.3\n",
      "548     3.2\n",
      "551     4.5\n",
      "556    13.0\n",
      "557     4.4\n",
      "Name: (Banner Elk, Banner Elk), dtype: float64\n",
      "558    15.1\n",
      "Name: (Banner Elk, Boone), dtype: float64\n",
      "549    22.4\n",
      "Name: (Banner Elk, Elk Park), dtype: float64\n",
      "552    28.1\n",
      "Name: (Banner Elk, Newland), dtype: float64\n",
      "357    12.9\n",
      "Name: (Bellevue, Seattle), dtype: float64\n",
      "967    2.3\n",
      "Name: (Berkeley, El Cerrito), dtype: float64\n",
      "832    3.0\n",
      "839    4.6\n",
      "920    3.6\n",
      "948    1.3\n",
      "962    2.5\n",
      "Name: (Berkeley, Emeryville), dtype: float64\n",
      "936    45.9\n",
      "Name: (Berkeley, Menlo Park), dtype: float64\n",
      "960    44.6\n",
      "Name: (Berkeley, Mountain View), dtype: float64\n",
      "437     5.1\n",
      "964     5.1\n",
      "969    16.3\n",
      "Name: (Berkeley, Oakland), dtype: float64\n",
      "916    11.8\n",
      "918    12.2\n",
      "922    11.4\n",
      "930    12.6\n",
      "Name: (Berkeley, San Francisco), dtype: float64\n",
      "841    47.7\n",
      "Name: (Berkeley, San Jose), dtype: float64\n",
      "555    23.8\n",
      "Name: (Boone, Banner Elk), dtype: float64\n",
      "559    180.2\n",
      "Name: (Boone, Cary), dtype: float64\n",
      "256    9.6\n",
      "Name: (Briar Meadow, Midtown), dtype: float64\n",
      "878    6.6\n",
      "Name: (Bryson City, Almond), dtype: float64\n",
      "880    68.4\n",
      "Name: (Bryson City, Asheville), dtype: float64\n",
      "875    16.3\n",
      "876     6.5\n",
      "877     6.3\n",
      "Name: (Bryson City, Bryson City), dtype: float64\n",
      "333    4.8\n",
      "Name: (Burtrose, Whitebridge), dtype: float64\n",
      "466    4.5\n",
      "Name: (CBD, Bywater), dtype: float64\n",
      "444    1.9\n",
      "Name: (CBD, Lower Garden District), dtype: float64\n",
      "527    7.7\n",
      "Name: (CBD, Pontchartrain Beach), dtype: float64\n",
      "534    1.3\n",
      "Name: (CBD, St Thomas), dtype: float64\n",
      "358    14.4\n",
      "Name: (Capitol One, Mcvan), dtype: float64\n",
      "353    4.5\n",
      "Name: (Capitol One, University District), dtype: float64\n",
      "57       5.8\n",
      "59       5.7\n",
      "79       5.6\n",
      "172      3.8\n",
      "409      5.7\n",
      "564      7.2\n",
      "615      6.9\n",
      "824      5.6\n",
      "854     11.2\n",
      "977      5.4\n",
      "1000     5.1\n",
      "1013     5.1\n",
      "1032     5.1\n",
      "1044     4.4\n",
      "Name: (Cary, Apex), dtype: float64\n",
      "7        0.8\n",
      "30       4.8\n",
      "37       1.6\n",
      "38       2.4\n",
      "39       1.0\n",
      "43       1.4\n",
      "44       0.5\n",
      "45       1.8\n",
      "53       4.6\n",
      "54       5.2\n",
      "65       6.0\n",
      "66       1.6\n",
      "69       1.6\n",
      "70       1.1\n",
      "71       1.6\n",
      "73       7.7\n",
      "975      1.5\n",
      "976      1.8\n",
      "979     39.2\n",
      "980      6.4\n",
      "981      2.7\n",
      "982     18.5\n",
      "983      2.5\n",
      "984      2.1\n",
      "988      5.5\n",
      "989      4.1\n",
      "990     12.7\n",
      "993      5.9\n",
      "994      1.9\n",
      "995      3.3\n",
      "996      1.3\n",
      "999      1.4\n",
      "1003     2.5\n",
      "1009     5.5\n",
      "1010     5.5\n",
      "1022     4.1\n",
      "1023     3.8\n",
      "1024     6.6\n",
      "1025     4.0\n",
      "1026     7.0\n",
      "1027     6.9\n",
      "1028     3.4\n",
      "1029     3.4\n",
      "1030     2.0\n",
      "1031     2.0\n",
      "1034     5.6\n",
      "1035    18.9\n",
      "1042     2.1\n",
      "1043     3.1\n",
      "1048     4.2\n",
      "1049     4.1\n",
      "1050     3.4\n",
      "1051     3.3\n",
      "Name: (Cary, Cary), dtype: float64\n",
      "61    19.4\n",
      "Name: (Cary, Chapel Hill), dtype: float64\n",
      "28     10.4\n",
      "55     10.4\n",
      "74     10.4\n",
      "95      8.5\n",
      "164    10.6\n",
      "183     9.9\n",
      "185    10.4\n",
      "304    10.5\n",
      "325    10.4\n",
      "337     9.9\n",
      "339    14.2\n",
      "394    10.4\n",
      "405     9.9\n",
      "411    10.4\n",
      "413    10.4\n",
      "424    10.4\n",
      "429    10.4\n",
      "490    10.4\n",
      "497    10.4\n",
      "499     9.9\n",
      "503    10.5\n",
      "505    10.1\n",
      "509     9.9\n",
      "513    11.8\n",
      "517     9.9\n",
      "520     9.9\n",
      "584     8.0\n",
      "593    10.4\n",
      "609    14.0\n",
      "644    12.9\n",
      "816    10.5\n",
      "851    16.4\n",
      "867    10.4\n",
      "887    16.5\n",
      "973    10.3\n",
      "997    10.3\n",
      "Name: (Cary, Durham), dtype: float64\n",
      "1036    15.6\n",
      "Name: (Cary, Fuquay-Varina), dtype: float64\n",
      "314    15.3\n",
      "Name: (Cary, Holly Springs), dtype: float64\n",
      "268    144.0\n",
      "Name: (Cary, Latta), dtype: float64\n",
      "8        8.3\n",
      "67       5.2\n",
      "81       6.1\n",
      "89       6.1\n",
      "99       8.4\n",
      "        ... \n",
      "1038     3.0\n",
      "1040     3.0\n",
      "1046     3.0\n",
      "1052     3.0\n",
      "1054    10.6\n",
      "Name: (Cary, Morrisville), Length: 67, dtype: float64\n",
      "34      17.1\n",
      "46      18.7\n",
      "50      19.0\n",
      "76      11.4\n",
      "91      17.3\n",
      "181      7.6\n",
      "187     15.7\n",
      "200     12.4\n",
      "307     19.1\n",
      "310      8.9\n",
      "316     11.9\n",
      "323     13.6\n",
      "334     12.4\n",
      "389     14.9\n",
      "455      6.0\n",
      "457     19.3\n",
      "461      8.6\n",
      "501     13.3\n",
      "642     14.9\n",
      "822     20.6\n",
      "845     17.2\n",
      "847     28.1\n",
      "1006     8.5\n",
      "Name: (Cary, Raleigh), dtype: float64\n",
      "985    6.7\n",
      "Name: (Cary, Unknown Location), dtype: float64\n",
      "1017    6.6\n",
      "Name: (Cary, Wake Co.), dtype: float64\n",
      "651    31.7\n",
      "Name: (Cary, Wake Forest), dtype: float64\n",
      "869    107.0\n",
      "Name: (Cary, Winston Salem), dtype: float64\n",
      "929    1.1\n",
      "950    2.3\n",
      "951    2.6\n",
      "Name: (Central, Central), dtype: float64\n",
      "940    2.9\n",
      "Name: (Central, College Avenue), dtype: float64\n",
      "942    2.3\n",
      "Name: (Central, South), dtype: float64\n",
      "952    1.9\n",
      "958    1.9\n",
      "Name: (Central, Southside), dtype: float64\n",
      "945    0.6\n",
      "Name: (Central, West Berkeley), dtype: float64\n",
      "468    1.1\n",
      "Name: (Chalmette, Arabi), dtype: float64\n",
      "62    23.3\n",
      "Name: (Chapel Hill, Cary), dtype: float64\n",
      "359    17.0\n",
      "Name: (Chapel Hill, Morrisville), dtype: float64\n",
      "330    1.9\n",
      "Name: (Chessington, Chessington), dtype: float64\n",
      "573    4.8\n",
      "Name: (Chessington, Whitebridge), dtype: float64\n",
      "941    2.6\n",
      "Name: (College Avenue, Central), dtype: float64\n",
      "110    2.6\n",
      "111    4.5\n",
      "112    1.7\n",
      "113    1.8\n",
      "114    6.0\n",
      "118    1.7\n",
      "Name: (Colombo, Colombo), dtype: float64\n",
      "119    21.4\n",
      "Name: (Colombo, Katunayaka), dtype: float64\n",
      "115    1.1\n",
      "Name: (Colombo, Nugegoda), dtype: float64\n",
      "629    1.5\n",
      "Name: (Columbia Heights, Kalorama Triangle), dtype: float64\n",
      "212    0.8\n",
      "Name: (Congress Ave District, Downtown), dtype: float64\n",
      "627    1.3\n",
      "Name: (Connecticut Avenue, Kalorama Triangle), dtype: float64\n",
      "229    2.0\n",
      "Name: (Convention Center District, West University), dtype: float64\n",
      "907    3.9\n",
      "Name: (Cory, Agnew), dtype: float64\n",
      "271    6.7\n",
      "Name: (Couples Glen, Isles of Buena Vista), dtype: float64\n",
      "280    27.2\n",
      "Name: (Couples Glen, Vista East), dtype: float64\n",
      "479    2.5\n",
      "480    8.6\n",
      "481    5.2\n",
      "482    7.6\n",
      "483    1.8\n",
      "Name: (Covington, Covington), dtype: float64\n",
      "484    4.7\n",
      "Name: (Covington, Mandeville), dtype: float64\n",
      "223    12.5\n",
      "Name: (Coxville, The Drag), dtype: float64\n",
      "296    80.5\n",
      "Name: (Daytona Beach, Jacksonville), dtype: float64\n",
      "442    9.3\n",
      "Name: (Downtown, Bay Farm Island), dtype: float64\n",
      "944    1.4\n",
      "Name: (Downtown, Central), dtype: float64\n",
      "23    11.2\n",
      "Name: (Downtown, Gulfton), dtype: float64\n",
      "258    0.9\n",
      "Name: (Downtown, Midtown), dtype: float64\n",
      "213    1.2\n",
      "Name: (Downtown, Red River District), dtype: float64\n",
      "924    6.2\n",
      "Name: (Downtown, Sunnyside), dtype: float64\n",
      "227    2.8\n",
      "231    1.7\n",
      "Name: (Downtown, The Drag), dtype: float64\n",
      "914    1.8\n",
      "Name: (Downtown, West Berkeley), dtype: float64\n",
      "645    15.3\n",
      "Name: (Durham, Apex), dtype: float64\n",
      "29     10.4\n",
      "56     10.1\n",
      "75     10.4\n",
      "165     9.9\n",
      "184     9.9\n",
      "186    10.9\n",
      "305     8.7\n",
      "326    10.0\n",
      "338    10.0\n",
      "340    18.2\n",
      "395     9.9\n",
      "406     9.9\n",
      "412     9.9\n",
      "414     9.9\n",
      "425     9.9\n",
      "430     9.9\n",
      "491     9.9\n",
      "498     9.9\n",
      "500     9.9\n",
      "504     9.9\n",
      "506     9.9\n",
      "510     9.9\n",
      "515     9.9\n",
      "521     9.9\n",
      "585     9.9\n",
      "594     9.9\n",
      "610    13.3\n",
      "868     9.9\n",
      "888    12.8\n",
      "974    10.5\n",
      "998    11.1\n",
      "Name: (Durham, Cary), dtype: float64\n",
      "96      2.6\n",
      "518     8.6\n",
      "817     8.1\n",
      "852    15.4\n",
      "Name: (Durham, Morrisville), dtype: float64\n",
      "26    3.9\n",
      "Name: (Eagan Park, Jamestown Court), dtype: float64\n",
      "856    3.6\n",
      "Name: (Eagle Rock, Cary), dtype: float64\n",
      "208    12.8\n",
      "Name: (East Austin, West University), dtype: float64\n",
      "102    2.7\n",
      "Name: (East Elmhurst, Jackson Heights), dtype: float64\n",
      "104    8.1\n",
      "Name: (East Elmhurst, New York), dtype: float64\n",
      "13    6.4\n",
      "Name: (East Harlem, NoMad), dtype: float64\n",
      "92    5.7\n",
      "Name: (Eastgate, Walnut Terrace), dtype: float64\n",
      "578    2.3\n",
      "Name: (Edgehill Farms, Burtrose), dtype: float64\n",
      "575    1.4\n",
      "Name: (Edgehill Farms, Preston), dtype: float64\n",
      "78     3.2\n",
      "84     2.7\n",
      "633    2.7\n",
      "638    2.7\n",
      "650    2.8\n",
      "828    3.3\n",
      "Name: (Edgehill Farms, Whitebridge), dtype: float64\n",
      "968    3.1\n",
      "Name: (El Cerrito, Berkeley), dtype: float64\n",
      "550    12.2\n",
      "Name: (Elk Park, Banner Elk), dtype: float64\n",
      "11    7.5\n",
      "Name: (Elmhurst, New York), dtype: float64\n",
      "436    3.9\n",
      "831    3.0\n",
      "840    3.1\n",
      "921    3.0\n",
      "949    3.7\n",
      "963    3.7\n",
      "Name: (Emeryville, Berkeley), dtype: float64\n",
      "441     5.1\n",
      "833     3.8\n",
      "843    13.2\n",
      "Name: (Emeryville, Oakland), dtype: float64\n",
      "439    9.8\n",
      "Name: (Emeryville, San Francisco), dtype: float64\n",
      "47    3.4\n",
      "Name: (Fairmont, Meredith Townes), dtype: float64\n",
      "637    4.0\n",
      "Name: (Farmington Woods, Edgehill Farms), dtype: float64\n",
      "31    4.7\n",
      "Name: (Farmington Woods, Whitebridge), dtype: float64\n",
      "189    0.8\n",
      "Name: (Fayetteville Street, Depot Historic District), dtype: float64\n",
      "201    5.9\n",
      "Name: (Fayetteville Street, Meredith Townes), dtype: float64\n",
      "35    15.1\n",
      "Name: (Fayetteville Street, Umstead), dtype: float64\n",
      "422    4.8\n",
      "Name: (Financial District, Kips Bay), dtype: float64\n",
      "14    1.6\n",
      "Name: (Flatiron District, Midtown), dtype: float64\n",
      "299    159.3\n",
      "Name: (Florence, Cary), dtype: float64\n",
      "0    5.1\n",
      "1    5.0\n",
      "2    4.8\n",
      "3    4.7\n",
      "Name: (Fort Pierce, Fort Pierce), dtype: float64\n",
      "4    63.7\n",
      "Name: (Fort Pierce, West Palm Beach), dtype: float64\n",
      "1037    15.6\n",
      "Name: (Fuquay-Varina, Cary), dtype: float64\n",
      "251    57.0\n",
      "Name: (Galveston, Houston), dtype: float64\n",
      "247    3.1\n",
      "Name: (Galveston, Port Bolivar), dtype: float64\n",
      "1154    48.2\n",
      "Name: (Gampaha, Ilukwatta), dtype: float64\n",
      "220    6.2\n",
      "Name: (Georgian Acres, The Drag), dtype: float64\n",
      "24    11.8\n",
      "Name: (Gulfton, Downtown), dtype: float64\n",
      "873    75.7\n",
      "Name: (Hayesville, Topton), dtype: float64\n",
      "583    9.1\n",
      "Name: (Hazelwood, Lexington Park at Amberly), dtype: float64\n",
      "567    0.9\n",
      "Name: (Hazelwood, Weston), dtype: float64\n",
      "41     2.3\n",
      "168    2.0\n",
      "175    6.6\n",
      "408    2.4\n",
      "899    2.4\n",
      "Name: (Hazelwood, Whitebridge), dtype: float64\n",
      "21    2.0\n",
      "Name: (Hell's Kitchen, Midtown), dtype: float64\n",
      "649    4.4\n",
      "Name: (Heritage Pines, Edgehill Farms), dtype: float64\n",
      "159    3.2\n",
      "Name: (Heritage Pines, Whitebridge), dtype: float64\n",
      "315     13.7\n",
      "1002    13.3\n",
      "Name: (Holly Springs, Cary), dtype: float64\n",
      "246    36.5\n",
      "Name: (Houston, Galveston), dtype: float64\n",
      "25     21.9\n",
      "235     4.9\n",
      "236    12.6\n",
      "Name: (Houston, Houston), dtype: float64\n",
      "244    12.0\n",
      "Name: (Houston, Sugar Land), dtype: float64\n",
      "20    2.4\n",
      "Name: (Hudson Square, Hell's Kitchen), dtype: float64\n",
      "18    4.0\n",
      "Name: (Hudson Square, Lower Manhattan), dtype: float64\n",
      "864    12.1\n",
      "Name: (Huntington Woods, Huntington Woods), dtype: float64\n",
      "399    1.7\n",
      "865    3.9\n",
      "Name: (Huntington Woods, Weston), dtype: float64\n",
      "926    5.5\n",
      "Name: (Ingleside, Potrero Flats), dtype: float64\n",
      "129      1.5\n",
      "130      1.0\n",
      "133      4.2\n",
      "137      3.0\n",
      "138      1.5\n",
      "146      4.6\n",
      "664      1.2\n",
      "681      3.2\n",
      "686      1.4\n",
      "691      1.5\n",
      "730      5.3\n",
      "733      4.3\n",
      "734      2.5\n",
      "739      2.6\n",
      "742      4.4\n",
      "779     37.7\n",
      "782      2.8\n",
      "783      1.6\n",
      "801      1.7\n",
      "806      1.0\n",
      "807      0.7\n",
      "808      2.3\n",
      "1076     1.8\n",
      "1077     1.4\n",
      "1085     2.1\n",
      "Name: (Islamabad, Islamabad), dtype: float64\n",
      "151    8.1\n",
      "Name: (Islamabad, Noorpur Shahan), dtype: float64\n",
      "139    18.4\n",
      "678     6.5\n",
      "Name: (Islamabad, R?walpindi), dtype: float64\n",
      "1061    5.9\n",
      "Name: (Islamabad, Rawalpindi), dtype: float64\n",
      "123      6.0\n",
      "125     10.0\n",
      "131      7.3\n",
      "134     13.6\n",
      "147      8.8\n",
      "665      5.7\n",
      "671      7.3\n",
      "682     12.5\n",
      "687     20.2\n",
      "689      6.3\n",
      "692     10.9\n",
      "702      5.0\n",
      "731     12.1\n",
      "735      5.7\n",
      "737      4.0\n",
      "743      5.3\n",
      "746     10.6\n",
      "765      5.7\n",
      "768     16.5\n",
      "777     20.5\n",
      "780     16.7\n",
      "784     12.7\n",
      "802      9.5\n",
      "809     10.9\n",
      "1069     2.2\n",
      "1078    10.3\n",
      "1081     3.5\n",
      "1086     7.2\n",
      "Name: (Islamabad, Unknown Location), dtype: float64\n",
      "103    1.8\n",
      "Name: (Jackson Heights, East Elmhurst), dtype: float64\n",
      "270    201.0\n",
      "Name: (Jacksonville, Kissimmee), dtype: float64\n",
      "297    174.2\n",
      "Name: (Jacksonville, Ridgeland), dtype: float64\n",
      "9      16.5\n",
      "416    22.3\n",
      "Name: (Jamaica, New York), dtype: float64\n",
      "488    1.0\n",
      "Name: (Jamestown Court, Jamestown Court), dtype: float64\n",
      "623    1.0\n",
      "625    1.1\n",
      "Name: (K Street, Kalorama Triangle), dtype: float64\n",
      "628    1.8\n",
      "Name: (Kalorama Triangle, Columbia Heights), dtype: float64\n",
      "626    1.5\n",
      "Name: (Kalorama Triangle, Downtown), dtype: float64\n",
      "622    1.0\n",
      "Name: (Kalorama Triangle, K Street), dtype: float64\n",
      "1119    4.9\n",
      "1120    5.0\n",
      "1121    0.6\n",
      "1124    5.5\n",
      "1127    2.0\n",
      "1130    3.8\n",
      "1131    5.1\n",
      "1132    3.8\n",
      "1135    1.4\n",
      "1136    1.1\n",
      "1137    4.1\n",
      "1138    6.1\n",
      "1139    1.3\n",
      "1142    7.2\n",
      "1145    2.8\n",
      "1146    2.9\n",
      "1147    4.6\n",
      "1148    4.6\n",
      "1149    0.8\n",
      "1150    0.7\n",
      "Name: (Kar?chi, Kar?chi), dtype: float64\n",
      "1122     3.1\n",
      "1125    10.3\n",
      "1128     8.5\n",
      "1133    11.6\n",
      "1140     3.0\n",
      "1143     6.4\n",
      "1151     3.9\n",
      "Name: (Kar?chi, Unknown Location), dtype: float64\n",
      "770    2.9\n",
      "798    3.6\n",
      "Name: (Karachi, Karachi), dtype: float64\n",
      "771    8.2\n",
      "799    8.0\n",
      "Name: (Karachi, Unknown Location), dtype: float64\n",
      "120    0.5\n",
      "Name: (Katunayaka, Katunayaka), dtype: float64\n",
      "108    43.7\n",
      "Name: (Katunayaka, Unknown Location), dtype: float64\n",
      "1153    6.4\n",
      "Name: (Katunayake, Gampaha), dtype: float64\n",
      "233    30.2\n",
      "Name: (Katy, Houston), dtype: float64\n",
      "476    2.2\n",
      "539    1.4\n",
      "Name: (Kenner, Kenner), dtype: float64\n",
      "532    4.9\n",
      "Name: (Kenner, Metairie), dtype: float64\n",
      "443    12.4\n",
      "450    15.0\n",
      "452    13.6\n",
      "465    12.8\n",
      "477    13.0\n",
      "526    12.8\n",
      "530    13.2\n",
      "537    13.4\n",
      "Name: (Kenner, New Orleans), dtype: float64\n",
      "385    1.7\n",
      "Name: (Kilarney Woods, Kildaire Farms), dtype: float64\n",
      "386    4.7\n",
      "Name: (Kilarney Woods, Whitebridge), dtype: float64\n",
      "295    77.3\n",
      "Name: (Kissimmee, Daytona Beach), dtype: float64\n",
      "289    11.0\n",
      "292     0.7\n",
      "293     5.5\n",
      "294     5.1\n",
      "Name: (Kissimmee, Kissimmee), dtype: float64\n",
      "272     8.8\n",
      "276     6.1\n",
      "279     3.6\n",
      "282    13.6\n",
      "287    16.1\n",
      "290    15.5\n",
      "Name: (Kissimmee, Orlando), dtype: float64\n",
      "972    6.1\n",
      "Name: (Krendle Woods, Whitebridge), dtype: float64\n",
      "716     7.4\n",
      "717     1.5\n",
      "720     3.4\n",
      "721     3.8\n",
      "724     7.0\n",
      "725     0.9\n",
      "774     9.8\n",
      "792     2.6\n",
      "795     2.4\n",
      "796     3.1\n",
      "797     6.1\n",
      "1096    2.1\n",
      "1097    2.1\n",
      "1098    3.0\n",
      "1105    5.3\n",
      "1106    1.6\n",
      "1107    3.6\n",
      "1108    1.7\n",
      "1109    2.9\n",
      "1110    0.6\n",
      "1111    0.6\n",
      "1112    2.3\n",
      "1113    2.3\n",
      "1114    3.2\n",
      "1115    6.2\n",
      "1116    7.7\n",
      "1117    3.8\n",
      "Name: (Lahore, Lahore), dtype: float64\n",
      "712      7.3\n",
      "718      7.9\n",
      "722      5.9\n",
      "726     86.6\n",
      "775      7.3\n",
      "793      5.8\n",
      "1099     6.2\n",
      "1103     6.3\n",
      "1118     7.9\n",
      "Name: (Lahore, Unknown Location), dtype: float64\n",
      "273    1.2\n",
      "274    2.1\n",
      "Name: (Lake Reams, Lake Reams), dtype: float64\n",
      "33    7.6\n",
      "Name: (Lake Wellingborough, Whitebridge), dtype: float64\n",
      "446    5.5\n",
      "Name: (Lakeview, Storyville), dtype: float64\n",
      "269    310.3\n",
      "Name: (Latta, Jacksonville), dtype: float64\n",
      "614    1.9\n",
      "Name: (Lexington Park at Amberly, Westpark Place), dtype: float64\n",
      "571    8.7\n",
      "Name: (Lexington Park at Amberly, Whitebridge), dtype: float64\n",
      "107    13.9\n",
      "Name: (Long Island City, Jamaica), dtype: float64\n",
      "445    6.4\n",
      "Name: (Lower Garden District, Lakeview), dtype: float64\n",
      "19    1.8\n",
      "Name: (Lower Manhattan, Hudson Square), dtype: float64\n",
      "485    2.8\n",
      "Name: (Mandeville, Mandeville), dtype: float64\n",
      "486    30.0\n",
      "Name: (Mandeville, Metairie), dtype: float64\n",
      "473    1.5\n",
      "Name: (Marigny, Storyville), dtype: float64\n",
      "352    14.5\n",
      "Name: (Mcvan, Capitol One), dtype: float64\n",
      "882    45.2\n",
      "Name: (Mebane, Cary), dtype: float64\n",
      "418    0.7\n",
      "Name: (Medical Centre, Tudor City), dtype: float64\n",
      "939    36.6\n",
      "Name: (Menlo Park, Berkeley), dtype: float64\n",
      "366    7.9\n",
      "Name: (Menlo Park, Newark), dtype: float64\n",
      "937    4.0\n",
      "Name: (Menlo Park, Palo Alto), dtype: float64\n",
      "311    7.5\n",
      "Name: (Meredith, Cedar Hill), dtype: float64\n",
      "317    1.4\n",
      "Name: (Meredith Townes, Harden Place), dtype: float64\n",
      "51     14.7\n",
      "202     9.4\n",
      "Name: (Meredith Townes, Leesville Hollow), dtype: float64\n",
      "449    2.7\n",
      "487    4.4\n",
      "Name: (Metairie, Kenner), dtype: float64\n",
      "471    15.5\n",
      "533     8.5\n",
      "Name: (Metairie, New Orleans), dtype: float64\n",
      "234    15.5\n",
      "Name: (Midtown, Alief), dtype: float64\n",
      "257    1.0\n",
      "Name: (Midtown, Downtown), dtype: float64\n",
      "12    6.2\n",
      "Name: (Midtown, East Harlem), dtype: float64\n",
      "261    23.0\n",
      "Name: (Midtown, Greater Greenspoint), dtype: float64\n",
      "17    1.9\n",
      "Name: (Midtown, Hudson Square), dtype: float64\n",
      "238    1.1\n",
      "239    1.1\n",
      "Name: (Midtown, Midtown), dtype: float64\n",
      "15    1.7\n",
      "Name: (Midtown, Midtown East), dtype: float64\n",
      "105    2.0\n",
      "Name: (Midtown, Midtown West), dtype: float64\n",
      "240    13.2\n",
      "243     9.4\n",
      "254    10.4\n",
      "259     8.8\n",
      "Name: (Midtown, Sharpstown), dtype: float64\n",
      "252    5.9\n",
      "Name: (Midtown, Washington Avenue), dtype: float64\n",
      "16    1.9\n",
      "Name: (Midtown East, Midtown), dtype: float64\n",
      "546    195.3\n",
      "Name: (Morrisville, Banner Elk), dtype: float64\n",
      "27      8.0\n",
      "68      9.7\n",
      "82      6.1\n",
      "90      6.1\n",
      "94      6.1\n",
      "       ... \n",
      "1021    3.4\n",
      "1039    3.1\n",
      "1041    4.8\n",
      "1047    3.0\n",
      "1053    3.1\n",
      "Name: (Morrisville, Cary), Length: 75, dtype: float64\n",
      "542     2.2\n",
      "545    11.8\n",
      "814     6.2\n",
      "863     5.9\n",
      "896     5.0\n",
      "Name: (Morrisville, Morrisville), dtype: float64\n",
      "97     17.0\n",
      "375     7.6\n",
      "604    14.7\n",
      "884    10.3\n",
      "Name: (Morrisville, Raleigh), dtype: float64\n",
      "961    43.6\n",
      "Name: (Mountain View, Berkeley), dtype: float64\n",
      "923    0.9\n",
      "Name: (NOMA, Downtown), dtype: float64\n",
      "467    11.8\n",
      "Name: (New Orleans, Chalmette), dtype: float64\n",
      "478    46.9\n",
      "Name: (New Orleans, Covington), dtype: float64\n",
      "451    12.9\n",
      "453    12.2\n",
      "474    12.6\n",
      "531    13.0\n",
      "536    13.6\n",
      "538    12.3\n",
      "Name: (New Orleans, Kenner), dtype: float64\n",
      "448    14.5\n",
      "529    12.5\n",
      "Name: (New Orleans, Metairie), dtype: float64\n",
      "423    16.3\n",
      "Name: (New York, Jamaica), dtype: float64\n",
      "106    13.0\n",
      "Name: (New York, Long Island City), dtype: float64\n",
      "10    10.8\n",
      "Name: (New York, Queens), dtype: float64\n",
      "22    15.1\n",
      "Name: (New York, Queens County), dtype: float64\n",
      "365    9.3\n",
      "Name: (Newark, Menlo Park), dtype: float64\n",
      "367    25.6\n",
      "Name: (Newark, San Francisco), dtype: float64\n",
      "554    41.9\n",
      "Name: (Newland, Boone), dtype: float64\n",
      "553    3.8\n",
      "Name: (Newland, Newland), dtype: float64\n",
      "677    3.3\n",
      "701    4.4\n",
      "729    6.2\n",
      "Name: (Noorpur Shahan, Islamabad), dtype: float64\n",
      "152    2.2\n",
      "699    7.5\n",
      "Name: (Noorpur Shahan, Unknown Location), dtype: float64\n",
      "222    7.2\n",
      "Name: (North Austin, Coxville), dtype: float64\n",
      "838    2.2\n",
      "Name: (North Berkeley Hills, Southside), dtype: float64\n",
      "562    3.3\n",
      "Name: (Northwoods, Preston), dtype: float64\n",
      "86    3.0\n",
      "Name: (Northwoods, Tanglewood), dtype: float64\n",
      "63    3.9\n",
      "Name: (Northwoods, Whitebridge), dtype: float64\n",
      "116    3.6\n",
      "Name: (Nugegoda, Unknown Location), dtype: float64\n",
      "928    2.6\n",
      "934    6.0\n",
      "Name: (Oakland, Berkeley), dtype: float64\n",
      "435    13.2\n",
      "830    13.0\n",
      "Name: (Oakland, Emeryville), dtype: float64\n",
      "834    9.5\n",
      "965    9.7\n",
      "Name: (Oakland, San Francisco), dtype: float64\n",
      "438    5.2\n",
      "Name: (Oakland, Unknown Location), dtype: float64\n",
      "377    11.2\n",
      "Name: (Old City, Hog Island), dtype: float64\n",
      "376    2.9\n",
      "Name: (Old City, Parkway Museums), dtype: float64\n",
      "275     6.6\n",
      "278     7.3\n",
      "281    25.7\n",
      "285    13.8\n",
      "286    28.8\n",
      "288    16.4\n",
      "291    20.3\n",
      "Name: (Orlando, Kissimmee), dtype: float64\n",
      "277    6.9\n",
      "Name: (Orlando, Orlando), dtype: float64\n",
      "938    2.5\n",
      "Name: (Palo Alto, Menlo Park), dtype: float64\n",
      "363    9.8\n",
      "Name: (Palo Alto, Sunnyvale), dtype: float64\n",
      "347    3.1\n",
      "433    2.8\n",
      "598    1.7\n",
      "859    8.7\n",
      "861    2.1\n",
      "Name: (Parkway, Whitebridge), dtype: float64\n",
      "514    1.2\n",
      "Name: (Parkwood, Parkwood), dtype: float64\n",
      "528    7.0\n",
      "Name: (Pontchartrain Beach, CBD), dtype: float64\n",
      "470    1.7\n",
      "475    4.8\n",
      "Name: (Pontchartrain Shores, Pontchartrain Shores), dtype: float64\n",
      "250    7.5\n",
      "Name: (Port Bolivar, Galveston), dtype: float64\n",
      "248    2.1\n",
      "249    1.2\n",
      "Name: (Port Bolivar, Port Bolivar), dtype: float64\n",
      "372    2.7\n",
      "Name: (Preston, Westpark Place), dtype: float64\n",
      "88     1.5\n",
      "157    1.7\n",
      "265    1.6\n",
      "563    4.7\n",
      "576    1.4\n",
      "Name: (Preston, Whitebridge), dtype: float64\n",
      "670    6.4\n",
      "Name: (R?walpindi, Islamabad), dtype: float64\n",
      "140    23.1\n",
      "696     4.1\n",
      "Name: (R?walpindi, R?walpindi), dtype: float64\n",
      "141     16.5\n",
      "656     15.6\n",
      "679      2.0\n",
      "697     18.7\n",
      "763     18.2\n",
      "788    112.6\n",
      "804     18.4\n",
      "811     12.4\n",
      "Name: (R?walpindi, Unknown Location), dtype: float64\n",
      "36     40.2\n",
      "49     12.9\n",
      "52     15.7\n",
      "77      9.0\n",
      "98     18.0\n",
      "182    17.3\n",
      "190    13.5\n",
      "203    11.9\n",
      "308    18.6\n",
      "318    15.2\n",
      "324    22.5\n",
      "335    32.8\n",
      "390    14.0\n",
      "456     5.9\n",
      "458    16.6\n",
      "502    11.3\n",
      "643    17.4\n",
      "823    17.6\n",
      "846    14.0\n",
      "848    28.2\n",
      "885    13.1\n",
      "Name: (Raleigh, Cary), dtype: float64\n",
      "93      13.5\n",
      "312     15.9\n",
      "462      9.0\n",
      "605     14.6\n",
      "1007     6.7\n",
      "Name: (Raleigh, Morrisville), dtype: float64\n",
      "48     2.7\n",
      "188    4.9\n",
      "Name: (Raleigh, Raleigh), dtype: float64\n",
      "1068    7.2\n",
      "Name: (Rawalpindi, Islamabad), dtype: float64\n",
      "1072    3.3\n",
      "Name: (Rawalpindi, Rawalpindi), dtype: float64\n",
      "1062      0.7\n",
      "1073     19.4\n",
      "1088    103.0\n",
      "Name: (Rawalpindi, Unknown Location), dtype: float64\n",
      "214    1.0\n",
      "Name: (Red River District, Downtown), dtype: float64\n",
      "356    2.9\n",
      "Name: (Redmond, Bellevue), dtype: float64\n",
      "909    2.8\n",
      "912    2.8\n",
      "Name: (Renaissance, Agnew), dtype: float64\n",
      "298    144.0\n",
      "Name: (Ridgeland, Florence), dtype: float64\n",
      "419    2.5\n",
      "Name: (Rose Hill, Soho), dtype: float64\n",
      "835    1.7\n",
      "Name: (SOMISSPO, French Quarter), dtype: float64\n",
      "932    1.1\n",
      "Name: (SOMISSPO, Tenderloin), dtype: float64\n",
      "836    10.8\n",
      "917    13.2\n",
      "919    11.3\n",
      "966    11.8\n",
      "Name: (San Francisco, Berkeley), dtype: float64\n",
      "440    11.6\n",
      "Name: (San Francisco, Emeryville), dtype: float64\n",
      "927    12.7\n",
      "933     9.9\n",
      "Name: (San Francisco, Oakland), dtype: float64\n",
      "362    20.5\n",
      "Name: (San Francisco, Palo Alto), dtype: float64\n",
      "842    44.6\n",
      "Name: (San Jose, Emeryville), dtype: float64\n",
      "905    3.8\n",
      "Name: (San Jose, Santa Clara), dtype: float64\n",
      "283    6.2\n",
      "Name: (Sand Lake Commons, Sky Lake), dtype: float64\n",
      "913    43.9\n",
      "Name: (Santa Clara, Berkeley), dtype: float64\n",
      "384    1.2\n",
      "Name: (Savon Height, Kilarney Woods), dtype: float64\n",
      "858    4.9\n",
      "Name: (Savon Height, Parkway), dtype: float64\n",
      "382    3.6\n",
      "886    9.6\n",
      "Name: (Savon Height, Whitebridge), dtype: float64\n",
      "417    3.3\n",
      "Name: (Seaport, Gramercy-Flatiron), dtype: float64\n",
      "355    14.2\n",
      "Name: (Seattle, Redmond), dtype: float64\n",
      "255    1.2\n",
      "Name: (Sharpstown, Briar Meadow), dtype: float64\n",
      "237    10.4\n",
      "242     9.2\n",
      "260    25.6\n",
      "Name: (Sharpstown, Midtown), dtype: float64\n",
      "241    1.0\n",
      "Name: (Sharpstown, Sharpstown), dtype: float64\n",
      "284    6.0\n",
      "Name: (Sky Lake, Sand Lake Commons), dtype: float64\n",
      "420    0.5\n",
      "Name: (Soho, Tribeca), dtype: float64\n",
      "943    6.4\n",
      "Name: (South, Downtown), dtype: float64\n",
      "947    0.8\n",
      "Name: (South, Southwest Berkeley), dtype: float64\n",
      "956    0.9\n",
      "Name: (South Berkeley, Southside), dtype: float64\n",
      "210    1.6\n",
      "Name: (South Congress, Arts District), dtype: float64\n",
      "219    8.4\n",
      "Name: (South Congress, North Austin), dtype: float64\n",
      "215    2.1\n",
      "217    1.9\n",
      "225    2.7\n",
      "Name: (South Congress, The Drag), dtype: float64\n",
      "957    2.4\n",
      "959    1.9\n",
      "Name: (Southside, Central), dtype: float64\n",
      "955    0.9\n",
      "Name: (Southside, South Berkeley), dtype: float64\n",
      "953    2.1\n",
      "Name: (Southside, West Berkeley), dtype: float64\n",
      "535    1.8\n",
      "Name: (St Thomas, CBD), dtype: float64\n",
      "570    3.0\n",
      "Name: (Stonewater, Lexington Park at Amberly), dtype: float64\n",
      "447    1.5\n",
      "Name: (Storyville, Faubourg Marigny), dtype: float64\n",
      "472    1.6\n",
      "Name: (Storyville, Marigny), dtype: float64\n",
      "245    35.1\n",
      "Name: (Sugar Land, Houston), dtype: float64\n",
      "512    8.7\n",
      "Name: (Summerwinds, Whitebridge), dtype: float64\n",
      "925    0.7\n",
      "Name: (Sunnyside, Ingleside), dtype: float64\n",
      "364    17.6\n",
      "Name: (Sunnyvale, Newark), dtype: float64\n",
      "346    7.5\n",
      "Name: (Tanglewood, Parkway), dtype: float64\n",
      "87    5.1\n",
      "Name: (Tanglewood, Preston), dtype: float64\n",
      "161    5.8\n",
      "328    6.5\n",
      "Name: (Tanglewood, Whitebridge), dtype: float64\n",
      "931    1.2\n",
      "Name: (Tenderloin, SOMISSPO), dtype: float64\n",
      "211    2.0\n",
      "228    1.7\n",
      "Name: (The Drag, Congress Ave District), dtype: float64\n",
      "218    5.7\n",
      "226    2.0\n",
      "Name: (The Drag, Convention Center District), dtype: float64\n",
      "221    10.5\n",
      "Name: (The Drag, North Austin), dtype: float64\n",
      "216    2.2\n",
      "224    2.0\n",
      "Name: (The Drag, South Congress), dtype: float64\n",
      "874    29.8\n",
      "Name: (Topton, Bryson City), dtype: float64\n",
      "872    40.7\n",
      "Name: (Topton, Hayesville), dtype: float64\n",
      "397    3.3\n",
      "Name: (Townes at Everett Crossing, Chessington), dtype: float64\n",
      "421    0.9\n",
      "Name: (Tribeca, Financial District), dtype: float64\n",
      "354    5.0\n",
      "Name: (University District, Capitol One), dtype: float64\n",
      "109    14.1\n",
      "117    14.7\n",
      "Name: (Unknown Location, Colombo), dtype: float64\n",
      "122     12.7\n",
      "124      5.2\n",
      "128      7.6\n",
      "132      3.5\n",
      "136     14.4\n",
      "144     14.5\n",
      "145      2.4\n",
      "150     13.0\n",
      "663      5.7\n",
      "680      5.7\n",
      "685     12.2\n",
      "688      9.8\n",
      "690      4.9\n",
      "732     10.8\n",
      "736      2.8\n",
      "738      5.5\n",
      "741      8.8\n",
      "745     13.0\n",
      "748      9.2\n",
      "764     10.5\n",
      "767     18.3\n",
      "778     12.6\n",
      "781     10.5\n",
      "805      9.8\n",
      "1060     7.7\n",
      "1075     5.7\n",
      "1080     4.9\n",
      "1084     2.1\n",
      "Name: (Unknown Location, Islamabad), dtype: float64\n",
      "1123     7.9\n",
      "1126    10.4\n",
      "1129     4.4\n",
      "1134    11.9\n",
      "1141     4.1\n",
      "1144    12.9\n",
      "Name: (Unknown Location, Kar?chi), dtype: float64\n",
      "711      9.2\n",
      "715      3.9\n",
      "719      2.9\n",
      "723      9.6\n",
      "791     33.2\n",
      "794      8.3\n",
      "1095    14.0\n",
      "1102     7.1\n",
      "1104    10.7\n",
      "Name: (Unknown Location, Lahore), dtype: float64\n",
      "986    3.5\n",
      "Name: (Unknown Location, Morrisville), dtype: float64\n",
      "676     7.6\n",
      "698     8.7\n",
      "700     7.7\n",
      "728    10.1\n",
      "Name: (Unknown Location, Noorpur Shahan), dtype: float64\n",
      "154    20.0\n",
      "669     1.4\n",
      "695     7.9\n",
      "750    17.2\n",
      "769     9.6\n",
      "787    17.9\n",
      "803    17.1\n",
      "810    12.7\n",
      "813    17.0\n",
      "Name: (Unknown Location, R?walpindi), dtype: float64\n",
      "1067    10.2\n",
      "1071    12.0\n",
      "1087    12.0\n",
      "Name: (Unknown Location, Rawalpindi), dtype: float64\n",
      "121     23.5\n",
      "126     18.3\n",
      "127     11.2\n",
      "135      2.5\n",
      "142      3.2\n",
      "        ... \n",
      "1093     3.2\n",
      "1094    12.3\n",
      "1100     9.6\n",
      "1101     1.3\n",
      "1152    16.2\n",
      "Name: (Unknown Location, Unknown Location), Length: 86, dtype: float64\n",
      "1018    1.8\n",
      "Name: (Wake Co., Morrisville), dtype: float64\n",
      "652    31.9\n",
      "Name: (Wake Forest, Cary), dtype: float64\n",
      "630    6.6\n",
      "Name: (Washington, Arlington), dtype: float64\n",
      "253    6.2\n",
      "Name: (Washington Avenue, Midtown), dtype: float64\n",
      "460    2.1\n",
      "Name: (Waverly Place, Macgregor Downs), dtype: float64\n",
      "170    6.8\n",
      "205    7.6\n",
      "342    6.8\n",
      "Name: (Waverly Place, Whitebridge), dtype: float64\n",
      "177    8.0\n",
      "301    8.0\n",
      "322    8.0\n",
      "588    8.0\n",
      "Name: (Wayne Ridge, Whitebridge), dtype: float64\n",
      "915    3.3\n",
      "935    0.8\n",
      "Name: (West Berkeley, Central), dtype: float64\n",
      "837    4.1\n",
      "Name: (West Berkeley, North Berkeley Hills), dtype: float64\n",
      "946    5.9\n",
      "Name: (West Berkeley, South), dtype: float64\n",
      "954    4.0\n",
      "Name: (West Berkeley, Southside), dtype: float64\n",
      "624    2.0\n",
      "Name: (West End, Northwest Rectangle), dtype: float64\n",
      "6    7.1\n",
      "Name: (West Palm Beach, Palm Beach), dtype: float64\n",
      "5    4.3\n",
      "Name: (West Palm Beach, West Palm Beach), dtype: float64\n",
      "230    2.1\n",
      "Name: (West University, Congress Ave District), dtype: float64\n",
      "209    2.3\n",
      "Name: (West University, South Congress), dtype: float64\n",
      "194    4.2\n",
      "403    3.8\n",
      "Name: (Weston, Weston), dtype: float64\n",
      "167    4.2\n",
      "Name: (Westpark Place, Hazelwood), dtype: float64\n",
      "163    1.7\n",
      "179    2.3\n",
      "267    2.2\n",
      "306    1.8\n",
      "344    2.3\n",
      "349    3.9\n",
      "373    1.9\n",
      "391    1.8\n",
      "428    1.7\n",
      "496    1.8\n",
      "600    2.1\n",
      "608    2.2\n",
      "611    1.8\n",
      "654    1.8\n",
      "894    1.8\n",
      "901    1.8\n",
      "Name: (Westpark Place, Whitebridge), dtype: float64\n",
      "612    6.2\n",
      "Name: (Whitebridge, Arlington Park at Amberly), dtype: float64\n",
      "332    4.9\n",
      "Name: (Whitebridge, Burtrose), dtype: float64\n",
      "572    3.9\n",
      "Name: (Whitebridge, Chessington), dtype: float64\n",
      "83     4.3\n",
      "199    2.8\n",
      "309    2.8\n",
      "574    2.8\n",
      "577    2.7\n",
      "617    2.8\n",
      "632    2.7\n",
      "827    3.3\n",
      "Name: (Whitebridge, Edgehill Farms), dtype: float64\n",
      "636    5.2\n",
      "Name: (Whitebridge, Farmington Woods), dtype: float64\n",
      "40     2.0\n",
      "174    2.6\n",
      "407    3.0\n",
      "898    2.5\n",
      "Name: (Whitebridge, Hazelwood), dtype: float64\n",
      "158    3.1\n",
      "648    2.2\n",
      "Name: (Whitebridge, Heritage Pines), dtype: float64\n",
      "383    4.5\n",
      "Name: (Whitebridge, Kildaire Farms), dtype: float64\n",
      "32    7.2\n",
      "Name: (Whitebridge, Lake Wellingborough), dtype: float64\n",
      "72    9.0\n",
      "Name: (Whitebridge, Macgregor Downs), dtype: float64\n",
      "85     5.3\n",
      "180    5.2\n",
      "Name: (Whitebridge, Northwoods), dtype: float64\n",
      "595    1.5\n",
      "819    2.1\n",
      "860    2.1\n",
      "Name: (Whitebridge, Parkway), dtype: float64\n",
      "156    1.5\n",
      "264    1.7\n",
      "371    2.8\n",
      "Name: (Whitebridge, Preston), dtype: float64\n",
      "381    3.6\n",
      "401    7.8\n",
      "857    3.6\n",
      "Name: (Whitebridge, Savon Height), dtype: float64\n",
      "569    6.4\n",
      "Name: (Whitebridge, Stonewater), dtype: float64\n",
      "511    8.8\n",
      "Name: (Whitebridge, Summerwinds), dtype: float64\n",
      "160    6.0\n",
      "327    6.0\n",
      "345    6.2\n",
      "Name: (Whitebridge, Tanglewood), dtype: float64\n",
      "169    7.7\n",
      "204    7.2\n",
      "341    7.7\n",
      "459    7.1\n",
      "641    6.9\n",
      "Name: (Whitebridge, Waverly Place), dtype: float64\n",
      "176    8.0\n",
      "196    7.8\n",
      "300    7.9\n",
      "321    8.2\n",
      "Name: (Whitebridge, Wayne Ridge), dtype: float64\n",
      "42     1.9\n",
      "162    6.3\n",
      "166    1.9\n",
      "171    2.1\n",
      "178    2.2\n",
      "266    2.0\n",
      "343    2.1\n",
      "348    2.2\n",
      "495    1.6\n",
      "599    2.2\n",
      "607    2.2\n",
      "653    1.9\n",
      "893    1.4\n",
      "900    1.4\n",
      "Name: (Whitebridge, Westpark Place), dtype: float64\n",
      "206    1.6\n",
      "263    1.4\n",
      "516    0.6\n",
      "889    1.2\n",
      "890    1.0\n",
      "891    4.1\n",
      "892    4.2\n",
      "Name: (Whitebridge, Whitebridge), dtype: float64\n",
      "64    8.3\n",
      "Name: (Whitebridge, Williamsburg Manor), dtype: float64\n",
      "870    133.6\n",
      "Name: (Winston Salem, Asheville), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "START*         STOP*             \n",
       "Agnew          Agnew                 None\n",
       "               Cory                  None\n",
       "               Renaissance           None\n",
       "Almond         Bryson City           None\n",
       "Apex           Apex                  None\n",
       "                                     ... \n",
       "Whitebridge    Wayne Ridge           None\n",
       "               Westpark Place        None\n",
       "               Whitebridge           None\n",
       "               Williamsburg Manor    None\n",
       "Winston Salem  Asheville             None\n",
       "Name: MILES*, Length: 363, dtype: object"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['START*','STOP*'])['MILES*'].apply(print)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ea0963fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START*            STOP*           \n",
       "Morrisville       Cary                395.7\n",
       "Cary              Durham              390.0\n",
       "                  Morrisville         380.0\n",
       "Raleigh           Cary                365.7\n",
       "Cary              Raleigh             336.5\n",
       "Durham            Cary                324.5\n",
       "Latta             Jacksonville        310.3\n",
       "Islamabad         Unknown Location    267.0\n",
       "Cary              Cary                255.9\n",
       "Unknown Location  Islamabad           243.8\n",
       "Name: MILES*, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['START*','STOP*'])['MILES*'].sum().sort_values(ascending=False)[1:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6eec869",
   "metadata": {},
   "source": [
    "- Cary-Durham & Cary-Morrisville and viceversa are the farthest distance ride.\n",
    "\n",
    "- Checking for Round Trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "eca5a257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ROUND_TRIP*', ylabel='count'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def is_roundtrip(df):\n",
    "    if df['START*'] == df['STOP*']:\n",
    "        return 'YES'\n",
    "    else:\n",
    "        return 'NO'\n",
    "    \n",
    "df['ROUND_TRIP*'] = df.apply(is_roundtrip, axis=1)\n",
    "\n",
    "sns.countplot(x='ROUND_TRIP*',data=df, order=df['ROUND_TRIP*'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c6a4b5cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ROUND_TRIP*\n",
       "NO     866\n",
       "YES    288\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ROUND_TRIP*'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20b3aac",
   "metadata": {},
   "source": [
    "- User mostly take sinle-trip Uber riders. *Around 75% trip is single-trip and 25% are Round-trip.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5cab02",
   "metadata": {},
   "source": [
    "### Calculating Ride Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5e1e3e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*    datetime64[ns]\n",
       "END_DATE*      datetime64[ns]\n",
       "CATEGORY*              object\n",
       "START*                 object\n",
       "STOP*                  object\n",
       "MILES*                float64\n",
       "ROUND_TRIP*            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "27a28eae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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_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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>0 days 00:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>0 days 00:12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>YES</td>\n",
       "      <td>0 days 00:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>YES</td>\n",
       "      <td>0 days 00:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>NO</td>\n",
       "      <td>0 days 01:07:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES* ROUND_TRIP*   Ride_duration  \n",
       "0      Fort Pierce     5.1         YES 0 days 00:06:00  \n",
       "1      Fort Pierce     5.0         YES 0 days 00:12:00  \n",
       "2      Fort Pierce     4.8         YES 0 days 00:13:00  \n",
       "3      Fort Pierce     4.7         YES 0 days 00:14:00  \n",
       "4  West Palm Beach    63.7          NO 0 days 01:07:00  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ride_duration'] = df['END_DATE*'] - df['START_DATE*']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99cb479",
   "metadata": {},
   "source": [
    "## Converting Ride_duration into Minute\n",
    "\n",
    "- This is a Python Lamda function that takes a single argument \"x\".\n",
    "- The function first calls the to_pytimedelta() method on pd.Timedelta, which converts the input x into a datetime.timedelta object.\n",
    "\n",
    "- The function then calculates the total number of minutes in the timedelta object, which is done by first getting the number of days using the days attribute and multipying it by 24 hours and 60 minutes per hour. Then, the number of seconds is divided by 60 to convert them into minutes, and added to the previously calculated number of minutes. The final result is the total numer of minutes in the timedelta object.\n",
    "\n",
    "- This function could be used to calculate the duration of a time interval in  minutes, which could be useful in a variety of applications such as analyzing time-series data or calculating the length of time between two events.\n",
    "\n",
    "- Note that this function assumes that the input x is a valid pd.Timedelta object and may raise errors if the input is not in the expected format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "3038de4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>YES</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>YES</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>NO</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES* ROUND_TRIP*  Ride_duration  \n",
       "0      Fort Pierce     5.1         YES            6.0  \n",
       "1      Fort Pierce     5.0         YES           12.0  \n",
       "2      Fort Pierce     4.8         YES           13.0  \n",
       "3      Fort Pierce     4.7         YES           14.0  \n",
       "4  West Palm Beach    63.7          NO           67.0  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using datetime.Timedelta\n",
    "# => https://pandas.pydata.org/pandas-docs/stable/user_guide/timedeltas.html\n",
    "df.loc[:, 'Ride_duration'] = df['Ride_duration'].apply(\n",
    "    lambda x: pd.Timedelta.to_pytimedelta(x).days/(24*60) + pd.Timedelta.to_pytimedelta(x).seconds/60)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d954bbf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "      <th>month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Fri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>YES</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>YES</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Tue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>NO</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES* ROUND_TRIP*  Ride_duration  month  Year  Day  Hour  \\\n",
       "0      Fort Pierce     5.1         YES            6.0      1  2016    1    21   \n",
       "1      Fort Pierce     5.0         YES           12.0      1  2016    2     1   \n",
       "2      Fort Pierce     4.8         YES           13.0      1  2016    2    20   \n",
       "3      Fort Pierce     4.7         YES           14.0      1  2016    5    17   \n",
       "4  West Palm Beach    63.7          NO           67.0      1  2016    6    14   \n",
       "\n",
       "  day_of_week  \n",
       "0         Fri  \n",
       "1         Sat  \n",
       "2         Sat  \n",
       "3         Tue  \n",
       "4         Wed  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capture Hour, Month & Year of Ride in a separate column\n",
    "\n",
    "df['month'] = pd.to_datetime(df['START_DATE*']).dt.month\n",
    "df['Year'] = pd.to_datetime(df['START_DATE*']).dt.year\n",
    "df['Day'] = pd.to_datetime(df['START_DATE*']).dt.day\n",
    "df['Hour'] = pd.to_datetime(df['START_DATE*']).dt.hour\n",
    "\n",
    "df['day_of_week'] = pd.to_datetime(df['START_DATE*']).dt.dayofweek\n",
    "days = {0: 'Mon',1: 'Tue', 2: 'Wed', 3: 'Thur', 4: 'Fri', 5: 'Sat', 6: 'Sun'}\n",
    "\n",
    "df['day_of_week'] = df['day_of_week'].apply(lambda x: days[x])\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5261a7d9",
   "metadata": {},
   "source": [
    "- Adding month name instead of month number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b257bdcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Replaceing non-numeric values with a placeholder value(-1) before conversion\n",
    "\n",
    "def convert_month_to_abbr(month):\n",
    "    try:\n",
    "        return calendar.month_abbr[int(month)]\n",
    "    except ValueError:\n",
    "        return -1  # Or any other placeholder\n",
    "\n",
    "df['month'] = df['month'].apply(convert_month_to_abbr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9b34ad39",
   "metadata": {},
   "outputs": [
    {
     "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_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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "      <th>month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Fri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>YES</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>YES</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Tue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>NO</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES* ROUND_TRIP*  Ride_duration month  Year  Day  Hour  \\\n",
       "0      Fort Pierce     5.1         YES            6.0   Jan  2016    1    21   \n",
       "1      Fort Pierce     5.0         YES           12.0   Jan  2016    2     1   \n",
       "2      Fort Pierce     4.8         YES           13.0   Jan  2016    2    20   \n",
       "3      Fort Pierce     4.7         YES           14.0   Jan  2016    5    17   \n",
       "4  West Palm Beach    63.7          NO           67.0   Jan  2016    6    14   \n",
       "\n",
       "  day_of_week  \n",
       "0         Fri  \n",
       "1         Sat  \n",
       "2         Sat  \n",
       "3         Tue  \n",
       "4         Wed  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "93a3cce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droppig month_abbr\n",
    "\n",
    "# df.drop(['month_abbr'],axis=1,inplace=True)\n",
    "\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103bb771",
   "metadata": {},
   "source": [
    "### Total Rides/Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "43563e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "Dec    146\n",
      "Aug    133\n",
      "Nov    122\n",
      "Feb    115\n",
      "Mar    113\n",
      "Jul    112\n",
      "Jun    107\n",
      "Oct    106\n",
      "Jan     61\n",
      "Apr     54\n",
      "May     49\n",
      "Sep     36\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['month'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a23bf90e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='month', ylabel='count'>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='month',data=df, order=pd.value_counts(df['month']).index, hue='CATEGORY*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481e6d38",
   "metadata": {},
   "source": [
    "- Most number of rides were in month of December (all of them were Business Trips) \n",
    "- Top 5 months aving most trips were: December, August, November, February & March.\n",
    "- Uber Ride was used at Fe, Mar,Jul, Jun & Aor for personal trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "f6e90f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day_of_week', ylabel='count'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='day_of_week',data=df, order=pd.value_counts(df['day_of_week']).index, hue='CATEGORY*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92aba453",
   "metadata": {},
   "source": [
    "- FRIDAY was the day at which Uber Rides were mostly used\n",
    "\n",
    "### Average distance covered/month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "7b6b50db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Handling non-numeric values using the replacement with a placeholder value eg.,1\n",
    "\n",
    "def convert_to_numeric (x):\n",
    "    try:\n",
    "        return float(x)\n",
    "    except ValueError:\n",
    "        return -1 # or any other placeholder\n",
    "    \n",
    "df['MILES*'] = df['MILES*'].apply(convert_to_numeric)\n",
    "df.groupby('month')['MILES*'].mean().sort_values(ascending=False).plot(kind='bar')\n",
    "\n",
    "plt.axhline(df['MILES*'].mean(), linestyle='--',\n",
    "           color='red',\n",
    "           label='Mean Distance')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86f3416",
   "metadata": {},
   "source": [
    "- User's Longest ride were on April & shortest were on November."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "522ccfc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Hour', ylabel='count'>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Hour', data=df, order=pd.value_counts(df['Hour']).index, \n",
    "             hue='CATEGORY*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563e80dc",
   "metadata": {},
   "source": [
    "- Maximum number of trips were on Evening & at Noon.\n",
    "\n",
    "### Calculating TRIP Speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "76560f4a",
   "metadata": {},
   "outputs": [
    {
     "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_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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "      <th>month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Fri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>YES</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Sat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>YES</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Tue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>NO</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "             STOP*  MILES* ROUND_TRIP*  Ride_duration month  Year  Day  Hour  \\\n",
       "0      Fort Pierce     5.1         YES            6.0   Jan  2016    1    21   \n",
       "1      Fort Pierce     5.0         YES           12.0   Jan  2016    2     1   \n",
       "2      Fort Pierce     4.8         YES           13.0   Jan  2016    2    20   \n",
       "3      Fort Pierce     4.7         YES           14.0   Jan  2016    5    17   \n",
       "4  West Palm Beach    63.7          NO           67.0   Jan  2016    6    14   \n",
       "\n",
       "  day_of_week  \n",
       "0         Fri  \n",
       "1         Sat  \n",
       "2         Sat  \n",
       "3         Tue  \n",
       "4         Wed  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "3f965960",
   "metadata": {},
   "outputs": [
    {
     "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_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>ROUND_TRIP*</th>\n",
       "      <th>Ride_duration</th>\n",
       "      <th>month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>Duration_hours</th>\n",
       "      <th>Speed_KM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>YES</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Fri</td>\n",
       "      <td>0.1</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>YES</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Sat</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*           END_DATE* CATEGORY*       START*        STOP*  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce  Fort Pierce   \n",
       "\n",
       "   MILES* ROUND_TRIP*  Ride_duration month  Year  Day  Hour day_of_week  \\\n",
       "0     5.1         YES            6.0   Jan  2016    1    21         Fri   \n",
       "1     5.0         YES           12.0   Jan  2016    2     1         Sat   \n",
       "\n",
       "   Duration_hours  Speed_KM  \n",
       "0             0.1      51.0  \n",
       "1             0.2      25.0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['Duration_hours'] = df['Ride_duration']/60\n",
    "\n",
    "df['Speed_KM'] = df['MILES*']/df['Duration_hours']\n",
    "\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f84450ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Speed_KM', ylabel='Count'>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.histplot(x='Speed_KM', data=df, kde=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbeb4a0e",
   "metadata": {},
   "source": [
    "- Speed is right skewed.\n",
    "\n",
    "## Conclusion\n",
    "- User mainly uses Uber cabs for its Business purpose.\n",
    "    - Around 94% miles was consumed during Business trips.\n",
    "    - Only 6% miles were consumed during personal trips.\n",
    "    \n",
    "- There are 177 unique starting points.\n",
    "    - Cary is most popular starting point for this driver.\n",
    "- There are 188 unique Stop points.\n",
    "    - Cary is the most popular drop point for this driver.\n",
    "- Cary-Durham & Cary-Morrisville and vice versa are the User's longest distance Uber ride.\n",
    "- User usually takes single-trip Uber riders.\n",
    "    - Around 75% trip is single-trip and 25% are Round-Trip.\n",
    "- User's most number of rides were in month of December & Least were in September.\n",
    "- Friday has maximum number of trips.\n",
    "- Afternoons and evenings seem to have th maximum number of trips.\n",
    "- User's Longest ride were on April & Shortest were on November.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f57d21b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "547a9053",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58379919",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3c8807",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f166e5b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
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