Uber Data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2ff7d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "558327ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "36817c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b66178f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "671e503d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"UberData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a956874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93442d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13d77f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['START_DATE*', 'END_DATE*', 'CATEGORY*', 'START*', 'STOP*', 'MILES*',\n",
       "       'PURPOSE*'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b99802f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove * from column attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69f3470d",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['START_DATE', 'END_DATE', 'CATEGORY', 'START', 'STOP', 'MILES',\n",
    "       'PURPOSE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e816d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "479e7324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1154, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "669c9805",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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",
       "      <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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ce435334",
   "metadata": {},
   "outputs": [],
   "source": [
    "# last row is not needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e806b63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4b3cce4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1153, 7)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6394502",
   "metadata": {},
   "source": [
    "## Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2550cd09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1153 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      START_DATE  END_DATE  CATEGORY  START   STOP  MILES  PURPOSE\n",
       "0          False     False     False  False  False  False    False\n",
       "1          False     False     False  False  False  False    False\n",
       "2          False     False     False  False  False  False    False\n",
       "3          False     False     False  False  False  False    False\n",
       "4          False     False     False  False  False  False    False\n",
       "...          ...       ...       ...    ...    ...    ...      ...\n",
       "1148       False     False     False  False  False  False    False\n",
       "1149       False     False     False  False  False  False    False\n",
       "1150       False     False     False  False  False  False    False\n",
       "1151       False     False     False  False  False  False    False\n",
       "1152       False     False     False  False  False  False    False\n",
       "\n",
       "[1153 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "644d6d27",
   "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       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9d42621b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing NaN with \"others\" since purpose contain strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd211c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna(\"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6bb9f4a6",
   "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       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4547fe34",
   "metadata": {},
   "source": [
    "### Dealing with data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d0054e9",
   "metadata": {},
   "outputs": [
    {
     "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ee0d1b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#object == string. Converting start_date and end_date to date time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c8303f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['START_DATE'] = pd.to_datetime(data['START_DATE'])\n",
    "data['END_DATE'] = pd.to_datetime(data['END_DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "50ed61c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>END_DATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>START</th>\n",
       "      <th>STOP</th>\n",
       "      <th>MILES</th>\n",
       "      <th>PURPOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>Meal/Entertain</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>Others</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>Errand/Supplies</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>Meeting</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>Customer Visit</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          PURPOSE  \n",
       "0      Fort Pierce    5.1   Meal/Entertain  \n",
       "1      Fort Pierce    5.0           Others  \n",
       "2      Fort Pierce    4.8  Errand/Supplies  \n",
       "3      Fort Pierce    4.7          Meeting  \n",
       "4  West Palm Beach   63.7   Customer Visit  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3eb30c1",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "42720cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "hour = []\n",
    "day = []\n",
    "month = []\n",
    "dayofweek = []\n",
    "dayname = []\n",
    "for i in data['START_DATE']:\n",
    "    hour.append(i.hour)\n",
    "    day.append(i.day)\n",
    "    month.append(i.month)\n",
    "    dayofweek.append(i.dayofweek)\n",
    "    dayname.append(i.day_name)\n",
    "data['HOUR'] = hour\n",
    "data['DAY'] = day\n",
    "data['MONTH'] = month\n",
    "data['DAYOFWEEK'] = dayofweek\n",
    "data['DAYNAME'] = dayname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ead6552",
   "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>PURPOSE</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>DAY</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAYOFWEEK</th>\n",
       "      <th>DAYNAME</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>Meal/Entertain</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;built-in method day_name of Timestamp object ...</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>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>&lt;built-in method day_name of Timestamp object ...</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>Errand/Supplies</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>&lt;built-in method day_name of Timestamp object ...</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>Meeting</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;built-in method day_name of Timestamp object ...</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>Customer Visit</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;built-in method day_name of Timestamp object ...</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          PURPOSE  HOUR  DAY  MONTH  DAYOFWEEK  \\\n",
       "0      Fort Pierce    5.1   Meal/Entertain    21    1      1          4   \n",
       "1      Fort Pierce    5.0           Others     1    2      1          5   \n",
       "2      Fort Pierce    4.8  Errand/Supplies    20    2      1          5   \n",
       "3      Fort Pierce    4.7          Meeting    17    5      1          1   \n",
       "4  West Palm Beach   63.7   Customer Visit    14    6      1          2   \n",
       "\n",
       "                                             DAYNAME  \n",
       "0  <built-in method day_name of Timestamp object ...  \n",
       "1  <built-in method day_name of Timestamp object ...  \n",
       "2  <built-in method day_name of Timestamp object ...  \n",
       "3  <built-in method day_name of Timestamp object ...  \n",
       "4  <built-in method day_name of Timestamp object ...  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f26f131",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce83674",
   "metadata": {},
   "source": [
    "###  1. How long people travel in uber?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "49211b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.1\n",
       "1        5.0\n",
       "2        4.8\n",
       "3        4.7\n",
       "4       63.7\n",
       "        ... \n",
       "1148     4.6\n",
       "1149     0.8\n",
       "1150     0.7\n",
       "1151     3.9\n",
       "1152    16.2\n",
       "Name: MILES, Length: 1153, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['MILES']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "68a4ca13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Distance (in miles)', ylabel='Frequency'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xlabel(\"Distance (in miles)\")\n",
    "plt.ylabel(\"No of people\")\n",
    "data['MILES'].plot(kind='hist', bins=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f66bd7a",
   "metadata": {},
   "source": [
    "<h4>Result :<br>\n",
    "        1. Most people use Uber for short distance<br>\n",
    "        2. Very few people use Uber for long distances</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc759dcd",
   "metadata": {},
   "source": [
    "### 2.What hour do most people take Uber?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a37c6c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       21\n",
       "1        1\n",
       "2       20\n",
       "3       17\n",
       "4       14\n",
       "        ..\n",
       "1148    16\n",
       "1149    23\n",
       "1150     1\n",
       "1151    13\n",
       "1152    15\n",
       "Name: HOUR, Length: 1153, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['HOUR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "76ad3ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15    98\n",
       "17    95\n",
       "18    94\n",
       "13    94\n",
       "14    89\n",
       "16    88\n",
       "12    77\n",
       "11    72\n",
       "20    71\n",
       "19    68\n",
       "10    65\n",
       "9     51\n",
       "21    50\n",
       "8     35\n",
       "22    30\n",
       "23    26\n",
       "0     19\n",
       "7     13\n",
       "1      5\n",
       "6      4\n",
       "5      4\n",
       "3      3\n",
       "2      2\n",
       "Name: HOUR, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['HOUR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2c406769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['HOUR'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5fe0f53f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Varada\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='HOUR', ylabel='count'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['HOUR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957fe162",
   "metadata": {},
   "source": [
    "<h4>Result :<br>\n",
    "        1. Most people take Uber at 15th hour<br>\n",
    "        2. Very few people take Uber at 2</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd667cf7",
   "metadata": {},
   "source": [
    "### What is the purpose of the trip?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cae8ffd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Meal/Entertain\n",
       "1                Others\n",
       "2       Errand/Supplies\n",
       "3               Meeting\n",
       "4        Customer Visit\n",
       "             ...       \n",
       "1148            Meeting\n",
       "1149     Customer Visit\n",
       "1150            Meeting\n",
       "1151     Temporary Site\n",
       "1152            Meeting\n",
       "Name: PURPOSE, Length: 1153, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PURPOSE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7fd924b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Others             502\n",
       "Meeting            187\n",
       "Meal/Entertain     160\n",
       "Errand/Supplies    128\n",
       "Customer Visit     101\n",
       "Temporary Site      48\n",
       "Between Offices     18\n",
       "Moving               4\n",
       "Airport/Travel       3\n",
       "Charity ($)          1\n",
       "Commute              1\n",
       "Name: PURPOSE, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PURPOSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a2fea289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "data['PURPOSE'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f96396",
   "metadata": {},
   "source": [
    "<h4>Result :<br>\n",
    "        1. Purpose of trip is mostly because of meeting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815e2c5a",
   "metadata": {},
   "source": [
    "### 4. Which day has the highest number of trips?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e9a568bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        2\n",
       "2        2\n",
       "3        5\n",
       "4        6\n",
       "        ..\n",
       "1148    30\n",
       "1149    30\n",
       "1150    31\n",
       "1151    31\n",
       "1152    31\n",
       "Name: DAY, Length: 1153, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DAY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "281a7ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    67\n",
       "29    51\n",
       "27    51\n",
       "26    45\n",
       "22    45\n",
       "21    45\n",
       "12    44\n",
       "1     43\n",
       "16    42\n",
       "20    38\n",
       "18    38\n",
       "8     37\n",
       "13    37\n",
       "11    37\n",
       "28    36\n",
       "10    35\n",
       "25    35\n",
       "23    34\n",
       "7     34\n",
       "17    33\n",
       "6     33\n",
       "14    33\n",
       "5     33\n",
       "4     33\n",
       "3     33\n",
       "30    32\n",
       "2     30\n",
       "24    29\n",
       "9     29\n",
       "15    28\n",
       "31    13\n",
       "Name: DAY, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['DAY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "461a9c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "data['DAY'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aafbea1",
   "metadata": {},
   "source": [
    "<h4>Result :<br>\n",
    "        1. Day 19 has highest number of trips<br>\n",
    "        2. Day 31 has lowest number of trips</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a31a793b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}