uber.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>01-01-2016 21:11</td>\n",
       "      <td>01-01-2016 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>01-02-2016 01:25</td>\n",
       "      <td>01-02-2016 01:37</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>01-02-2016 20:25</td>\n",
       "      <td>01-02-2016 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>01-05-2016 17:31</td>\n",
       "      <td>01-05-2016 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>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2016 17:15</td>\n",
       "      <td>01-06-2016 17:19</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2016 17:30</td>\n",
       "      <td>01-06-2016 17:35</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01-07-2016 13:27</td>\n",
       "      <td>01-07-2016 13:33</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01-10-2016 08:05</td>\n",
       "      <td>01-10-2016 08:25</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01-10-2016 12:17</td>\n",
       "      <td>01-10-2016 12:44</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         START_DATE          END_DATE  CATEGORY            START  \\\n",
       "0  01-01-2016 21:11  01-01-2016 21:17  Business      Fort Pierce   \n",
       "1  01-02-2016 01:25  01-02-2016 01:37  Business      Fort Pierce   \n",
       "2  01-02-2016 20:25  01-02-2016 20:38  Business      Fort Pierce   \n",
       "3  01-05-2016 17:31  01-05-2016 17:45  Business      Fort Pierce   \n",
       "4  01-06-2016 14:42  01-06-2016 15:49  Business      Fort Pierce   \n",
       "5  01-06-2016 17:15  01-06-2016 17:19  Business  West Palm Beach   \n",
       "6  01-06-2016 17:30  01-06-2016 17:35  Business  West Palm Beach   \n",
       "7  01-07-2016 13:27  01-07-2016 13:33  Business             Cary   \n",
       "8  01-10-2016 08:05  01-10-2016 08:25  Business             Cary   \n",
       "9  01-10-2016 12:17  01-10-2016 12:44  Business          Jamaica   \n",
       "\n",
       "              STOP  MILES          PURPOSE  \n",
       "0      Fort Pierce    5.1   Meal/Entertain  \n",
       "1      Fort Pierce    5.0              NaN  \n",
       "2      Fort Pierce    4.8  Errand/Supplies  \n",
       "3      Fort Pierce    4.7          Meeting  \n",
       "4  West Palm Beach   63.7   Customer Visit  \n",
       "5  West Palm Beach    4.3   Meal/Entertain  \n",
       "6       Palm Beach    7.1          Meeting  \n",
       "7             Cary    0.8          Meeting  \n",
       "8      Morrisville    8.3          Meeting  \n",
       "9         New York   16.5   Customer Visit  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber=pd.read_csv('UberDataset.csv')\n",
    "uber.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MILES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.115398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>359.299007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12204.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MILES\n",
       "count   1156.000000\n",
       "mean      21.115398\n",
       "std      359.299007\n",
       "min        0.500000\n",
       "25%        2.900000\n",
       "50%        6.000000\n",
       "75%       10.400000\n",
       "max    12204.700000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1156 entries, 0 to 1155\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   START_DATE  1156 non-null   object \n",
      " 1   END_DATE    1155 non-null   object \n",
      " 2   CATEGORY    1155 non-null   object \n",
      " 3   START       1155 non-null   object \n",
      " 4   STOP        1155 non-null   object \n",
      " 5   MILES       1156 non-null   float64\n",
      " 6   PURPOSE     653 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 63.3+ KB\n"
     ]
    }
   ],
   "source": [
    "uber.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for duplicate values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for unique values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE    1155\n",
       "END_DATE      1154\n",
       "CATEGORY         2\n",
       "START          177\n",
       "STOP           188\n",
       "MILES          257\n",
       "PURPOSE         10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### solve the problem of missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meeting            187\n",
       "Meal/Entertain     160\n",
       "Errand/Supplies    128\n",
       "Customer Visit     101\n",
       "Temporary Site      50\n",
       "Between Offices     18\n",
       "Moving               4\n",
       "Airport/Travel       3\n",
       "Charity ($)          1\n",
       "Commute              1\n",
       "Name: PURPOSE, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber['PURPOSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber['PURPOSE'].fillna(method='ffill',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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",
       "      <th>PURPOSE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2016 21:11</td>\n",
       "      <td>01-01-2016 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>01-02-2016 01:25</td>\n",
       "      <td>01-02-2016 01:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2016 20:25</td>\n",
       "      <td>01-02-2016 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>01-05-2016 17:31</td>\n",
       "      <td>01-05-2016 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>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2016 17:15</td>\n",
       "      <td>01-06-2016 17:19</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2016 17:30</td>\n",
       "      <td>01-06-2016 17:35</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01-07-2016 13:27</td>\n",
       "      <td>01-07-2016 13:33</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01-10-2016 08:05</td>\n",
       "      <td>01-10-2016 08:25</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01-10-2016 12:17</td>\n",
       "      <td>01-10-2016 12:44</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         START_DATE          END_DATE  CATEGORY            START  \\\n",
       "0  01-01-2016 21:11  01-01-2016 21:17  Business      Fort Pierce   \n",
       "1  01-02-2016 01:25  01-02-2016 01:37  Business      Fort Pierce   \n",
       "2  01-02-2016 20:25  01-02-2016 20:38  Business      Fort Pierce   \n",
       "3  01-05-2016 17:31  01-05-2016 17:45  Business      Fort Pierce   \n",
       "4  01-06-2016 14:42  01-06-2016 15:49  Business      Fort Pierce   \n",
       "5  01-06-2016 17:15  01-06-2016 17:19  Business  West Palm Beach   \n",
       "6  01-06-2016 17:30  01-06-2016 17:35  Business  West Palm Beach   \n",
       "7  01-07-2016 13:27  01-07-2016 13:33  Business             Cary   \n",
       "8  01-10-2016 08:05  01-10-2016 08:25  Business             Cary   \n",
       "9  01-10-2016 12:17  01-10-2016 12:44  Business          Jamaica   \n",
       "\n",
       "              STOP  MILES          PURPOSE  \n",
       "0      Fort Pierce    5.1   Meal/Entertain  \n",
       "1      Fort Pierce    5.0   Meal/Entertain  \n",
       "2      Fort Pierce    4.8  Errand/Supplies  \n",
       "3      Fort Pierce    4.7          Meeting  \n",
       "4  West Palm Beach   63.7   Customer Visit  \n",
       "5  West Palm Beach    4.3   Meal/Entertain  \n",
       "6       Palm Beach    7.1          Meeting  \n",
       "7             Cary    0.8          Meeting  \n",
       "8      Morrisville    8.3          Meeting  \n",
       "9         New York   16.5   Customer Visit  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### solve start and end date and time converting to datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber['START_DATE'] = pd.to_datetime(uber['START_DATE'],errors='coerce')\n",
    "uber['END_DATE'] = pd.to_datetime(uber['END_DATE'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>Meal/Entertain</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06 17:15:00</td>\n",
       "      <td>2016-01-06 17:19:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-06 17:30:00</td>\n",
       "      <td>2016-01-06 17:35:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-07 13:27:00</td>\n",
       "      <td>2016-01-07 13:33:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-10 08:05:00</td>\n",
       "      <td>2016-01-10 08:25:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-10 12:17:00</td>\n",
       "      <td>2016-01-10 12:44:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</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",
       "5 2016-01-06 17:15:00 2016-01-06 17:19:00  Business  West Palm Beach   \n",
       "6 2016-01-06 17:30:00 2016-01-06 17:35:00  Business  West Palm Beach   \n",
       "7 2016-01-07 13:27:00 2016-01-07 13:33:00  Business             Cary   \n",
       "8 2016-01-10 08:05:00 2016-01-10 08:25:00  Business             Cary   \n",
       "9 2016-01-10 12:17:00 2016-01-10 12:44:00  Business          Jamaica   \n",
       "\n",
       "              STOP  MILES          PURPOSE  \n",
       "0      Fort Pierce    5.1   Meal/Entertain  \n",
       "1      Fort Pierce    5.0   Meal/Entertain  \n",
       "2      Fort Pierce    4.8  Errand/Supplies  \n",
       "3      Fort Pierce    4.7          Meeting  \n",
       "4  West Palm Beach   63.7   Customer Visit  \n",
       "5  West Palm Beach    4.3   Meal/Entertain  \n",
       "6       Palm Beach    7.1          Meeting  \n",
       "7             Cary    0.8          Meeting  \n",
       "8      Morrisville    8.3          Meeting  \n",
       "9         New York   16.5   Customer Visit  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seperate the date and time and month and year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperate the date and time and month and year\n",
    "uber['start_date'] = uber['START_DATE'].dt.date\n",
    "uber['start_time'] = uber['START_DATE'].dt.time\n",
    "uber['end_date'] = uber['END_DATE'].dt.date\n",
    "uber['end_time'] = uber['END_DATE'].dt.time\n",
    "uber['month'] = uber['START_DATE'].dt.month\n",
    "uber['year'] = uber['START_DATE'].dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "      <th>start_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_time</th>\n",
       "      <th>month</th>\n",
       "      <th>year</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",
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       "      <td>1.0</td>\n",
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       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2016.0</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>Errand/Supplies</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20:25:00</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20:38:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</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>Meeting</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17:31:00</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17:45:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</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>Customer Visit</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>14:42:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15:49:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06 17:15:00</td>\n",
       "      <td>2016-01-06 17:19:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:15:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:19:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-06 17:30:00</td>\n",
       "      <td>2016-01-06 17:35:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:30:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:35:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-07 13:27:00</td>\n",
       "      <td>2016-01-07 13:33:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>13:27:00</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>13:33:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-10 08:05:00</td>\n",
       "      <td>2016-01-10 08:25:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>08:05:00</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>08:25:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-10 12:17:00</td>\n",
       "      <td>2016-01-10 12:44:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>12:17:00</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>12:44:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</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",
       "5 2016-01-06 17:15:00 2016-01-06 17:19:00  Business  West Palm Beach   \n",
       "6 2016-01-06 17:30:00 2016-01-06 17:35:00  Business  West Palm Beach   \n",
       "7 2016-01-07 13:27:00 2016-01-07 13:33:00  Business             Cary   \n",
       "8 2016-01-10 08:05:00 2016-01-10 08:25:00  Business             Cary   \n",
       "9 2016-01-10 12:17:00 2016-01-10 12:44:00  Business          Jamaica   \n",
       "\n",
       "              STOP  MILES          PURPOSE  start_date start_time    end_date  \\\n",
       "0      Fort Pierce    5.1   Meal/Entertain  2016-01-01   21:11:00  2016-01-01   \n",
       "1      Fort Pierce    5.0   Meal/Entertain  2016-01-02   01:25:00  2016-01-02   \n",
       "2      Fort Pierce    4.8  Errand/Supplies  2016-01-02   20:25:00  2016-01-02   \n",
       "3      Fort Pierce    4.7          Meeting  2016-01-05   17:31:00  2016-01-05   \n",
       "4  West Palm Beach   63.7   Customer Visit  2016-01-06   14:42:00  2016-01-06   \n",
       "5  West Palm Beach    4.3   Meal/Entertain  2016-01-06   17:15:00  2016-01-06   \n",
       "6       Palm Beach    7.1          Meeting  2016-01-06   17:30:00  2016-01-06   \n",
       "7             Cary    0.8          Meeting  2016-01-07   13:27:00  2016-01-07   \n",
       "8      Morrisville    8.3          Meeting  2016-01-10   08:05:00  2016-01-10   \n",
       "9         New York   16.5   Customer Visit  2016-01-10   12:17:00  2016-01-10   \n",
       "\n",
       "   end_time  month    year  \n",
       "0  21:17:00    1.0  2016.0  \n",
       "1  01:37:00    1.0  2016.0  \n",
       "2  20:38:00    1.0  2016.0  \n",
       "3  17:45:00    1.0  2016.0  \n",
       "4  15:49:00    1.0  2016.0  \n",
       "5  17:19:00    1.0  2016.0  \n",
       "6  17:35:00    1.0  2016.0  \n",
       "7  13:33:00    1.0  2016.0  \n",
       "8  08:25:00    1.0  2016.0  \n",
       "9  12:44:00    1.0  2016.0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### remove START_DATE END_DATE and rearrange the columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber.drop(['START_DATE','END_DATE'],axis=1,inplace=True)\n",
    "uber = uber[['start_date','start_time','end_date','end_time','month','year','CATEGORY','START','STOP','MILES','PURPOSE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0    146\n",
       "8.0     133\n",
       "11.0    122\n",
       "2.0     115\n",
       "3.0     113\n",
       "7.0     112\n",
       "6.0     108\n",
       "10.0    106\n",
       "1.0      61\n",
       "4.0      54\n",
       "5.0      49\n",
       "9.0      36\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.month.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### convert month to integer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uber.month.fillna(fill,inplace=True)\n",
    "uber['month'] = uber['month'].fillna(uber['month'].mode()[0])\n",
    "uber['month'] = uber['month'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>end_time</th>\n",
       "      <th>month</th>\n",
       "      <th>year</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</td>\n",
       "      <td>21:11:00</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>21:17:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</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</td>\n",
       "      <td>01:25:00</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>01:37:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20:25:00</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20:38:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</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</td>\n",
       "      <td>17:31:00</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17:45:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</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</td>\n",
       "      <td>14:42:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15:49:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:15:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:19:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:30:00</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17:35:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>13:27:00</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>13:33:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>08:05:00</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>08:25:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>12:17:00</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>12:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_date start_time    end_date  end_time  month    year  CATEGORY  \\\n",
       "0  2016-01-01   21:11:00  2016-01-01  21:17:00      1  2016.0  Business   \n",
       "1  2016-01-02   01:25:00  2016-01-02  01:37:00      1  2016.0  Business   \n",
       "2  2016-01-02   20:25:00  2016-01-02  20:38:00      1  2016.0  Business   \n",
       "3  2016-01-05   17:31:00  2016-01-05  17:45:00      1  2016.0  Business   \n",
       "4  2016-01-06   14:42:00  2016-01-06  15:49:00      1  2016.0  Business   \n",
       "5  2016-01-06   17:15:00  2016-01-06  17:19:00      1  2016.0  Business   \n",
       "6  2016-01-06   17:30:00  2016-01-06  17:35:00      1  2016.0  Business   \n",
       "7  2016-01-07   13:27:00  2016-01-07  13:33:00      1  2016.0  Business   \n",
       "8  2016-01-10   08:05:00  2016-01-10  08:25:00      1  2016.0  Business   \n",
       "9  2016-01-10   12:17:00  2016-01-10  12:44:00      1  2016.0  Business   \n",
       "\n",
       "             START             STOP  MILES          PURPOSE  \n",
       "0      Fort Pierce      Fort Pierce    5.1   Meal/Entertain  \n",
       "1      Fort Pierce      Fort Pierce    5.0   Meal/Entertain  \n",
       "2      Fort Pierce      Fort Pierce    4.8  Errand/Supplies  \n",
       "3      Fort Pierce      Fort Pierce    4.7          Meeting  \n",
       "4      Fort Pierce  West Palm Beach   63.7   Customer Visit  \n",
       "5  West Palm Beach  West Palm Beach    4.3   Meal/Entertain  \n",
       "6  West Palm Beach       Palm Beach    7.1          Meeting  \n",
       "7             Cary             Cary    0.8          Meeting  \n",
       "8             Cary      Morrisville    8.3          Meeting  \n",
       "9          Jamaica         New York   16.5   Customer Visit  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1156"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.month.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    147\n",
       "8     133\n",
       "11    122\n",
       "2     115\n",
       "3     113\n",
       "7     112\n",
       "6     108\n",
       "10    106\n",
       "1      61\n",
       "4      54\n",
       "5      49\n",
       "9      36\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.month.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### convert year to integer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber['year'] = uber['year'].fillna(uber['year'].mode()[0])\n",
    "uber['year'] = uber['year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_date     object\n",
       "start_time     object\n",
       "end_date       object\n",
       "end_time       object\n",
       "month           int32\n",
       "year            int32\n",
       "CATEGORY       object\n",
       "START          object\n",
       "STOP           object\n",
       "MILES         float64\n",
       "PURPOSE        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert start_time and end_time to hours and minutes\n",
    "uber['start_hour'] = uber['start_time'].apply(lambda x: x.hour)\n",
    "uber['start_minute'] = uber['start_time'].apply(lambda x: x.minute)\n",
    "uber['end_hour'] = uber['end_time'].apply(lambda x: x.hour)\n",
    "uber['end_minute'] = uber['end_time'].apply(lambda x: x.minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearrange the columns and drop start_time and end_time columns\n",
    "uber = uber[['start_date','start_hour','start_minute','end_date','end_hour','end_minute','month','year','CATEGORY','START','STOP','MILES','PURPOSE']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column for duration of the trip to be in minutes\n",
    "uber['duration'] = uber['end_hour']*60 + uber['end_minute'] - (uber['start_hour']*60 + uber['start_minute'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert miles to km\n",
    "uber['MILES'] = uber['MILES']*1.60934\n",
    "#calculate speed\n",
    "uber['speed'] = uber['MILES']/(uber['duration']/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>start_hour</th>\n",
       "      <th>start_minute</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>end_minute</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>START</th>\n",
       "      <th>STOP</th>\n",
       "      <th>MILES</th>\n",
       "      <th>PURPOSE</th>\n",
       "      <th>duration</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>8.207634</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.076340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>8.046700</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.233500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>7.724832</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.653071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>7.563898</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.416706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>102.514958</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>67.0</td>\n",
       "      <td>91.804440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_date  start_hour  start_minute    end_date  end_hour  end_minute  \\\n",
       "0  2016-01-01        21.0          11.0  2016-01-01      21.0        17.0   \n",
       "1  2016-01-02         1.0          25.0  2016-01-02       1.0        37.0   \n",
       "2  2016-01-02        20.0          25.0  2016-01-02      20.0        38.0   \n",
       "3  2016-01-05        17.0          31.0  2016-01-05      17.0        45.0   \n",
       "4  2016-01-06        14.0          42.0  2016-01-06      15.0        49.0   \n",
       "\n",
       "   month  year  CATEGORY        START             STOP       MILES  \\\n",
       "0      1  2016  Business  Fort Pierce      Fort Pierce    8.207634   \n",
       "1      1  2016  Business  Fort Pierce      Fort Pierce    8.046700   \n",
       "2      1  2016  Business  Fort Pierce      Fort Pierce    7.724832   \n",
       "3      1  2016  Business  Fort Pierce      Fort Pierce    7.563898   \n",
       "4      1  2016  Business  Fort Pierce  West Palm Beach  102.514958   \n",
       "\n",
       "           PURPOSE  duration      speed  \n",
       "0   Meal/Entertain       6.0  82.076340  \n",
       "1   Meal/Entertain      12.0  40.233500  \n",
       "2  Errand/Supplies      13.0  35.653071  \n",
       "3          Meeting      14.0  32.416706  \n",
       "4   Customer Visit      67.0  91.804440  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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_hour</th>\n",
       "      <th>start_minute</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>end_minute</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>MILES</th>\n",
       "      <th>duration</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1155.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>1156.000000</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>1156.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "      <td>1155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.696104</td>\n",
       "      <td>29.128139</td>\n",
       "      <td>14.794805</td>\n",
       "      <td>28.994805</td>\n",
       "      <td>6.987024</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>33.981854</td>\n",
       "      <td>5.788745</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.575226</td>\n",
       "      <td>17.191727</td>\n",
       "      <td>4.775222</td>\n",
       "      <td>17.547755</td>\n",
       "      <td>3.546452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>578.234265</td>\n",
       "      <td>154.891874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.804670</td>\n",
       "      <td>-1432.000000</td>\n",
       "      <td>-17.107984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.667086</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.571879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>9.656040</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>33.357229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16.737136</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>45.622351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>19641.511898</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        start_hour  start_minute     end_hour   end_minute        month  \\\n",
       "count  1155.000000   1155.000000  1155.000000  1155.000000  1156.000000   \n",
       "mean     14.696104     29.128139    14.794805    28.994805     6.987024   \n",
       "std       4.575226     17.191727     4.775222    17.547755     3.546452   \n",
       "min       0.000000      0.000000     0.000000     0.000000     1.000000   \n",
       "25%      12.000000     14.000000    12.000000    14.000000     3.750000   \n",
       "50%      15.000000     29.000000    15.000000    29.000000     7.000000   \n",
       "75%      18.000000     44.000000    18.000000    44.000000    10.000000   \n",
       "max      23.000000     59.000000    23.000000    59.000000    12.000000   \n",
       "\n",
       "         year         MILES     duration        speed  \n",
       "count  1156.0   1156.000000  1155.000000  1155.000000  \n",
       "mean   2016.0     33.981854     5.788745          inf  \n",
       "std       0.0    578.234265   154.891874          NaN  \n",
       "min    2016.0      0.804670 -1432.000000   -17.107984  \n",
       "25%    2016.0      4.667086    10.000000    23.571879  \n",
       "50%    2016.0      9.656040    16.000000    33.357229  \n",
       "75%    2016.0     16.737136    27.000000    45.622351  \n",
       "max    2016.0  19641.511898   330.000000          inf  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>start_hour</th>\n",
       "      <th>start_minute</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>end_minute</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>START</th>\n",
       "      <th>STOP</th>\n",
       "      <th>MILES</th>\n",
       "      <th>PURPOSE</th>\n",
       "      <th>duration</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>314.786904</td>\n",
       "      <td>Temporary Site</td>\n",
       "      <td>-1104.0</td>\n",
       "      <td>-17.107984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_date  start_hour  start_minute    end_date  end_hour  end_minute  \\\n",
       "776  2016-09-27        21.0           1.0  2016-09-28       2.0        37.0   \n",
       "\n",
       "     month  year  CATEGORY             START              STOP       MILES  \\\n",
       "776      9  2016  Business  Unknown Location  Unknown Location  314.786904   \n",
       "\n",
       "            PURPOSE  duration      speed  \n",
       "776  Temporary Site   -1104.0 -17.107984  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# found problem in speed that have negative value\n",
    "uber[uber[\"speed\"]==-17.10798391304348]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-17.10798391304348"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.speed.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mega\\AppData\\Local\\Temp\\ipykernel_9084\\997425509.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(uber.corr(),annot=True)\n",
      "c:\\Users\\Mega\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\matrix.py:260: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior\n",
      "  annotation = (\"{:\" + self.fmt + \"}\").format(val)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the heatmap\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(uber.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "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=uber)\n",
    "plt.title(\" Distrubution of Months\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each category\n",
    "category_counts = uber['CATEGORY'].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.bar(category_counts.index, category_counts.values)\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Categories')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each purpose\n",
    "purpose_counts = uber['PURPOSE'].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.bar(purpose_counts.index, purpose_counts.values)\n",
    "\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Purpose')\n",
    "\n",
    "# Rotate x-axis labels for better readability\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "sns.countplot(data=uber, x='PURPOSE', hue='CATEGORY')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}