EDA (1).ipynb

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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order No</th>\n",
       "      <td>Order_No_4211</td>\n",
       "      <td>Order_No_25375</td>\n",
       "      <td>Order_No_1899</td>\n",
       "      <td>Order_No_9336</td>\n",
       "      <td>Order_No_27883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User Id</th>\n",
       "      <td>User_Id_633</td>\n",
       "      <td>User_Id_2285</td>\n",
       "      <td>User_Id_265</td>\n",
       "      <td>User_Id_1402</td>\n",
       "      <td>User_Id_1737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle Type</th>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform Type</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal or Business</th>\n",
       "      <td>Business</td>\n",
       "      <td>Personal</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>Personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Placement - Time</th>\n",
       "      <td>9:35:46 AM</td>\n",
       "      <td>11:16:16 AM</td>\n",
       "      <td>12:39:25 PM</td>\n",
       "      <td>9:25:34 AM</td>\n",
       "      <td>9:55:18 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmation - Time</th>\n",
       "      <td>9:40:10 AM</td>\n",
       "      <td>11:23:21 AM</td>\n",
       "      <td>12:42:44 PM</td>\n",
       "      <td>9:26:05 AM</td>\n",
       "      <td>9:56:18 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Pickup - Time</th>\n",
       "      <td>10:04:47 AM</td>\n",
       "      <td>11:40:22 AM</td>\n",
       "      <td>12:49:34 PM</td>\n",
       "      <td>9:37:56 AM</td>\n",
       "      <td>10:03:53 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup - Time</th>\n",
       "      <td>10:27:30 AM</td>\n",
       "      <td>11:44:09 AM</td>\n",
       "      <td>12:53:03 PM</td>\n",
       "      <td>9:43:06 AM</td>\n",
       "      <td>10:05:23 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Day of Month</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival at Destination - Time</th>\n",
       "      <td>10:39:55 AM</td>\n",
       "      <td>12:17:22 PM</td>\n",
       "      <td>1:00:38 PM</td>\n",
       "      <td>10:05:27 AM</td>\n",
       "      <td>10:25:37 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance (KM)</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>20.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.2</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precipitation in millimeters</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup Lat</th>\n",
       "      <td>-1.31775</td>\n",
       "      <td>-1.35145</td>\n",
       "      <td>-1.30828</td>\n",
       "      <td>-1.2813</td>\n",
       "      <td>-1.2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pickup Long</th>\n",
       "      <td>36.8304</td>\n",
       "      <td>36.8993</td>\n",
       "      <td>36.8434</td>\n",
       "      <td>36.8324</td>\n",
       "      <td>36.7921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination Lat</th>\n",
       "      <td>-1.30041</td>\n",
       "      <td>-1.295</td>\n",
       "      <td>-1.30092</td>\n",
       "      <td>-1.25715</td>\n",
       "      <td>-1.29504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination Long</th>\n",
       "      <td>36.8297</td>\n",
       "      <td>36.8144</td>\n",
       "      <td>36.8282</td>\n",
       "      <td>36.7951</td>\n",
       "      <td>36.8098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rider Id</th>\n",
       "      <td>Rider_Id_432</td>\n",
       "      <td>Rider_Id_856</td>\n",
       "      <td>Rider_Id_155</td>\n",
       "      <td>Rider_Id_855</td>\n",
       "      <td>Rider_Id_770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "      <td>745</td>\n",
       "      <td>1993</td>\n",
       "      <td>455</td>\n",
       "      <td>1341</td>\n",
       "      <td>1214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       0               1  \\\n",
       "Order No                                   Order_No_4211  Order_No_25375   \n",
       "User Id                                      User_Id_633    User_Id_2285   \n",
       "Vehicle Type                                        Bike            Bike   \n",
       "Platform Type                                          3               3   \n",
       "Personal or Business                            Business        Personal   \n",
       "Placement - Day of Month                               9              12   \n",
       "Placement - Weekday (Mo = 1)                           5               5   \n",
       "Placement - Time                              9:35:46 AM     11:16:16 AM   \n",
       "Confirmation - Day of Month                            9              12   \n",
       "Confirmation - Weekday (Mo = 1)                        5               5   \n",
       "Confirmation - Time                           9:40:10 AM     11:23:21 AM   \n",
       "Arrival at Pickup - Day of Month                       9              12   \n",
       "Arrival at Pickup - Weekday (Mo = 1)                   5               5   \n",
       "Arrival at Pickup - Time                     10:04:47 AM     11:40:22 AM   \n",
       "Pickup - Day of Month                                  9              12   \n",
       "Pickup - Weekday (Mo = 1)                              5               5   \n",
       "Pickup - Time                                10:27:30 AM     11:44:09 AM   \n",
       "Arrival at Destination - Day of Month                  9              12   \n",
       "Arrival at Destination - Weekday (Mo = 1)              5               5   \n",
       "Arrival at Destination - Time                10:39:55 AM     12:17:22 PM   \n",
       "Distance (KM)                                          4              16   \n",
       "Temperature                                         20.4            26.4   \n",
       "Precipitation in millimeters                         NaN             NaN   \n",
       "Pickup Lat                                      -1.31775        -1.35145   \n",
       "Pickup Long                                      36.8304         36.8993   \n",
       "Destination Lat                                 -1.30041          -1.295   \n",
       "Destination Long                                 36.8297         36.8144   \n",
       "Rider Id                                    Rider_Id_432    Rider_Id_856   \n",
       "Time from Pickup to Arrival                          745            1993   \n",
       "\n",
       "                                                       2              3  \\\n",
       "Order No                                   Order_No_1899  Order_No_9336   \n",
       "User Id                                      User_Id_265   User_Id_1402   \n",
       "Vehicle Type                                        Bike           Bike   \n",
       "Platform Type                                          3              3   \n",
       "Personal or Business                            Business       Business   \n",
       "Placement - Day of Month                              30             15   \n",
       "Placement - Weekday (Mo = 1)                           2              5   \n",
       "Placement - Time                             12:39:25 PM     9:25:34 AM   \n",
       "Confirmation - Day of Month                           30             15   \n",
       "Confirmation - Weekday (Mo = 1)                        2              5   \n",
       "Confirmation - Time                          12:42:44 PM     9:26:05 AM   \n",
       "Arrival at Pickup - Day of Month                      30             15   \n",
       "Arrival at Pickup - Weekday (Mo = 1)                   2              5   \n",
       "Arrival at Pickup - Time                     12:49:34 PM     9:37:56 AM   \n",
       "Pickup - Day of Month                                 30             15   \n",
       "Pickup - Weekday (Mo = 1)                              2              5   \n",
       "Pickup - Time                                12:53:03 PM     9:43:06 AM   \n",
       "Arrival at Destination - Day of Month                 30             15   \n",
       "Arrival at Destination - Weekday (Mo = 1)              2              5   \n",
       "Arrival at Destination - Time                 1:00:38 PM    10:05:27 AM   \n",
       "Distance (KM)                                          3              9   \n",
       "Temperature                                          NaN           19.2   \n",
       "Precipitation in millimeters                         NaN            NaN   \n",
       "Pickup Lat                                      -1.30828        -1.2813   \n",
       "Pickup Long                                      36.8434        36.8324   \n",
       "Destination Lat                                 -1.30092       -1.25715   \n",
       "Destination Long                                 36.8282        36.7951   \n",
       "Rider Id                                    Rider_Id_155   Rider_Id_855   \n",
       "Time from Pickup to Arrival                          455           1341   \n",
       "\n",
       "                                                        4  \n",
       "Order No                                   Order_No_27883  \n",
       "User Id                                      User_Id_1737  \n",
       "Vehicle Type                                         Bike  \n",
       "Platform Type                                           1  \n",
       "Personal or Business                             Personal  \n",
       "Placement - Day of Month                               13  \n",
       "Placement - Weekday (Mo = 1)                            1  \n",
       "Placement - Time                               9:55:18 AM  \n",
       "Confirmation - Day of Month                            13  \n",
       "Confirmation - Weekday (Mo = 1)                         1  \n",
       "Confirmation - Time                            9:56:18 AM  \n",
       "Arrival at Pickup - Day of Month                       13  \n",
       "Arrival at Pickup - Weekday (Mo = 1)                    1  \n",
       "Arrival at Pickup - Time                      10:03:53 AM  \n",
       "Pickup - Day of Month                                  13  \n",
       "Pickup - Weekday (Mo = 1)                               1  \n",
       "Pickup - Time                                 10:05:23 AM  \n",
       "Arrival at Destination - Day of Month                  13  \n",
       "Arrival at Destination - Weekday (Mo = 1)               1  \n",
       "Arrival at Destination - Time                 10:25:37 AM  \n",
       "Distance (KM)                                           9  \n",
       "Temperature                                          15.4  \n",
       "Precipitation in millimeters                          NaN  \n",
       "Pickup Lat                                        -1.2666  \n",
       "Pickup Long                                       36.7921  \n",
       "Destination Lat                                  -1.29504  \n",
       "Destination Long                                  36.8098  \n",
       "Rider Id                                     Rider_Id_770  \n",
       "Time from Pickup to Arrival                          1214  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import ast, json\n",
    "\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"JCP/Train.csv\")\n",
    "\n",
    "df.head().T\n"
   ]
  },
  {
   "cell_type": "code",
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform Type</th>\n",
       "      <th>Placement - Day of Month</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Confirmation - Day of Month</th>\n",
       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
       "      <th>Arrival at Pickup - Day of Month</th>\n",
       "      <th>Arrival at Pickup - Weekday (Mo = 1)</th>\n",
       "      <th>Pickup - Day of Month</th>\n",
       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
       "      <th>Arrival at Destination - Day of Month</th>\n",
       "      <th>Arrival at Destination - Weekday (Mo = 1)</th>\n",
       "      <th>Distance (KM)</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation in millimeters</th>\n",
       "      <th>Pickup Lat</th>\n",
       "      <th>Pickup Long</th>\n",
       "      <th>Destination Lat</th>\n",
       "      <th>Destination Long</th>\n",
       "      <th>Time from Pickup to Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>16835.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "      <td>21201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.752182</td>\n",
       "      <td>15.653696</td>\n",
       "      <td>3.240083</td>\n",
       "      <td>15.653837</td>\n",
       "      <td>3.240225</td>\n",
       "      <td>15.653837</td>\n",
       "      <td>3.240225</td>\n",
       "      <td>15.653837</td>\n",
       "      <td>3.240225</td>\n",
       "      <td>15.653837</td>\n",
       "      <td>3.240225</td>\n",
       "      <td>9.506533</td>\n",
       "      <td>23.258889</td>\n",
       "      <td>7.905797</td>\n",
       "      <td>-1.281470</td>\n",
       "      <td>36.811264</td>\n",
       "      <td>-1.282581</td>\n",
       "      <td>36.811220</td>\n",
       "      <td>1556.920947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.625178</td>\n",
       "      <td>8.798916</td>\n",
       "      <td>1.567295</td>\n",
       "      <td>8.798886</td>\n",
       "      <td>1.567228</td>\n",
       "      <td>8.798886</td>\n",
       "      <td>1.567228</td>\n",
       "      <td>8.798886</td>\n",
       "      <td>1.567228</td>\n",
       "      <td>8.798886</td>\n",
       "      <td>1.567228</td>\n",
       "      <td>5.668963</td>\n",
       "      <td>3.615768</td>\n",
       "      <td>17.089971</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>0.037473</td>\n",
       "      <td>0.034824</td>\n",
       "      <td>0.044721</td>\n",
       "      <td>987.270788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.438302</td>\n",
       "      <td>36.653621</td>\n",
       "      <td>-1.430298</td>\n",
       "      <td>36.606594</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>1.075000</td>\n",
       "      <td>-1.300921</td>\n",
       "      <td>36.784605</td>\n",
       "      <td>-1.301201</td>\n",
       "      <td>36.785661</td>\n",
       "      <td>882.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>-1.279395</td>\n",
       "      <td>36.807040</td>\n",
       "      <td>-1.284382</td>\n",
       "      <td>36.808002</td>\n",
       "      <td>1369.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>-1.257147</td>\n",
       "      <td>36.829741</td>\n",
       "      <td>-1.261177</td>\n",
       "      <td>36.829477</td>\n",
       "      <td>2040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>99.100000</td>\n",
       "      <td>-1.147170</td>\n",
       "      <td>36.991046</td>\n",
       "      <td>-1.030225</td>\n",
       "      <td>37.016779</td>\n",
       "      <td>7883.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Platform Type  Placement - Day of Month  Placement - Weekday (Mo = 1)  \\\n",
       "count   21201.000000              21201.000000                  21201.000000   \n",
       "mean        2.752182                 15.653696                      3.240083   \n",
       "std         0.625178                  8.798916                      1.567295   \n",
       "min         1.000000                  1.000000                      1.000000   \n",
       "25%         3.000000                  8.000000                      2.000000   \n",
       "50%         3.000000                 15.000000                      3.000000   \n",
       "75%         3.000000                 23.000000                      5.000000   \n",
       "max         4.000000                 31.000000                      7.000000   \n",
       "\n",
       "       Confirmation - Day of Month  Confirmation - Weekday (Mo = 1)  \\\n",
       "count                 21201.000000                     21201.000000   \n",
       "mean                     15.653837                         3.240225   \n",
       "std                       8.798886                         1.567228   \n",
       "min                       1.000000                         1.000000   \n",
       "25%                       8.000000                         2.000000   \n",
       "50%                      15.000000                         3.000000   \n",
       "75%                      23.000000                         5.000000   \n",
       "max                      31.000000                         7.000000   \n",
       "\n",
       "       Arrival at Pickup - Day of Month  Arrival at Pickup - Weekday (Mo = 1)  \\\n",
       "count                      21201.000000                          21201.000000   \n",
       "mean                          15.653837                              3.240225   \n",
       "std                            8.798886                              1.567228   \n",
       "min                            1.000000                              1.000000   \n",
       "25%                            8.000000                              2.000000   \n",
       "50%                           15.000000                              3.000000   \n",
       "75%                           23.000000                              5.000000   \n",
       "max                           31.000000                              7.000000   \n",
       "\n",
       "       Pickup - Day of Month  Pickup - Weekday (Mo = 1)  \\\n",
       "count           21201.000000               21201.000000   \n",
       "mean               15.653837                   3.240225   \n",
       "std                 8.798886                   1.567228   \n",
       "min                 1.000000                   1.000000   \n",
       "25%                 8.000000                   2.000000   \n",
       "50%                15.000000                   3.000000   \n",
       "75%                23.000000                   5.000000   \n",
       "max                31.000000                   7.000000   \n",
       "\n",
       "       Arrival at Destination - Day of Month  \\\n",
       "count                           21201.000000   \n",
       "mean                               15.653837   \n",
       "std                                 8.798886   \n",
       "min                                 1.000000   \n",
       "25%                                 8.000000   \n",
       "50%                                15.000000   \n",
       "75%                                23.000000   \n",
       "max                                31.000000   \n",
       "\n",
       "       Arrival at Destination - Weekday (Mo = 1)  Distance (KM)   Temperature  \\\n",
       "count                               21201.000000   21201.000000  16835.000000   \n",
       "mean                                    3.240225       9.506533     23.258889   \n",
       "std                                     1.567228       5.668963      3.615768   \n",
       "min                                     1.000000       1.000000     11.200000   \n",
       "25%                                     2.000000       5.000000     20.600000   \n",
       "50%                                     3.000000       8.000000     23.500000   \n",
       "75%                                     5.000000      13.000000     26.000000   \n",
       "max                                     7.000000      49.000000     32.100000   \n",
       "\n",
       "       Precipitation in millimeters    Pickup Lat   Pickup Long  \\\n",
       "count                    552.000000  21201.000000  21201.000000   \n",
       "mean                       7.905797     -1.281470     36.811264   \n",
       "std                       17.089971      0.030507      0.037473   \n",
       "min                        0.100000     -1.438302     36.653621   \n",
       "25%                        1.075000     -1.300921     36.784605   \n",
       "50%                        2.900000     -1.279395     36.807040   \n",
       "75%                        4.900000     -1.257147     36.829741   \n",
       "max                       99.100000     -1.147170     36.991046   \n",
       "\n",
       "       Destination Lat  Destination Long  Time from Pickup to Arrival  \n",
       "count     21201.000000      21201.000000                 21201.000000  \n",
       "mean         -1.282581         36.811220                  1556.920947  \n",
       "std           0.034824          0.044721                   987.270788  \n",
       "min          -1.430298         36.606594                     1.000000  \n",
       "25%          -1.301201         36.785661                   882.000000  \n",
       "50%          -1.284382         36.808002                  1369.000000  \n",
       "75%          -1.261177         36.829477                  2040.000000  \n",
       "max          -1.030225         37.016779                  7883.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bike'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Vehicle Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21201, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropped vehicle type because they were only bike\n",
    "df = df.drop(['Vehicle Type'], axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot = sns.distplot(df['Time from Pickup to Arrival'], kde=False, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e9b078810>"
      ]
     },
     "execution_count": 7,
     "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.boxplot(df['Time from Pickup to Arrival'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjQog0iSNCiDSJI0KINIkjSoJUMXsDnYerd92PNtK4cuQ5IekhwRSZIGZRBJkgZlEEmSBmUQSZIGZRBJkgZlEEmSBmUQSZIGZRBJkgZlEEmSBmUQSZIGlaoauoZFL8mdwBVD17GRdgZuHrqIB8G6J8u6J2ua6n5MVe3SZ0WvNdfPFVW1YugiNkaSlZtbzWDdk2bdk2XdG+bUnCRpUAaRJGlQBlE/Hx26gAdhc6wZrHvSrHuyrHsDPFhBkjQoR0SSpEEZRHNI8vwkVyS5KsmbF0E9eyQ5N8l3klyW5PWt/e1Jrkuypj0OGfmdt7T6r0jyvJH2iW5bkmuSXNrqW9nadkry5SRXtp87tvYkOa7VdkmS/Ube5xVt/SuTvGKM9f7mSH+uSXJHkjcsxr5O8vEkNyX59kjbgvVtkqe0/3ZXtd/NGOt+b5LvttrOSPKI1r48yT0j/f7h+eqbrQ/GVPeCfS+S7JXkW6391CRbj7HuU0dqvibJmtY+2f6uKh8beABbAt8HHgtsDVwM7D1wTbsB+7XlpcD3gL2BtwN/uoH19251PwzYq23PlkNsG3ANsPN6be8B3tyW3wy8uy0fAnwRCPA04FutfSfg6vZzx7a844S+CzcAj1mMfQ08C9gP+PY4+ha4oK2b9rsvGGPdBwNL2vK7R+pePrreeu+zwfpm64Mx1b1g3wvgH4CXt+UPA68dV93rvf5+4G1D9Lcjotk9Fbiqqq6uqvuAvwdePGRBVXV9Va1uy3cClwO7z/ErLwb+vqruraofAFfRbddi2bYXAye15ZOAw0baP1GdbwKPSLIb8Dzgy1V1a1XdBnwZeP4E6jwI+H5VXTvHOoP1dVWdD9y6gXo2uW/ba8uq6pvV/QvziZH3WvC6q+rsqrq/Pf0m8Oi53mOe+mbrgwWvew4b9b1oo4vnAJ+aZN3tc18G/N1c7zGu/jaIZrc78KOR5z9m7n/0JyrJcuDJwLda0x+36YyPjwyJZ9uGIbatgLOTrEry6tb2qKq6vi3fADyqLS+mugFezgP/B13sfQ0L17e7t+X12yfhGLq/uGfsleSiJF9N8szWNld9s/XBuCzE9+KRwL+OhPGk+vuZwI1VdeVI28T62yDaDCXZATgdeENV3QH8P+DXgX2B6+mG2IvNM6pqP+AFwB8ledboi+2vq0V3CGebnz8UOK01bQ59/QCLtW/nkuStwP3AKa3pemDPqnoy8Cbgk0mW9X2/CfTBZve9WM/v8cA/tiba3wbR7K4D9hh5/ujWNqgkW9GF0ClV9WmAqrqxqtZV1S+Aj9EN+2H2bZj4tlXVde3nTcAZrcYb21B/Zsh/02Krmy44V1fVjbB59HWzUH17HQ+cHht7/UleCbwQOLL9g0ab2rqlLa+i27/yG/PUN1sfLLgF/F7cQjddumS99rFpn3U4cOpM26T72yCa3YXA49oRLFvTTc+cOWRBbR73eODyqvrASPtuI6u9BJg5KuZM4OVJHpZkL+BxdDsaJ7ptSbZPsnRmmW6H9LfbZ84cnfUK4LMjdR+VztOA29uQ/yzg4CQ7tqmPg1vbOD3gL8XF3tcjFqRv22t3JHla+/4dNfJeCy7J84H/BhxaVXePtO+SZMu2/Fi6/r16nvpm64Nx1L0g34sWvOcCR0yi7ua5wHer6pdTbhPv775HNUzjg+4Io+/R/TXw1kVQzzPohruXAGva4xDgZODS1n4msNvI77y11X8FI0c7TXLb6I4Murg9Lpv5PLr58HOAK4F/AnZq7QE+1Gq7FFgx8l7H0O3wvQo4esx1b0/3F+rDR9oWXV/TBeX1wFq6OftXLWTfAivo/mH9PvBXtBPhx1T3VXT7Tma+3x9u6760fXfWAKuBF81X32x9MKa6F+x70f5/uaD1xWnAw8ZVd2s/EXjNeutOtL+9soIkaVBOzUmSBmUQSZIGZRBJkgZlEEmSBmUQSZIGZRBJUyDdlcO3G7oOaUM8fFuaAkmuoTtn6Oaha5HW54hIWiSSHNUumnlxkpPT3RPmK63tnCR7tvVOTHLEyO/d1X4+O8l5ST6V7p4+p7QrKPwJ8GvAuUnOHWbrpNktmX8VSeOWZB/gz4CnV9XNSXaiu5T+SVV1UpJjgOOY/9L6Twb2AX4CfB34nao6LsmbgAMdEWkxckQkLQ7PAU6bCYqquhU4APhke/1kuks8zeeCqvpxdRffXEN3gzNpUTOIpM3P/bT/d5NsQXeHzxn3jiyvw1kPbQYMImlx+Arwn5I8EqBNzX2D7qrMAEcC/9yWrwGe0pYPBbbq8f530t1eXlp0/GtJWgSq6rIk7wC+mmQdcBHwOuCEJMcCPwWObqt/DPhskouBLwE/6/ERHwW+lOQnVXXgwm+B9OB5+LYkaVBOzUmSBmUQSZIGZRBJkgZlEEmSBmUQSZIGZRBJkgZlEEmSBmUQSZIG9f8BoiCE4vr02VIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing personal and business customers\n",
    "sns.countplot(y = df['Personal or Business']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#types of platforms.\n",
    "sns.countplot(y = df['Platform Type']);\n",
    "\n",
    "#3 is mostly used"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}