sendy-logistics-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Order No</th>\n",
       "      <th>User Id</th>\n",
       "      <th>Vehicle Type</th>\n",
       "      <th>Platform Type</th>\n",
       "      <th>Personal or Business</th>\n",
       "      <th>Placement - Day of Month</th>\n",
       "      <th>Placement - Weekday (Mo = 1)</th>\n",
       "      <th>Placement - Time</th>\n",
       "      <th>Confirmation - Day of Month</th>\n",
       "      <th>Confirmation - Weekday (Mo = 1)</th>\n",
       "      <th>...</th>\n",
       "      <th>Pickup - Weekday (Mo = 1)</th>\n",
       "      <th>Pickup - Time</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>Rider Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Order_No_19248</td>\n",
       "      <td>User_Id_3355</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>4:44:10 PM</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5:06:47 PM</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.333275</td>\n",
       "      <td>36.870815</td>\n",
       "      <td>-1.305249</td>\n",
       "      <td>36.822390</td>\n",
       "      <td>Rider_Id_192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Order_No_12736</td>\n",
       "      <td>User_Id_3647</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12:57:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1:25:37 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.272639</td>\n",
       "      <td>36.794723</td>\n",
       "      <td>-1.277007</td>\n",
       "      <td>36.823907</td>\n",
       "      <td>Rider_Id_868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Order_No_768</td>\n",
       "      <td>User_Id_2154</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11:08:14 AM</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>11:57:54 AM</td>\n",
       "      <td>5</td>\n",
       "      <td>22.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.290894</td>\n",
       "      <td>36.822971</td>\n",
       "      <td>-1.276574</td>\n",
       "      <td>36.851365</td>\n",
       "      <td>Rider_Id_26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Order_No_15332</td>\n",
       "      <td>User_Id_2910</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1:51:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2:16:52 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.290503</td>\n",
       "      <td>36.809646</td>\n",
       "      <td>-1.303382</td>\n",
       "      <td>36.790658</td>\n",
       "      <td>Rider_Id_685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Order_No_21373</td>\n",
       "      <td>User_Id_1205</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11:30:28 AM</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11:56:04 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.281081</td>\n",
       "      <td>36.814423</td>\n",
       "      <td>-1.266467</td>\n",
       "      <td>36.792161</td>\n",
       "      <td>Rider_Id_858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order No       User Id Vehicle Type  Platform Type  \\\n",
       "0  Order_No_19248  User_Id_3355         Bike              3   \n",
       "1  Order_No_12736  User_Id_3647         Bike              3   \n",
       "2    Order_No_768  User_Id_2154         Bike              3   \n",
       "3  Order_No_15332  User_Id_2910         Bike              3   \n",
       "4  Order_No_21373  User_Id_1205         Bike              3   \n",
       "\n",
       "  Personal or Business  Placement - Day of Month  \\\n",
       "0             Business                        27   \n",
       "1             Business                        17   \n",
       "2             Business                        27   \n",
       "3             Business                        17   \n",
       "4             Business                        11   \n",
       "\n",
       "   Placement - Weekday (Mo = 1) Placement - Time  Confirmation - Day of Month  \\\n",
       "0                             3       4:44:10 PM                           27   \n",
       "1                             5      12:57:35 PM                           17   \n",
       "2                             4      11:08:14 AM                           27   \n",
       "3                             1       1:51:35 PM                           17   \n",
       "4                             2      11:30:28 AM                           11   \n",
       "\n",
       "   Confirmation - Weekday (Mo = 1)  ... Pickup - Weekday (Mo = 1)  \\\n",
       "0                                3  ...                         3   \n",
       "1                                5  ...                         5   \n",
       "2                                4  ...                         4   \n",
       "3                                1  ...                         1   \n",
       "4                                2  ...                         2   \n",
       "\n",
       "   Pickup - Time  Distance (KM) Temperature  Precipitation in millimeters  \\\n",
       "0     5:06:47 PM              8         NaN                           NaN   \n",
       "1     1:25:37 PM              5         NaN                           NaN   \n",
       "2    11:57:54 AM              5        22.8                           NaN   \n",
       "3     2:16:52 PM              5        24.5                           NaN   \n",
       "4    11:56:04 AM              6        24.4                           NaN   \n",
       "\n",
       "   Pickup Lat Pickup Long  Destination Lat  Destination Long      Rider Id  \n",
       "0   -1.333275   36.870815        -1.305249         36.822390  Rider_Id_192  \n",
       "1   -1.272639   36.794723        -1.277007         36.823907  Rider_Id_868  \n",
       "2   -1.290894   36.822971        -1.276574         36.851365   Rider_Id_26  \n",
       "3   -1.290503   36.809646        -1.303382         36.790658  Rider_Id_685  \n",
       "4   -1.281081   36.814423        -1.266467         36.792161  Rider_Id_858  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"data/Test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7068, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order No                                 object\n",
       "User Id                                  object\n",
       "Vehicle Type                             object\n",
       "Platform Type                             int64\n",
       "Personal or Business                     object\n",
       "Placement - Day of Month                  int64\n",
       "Placement - Weekday (Mo = 1)              int64\n",
       "Placement - Time                         object\n",
       "Confirmation - Day of Month               int64\n",
       "Confirmation - Weekday (Mo = 1)           int64\n",
       "Confirmation - Time                      object\n",
       "Arrival at Pickup - Day of Month          int64\n",
       "Arrival at Pickup - Weekday (Mo = 1)      int64\n",
       "Arrival at Pickup - Time                 object\n",
       "Pickup - Day of Month                     int64\n",
       "Pickup - Weekday (Mo = 1)                 int64\n",
       "Pickup - Time                            object\n",
       "Distance (KM)                             int64\n",
       "Temperature                             float64\n",
       "Precipitation in millimeters            float64\n",
       "Pickup Lat                              float64\n",
       "Pickup Long                             float64\n",
       "Destination Lat                         float64\n",
       "Destination Long                        float64\n",
       "Rider Id                                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns\n",
    "test.rename(columns={\n",
    "    \"Order No\": \"order_id\",\n",
    "    \"User Id\": \"user_id\",\n",
    "    \"Vehicle Type\": \"vehicle\",\n",
    "    \"Platform Type\": \"platform\",\n",
    "    \"Personal or Business\": \"personal_or_business\",\n",
    "    \"Placement - Day of Month\": \"placement_date\",\n",
    "    \"Placement - Weekday (Mo = 1)\": \"placement_weekday\",\n",
    "    \"Placement - Time\": \"placement_time\",\n",
    "    \"Confirmation - Day of Month\": \"confirm_date\",\n",
    "    \"Confirmation - Weekday (Mo = 1)\": \"confirm_weekday\",\n",
    "    \"Confirmation - Time\": \"confirm_time\",\n",
    "    \"Arrival at Pickup - Day of Month\": \"arrival_pickup_date\",\n",
    "    \"Arrival at Pickup - Weekday (Mo = 1)\": \"arrive_pickup_weekday\",\n",
    "    \"Arrival at Pickup - Time\": \"arrive_time\",\n",
    "    \"Pickup - Day of Month\": \"pickup_date\",\n",
    "    \"Pickup - Weekday (Mo = 1)\": \"pickup_weekday\",\n",
    "    \"Pickup - Time\": \"pickup_time\",\n",
    "    \"Arrival at Destination - Day of Month\": \"arrival_dest_date\",\n",
    "    \"Arrival at Destination - Weekday (Mo = 1)\": \"arrival_dest_weekday\",\n",
    "    \"Arrival at Destination - Time\": \"arrival_dest_time\",\n",
    "    \"Distance (KM)\": \"distance\",\n",
    "    \"Temperature\": \"temperature\",\n",
    "    \"Precipitation in millimeters\": \"precipitation\",\n",
    "    \"Pickup Lat\": \"pickup_lat\",\n",
    "    \"Pickup Long\": \"pickup_long\",\n",
    "    \"Destination Lat\": \"dest_lat\",\n",
    "    \"Destination Long\": \"dest_long\",\n",
    "    \"Rider Id\": \"rider_id\",\n",
    "    \"Time from Pickup to Arrival\": \"pickup_to_arrival_time\"\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.astype({\"vehicle\": \"category\", \n",
    "                      \"platform\": \"category\", \n",
    "                      \"personal_or_business\": \"category\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"order_id\"] = test[\"order_id\"].str.replace(\"Order_No_\", \"\").astype(int)\n",
    "test[\"user_id\"] = test[\"user_id\"].str.replace(\"User_Id_\", \"\").astype(int)\n",
    "test[\"rider_id\"] = test[\"rider_id\"].str.replace(\"Rider_Id_\", \"\").astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d6f4400a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(columns=\"precipitation\", inplace=True)\n",
    "# Precipitation is messed up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.temperature.fillna(test.temperature.mean(), inplace=True)\n",
    "# Temperature is mostly ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>platform</th>\n",
       "      <th>personal_or_business</th>\n",
       "      <th>placement_date</th>\n",
       "      <th>placement_weekday</th>\n",
       "      <th>placement_time</th>\n",
       "      <th>confirm_date</th>\n",
       "      <th>confirm_weekday</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_weekday</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>temperature</th>\n",
       "      <th>pickup_lat</th>\n",
       "      <th>pickup_long</th>\n",
       "      <th>dest_lat</th>\n",
       "      <th>dest_long</th>\n",
       "      <th>rider_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19248</td>\n",
       "      <td>3355</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>4:44:10 PM</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>5:06:47 PM</td>\n",
       "      <td>8</td>\n",
       "      <td>23.24612</td>\n",
       "      <td>-1.333275</td>\n",
       "      <td>36.870815</td>\n",
       "      <td>-1.305249</td>\n",
       "      <td>36.822390</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12736</td>\n",
       "      <td>3647</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12:57:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>1:25:37 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>23.24612</td>\n",
       "      <td>-1.272639</td>\n",
       "      <td>36.794723</td>\n",
       "      <td>-1.277007</td>\n",
       "      <td>36.823907</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>768</td>\n",
       "      <td>2154</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11:08:14 AM</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11:57:54 AM</td>\n",
       "      <td>5</td>\n",
       "      <td>22.80000</td>\n",
       "      <td>-1.290894</td>\n",
       "      <td>36.822971</td>\n",
       "      <td>-1.276574</td>\n",
       "      <td>36.851365</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15332</td>\n",
       "      <td>2910</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1:51:35 PM</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2:16:52 PM</td>\n",
       "      <td>5</td>\n",
       "      <td>24.50000</td>\n",
       "      <td>-1.290503</td>\n",
       "      <td>36.809646</td>\n",
       "      <td>-1.303382</td>\n",
       "      <td>36.790658</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21373</td>\n",
       "      <td>1205</td>\n",
       "      <td>Bike</td>\n",
       "      <td>3</td>\n",
       "      <td>Business</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11:30:28 AM</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11:56:04 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>24.40000</td>\n",
       "      <td>-1.281081</td>\n",
       "      <td>36.814423</td>\n",
       "      <td>-1.266467</td>\n",
       "      <td>36.792161</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id vehicle platform personal_or_business  placement_date  \\\n",
       "0     19248     3355    Bike        3             Business              27   \n",
       "1     12736     3647    Bike        3             Business              17   \n",
       "2       768     2154    Bike        3             Business              27   \n",
       "3     15332     2910    Bike        3             Business              17   \n",
       "4     21373     1205    Bike        3             Business              11   \n",
       "\n",
       "   placement_weekday placement_time  confirm_date  confirm_weekday  ...  \\\n",
       "0                  3     4:44:10 PM            27                3  ...   \n",
       "1                  5    12:57:35 PM            17                5  ...   \n",
       "2                  4    11:08:14 AM            27                4  ...   \n",
       "3                  1     1:51:35 PM            17                1  ...   \n",
       "4                  2    11:30:28 AM            11                2  ...   \n",
       "\n",
       "  pickup_date  pickup_weekday  pickup_time distance  temperature  pickup_lat  \\\n",
       "0          27               3   5:06:47 PM        8     23.24612   -1.333275   \n",
       "1          17               5   1:25:37 PM        5     23.24612   -1.272639   \n",
       "2          27               4  11:57:54 AM        5     22.80000   -1.290894   \n",
       "3          17               1   2:16:52 PM        5     24.50000   -1.290503   \n",
       "4          11               2  11:56:04 AM        6     24.40000   -1.281081   \n",
       "\n",
       "  pickup_long  dest_lat  dest_long  rider_id  \n",
       "0   36.870815 -1.305249  36.822390       192  \n",
       "1   36.794723 -1.277007  36.823907       868  \n",
       "2   36.822971 -1.276574  36.851365        26  \n",
       "3   36.809646 -1.303382  36.790658       685  \n",
       "4   36.814423 -1.266467  36.792161       858  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "platform\n",
       "1    9.451477\n",
       "2    8.937500\n",
       "3    9.489776\n",
       "4    8.666667\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('platform').distance.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehicle\n",
       "Bike    9.45897\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('vehicle').distance.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>platform</th>\n",
       "      <th>personal_or_business</th>\n",
       "      <th>placement_date</th>\n",
       "      <th>placement_weekday</th>\n",
       "      <th>placement_time</th>\n",
       "      <th>confirm_date</th>\n",
       "      <th>confirm_weekday</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_weekday</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>temperature</th>\n",
       "      <th>pickup_lat</th>\n",
       "      <th>pickup_long</th>\n",
       "      <th>dest_lat</th>\n",
       "      <th>dest_long</th>\n",
       "      <th>rider_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [order_id, user_id, vehicle, platform, personal_or_business, placement_date, placement_weekday, placement_time, confirm_date, confirm_weekday, confirm_time, arrival_pickup_date, arrive_pickup_weekday, arrive_time, pickup_date, pickup_weekday, pickup_time, distance, temperature, pickup_lat, pickup_long, dest_lat, dest_long, rider_id]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_2 = test['placement_date'] == test['arrival_pickup_date']\n",
    "test[~bool_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_time(time):\n",
    "    #time is in the form \"XX:XX:XX AM/PM\" or \"X:XX:XX AM/PM\". Convert it to a seconds format.\n",
    "    #for example, \"1:00:00 AM\" is converted to 3600. \"1:00:00 PM\" is converted to 46800.\n",
    "    \n",
    "    time_one_twelve = time[:-3] \n",
    "    if time_one_twelve[1] == \":\":\n",
    "        time_one_twelve = \"0\" + time_one_twelve[:]\n",
    "    #this is the part consisting of \"XX:XX:XX\"\n",
    "    \n",
    "    ampm = time[-2:] #this is the part consisting of \"AM/PM\"\n",
    "    \n",
    "    if ampm == 'AM':\n",
    "        return convert_am_time(time_one_twelve)\n",
    "    elif ampm == 'PM':\n",
    "        return convert_am_time(time_one_twelve) + 3600 * 12\n",
    "    else:\n",
    "        return -1 #this should never happen when your input is valid, it's to deal with naughty inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_am_time(time_one_twelve):\n",
    "    hour_string = time_one_twelve[:2]\n",
    "    minute_string = time_one_twelve[3:5]\n",
    "    second_string = time_one_twelve[6:8]\n",
    "    \n",
    "    if hour_string == \"12\":\n",
    "        hour_int = 0\n",
    "    else:\n",
    "        hour_int = convert_str_to_int(hour_string)\n",
    "    minute_int = convert_str_to_int(minute_string)\n",
    "    second_int = convert_str_to_int(second_string)\n",
    "    \n",
    "    return 3600 * hour_int + 60 * minute_int + second_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_str_to_int(string):\n",
    "    # Precondition: string has length 2 and should only consist of digits\n",
    "    if string[0] == \"0\":\n",
    "        return int(string[1])\n",
    "    return int(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['placement_time'] = test.apply(lambda x: convert_time(x.placement_time), axis=1)\n",
    "test['pickup_time'] = test.apply(lambda x: convert_time(x.pickup_time), axis=1)\n",
    "test['confirm_time'] = test.apply(lambda x: convert_time(x.confirm_time), axis=1)\n",
    "test['arrive_time'] = test.apply(lambda x: convert_time(x.arrive_time), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>placement_time</th>\n",
       "      <th>confirm_time</th>\n",
       "      <th>arrive_time</th>\n",
       "      <th>pickup_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60250</td>\n",
       "      <td>60269</td>\n",
       "      <td>60784</td>\n",
       "      <td>61607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46655</td>\n",
       "      <td>46757</td>\n",
       "      <td>48027</td>\n",
       "      <td>48337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40094</td>\n",
       "      <td>41105</td>\n",
       "      <td>41600</td>\n",
       "      <td>43074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49895</td>\n",
       "      <td>50007</td>\n",
       "      <td>50561</td>\n",
       "      <td>51412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41428</td>\n",
       "      <td>41685</td>\n",
       "      <td>42439</td>\n",
       "      <td>42964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7063</th>\n",
       "      <td>38149</td>\n",
       "      <td>38224</td>\n",
       "      <td>38413</td>\n",
       "      <td>38898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7064</th>\n",
       "      <td>53982</td>\n",
       "      <td>54057</td>\n",
       "      <td>55117</td>\n",
       "      <td>55304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7065</th>\n",
       "      <td>53112</td>\n",
       "      <td>53136</td>\n",
       "      <td>54015</td>\n",
       "      <td>54472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7066</th>\n",
       "      <td>55106</td>\n",
       "      <td>55185</td>\n",
       "      <td>56447</td>\n",
       "      <td>56795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7067</th>\n",
       "      <td>56229</td>\n",
       "      <td>56620</td>\n",
       "      <td>57485</td>\n",
       "      <td>57883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7068 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      placement_time  confirm_time  arrive_time  pickup_time\n",
       "0              60250         60269        60784        61607\n",
       "1              46655         46757        48027        48337\n",
       "2              40094         41105        41600        43074\n",
       "3              49895         50007        50561        51412\n",
       "4              41428         41685        42439        42964\n",
       "...              ...           ...          ...          ...\n",
       "7063           38149         38224        38413        38898\n",
       "7064           53982         54057        55117        55304\n",
       "7065           53112         53136        54015        54472\n",
       "7066           55106         55185        56447        56795\n",
       "7067           56229         56620        57485        57883\n",
       "\n",
       "[7068 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[[\"placement_time\",\"confirm_time\", \"arrive_time\", \"pickup_time\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['placement_to_confirm_time'] = test.confirm_time - test.placement_time\n",
    "#first time interval\n",
    "test['confirm_to_pickup_time'] = test.pickup_time - test.confirm_time\n",
    "#second time interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                        int64\n",
       "user_id                         int64\n",
       "vehicle                      category\n",
       "platform                     category\n",
       "personal_or_business         category\n",
       "placement_date                  int64\n",
       "placement_weekday               int64\n",
       "placement_time                  int64\n",
       "confirm_date                    int64\n",
       "confirm_weekday                 int64\n",
       "confirm_time                    int64\n",
       "arrival_pickup_date             int64\n",
       "arrive_pickup_weekday           int64\n",
       "arrive_time                     int64\n",
       "pickup_date                     int64\n",
       "pickup_weekday                  int64\n",
       "pickup_time                     int64\n",
       "distance                        int64\n",
       "temperature                   float64\n",
       "pickup_lat                    float64\n",
       "pickup_long                   float64\n",
       "dest_lat                      float64\n",
       "dest_long                     float64\n",
       "rider_id                        int64\n",
       "placement_to_confirm_time       int64\n",
       "confirm_to_pickup_time          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43199\n",
      "43201\n",
      "86399\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(convert_time(\"11:59:59 AM\"))\n",
    "print(convert_time(\"12:00:01 PM\"))\n",
    "print(convert_time(\"11:59:59 PM\"))\n",
    "print(convert_time(\"12:00:01 AM\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.to_pickle(\"clean-data/test.pkl\")"
   ]
  }
 ],
 "metadata": {
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