Ride Hailing Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f0ae762",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86766434",
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:07:55</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-73.976746</td>\n",
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       "      <td>40.746128</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:11:06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.90</td>\n",
       "      <td>-73.983482</td>\n",
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       "      <td>N</td>\n",
       "      <td>-74.005943</td>\n",
       "      <td>40.733166</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:31:06</td>\n",
       "      <td>2</td>\n",
       "      <td>19.98</td>\n",
       "      <td>-73.782021</td>\n",
       "      <td>40.644810</td>\n",
       "      <td>1</td>\n",
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       "      <td>-73.974541</td>\n",
       "      <td>40.675770</td>\n",
       "      <td>1</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>10.78</td>\n",
       "      <td>-73.863419</td>\n",
       "      <td>40.769814</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.969650</td>\n",
       "      <td>40.757767</td>\n",
       "      <td>1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>30.43</td>\n",
       "      <td>-73.971741</td>\n",
       "      <td>40.792183</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.177170</td>\n",
       "      <td>40.695053</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0.3</td>\n",
       "      <td>113.80</td>\n",
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       "      <th>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:15</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-73.990898</td>\n",
       "      <td>40.750519</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.998245</td>\n",
       "      <td>40.750462</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:32:41</td>\n",
       "      <td>1</td>\n",
       "      <td>3.40</td>\n",
       "      <td>-74.014488</td>\n",
       "      <td>40.718296</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.982361</td>\n",
       "      <td>40.752529</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.80</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:37:23</td>\n",
       "      <td>1</td>\n",
       "      <td>9.70</td>\n",
       "      <td>-73.963379</td>\n",
       "      <td>40.774097</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.865028</td>\n",
       "      <td>40.770512</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.80</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>44.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-73.984901</td>\n",
       "      <td>40.763111</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.970695</td>\n",
       "      <td>40.759148</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01 06:17:11</td>\n",
       "      <td>2016-03-01 06:22:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-73.990685</td>\n",
       "      <td>40.750473</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.980354</td>\n",
       "      <td>40.754910</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count   \n",
       "0             1  2016-03-01 00:00:00   2016-03-01 00:07:55                1  \\\n",
       "1             1  2016-03-01 00:00:00   2016-03-01 00:11:06                1   \n",
       "2             2  2016-03-01 00:00:00   2016-03-01 00:31:06                2   \n",
       "3             2  2016-03-01 00:00:00   2016-03-01 00:00:00                3   \n",
       "4             2  2016-03-01 00:00:00   2016-03-01 00:00:00                5   \n",
       "...         ...                  ...                   ...              ...   \n",
       "99995         1  2016-03-01 06:17:10   2016-03-01 06:22:15                1   \n",
       "99996         1  2016-03-01 06:17:10   2016-03-01 06:32:41                1   \n",
       "99997         1  2016-03-01 06:17:10   2016-03-01 06:37:23                1   \n",
       "99998         2  2016-03-01 06:17:10   2016-03-01 06:22:09                1   \n",
       "99999         1  2016-03-01 06:17:11   2016-03-01 06:22:00                1   \n",
       "\n",
       "       trip_distance  pickup_longitude  pickup_latitude  RatecodeID   \n",
       "0               2.50        -73.976746        40.765152           1  \\\n",
       "1               2.90        -73.983482        40.767925           1   \n",
       "2              19.98        -73.782021        40.644810           1   \n",
       "3              10.78        -73.863419        40.769814           1   \n",
       "4              30.43        -73.971741        40.792183           3   \n",
       "...              ...               ...              ...         ...   \n",
       "99995           0.50        -73.990898        40.750519           1   \n",
       "99996           3.40        -74.014488        40.718296           1   \n",
       "99997           9.70        -73.963379        40.774097           1   \n",
       "99998           0.92        -73.984901        40.763111           1   \n",
       "99999           1.00        -73.990685        40.750473           1   \n",
       "\n",
       "      store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type   \n",
       "0                      N         -74.004265         40.746128             1  \\\n",
       "1                      N         -74.005943         40.733166             1   \n",
       "2                      N         -73.974541         40.675770             1   \n",
       "3                      N         -73.969650         40.757767             1   \n",
       "4                      N         -74.177170         40.695053             1   \n",
       "...                  ...                ...               ...           ...   \n",
       "99995                  N         -73.998245         40.750462             2   \n",
       "99996                  N         -73.982361         40.752529             1   \n",
       "99997                  N         -73.865028         40.770512             1   \n",
       "99998                  N         -73.970695         40.759148             1   \n",
       "99999                  N         -73.980354         40.754910             2   \n",
       "\n",
       "       fare_amount  extra  mta_tax  tip_amount  tolls_amount   \n",
       "0              9.0    0.5      0.5        2.05          0.00  \\\n",
       "1             11.0    0.5      0.5        3.05          0.00   \n",
       "2             54.5    0.5      0.5        8.00          0.00   \n",
       "3             31.5    0.0      0.5        3.78          5.54   \n",
       "4             98.0    0.0      0.0        0.00         15.50   \n",
       "...            ...    ...      ...         ...           ...   \n",
       "99995          5.0    0.0      0.5        0.00          0.00   \n",
       "99996         14.0    0.0      0.5        2.00          0.00   \n",
       "99997         29.0    0.0      0.5        8.80          5.54   \n",
       "99998          5.5    0.5      0.5        1.36          0.00   \n",
       "99999          6.0    0.0      0.5        0.00          0.00   \n",
       "\n",
       "       improvement_surcharge  total_amount  \n",
       "0                        0.3         12.35  \n",
       "1                        0.3         15.35  \n",
       "2                        0.3         63.80  \n",
       "3                        0.3         41.62  \n",
       "4                        0.3        113.80  \n",
       "...                      ...           ...  \n",
       "99995                    0.3          5.80  \n",
       "99996                    0.3         16.80  \n",
       "99997                    0.3         44.14  \n",
       "99998                    0.3          8.16  \n",
       "99999                    0.3          6.80  \n",
       "\n",
       "[100000 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/uber_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fab35de9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 19 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   VendorID               100000 non-null  int64  \n",
      " 1   tpep_pickup_datetime   100000 non-null  object \n",
      " 2   tpep_dropoff_datetime  100000 non-null  object \n",
      " 3   passenger_count        100000 non-null  int64  \n",
      " 4   trip_distance          100000 non-null  float64\n",
      " 5   pickup_longitude       100000 non-null  float64\n",
      " 6   pickup_latitude        100000 non-null  float64\n",
      " 7   RatecodeID             100000 non-null  int64  \n",
      " 8   store_and_fwd_flag     100000 non-null  object \n",
      " 9   dropoff_longitude      100000 non-null  float64\n",
      " 10  dropoff_latitude       100000 non-null  float64\n",
      " 11  payment_type           100000 non-null  int64  \n",
      " 12  fare_amount            100000 non-null  float64\n",
      " 13  extra                  100000 non-null  float64\n",
      " 14  mta_tax                100000 non-null  float64\n",
      " 15  tip_amount             100000 non-null  float64\n",
      " 16  tolls_amount           100000 non-null  float64\n",
      " 17  improvement_surcharge  100000 non-null  float64\n",
      " 18  total_amount           100000 non-null  float64\n",
      "dtypes: float64(12), int64(4), object(3)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfcf7f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'])\n",
    "df['tpep_dropoff_datetime'] = pd.to_datetime(df['tpep_dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c717e06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 19 columns):\n",
      " #   Column                 Non-Null Count   Dtype         \n",
      "---  ------                 --------------   -----         \n",
      " 0   VendorID               100000 non-null  int64         \n",
      " 1   tpep_pickup_datetime   100000 non-null  datetime64[ns]\n",
      " 2   tpep_dropoff_datetime  100000 non-null  datetime64[ns]\n",
      " 3   passenger_count        100000 non-null  int64         \n",
      " 4   trip_distance          100000 non-null  float64       \n",
      " 5   pickup_longitude       100000 non-null  float64       \n",
      " 6   pickup_latitude        100000 non-null  float64       \n",
      " 7   RatecodeID             100000 non-null  int64         \n",
      " 8   store_and_fwd_flag     100000 non-null  object        \n",
      " 9   dropoff_longitude      100000 non-null  float64       \n",
      " 10  dropoff_latitude       100000 non-null  float64       \n",
      " 11  payment_type           100000 non-null  int64         \n",
      " 12  fare_amount            100000 non-null  float64       \n",
      " 13  extra                  100000 non-null  float64       \n",
      " 14  mta_tax                100000 non-null  float64       \n",
      " 15  tip_amount             100000 non-null  float64       \n",
      " 16  tolls_amount           100000 non-null  float64       \n",
      " 17  improvement_surcharge  100000 non-null  float64       \n",
      " 18  total_amount           100000 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(12), int64(4), object(1)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a41a38d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates().reset_index(drop=True)\n",
    "df['trip_id'] = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aea0925b",
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-01 00:07:55</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-73.976746</td>\n",
       "      <td>40.765152</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.004265</td>\n",
       "      <td>40.746128</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-01 00:11:06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.90</td>\n",
       "      <td>-73.983482</td>\n",
       "      <td>40.767925</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.005943</td>\n",
       "      <td>40.733166</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-01 00:31:06</td>\n",
       "      <td>2</td>\n",
       "      <td>19.98</td>\n",
       "      <td>-73.782021</td>\n",
       "      <td>40.644810</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.974541</td>\n",
       "      <td>40.675770</td>\n",
       "      <td>1</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>10.78</td>\n",
       "      <td>-73.863419</td>\n",
       "      <td>40.769814</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.969650</td>\n",
       "      <td>40.757767</td>\n",
       "      <td>1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.62</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>30.43</td>\n",
       "      <td>-73.971741</td>\n",
       "      <td>40.792183</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.177170</td>\n",
       "      <td>40.695053</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0.3</td>\n",
       "      <td>113.80</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count   \n",
       "0         1           2016-03-01   2016-03-01 00:07:55                1  \\\n",
       "1         1           2016-03-01   2016-03-01 00:11:06                1   \n",
       "2         2           2016-03-01   2016-03-01 00:31:06                2   \n",
       "3         2           2016-03-01   2016-03-01 00:00:00                3   \n",
       "4         2           2016-03-01   2016-03-01 00:00:00                5   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  RatecodeID   \n",
       "0           2.50        -73.976746        40.765152           1  \\\n",
       "1           2.90        -73.983482        40.767925           1   \n",
       "2          19.98        -73.782021        40.644810           1   \n",
       "3          10.78        -73.863419        40.769814           1   \n",
       "4          30.43        -73.971741        40.792183           3   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type   \n",
       "0                  N         -74.004265         40.746128             1  \\\n",
       "1                  N         -74.005943         40.733166             1   \n",
       "2                  N         -73.974541         40.675770             1   \n",
       "3                  N         -73.969650         40.757767             1   \n",
       "4                  N         -74.177170         40.695053             1   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount   \n",
       "0          9.0    0.5      0.5        2.05          0.00  \\\n",
       "1         11.0    0.5      0.5        3.05          0.00   \n",
       "2         54.5    0.5      0.5        8.00          0.00   \n",
       "3         31.5    0.0      0.5        3.78          5.54   \n",
       "4         98.0    0.0      0.0        0.00         15.50   \n",
       "\n",
       "   improvement_surcharge  total_amount  trip_id  \n",
       "0                    0.3         12.35        0  \n",
       "1                    0.3         15.35        1  \n",
       "2                    0.3         63.80        2  \n",
       "3                    0.3         41.62        3  \n",
       "4                    0.3        113.80        4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4cf3c64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>pick_hour</th>\n",
       "      <th>pick_day</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2016</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:15</td>\n",
       "      <td>6</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:32:41</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:37:23</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tpep_pickup_datetime tpep_dropoff_datetime  pick_hour  pick_day   \n",
       "0      2016-03-01 00:00:00   2016-03-01 00:07:55          0         1  \\\n",
       "1      2016-03-01 00:00:00   2016-03-01 00:11:06          0         1   \n",
       "2      2016-03-01 00:00:00   2016-03-01 00:31:06          0         1   \n",
       "3      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "4      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "...                    ...                   ...        ...       ...   \n",
       "99995  2016-03-01 06:17:10   2016-03-01 06:22:15          6         1   \n",
       "99996  2016-03-01 06:17:10   2016-03-01 06:32:41          6         1   \n",
       "99997  2016-03-01 06:17:10   2016-03-01 06:37:23          6         1   \n",
       "99998  2016-03-01 06:17:10   2016-03-01 06:22:09          6         1   \n",
       "99999  2016-03-01 06:17:11   2016-03-01 06:22:00          6         1   \n",
       "\n",
       "       pick_month  pick_year  pick_weekday  \n",
       "0               3       2016             1  \n",
       "1               3       2016             1  \n",
       "2               3       2016             1  \n",
       "3               3       2016             1  \n",
       "4               3       2016             1  \n",
       "...           ...        ...           ...  \n",
       "99995           3       2016             1  \n",
       "99996           3       2016             1  \n",
       "99997           3       2016             1  \n",
       "99998           3       2016             1  \n",
       "99999           3       2016             1  \n",
       "\n",
       "[100000 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_dim = df[['tpep_pickup_datetime','tpep_dropoff_datetime']].reset_index(drop=True)\n",
    "datetime_dim['tpep_pickup_datetime'] = datetime_dim['tpep_pickup_datetime']\n",
    "datetime_dim['pick_hour'] = datetime_dim['tpep_pickup_datetime'].dt.hour\n",
    "datetime_dim['pick_day'] = datetime_dim['tpep_pickup_datetime'].dt.day\n",
    "datetime_dim['pick_month'] = datetime_dim['tpep_pickup_datetime'].dt.month\n",
    "datetime_dim['pick_year'] = datetime_dim['tpep_pickup_datetime'].dt.year\n",
    "datetime_dim['pick_weekday'] = datetime_dim['tpep_pickup_datetime'].dt.weekday\n",
    "\n",
    "datetime_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ee81765",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2016-03-01 06:17:10</td>\n",
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       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:32:41</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:37:23</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:09</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2016-03-01 06:17:11</td>\n",
       "      <td>2016-03-01 06:22:00</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tpep_pickup_datetime tpep_dropoff_datetime  pick_hour  pick_day   \n",
       "0      2016-03-01 00:00:00   2016-03-01 00:07:55          0         1  \\\n",
       "1      2016-03-01 00:00:00   2016-03-01 00:11:06          0         1   \n",
       "2      2016-03-01 00:00:00   2016-03-01 00:31:06          0         1   \n",
       "3      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "4      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "...                    ...                   ...        ...       ...   \n",
       "99995  2016-03-01 06:17:10   2016-03-01 06:22:15          6         1   \n",
       "99996  2016-03-01 06:17:10   2016-03-01 06:32:41          6         1   \n",
       "99997  2016-03-01 06:17:10   2016-03-01 06:37:23          6         1   \n",
       "99998  2016-03-01 06:17:10   2016-03-01 06:22:09          6         1   \n",
       "99999  2016-03-01 06:17:11   2016-03-01 06:22:00          6         1   \n",
       "\n",
       "       pick_month  pick_year  pick_weekday  drop_hour  drop_day  drop_month   \n",
       "0               3       2016             1          0         1           3  \\\n",
       "1               3       2016             1          0         1           3   \n",
       "2               3       2016             1          0         1           3   \n",
       "3               3       2016             1          0         1           3   \n",
       "4               3       2016             1          0         1           3   \n",
       "...           ...        ...           ...        ...       ...         ...   \n",
       "99995           3       2016             1          6         1           3   \n",
       "99996           3       2016             1          6         1           3   \n",
       "99997           3       2016             1          6         1           3   \n",
       "99998           3       2016             1          6         1           3   \n",
       "99999           3       2016             1          6         1           3   \n",
       "\n",
       "       drop_year  drop_weekday  \n",
       "0           2016             1  \n",
       "1           2016             1  \n",
       "2           2016             1  \n",
       "3           2016             1  \n",
       "4           2016             1  \n",
       "...          ...           ...  \n",
       "99995       2016             1  \n",
       "99996       2016             1  \n",
       "99997       2016             1  \n",
       "99998       2016             1  \n",
       "99999       2016             1  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_dim['tpep_dropoff_datetime'] = datetime_dim['tpep_dropoff_datetime']\n",
    "datetime_dim['drop_hour'] = datetime_dim['tpep_dropoff_datetime'].dt.hour\n",
    "datetime_dim['drop_day'] = datetime_dim['tpep_dropoff_datetime'].dt.day\n",
    "datetime_dim['drop_month'] = datetime_dim['tpep_dropoff_datetime'].dt.month\n",
    "datetime_dim['drop_year'] = datetime_dim['tpep_dropoff_datetime'].dt.year\n",
    "datetime_dim['drop_weekday'] = datetime_dim['tpep_dropoff_datetime'].dt.weekday\n",
    "datetime_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac25f3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_dim['datetime_id'] = datetime_dim.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ccfb2d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
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       "      <td>2016-03-01 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:31:06</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>2016-03-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>99995</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:32:41</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>99996</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:37:23</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2016-03-01 06:17:10</td>\n",
       "      <td>2016-03-01 06:22:09</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>99998</td>\n",
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       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2016-03-01 06:17:11</td>\n",
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       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tpep_pickup_datetime tpep_dropoff_datetime  pick_hour  pick_day   \n",
       "0      2016-03-01 00:00:00   2016-03-01 00:07:55          0         1  \\\n",
       "1      2016-03-01 00:00:00   2016-03-01 00:11:06          0         1   \n",
       "2      2016-03-01 00:00:00   2016-03-01 00:31:06          0         1   \n",
       "3      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "4      2016-03-01 00:00:00   2016-03-01 00:00:00          0         1   \n",
       "...                    ...                   ...        ...       ...   \n",
       "99995  2016-03-01 06:17:10   2016-03-01 06:22:15          6         1   \n",
       "99996  2016-03-01 06:17:10   2016-03-01 06:32:41          6         1   \n",
       "99997  2016-03-01 06:17:10   2016-03-01 06:37:23          6         1   \n",
       "99998  2016-03-01 06:17:10   2016-03-01 06:22:09          6         1   \n",
       "99999  2016-03-01 06:17:11   2016-03-01 06:22:00          6         1   \n",
       "\n",
       "       pick_month  pick_year  pick_weekday  drop_hour  drop_day  drop_month   \n",
       "0               3       2016             1          0         1           3  \\\n",
       "1               3       2016             1          0         1           3   \n",
       "2               3       2016             1          0         1           3   \n",
       "3               3       2016             1          0         1           3   \n",
       "4               3       2016             1          0         1           3   \n",
       "...           ...        ...           ...        ...       ...         ...   \n",
       "99995           3       2016             1          6         1           3   \n",
       "99996           3       2016             1          6         1           3   \n",
       "99997           3       2016             1          6         1           3   \n",
       "99998           3       2016             1          6         1           3   \n",
       "99999           3       2016             1          6         1           3   \n",
       "\n",
       "       drop_year  drop_weekday  datetime_id  \n",
       "0           2016             1            0  \n",
       "1           2016             1            1  \n",
       "2           2016             1            2  \n",
       "3           2016             1            3  \n",
       "4           2016             1            4  \n",
       "...          ...           ...          ...  \n",
       "99995       2016             1        99995  \n",
       "99996       2016             1        99996  \n",
       "99997       2016             1        99997  \n",
       "99998       2016             1        99998  \n",
       "99999       2016             1        99999  \n",
       "\n",
       "[100000 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3cc3dbf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_dim = datetime_dim[['datetime_id', 'tpep_pickup_datetime', 'pick_hour', 'pick_day', 'pick_month', 'pick_year', 'pick_weekday',\n",
    "                             'tpep_dropoff_datetime', 'drop_hour', 'drop_day', 'drop_month', 'drop_year', 'drop_weekday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa6ba83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   datetime_id tpep_pickup_datetime  pick_hour  pick_day  pick_month   \n",
       "0            0           2016-03-01          0         1           3  \\\n",
       "1            1           2016-03-01          0         1           3   \n",
       "2            2           2016-03-01          0         1           3   \n",
       "3            3           2016-03-01          0         1           3   \n",
       "4            4           2016-03-01          0         1           3   \n",
       "\n",
       "   pick_year  pick_weekday tpep_dropoff_datetime  drop_hour  drop_day   \n",
       "0       2016             1   2016-03-01 00:07:55          0         1  \\\n",
       "1       2016             1   2016-03-01 00:11:06          0         1   \n",
       "2       2016             1   2016-03-01 00:31:06          0         1   \n",
       "3       2016             1   2016-03-01 00:00:00          0         1   \n",
       "4       2016             1   2016-03-01 00:00:00          0         1   \n",
       "\n",
       "   drop_month  drop_year  drop_weekday  \n",
       "0           3       2016             1  \n",
       "1           3       2016             1  \n",
       "2           3       2016             1  \n",
       "3           3       2016             1  \n",
       "4           3       2016             1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f650af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "passenger_count_dim = df[['passenger_count']].reset_index(drop=True)\n",
    "passenger_count_dim['passenger_count_id'] = passenger_count_dim.index\n",
    "passenger_count_dim = passenger_count_dim[['passenger_count_id','passenger_count']]\n",
    "\n",
    "trip_distance_dim = df[['trip_distance']].reset_index(drop=True)\n",
    "trip_distance_dim['trip_distance_id'] = trip_distance_dim.index\n",
    "trip_distance_dim = trip_distance_dim[['trip_distance_id','trip_distance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34da88ae",
   "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>passenger_count_id</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count_id  passenger_count\n",
       "0                   0                1\n",
       "1                   1                1\n",
       "2                   2                2\n",
       "3                   3                3\n",
       "4                   4                5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passenger_count_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d0219cd",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_distance_id</th>\n",
       "      <th>trip_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_distance_id  trip_distance\n",
       "0                 0           2.50\n",
       "1                 1           2.90\n",
       "2                 2          19.98\n",
       "3                 3          10.78\n",
       "4                 4          30.43"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_distance_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09b27211",
   "metadata": {},
   "outputs": [],
   "source": [
    "rate_code_type = {\n",
    "    1:\"Standard rate\",\n",
    "    2:\"JFK\",\n",
    "    3:\"Newark\",\n",
    "    4:\"Nassau or Westchester\",\n",
    "    5:\"Negotiated fare\",\n",
    "    6:\"Group ride\"\n",
    "}\n",
    "\n",
    "rate_code_dim = df[['RatecodeID']].reset_index(drop=True)\n",
    "rate_code_dim['rate_code_id'] = rate_code_dim.index\n",
    "rate_code_dim['rate_code_name'] = rate_code_dim['RatecodeID'].map(rate_code_type)\n",
    "rate_code_dim = rate_code_dim[['rate_code_id','RatecodeID','rate_code_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b6a9df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>rate_code_id</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>rate_code_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Standard rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Standard rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Standard rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Standard rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Newark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_code_id  RatecodeID rate_code_name\n",
       "0             0           1  Standard rate\n",
       "1             1           1  Standard rate\n",
       "2             2           1  Standard rate\n",
       "3             3           1  Standard rate\n",
       "4             4           3         Newark"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_code_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "184ae8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickup_location_dim = df[['pickup_longitude', 'pickup_latitude']].reset_index(drop=True)\n",
    "pickup_location_dim['pickup_location_id'] = pickup_location_dim.index\n",
    "pickup_location_dim = pickup_location_dim[['pickup_location_id','pickup_latitude','pickup_longitude']] \n",
    "\n",
    "\n",
    "dropoff_location_dim = df[['dropoff_longitude', 'dropoff_latitude']].reset_index(drop=True)\n",
    "dropoff_location_dim['dropoff_location_id'] = dropoff_location_dim.index\n",
    "dropoff_location_dim = dropoff_location_dim[['dropoff_location_id','dropoff_latitude','dropoff_longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ca14726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_location_id</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>40.765152</td>\n",
       "      <td>-73.976746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40.767925</td>\n",
       "      <td>-73.983482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40.644810</td>\n",
       "      <td>-73.782021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>40.769814</td>\n",
       "      <td>-73.863419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40.792183</td>\n",
       "      <td>-73.971741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_location_id  pickup_latitude  pickup_longitude\n",
       "0                   0        40.765152        -73.976746\n",
       "1                   1        40.767925        -73.983482\n",
       "2                   2        40.644810        -73.782021\n",
       "3                   3        40.769814        -73.863419\n",
       "4                   4        40.792183        -73.971741"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickup_location_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8a877f7f",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dropoff_location_id</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>40.746128</td>\n",
       "      <td>-74.004265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40.733166</td>\n",
       "      <td>-74.005943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40.675770</td>\n",
       "      <td>-73.974541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>40.757767</td>\n",
       "      <td>-73.969650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40.695053</td>\n",
       "      <td>-74.177170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dropoff_location_id  dropoff_latitude  dropoff_longitude\n",
       "0                    0         40.746128         -74.004265\n",
       "1                    1         40.733166         -74.005943\n",
       "2                    2         40.675770         -73.974541\n",
       "3                    3         40.757767         -73.969650\n",
       "4                    4         40.695053         -74.177170"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropoff_location_dim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "62c40a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "payment_type_name = {\n",
    "    1:\"Credit card\",\n",
    "    2:\"Cash\",\n",
    "    3:\"No charge\",\n",
    "    4:\"Dispute\",\n",
    "    5:\"Unknown\",\n",
    "    6:\"Voided trip\"\n",
    "}\n",
    "payment_type_dim = df[['payment_type']].reset_index(drop=True)\n",
    "payment_type_dim['payment_type_id'] = payment_type_dim.index\n",
    "payment_type_dim['payment_type_name'] = payment_type_dim['payment_type'].map(payment_type_name)\n",
    "payment_type_dim = payment_type_dim[['payment_type_id','payment_type','payment_type_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75c8b403",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact_table = df.merge(passenger_count_dim, left_on='trip_id', right_on='passenger_count_id') \\\n",
    "             .merge(trip_distance_dim, left_on='trip_id', right_on='trip_distance_id') \\\n",
    "             .merge(rate_code_dim, left_on='trip_id', right_on='rate_code_id') \\\n",
    "             .merge(pickup_location_dim, left_on='trip_id', right_on='pickup_location_id') \\\n",
    "             .merge(dropoff_location_dim, left_on='trip_id', right_on='dropoff_location_id')\\\n",
    "             .merge(datetime_dim, left_on='trip_id', right_on='datetime_id') \\\n",
    "             .merge(payment_type_dim, left_on='trip_id', right_on='payment_type_id') \\\n",
    "             [['trip_id','VendorID', 'datetime_id', 'passenger_count_id',\n",
    "               'trip_distance_id', 'rate_code_id', 'store_and_fwd_flag', 'pickup_location_id', 'dropoff_location_id',\n",
    "               'payment_type_id', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount',\n",
    "               'improvement_surcharge', 'total_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06ac9581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trip_id', 'VendorID', 'datetime_id', 'passenger_count_id',\n",
       "       'trip_distance_id', 'rate_code_id', 'store_and_fwd_flag',\n",
       "       'pickup_location_id', 'dropoff_location_id', 'payment_type_id',\n",
       "       'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount',\n",
       "       'improvement_surcharge', 'total_amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c13a11d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>datetime_id</th>\n",
       "      <th>passenger_count_id</th>\n",
       "      <th>trip_distance_id</th>\n",
       "      <th>rate_code_id</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pickup_location_id</th>\n",
       "      <th>dropoff_location_id</th>\n",
       "      <th>payment_type_id</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.78</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>41.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0.3</td>\n",
       "      <td>113.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_id  VendorID  datetime_id  passenger_count_id  trip_distance_id   \n",
       "0        0         1            0                   0                 0  \\\n",
       "1        1         1            1                   1                 1   \n",
       "2        2         2            2                   2                 2   \n",
       "3        3         2            3                   3                 3   \n",
       "4        4         2            4                   4                 4   \n",
       "\n",
       "   rate_code_id store_and_fwd_flag  pickup_location_id  dropoff_location_id   \n",
       "0             0                  N                   0                    0  \\\n",
       "1             1                  N                   1                    1   \n",
       "2             2                  N                   2                    2   \n",
       "3             3                  N                   3                    3   \n",
       "4             4                  N                   4                    4   \n",
       "\n",
       "   payment_type_id  fare_amount  extra  mta_tax  tip_amount  tolls_amount   \n",
       "0                0          9.0    0.5      0.5        2.05          0.00  \\\n",
       "1                1         11.0    0.5      0.5        3.05          0.00   \n",
       "2                2         54.5    0.5      0.5        8.00          0.00   \n",
       "3                3         31.5    0.0      0.5        3.78          5.54   \n",
       "4                4         98.0    0.0      0.0        0.00         15.50   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3         12.35  \n",
       "1                    0.3         15.35  \n",
       "2                    0.3         63.80  \n",
       "3                    0.3         41.62  \n",
       "4                    0.3        113.80  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact_table.head()"
   ]
  }
 ],
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