Cleaning3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression, LassoCV, RidgeCV\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_graphviz\n",
    "from subprocess import call\n",
    "from IPython.display import SVG\n",
    "#from graphviz import Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/taxi_FINAL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>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>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>Location</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>CRD</th>\n",
       "      <th>UNK</th>\n",
       "      <th>NOC</th>\n",
       "      <th>DIS</th>\n",
       "      <th>CSH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:45:25</td>\n",
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       "      <td>-73.982227</td>\n",
       "      <td>40.731790</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.90</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:46:12</td>\n",
       "      <td>2014-01-09 20:55:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-73.982392</td>\n",
       "      <td>40.773382</td>\n",
       "      <td>-73.960449</td>\n",
       "      <td>40.763995</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>540</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:47</td>\n",
       "      <td>2014-01-09 20:59:46</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-73.988570</td>\n",
       "      <td>40.739406</td>\n",
       "      <td>-73.986626</td>\n",
       "      <td>40.765217</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>899</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:57</td>\n",
       "      <td>2014-01-09 20:51:40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-73.960213</td>\n",
       "      <td>40.770464</td>\n",
       "      <td>-73.979863</td>\n",
       "      <td>40.777050</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.20</td>\n",
       "      <td>403</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:47:09</td>\n",
       "      <td>2014-01-09 20:53:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-73.995371</td>\n",
       "      <td>40.717248</td>\n",
       "      <td>-73.984367</td>\n",
       "      <td>40.720524</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.75</td>\n",
       "      <td>383</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          1  2014-01-09 20:45:25  2014-01-09 20:52:31                1   \n",
       "1          1  2014-01-09 20:46:12  2014-01-09 20:55:12                1   \n",
       "2          1  2014-01-09 20:44:47  2014-01-09 20:59:46                2   \n",
       "3          1  2014-01-09 20:44:57  2014-01-09 20:51:40                1   \n",
       "4          1  2014-01-09 20:47:09  2014-01-09 20:53:32                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0            0.7        -73.994770        40.736828         -73.982227   \n",
       "1            1.4        -73.982392        40.773382         -73.960449   \n",
       "2            2.3        -73.988570        40.739406         -73.986626   \n",
       "3            1.7        -73.960213        40.770464         -73.979863   \n",
       "4            0.9        -73.995371        40.717248         -73.984367   \n",
       "\n",
       "   dropoff_latitude  fare_amount  ...  tolls_amount  total_amount  duration  \\\n",
       "0         40.731790          6.5  ...           0.0          8.90       426   \n",
       "1         40.763995          8.5  ...           0.0         11.40       540   \n",
       "2         40.765217         11.5  ...           0.0         14.00       899   \n",
       "3         40.777050          7.5  ...           0.0         10.20       403   \n",
       "4         40.720524          6.0  ...           0.0          8.75       383   \n",
       "\n",
       "   Location  time_of_day  CRD  UNK  NOC  DIS  CSH  \n",
       "0         5            4    1    0    0    0    0  \n",
       "1         4            4    1    0    0    0    0  \n",
       "2         5            4    1    0    0    0    0  \n",
       "3         5            4    1    0    0    0    0  \n",
       "4         5            4    1    0    0    0    0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"tip_percent\"]= data.tip_amount/(data.total_amount-data.tip_amount) *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
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       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>Location</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>CRD</th>\n",
       "      <th>UNK</th>\n",
       "      <th>NOC</th>\n",
       "      <th>DIS</th>\n",
       "      <th>CSH</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:45:25</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:46:12</td>\n",
       "      <td>2014-01-09 20:55:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-73.982392</td>\n",
       "      <td>40.773382</td>\n",
       "      <td>-73.960449</td>\n",
       "      <td>40.763995</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>11.40</td>\n",
       "      <td>540</td>\n",
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       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:47</td>\n",
       "      <td>2014-01-09 20:59:46</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-73.988570</td>\n",
       "      <td>40.739406</td>\n",
       "      <td>-73.986626</td>\n",
       "      <td>40.765217</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>14.00</td>\n",
       "      <td>899</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:57</td>\n",
       "      <td>2014-01-09 20:51:40</td>\n",
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       "      <td>1.7</td>\n",
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       "      <td>40.770464</td>\n",
       "      <td>-73.979863</td>\n",
       "      <td>40.777050</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>10.20</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:47:09</td>\n",
       "      <td>2014-01-09 20:53:32</td>\n",
       "      <td>1</td>\n",
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       "      <td>40.717248</td>\n",
       "      <td>-73.984367</td>\n",
       "      <td>40.720524</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          1  2014-01-09 20:45:25  2014-01-09 20:52:31                1   \n",
       "1          1  2014-01-09 20:46:12  2014-01-09 20:55:12                1   \n",
       "2          1  2014-01-09 20:44:47  2014-01-09 20:59:46                2   \n",
       "3          1  2014-01-09 20:44:57  2014-01-09 20:51:40                1   \n",
       "4          1  2014-01-09 20:47:09  2014-01-09 20:53:32                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0            0.7        -73.994770        40.736828         -73.982227   \n",
       "1            1.4        -73.982392        40.773382         -73.960449   \n",
       "2            2.3        -73.988570        40.739406         -73.986626   \n",
       "3            1.7        -73.960213        40.770464         -73.979863   \n",
       "4            0.9        -73.995371        40.717248         -73.984367   \n",
       "\n",
       "   dropoff_latitude  fare_amount  ...  total_amount  duration  Location  \\\n",
       "0         40.731790          6.5  ...          8.90       426         5   \n",
       "1         40.763995          8.5  ...         11.40       540         4   \n",
       "2         40.765217         11.5  ...         14.00       899         5   \n",
       "3         40.777050          7.5  ...         10.20       403         5   \n",
       "4         40.720524          6.0  ...          8.75       383         5   \n",
       "\n",
       "   time_of_day  CRD  UNK  NOC  DIS  CSH  tip_percent  \n",
       "0            4    1    0    0    0    0    18.666667  \n",
       "1            4    1    0    0    0    0    20.000000  \n",
       "2            4    1    0    0    0    0    12.000000  \n",
       "3            4    1    0    0    0    0    20.000000  \n",
       "4            4    1    0    0    0    0    25.000000  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.000000    1589107\n",
       "25.000000     206087\n",
       "0.000000      196794\n",
       "18.181818     150846\n",
       "14.285714     142446\n",
       "Name: tip_percent, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"tip_percent\"].value_counts().head() #use this as the number that pay 20 and 25 and 0!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7420cec50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.tip_percent.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tip_percent.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [0,5,10,15,20,25,30,150]\n",
    "l = [0,1,2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"tip_percent\"] = pd.cut(data[\"tip_percent\"], bins=b, labels=l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>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>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>Location</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>CRD</th>\n",
       "      <th>UNK</th>\n",
       "      <th>NOC</th>\n",
       "      <th>DIS</th>\n",
       "      <th>CSH</th>\n",
       "      <th>tip_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:45:25</td>\n",
       "      <td>2014-01-09 20:52:31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-73.994770</td>\n",
       "      <td>40.736828</td>\n",
       "      <td>-73.982227</td>\n",
       "      <td>40.731790</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>8.90</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:46:12</td>\n",
       "      <td>2014-01-09 20:55:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-73.982392</td>\n",
       "      <td>40.773382</td>\n",
       "      <td>-73.960449</td>\n",
       "      <td>40.763995</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>11.40</td>\n",
       "      <td>540</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:47</td>\n",
       "      <td>2014-01-09 20:59:46</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-73.988570</td>\n",
       "      <td>40.739406</td>\n",
       "      <td>-73.986626</td>\n",
       "      <td>40.765217</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>14.00</td>\n",
       "      <td>899</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:57</td>\n",
       "      <td>2014-01-09 20:51:40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-73.960213</td>\n",
       "      <td>40.770464</td>\n",
       "      <td>-73.979863</td>\n",
       "      <td>40.777050</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>10.20</td>\n",
       "      <td>403</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:47:09</td>\n",
       "      <td>2014-01-09 20:53:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-73.995371</td>\n",
       "      <td>40.717248</td>\n",
       "      <td>-73.984367</td>\n",
       "      <td>40.720524</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.75</td>\n",
       "      <td>383</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          1  2014-01-09 20:45:25  2014-01-09 20:52:31                1   \n",
       "1          1  2014-01-09 20:46:12  2014-01-09 20:55:12                1   \n",
       "2          1  2014-01-09 20:44:47  2014-01-09 20:59:46                2   \n",
       "3          1  2014-01-09 20:44:57  2014-01-09 20:51:40                1   \n",
       "4          1  2014-01-09 20:47:09  2014-01-09 20:53:32                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0            0.7        -73.994770        40.736828         -73.982227   \n",
       "1            1.4        -73.982392        40.773382         -73.960449   \n",
       "2            2.3        -73.988570        40.739406         -73.986626   \n",
       "3            1.7        -73.960213        40.770464         -73.979863   \n",
       "4            0.9        -73.995371        40.717248         -73.984367   \n",
       "\n",
       "   dropoff_latitude  fare_amount  ...  total_amount  duration  Location  \\\n",
       "0         40.731790          6.5  ...          8.90       426         5   \n",
       "1         40.763995          8.5  ...         11.40       540         4   \n",
       "2         40.765217         11.5  ...         14.00       899         5   \n",
       "3         40.777050          7.5  ...         10.20       403         5   \n",
       "4         40.720524          6.0  ...          8.75       383         5   \n",
       "\n",
       "   time_of_day  CRD  UNK  NOC  DIS  CSH  tip_percent  \n",
       "0            4    1    0    0    0    0            3  \n",
       "1            4    1    0    0    0    0            3  \n",
       "2            4    1    0    0    0    0            2  \n",
       "3            4    1    0    0    0    0            3  \n",
       "4            4    1    0    0    0    0            4  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EMiA2WProVOfxZ38c/XPNQNk6setryyPuW0mRUQDLhsm4+w6XuSfP10mOJit6CphMxBkwWJxRg53ozmPCVLdv0uS6Fank6vXasWprKrnG0dSGXdCtw0CHk/mHqRRsCbhN+rLFy4Fj074eBczu6QNUdVXn30XkT+TxmkpXQYNlk4jszvv9WI4A2nJWVfe2GthnUTvNfeyZyyK37lIjWw/tbptHaqpbSTvMvn2KO+moVzqeGbrRW7upLwGTiUhFjUSG7+tEhu80Ikm1Y6sm17+VSq5apx2rtqSSa0RTbQPRrXtAagTFPTByU7YN/OZpERF5HPgYsAj4s4gcifdzugdwEYCIPIC3BA7Ag8B3VVVFZLiqrvSf/yzwsr/9/sAdwAdF5PvpF3dFpBVvyeQk0KGqE/3nd8M7K4kBrcBpfo1fBC4HWlT1xO6+H0mbAtQt/9by74EP+cXuAXxeVV/K+uaQxeKJ7ZThgKPeUf2y++9nfhS5bXC1bNs/29ZfHr7n3JcHVE5Kf27we7rm2quTOMoeXbdPibv9jXEnzl826uN7q+PmfO6WarJdU++8pR1r1qaSb29OJdegyXer0C1DITmKwv/3cN0Ff3ngm9k2EpGngIWqOt2/5jIN+AHekdxg4MOqukJENuFd5L0PL1h+p6qzRORW4EC8A4BW4JuqulJEhuE1oL8e+GWGYJmoqmu71PJLYL2qNopIHBjSubaYiBwLfL+nYAl6V+h5EZmMt0i8AK+qanuQ9+bAMmBvQ/sucKqnu/+af3Hk9iHVsv3woO96raJip6OPtoGyxzV1zrPTH0jtFCzeEcy9k/Ze8sD2N8adODfXASPiRsUdGsMdGnOp3eE11VRSU23LNbl6dapj1SZNrk6lUu9Ukdq8OyRHsuPphSnrAm5XAwwHUNXtwHXAdf7ZwkIAEdkXSPrrqiMitwCnALNU9auZPlRVV4tIbw8CTub9U7Gb8U7DAi9aGPSu0ADg28DReGk4V0RmqqqJU5M3scHSheoZ7iPzLor839Aq2d6rpVHWOc7a7Y7sk+m1OROcQz/5fGruB1YwKdPr+Q6YTEQcV9who3CHjHIr9tvhNVVVTW1Yqck1q1LJtzdqx+qUpt6p1NSm3aBjJN4Pcj6szraBf4SyJ/BbEXH9sSyjgQQwHrjQP1qZCDyT9tasd4tEZBJeQ/zNGV5W4BERUeBaVb3Of37PztOqtKOewIJeY7kF7zzs9/7XXwZuBb7Qm52FpNXAPguSkEqd6T487weRO4cNkPaP9uUzHq2pXox3RyKjn3zFPeSmGckl0STjutsmPWCWxOrmLh39ibwHTCYiIuIOHo47eLjL+J1e19R7q1PJNau0Y9WGVHJVhybXV/hjdUaQeaxOX70dYJuhwLuqeuN/61NdBnxYREYA/xCRv9GHu0WqOheYICINGV4+yg+sYcA/RaRFVR8PUG+PggbLfqr6kbSvHxORF/u78z4qqxZ/mQip1NfdWfMuiPx1zwHS3q/1gR+uqe7xTtv2qFT/9Mvu1h/flmyXLNcyHE1W7LPkvknjWhMFFTDdEWfgMNcZOIzozpmpqc3rU8k1K72xOqvbvQGCGwfD9uHAbr3c1VsBtul6V+j9Wrwf/EXAJOBJdmzDMApY0ct6dvhs/8/VInIPcBjwOLCq82KwiAwnwFFXuqDBslBEjlDVeQAicjjeN2hCq6H9GueQSp7tJuadH7lreKV0hLLg+MuVFVkPcVtGS+3jH5LZk1/WY4N8ZrEFTCbiVO/mOmN3Izp2p9f8sTorUslV73pjddZ1jtXZCzTTf88l2fbn33FxRWSAqm4V7/b/OlXdIiJD8FZU/LX/g77RvzM7HzgD/0xCRM71P+vqQN+jSA3gqOpG/+/H8f60nfuAeqDR//PeIJ/ZKfC6QsAZItI552EM0CwiTXinsh/uzU77qeyOWBxSyXPc++d9L3L3iArpCG3JzvdENm4W+UCQbf94onPMwYuTL+6ylY9k39rzfsA80L4kdmJRBkwmWcbqvKfJdStSHavWa3LVtlRyjWpydZAjFoBH8K5j/guoBa7yr30I8CtVbfK3+xbeoNUqYJb/AO8W9E6/8EVkL7yRuIOAlIh8D2+FxqHAPf4srwhwh6o+5L+tEfiriHwNb65Try57BL3dvHNs72hDvoYOx+KJGAF+A5QCl2THt917538n8o+RFdIRC/vzH6ypXvDDYUN3GjDXnT3f0eW/m5ncRfp4/SElTvuS2Inzlo7+xDh13HLpqrZo+swpHwqyoYgcBJzf3d2dAO9/APicf0cpZ8K83dzjUYKIPA/kq43CcrzBPMU8YKpHLsmO70TueXq6e++YqCRztqj4rJrqrAO30q0aIqPuONZ5aursVJ9OwxxNRbscwZRDwLQE3VBVF4rIY513hXq7o55+0MPiD5C7DHiux+2CHLEE2NlCVT2o3x8UUCyeeBPvdKykROho/27k7/POce+PRSWZ8wbMR40Z+eIG1w18atNpxrUdT41cT7+v8ZTJEcwV02dO+d/sm5WWsPqb9D+deqcp+ybFI0JH+4WRO+e2VE5b9Z3IPyblI1S2w7YNjpN1VG4ml5zhHpCUvt+J6NR5BDN57vf2HPvmw3MllVze388sQHkfnV4IirVx0lzTBYQhSsf2iyJ3zG2pnLZ6euS+SRFJ5e239tNVA15FpE89hDdVyeCrPues1pDW794xYB4qtYB52nQBJoQVLPmelVrUwVJB+7ZLIrc93lw5bd03Iw9Mikgq73dKZg2sWd+f9y/4gHPgi+Ok3wOp0nkBc38pBcxb02dOCdo9rqQEDha/z+3/iMh3MvS7/XjIdWXzLMGmoheUSrZvvTRyy5zmyjPXfz3y4DERSQ03Vcv8AQN26e9n/PILzpFbo7waRj3pSihgyvJoBQIGi4hcijcRaXe8e983icglna93ts/Ll9bGuna8wUFFYQDbtv448uc5r1Se2XZW5KHJrsFAAUhCcq3rBBq/0pMOVyouPd2NaI5CPj1gYq2znijCgLHBksWXgUNV9TJVvQxvCvbU3JUVSKiH4blQxbbNP4vcMGdR5Vkb6iOPTHYlrx3UurWwsvI1RPp9xALQupfs8+BEeTaMz+qOo6no3q0PHF2EAfOU6QJMCRosrew4j6ES+E/o1fROwV5nqWLb5p9H/jTn5cqzNp0eeXSyKxmHeRvz0MDqXs37yObmT7rHrB8YuGF0n2UImGXZ32XMNuB500WYEnTk7T+AQ4F/4t1a/iTwBP7EJFX9nxzWmFEsnqjB6xFaMN3Yq9m66bLIzc9+wX38Q45otzOGTTt+1Ij5K6KRwP1aghiyUVdfc3XSdbzT5bxIidPeOvbT898cc9xYddxCW3j939NnTsn3tceCETRYelwsSVVvDq2iXojFE/PxZmMaVcOW934cvXnB55y5ExzRQulSn5GCfiQ2er16zYNCNeWF1PxzZqVCDawgUuJ0tI791Lw3xxxfSAFz/vSZU2aYLsKUoEP6jQRHALMxGCw1bNn4s+iNz53sPPVhR4LN/DWtpSL6hkrmxk799e8DncM/uTA1d5+3MzeGyhVHU5G9WxNHx96c1dE69lNPFEjA3G94/0b1eI1FRP7q/9kkIi91feSnxB7daWKnu7Cp7XfR389uqvxa8rPuk8c6or3tz2HMQzXVQWfa9knDVPfgdsdMa4vOgJk893vDY60PmrwG8+r0mVMWZ9sofUlVETlQRJ4WkUX+z9cX07b7s4gsSVs69cAsnztWRJ7zt10kIuekvXaI//O8WER+J/7UZhG5UkTeFpHv9+cb75Tt4u13/T+bgZPSHp+hF5OrcqW1sW4hXjfzvBjEexuujv529kuVZ/MZ9+ljHWHXfO07LLOrq3N6TWpbhdRc8SVnk0JHLvfTkwIImLsDbvffJVXx2kaeoaoH4K14+BsRSf/3daGqHug/XsjyuSuBI1X1QLyWJ3G/Cx14aw99A9jXf5wAoKoXAjMD1p1Vj8GStpTAeFV9M+3RyvvLD5h2W653MJj33v1j9DezX6z8hp7ozj9WJNS2hXn1ZjQSy/U+Fo11Dnjyg/JErveTzY4Bk3hCUh35GgUbNFim4jdQUtXXVPV1/+8r8G6M7NTIPAhV3a6qnZ0BK/F/zv1OcINU9Wn1Lq52NuIOXbZToW/5zZz263IatITCmVx1OzmaBLkrG9+5Nvrr2S9UfsP5tPtMUQcKwNJI5K3k+7+5curqk5xJmyoLY7KoFzAPHj157nkj8hAwb0yfOSXrbWbZeUnV9NcOw1uaNX1Ix+X+z94MCTDHS0RG+5crlgG/8MNqJF7bkU5Bl23ttWynQnfgnfrcx46nQoeo6um5KKi3WhvrlpFlhbjeGsKG9ddHr5y9sPKbkePdBceKMCjMzzflYW9hsrxIOeJeXO8OVq8Je0HIU8DcFHC7rkuqAv89qrgVOFNVOyd5XoR3hnAoXr/drMtwqOoyv7PjeKBevPXX+7Jsa59kOxVqU9VWVf1yl1OhvA7hDyCU06HdaFt3Y/SXs5+vPKfiE+7CY0UIZXRqoXi0piqU2chBrdxdxvx1kmOq6Xq3chgwHQRfz3yn5tkiMghvuY9LOvtLg3dJQj3b8IIr8J1Q/0ilsxH3ckJsxN2TYm2b0NXf6Md8laG8u+bmaOPs5yq/NWCK+8KxIgwMsbaC8VpFRd6bKd19tHP027sW5pyZ9IAZt+SBJyTV0d9+yvdPnzllZfbNvObZgOuv2dV5anQPcIuq3pW+rX8Ug38H5xTeXzr1MH/BMrpsP0pEqvy/dzbiftW/ZrpRRI7wP+sMetkkO6iSCJbWxroNeKdrvTKMd9bcGv35nGcrv10z2X3pWJG8LWCVd2tcZ027SLdrA+XSxfXu/kkh0A+cCY6mIuPenHX05LnnjexnwFzby+07m2cDnAYcA0zLcFv5dv9aZxPeKdTP/OfHkPkXai0w31+iZw47N+K+HliMdw1nVob391vBDIcPwS3AF7NuBezJ+tUzon9s/qjzymEiTM5xXQXhX9XV/6GPdxn6671qGfLbk53nz/tHai/Jf++ewDoDZuzShzveHHP8E61jTxitTiRbI/lOrXhB0RtXA+cD/1LV2+jmlF5Vp3Tz/sOBP2TY/p9AxpUzVHUB3hrsOVUSRyy+h6Dn3iDDWff2nRU/nTOv8txBR7qvTBahKk+1GfdIloXJcm1erXPwy2PDbQyVKzsewdz/ZMAjmD9NnzmlVxdCVXUh3uJ/fWoMr6oXqmood2dF5ErgdKBXDda7/bwwmmkXilg8cSZwY9fnR7B25W8q/vDaofLq4SIFsUh43h02dtSrWxxnv+xb5k6kQ7fdNCO5tLKDfU3W0VspcTreHHPc/NaxnxrVzRHMVmDs9JlTQp01XsxKLViieOeNowFGyZoVv4n+YfEh8trhIvSpv2sp2OBI21FjRu2CiPEj1H1W6utX/Dk5Rii+/x9pATNanUj6KhFXT5855TvGCitAxv+hhcnvLHfVaFn91j0Vl86dW/HdoROd144p51ABmFNd9XohhArAf4bLvo8cLPOyb1l4vFOkh45KO0VaCmwHfmm6tkJTUkcsAF+7+CdV10evelUE07NbC8b0PfeY/Xh11bGm6/gvVb3u98nnd93EIaZL6Q9FkstHTr7iuEevudR0LYWmIH6LhemGKy7dIsKvTNdRSBZWVhZWjxgRuWiaO1IhL8vy5oqg7aPfmn2d6ToKUckFi+9avDkSZW+bsHWjI0Yv2maybpDsdcNxTugd/vPsmtqW5mLpv5tXpRksDW3bgJ+aLqMQPFVV9SreqM6C88ghzhGtwzA+C7qPNgJXmC6iUJVmsHhuwusjU9Zm1VQX9OnGZae7H+lwKMZFvWbUtjSvNV1EoSrdYGlo6wDOJv/rSheUZ6oGFPTM7C2VskvjF5w2haTpWnphGdjreD0p3WABaGh7khC7YhWbDuhYZ3hQXBAv7e1MeGY/842heuHrtS3NBdMOohCVdrB44kBO+7wWqucHVL6KSFFMrJxxinPU5or8tRnth+trW5p7Oyeo7JR+sDS0bQC+bboMEx6sqSmaawApRyL/W+8O1JDmquTIMuAC00UUg9IPFoCGtvvweraUlaeqBxTVJMu3hsrYvx8phbxb4fdaAAALRUlEQVR64Nm1Lc0bTBdRDMojWDznUuQDsnpDQd923aKa7Afwl8nupNWDmW+6jgxurG1pfth0EcWifIKloW0VcKHpMvLllYqKxep1Dys6F9e741PCKtN1pFmO1zfFCqh8ggWgoe0GvL4tJW/WwOqC7diWzYYa2f33JznLtHCGCnyjtqW5zXQRxaS8gsXzFXZcVqEkzamqKurugE8e4ExsHk0hNIa6qbalOSftG0tZ+QVLQ9s7wMnAe6ZLyaWl0YiR/rZhuvxL7uHbXaO/BJqB7xncf9Eqv2ABaGhbhNehvFAOtUPVGoksTfmd3YtZe0QGNEx1O9TreZJv64HP2LtAfVOewQLQ0HYP73c7LykPDawumZndi0fKfv/+iOR7+ZAO4LTaluasC7tbmZVvsHguA+43XUTY/l1dndeFyXLtuk85kzZUsTCPuzyvtqX50Tzur+SUd7A0tCleZ/IW06WEaXFFdEz2rYqHijgXTXP3VMjHnZk/1rY0X52H/ZS08g4W6BzyfzIZ1tEtRqtdd3W7SNC1cIrGml1lxM0fd3I9l+huwDbFDoENFoCGtteA48nPb8SceqSmqmRvpT94mHPksqE8maOPfxyYWtvSXFKnkabYYOnU0PYM8EmK/MjlkZrqdtM15NIlX3UnJB3Cbgf5MnBybUtz1kXdRKRKROZ0LjImIsm0JVGzLvMrIseIyPMi0iEin+/yWr2IvO4/6tOef0xE3hORiX343oywwZKuoe1Zijxcmisq9jJdQy5tGSCDfnmqs04hrCOLV4ATaluag/4/Pwv4u6p2NqbaoqoH+o/PBHj/UmAacEf6kyKyG97NhMOBw4DL/AXdUdWPAQsC1lcQbLB01dC2APgERThhsc1x3t0qMt50Hbm2cLzzkefGy9wQPuppYFJtS3Nv+vVMBe7t6w5VtdVfFrVrMB4P/FNV16vqO8A/gRP6uh/TbLBk0tD2HEUYLo9VV71WKAuT5dpVn3OO3BLtV0/jBPCJ2pbm9UHfIF5T8r1VtTXt6QEiskBE5onIKf2oZyQ7riyx3H+uKJXFP8I+aWh7Hvg43gjMovBQTfUW0zXkS9KV6CVnuAMUNvfh7TcDp9S2NPf2vUPZ+TR5jKpOxJuD9hsR2acP9QBIhueKdmS4DZaeNLQtxAuXt02XEsSLAwpsYbIcWzZMxt13hPT22sMva1uap9W2NHf0YZdbgAHpT6jqCv/PN4DZwEF9+FzwjlDSV+8cBazo42cZZ4Mlm4a2F/AupuVz5GevbRHZ/J4U3sJkuXb7x9xj1u7CMwE2VeCC2pbmH/Z1X/61D1dEBgCIyBARqfT/PhQ4Cu9iMCLycxH5bC8+/mHgOP8zhwDH+c8VJRssQTS0LQOOpoDbWz5ZNeBVRKKm6zDhomnuuJSwpodN2oEzaluafx3C7h7B+7cAUAssEJEXgceARlV9xX9tAhmOdEXkUBFZDnwBuFZEFgGo6nq8Rfae9R8/8Z8rSjZYgmpo2wychndLsODOfWcNrCnbWbhtA2WPa+qc1m5eXg+cVNvSfFtIu7saqAdQ1adUdYKqfsT/84a07aKqutPkSVV9VlVHqWqNqu6uqgekvXajqo73HzeFVK8RNlh6o6FNaWj7CfBpYJ3pctI9O6CyoBcmy7U5E5xDXxtB11vQTwAHhtmrVlUXAo91DpDrYbvjw9qniDwG7I135FUURLXgfvkWh4bBY4C78K6/GNUO7QfHRrcjUm26FpMq2nXzTTOSq6JJxgI/By6rbWkuphUWS4Y9YumrhralwCTgKgwvD/qctzBZWYcKwPaoVF9xmrMcOK62pfkSGyrmFESwZJh/8ZCIvCsiD3TZbpyIzPfnUvzFH7DU0+funjbP4uour80WkVfT5nkM858/T0SWdt0+o4a27TS0fR84Anihl992aB4cWFNQp2UG3b4o5pxse6mYVxDBws7zL64Evpphu18AM1R1X7xRsV/L8rlbgR8B3+/m9alp8zxWA6jqDODSXlXvTQM4FPgBfRuw1S9PVRXXwmQ5sAY4tam+6fSm+qaiGi1dqgolWHaYf6GqjwI7LLotIgJM4f1bvjcDPQ6hVtVNqvoEXsDkVkNbBw1tVwIfIo/jD1KQWuW6ZTd+xZcErgcOaKpv+rvpYqz3GV8iopv5F5nsDryrqp0jJsOYS3GTiCTxGvz8TMO4kt3QtgQ4gYbBXwFmAMP6/Zk9eLmyYjEiH8jlPgrUQ8CFTfVNL5suxNpZIRyxZJp/kUnYcymmquoEvAuwk8h86tV3DW134A2gmkkObxPOqinehcn66CXg+Kb6pk/ZUClchRAsO82/6MZaYFcR6TzK6tdcClV9y/9zI15vjPBvGze0raeh7VvAeOCPQNZGQr31eHVVjxewS8gKvGtqBzXVNz1iuhirZ8aDpev8ix62U7xh051dt+rxr8uIyGdF5OdB9ykiEX9uB+INgz8Rr4tYbjS0LaWhbTreIKff4IVpKJZHIn2dTVss2oAG4ANN9U03NtU32daRRcD4NRZf5/yLfwGIyFxgf2CgP6/ia6r6MPBD4E4R+RnepMDOIdT7ABmHtItIKzAIqPD7ZRwHvAk87IeK6+/3T7n51tI0tK0AzqNh8M+BC4BvAwP7+nH/iUbeTJVg42zfIrzh87c21TdtMl2M1TsFMfJWRA4CzlfVPl3nEJHbgPNUtaeJaL35vGnARFU9N4zP61bD4N3xlvD8JrBHb9/+h10Hz505ZPCk0OsyJwncB/y+qb7pMdPFWH1XEMECICJnATenjWUxVcd5wDnA3ap6cV522jA4gtea8HS8pUgCjUs5dcReT75WWXFULkvLk3V4R4zXNNU3LTVdjNV/BRMslq9h8C7A5/BCZgo9XAc7KDZ6aYdIsS5OtgnvFPge4K6m+qbcjzWy8sYGSyFrGDwC+DJeyByY/tJK11153JiRxbbw+wq8JW3vAx5tqm8K/S6ZVRhssBSLhsGj8I5gpgAfv2XQLkuv3H3IkYarCuJFvCC5D3iuqb7J/oMrAzZYitQ514zf+8nqqqPxJkB+FK9jWY89QvLgHd7vgPYM8ExTfVNR9Au2wmWDpURMuHlCDfBBvLEy4/xH59/HAGG1rdyGN+lvFdACNOGNAWqyF16tTjZYysCEmye4eCOVxwF7AZVARQ8PwbtTswZY7f+5BljTVN+0sevnW1ZXNlgsywqd8SH9lmWVHhsslmWFzgaLlRPp7UZF5EAReVpEFonISyLyxbTtettu9JMi8pyINPl/Tkl7rf/tRq1Q2GCxciW93ehm4Ax/DZ0T8NY43tXfrrftRtcCJ/m9dOqBW7u8Hk67UatfbLBYufLfdqOq+pqqvu7/fQXenaY9+thudGHnesl4M6AHdC5zahUOGyxW6HpqNyoih+Hd0v4P/W83eiqwUFXTpwbc5J8G/cgPLssAGyxWLmRsNyoiw/FOXc5U1RT9aDcqIgfgnUZ9M+3p3LYbtQKzwWLlwk7tRkVkEJAALlHVef7TfWo3KiKj8GZFn6Gq/+l8Pi/tRq1AbLBYoevabtQ/NboHuEVV70rbrtftRv2LvgngIlV9Mu35/LYbtXpkg8XKlc52owCnAccA09JuBXe2gfghcL6ILMa75pKt3ei5eM3Jf9TltnIlXrvRl/BWpXyLfLQbtTKyQ/qtnCjbdqMWYI9YrBxR1YXAY53rcffh/aeHGCrnARfRTcN1K3z2iMWyrNDZIxbLskJng8WyrNDZYLEsK3Q2WCzLCp0NFsuyQmeDxbKs0NlgsSwrdDZYLMsKnQ0Wy7JCZ4PFsqzQ2WCxLCt0NlgsywqdDRbLskJng8WyrNDZYLEsK3Q2WCzLCp0NFsuyQmeDxbKs0P0/t7wKYrfgXrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.tip_percent.value_counts().plot.pie();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id               int64\n",
       "pickup_datetime        object\n",
       "dropoff_datetime       object\n",
       "passenger_count         int64\n",
       "trip_distance         float64\n",
       "pickup_longitude      float64\n",
       "pickup_latitude       float64\n",
       "dropoff_longitude     float64\n",
       "dropoff_latitude      float64\n",
       "fare_amount           float64\n",
       "surcharge             float64\n",
       "mta_tax               float64\n",
       "tip_amount            float64\n",
       "tolls_amount          float64\n",
       "total_amount          float64\n",
       "duration                int64\n",
       "Location                int64\n",
       "time_of_day             int64\n",
       "CRD                     int64\n",
       "UNK                     int64\n",
       "NOC                     int64\n",
       "DIS                     int64\n",
       "CSH                     int64\n",
       "tip_percent          category\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c73ef62198>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.tip_amount.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [0,0.5,1.0,1.5,2.0,2.5,3,3.5,4,4.5,5]\n",
    "l= [0,1,2,3,4,5,6,7,8,9]\n",
    "data[\"tip_amount_binned\"] = pd.cut(data[\"tip_amount\"], bins=b, labels=l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.tip_amount_binned.value_counts().plot.pie();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>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>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>duration</th>\n",
       "      <th>Location</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>CRD</th>\n",
       "      <th>UNK</th>\n",
       "      <th>NOC</th>\n",
       "      <th>DIS</th>\n",
       "      <th>CSH</th>\n",
       "      <th>tip_percent</th>\n",
       "      <th>tip_amount_binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:45:25</td>\n",
       "      <td>2014-01-09 20:52:31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-73.994770</td>\n",
       "      <td>40.736828</td>\n",
       "      <td>-73.982227</td>\n",
       "      <td>40.731790</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:46:12</td>\n",
       "      <td>2014-01-09 20:55:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-73.982392</td>\n",
       "      <td>40.773382</td>\n",
       "      <td>-73.960449</td>\n",
       "      <td>40.763995</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>540</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:47</td>\n",
       "      <td>2014-01-09 20:59:46</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-73.988570</td>\n",
       "      <td>40.739406</td>\n",
       "      <td>-73.986626</td>\n",
       "      <td>40.765217</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>899</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:57</td>\n",
       "      <td>2014-01-09 20:51:40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-73.960213</td>\n",
       "      <td>40.770464</td>\n",
       "      <td>-73.979863</td>\n",
       "      <td>40.777050</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>403</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:47:09</td>\n",
       "      <td>2014-01-09 20:53:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-73.995371</td>\n",
       "      <td>40.717248</td>\n",
       "      <td>-73.984367</td>\n",
       "      <td>40.720524</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>383</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          1  2014-01-09 20:45:25  2014-01-09 20:52:31                1   \n",
       "1          1  2014-01-09 20:46:12  2014-01-09 20:55:12                1   \n",
       "2          1  2014-01-09 20:44:47  2014-01-09 20:59:46                2   \n",
       "3          1  2014-01-09 20:44:57  2014-01-09 20:51:40                1   \n",
       "4          1  2014-01-09 20:47:09  2014-01-09 20:53:32                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0            0.7        -73.994770        40.736828         -73.982227   \n",
       "1            1.4        -73.982392        40.773382         -73.960449   \n",
       "2            2.3        -73.988570        40.739406         -73.986626   \n",
       "3            1.7        -73.960213        40.770464         -73.979863   \n",
       "4            0.9        -73.995371        40.717248         -73.984367   \n",
       "\n",
       "   dropoff_latitude  fare_amount  ...  duration  Location  time_of_day  CRD  \\\n",
       "0         40.731790          6.5  ...       426         5            4    1   \n",
       "1         40.763995          8.5  ...       540         4            4    1   \n",
       "2         40.765217         11.5  ...       899         5            4    1   \n",
       "3         40.777050          7.5  ...       403         5            4    1   \n",
       "4         40.720524          6.0  ...       383         5            4    1   \n",
       "\n",
       "   UNK  NOC  DIS  CSH  tip_percent  tip_amount_binned  \n",
       "0    0    0    0    0            3                  2  \n",
       "1    0    0    0    0            3                  3  \n",
       "2    0    0    0    0            2                  2  \n",
       "3    0    0    0    0            3                  3  \n",
       "4    0    0    0    0            4                  3  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id            False\n",
       "pickup_datetime      False\n",
       "dropoff_datetime     False\n",
       "passenger_count      False\n",
       "trip_distance        False\n",
       "pickup_longitude     False\n",
       "pickup_latitude      False\n",
       "dropoff_longitude    False\n",
       "dropoff_latitude     False\n",
       "fare_amount          False\n",
       "surcharge            False\n",
       "mta_tax              False\n",
       "tip_amount           False\n",
       "tolls_amount         False\n",
       "total_amount         False\n",
       "duration             False\n",
       "Location             False\n",
       "time_of_day          False\n",
       "CRD                  False\n",
       "UNK                  False\n",
       "NOC                  False\n",
       "DIS                  False\n",
       "CSH                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.tip_percent.fillna(0, inplace=True)\n",
    "data.tip_amount_binned.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding location dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b= \n",
    "l = ['Financial District','Tribeca','Greenwich','Midtown','Upper East Side','Other']\n",
    "## Financial Distric (0), Tribeca (1), Greenwich (2), Midtown (3), Upper East Side (4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = data['Location']\n",
    "ll=[]\n",
    "\n",
    "for i in loc:\n",
    "  if i==0:\n",
    "    ll.append(\"Financial District\")\n",
    "  elif i==1:\n",
    "    ll.append(\"Tribeca\")\n",
    "  elif i==2:\n",
    "    ll.append(\"Greenwich\")\n",
    "  elif i==3:\n",
    "    ll.append(\"Midtown\")\n",
    "  elif i==4:\n",
    "    ll.append(\"Upper East Side\")\n",
    "  elif i==5:\n",
    "    ll.append(\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Location']= ll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other', 'Upper East Side', 'Midtown', 'Greenwich', 'Tribeca',\n",
       "       'Financial District'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_types = data.Location.unique()\n",
    "unique_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>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>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>UNK</th>\n",
       "      <th>NOC</th>\n",
       "      <th>DIS</th>\n",
       "      <th>CSH</th>\n",
       "      <th>Other</th>\n",
       "      <th>Upper East Side</th>\n",
       "      <th>Midtown</th>\n",
       "      <th>Greenwich</th>\n",
       "      <th>Tribeca</th>\n",
       "      <th>Financial District</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:45:25</td>\n",
       "      <td>2014-01-09 20:52:31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-73.994770</td>\n",
       "      <td>40.736828</td>\n",
       "      <td>-73.982227</td>\n",
       "      <td>40.731790</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:46:12</td>\n",
       "      <td>2014-01-09 20:55:12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-73.982392</td>\n",
       "      <td>40.773382</td>\n",
       "      <td>-73.960449</td>\n",
       "      <td>40.763995</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:47</td>\n",
       "      <td>2014-01-09 20:59:46</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-73.988570</td>\n",
       "      <td>40.739406</td>\n",
       "      <td>-73.986626</td>\n",
       "      <td>40.765217</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:44:57</td>\n",
       "      <td>2014-01-09 20:51:40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-73.960213</td>\n",
       "      <td>40.770464</td>\n",
       "      <td>-73.979863</td>\n",
       "      <td>40.777050</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-09 20:47:09</td>\n",
       "      <td>2014-01-09 20:53:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-73.995371</td>\n",
       "      <td>40.717248</td>\n",
       "      <td>-73.984367</td>\n",
       "      <td>40.720524</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          1  2014-01-09 20:45:25  2014-01-09 20:52:31                1   \n",
       "1          1  2014-01-09 20:46:12  2014-01-09 20:55:12                1   \n",
       "2          1  2014-01-09 20:44:47  2014-01-09 20:59:46                2   \n",
       "3          1  2014-01-09 20:44:57  2014-01-09 20:51:40                1   \n",
       "4          1  2014-01-09 20:47:09  2014-01-09 20:53:32                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0            0.7        -73.994770        40.736828         -73.982227   \n",
       "1            1.4        -73.982392        40.773382         -73.960449   \n",
       "2            2.3        -73.988570        40.739406         -73.986626   \n",
       "3            1.7        -73.960213        40.770464         -73.979863   \n",
       "4            0.9        -73.995371        40.717248         -73.984367   \n",
       "\n",
       "   dropoff_latitude  fare_amount  ...  UNK  NOC  DIS  CSH  Other  \\\n",
       "0         40.731790          6.5  ...    0    0    0    0      1   \n",
       "1         40.763995          8.5  ...    0    0    0    0      0   \n",
       "2         40.765217         11.5  ...    0    0    0    0      1   \n",
       "3         40.777050          7.5  ...    0    0    0    0      1   \n",
       "4         40.720524          6.0  ...    0    0    0    0      1   \n",
       "\n",
       "   Upper East Side  Midtown  Greenwich  Tribeca  Financial District  \n",
       "0                0        0          0        0                   0  \n",
       "1                1        0          0        0                   0  \n",
       "2                0        0          0        0                   0  \n",
       "3                0        0          0        0                   0  \n",
       "4                0        0          0        0                   0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for unique in unique_types:\n",
    "    data[unique] = data.Location.apply(lambda x: 1 if x == unique else 0)\n",
    "data = data.drop(columns=['Location'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"data/taxi_FINAL_FINAL.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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