EDA-NYC-Taxi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples in train file :  1458644\n",
      "Total number of samples in test file :  625134\n"
     ]
    }
   ],
   "source": [
    "# Read the train and test data file\n",
    "train = pd.read_csv('Taxi-Trip-Duration-Data/train.csv')\n",
    "test = pd.read_csv('Taxi-Trip-Duration-Data/test.csv')\n",
    "print(\"Total number of samples in train file : \", train.shape[0])\n",
    "print(\"Total number of samples in test file : \", test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A view of the train dataframe\n",
      "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
      "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
      "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
      "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
      "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
      "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
      "\n",
      "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
      "0                1        -73.982155        40.767937         -73.964630   \n",
      "1                1        -73.980415        40.738564         -73.999481   \n",
      "2                1        -73.979027        40.763939         -74.005333   \n",
      "3                1        -74.010040        40.719971         -74.012268   \n",
      "4                1        -73.973053        40.793209         -73.972923   \n",
      "\n",
      "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
      "0         40.765602                  N            455  \n",
      "1         40.731152                  N            663  \n",
      "2         40.710087                  N           2124  \n",
      "3         40.706718                  N            429  \n",
      "4         40.782520                  N            435  \n",
      "\n",
      "Columns in train dataset :  Index(['id', 'vendor_id', 'pickup_datetime', 'dropoff_datetime',\n",
      "       'passenger_count', 'pickup_longitude', 'pickup_latitude',\n",
      "       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',\n",
      "       'trip_duration'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "Overall description of the train dataset : \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1458644 entries, 0 to 1458643\n",
      "Data columns (total 11 columns):\n",
      "id                    1458644 non-null object\n",
      "vendor_id             1458644 non-null int64\n",
      "pickup_datetime       1458644 non-null object\n",
      "dropoff_datetime      1458644 non-null object\n",
      "passenger_count       1458644 non-null int64\n",
      "pickup_longitude      1458644 non-null float64\n",
      "pickup_latitude       1458644 non-null float64\n",
      "dropoff_longitude     1458644 non-null float64\n",
      "dropoff_latitude      1458644 non-null float64\n",
      "store_and_fwd_flag    1458644 non-null object\n",
      "trip_duration         1458644 non-null int64\n",
      "dtypes: float64(4), int64(3), object(4)\n",
      "memory usage: 122.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Let's look at the train data first\n",
    "print(\"A view of the train dataframe\")\n",
    "print(train.head())\n",
    "print(\"\\nColumns in train dataset : \", train.columns)\n",
    "print(\"\\n\")\n",
    "print(\"Overall description of the train dataset : \")\n",
    "print(train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique id in train dataset :  1458644\n",
      "Number of unique id in test dataset :  625134\n",
      "Number of common id in the train and test datasets :  0\n"
     ]
    }
   ],
   "source": [
    "# Okay  we have an overview of the train dataset. Before exploring further, let's see if the id column has some overlap with the test ids or not\n",
    "train_id = set(train['id'].values)\n",
    "test_id = set(test['id'].values)\n",
    "print(\"Number of unique id in train dataset : \", len(train_id))\n",
    "print(\"Number of unique id in test dataset : \", len(test_id))\n",
    "common_ids = train_id.intersection(test_id)\n",
    "print(\"Number of common id in the train and test datasets : \", len(common_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No overlap in train and test ids. Hence we can drop the id column for now until unless we figure out that there actually was some overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longest trip duration 3526282 or 58771 minutes: \n",
      "Smallest trip duration 1 or 0 minutes: \n",
      "Average trip duration : 959.4922729603659 or 15.0 minutes\n"
     ]
    }
   ],
   "source": [
    "# Let's have a look at the traget variable(trip duration) first\n",
    "target = train['trip_duration']\n",
    "print(\"Longest trip duration {} or {} minutes: \" .format(np.max(target.values), np.max(target.values)//60))\n",
    "print(\"Smallest trip duration {} or {} minutes: \".format(np.min(target.values),np.min(target.values)//60))\n",
    "print(\"Average trip duration : {} or {} minutes\".format(np.mean(target.values), np.mean(target.values)//60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did the rider just opened the door and got out at once? And 58771 minutes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109edc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization is always better \n",
    "f = plt.figure(figsize=(8,6))\n",
    "plt.scatter(range(len(target)), np.sort(target.values), alpha=0.5)\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Trip duration in seconds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are four outliers in the datset. Should we drop them? We will see about that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique vendors :  2\n",
      "How popular is the vendor? \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e94588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Moving on to the vendor_id column\n",
    "unique_vendors = set(train['vendor_id'].values)\n",
    "print(\"Number of unique vendors : \", len(unique_vendors))\n",
    "print(\"How popular is the vendor? \")\n",
    "vendor_popularity = train['vendor_id'].value_counts()\n",
    "\n",
    "f = plt.figure(figsize=(10,5))\n",
    "sns.barplot(vendor_popularity.index, vendor_popularity.values, alpha=0.7)\n",
    "plt.xlabel('Vendor', fontsize=14)\n",
    "plt.ylabel('Trips offered', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of passengers on a trip :  9\n",
      "Minimum number of passengers on a trip :  0\n",
      "Average number of passengers on a trip :  1.6645295219395548\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1040cf198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Moving to passengers count column\n",
    "pass_count = train['passenger_count']\n",
    "print(\"Maximum number of passengers on a trip : \", np.max(pass_count.values))\n",
    "print(\"Minimum number of passengers on a trip : \", np.min(pass_count.values))\n",
    "print(\"Average number of passengers on a trip : \", np.mean(pass_count.values))\n",
    "\n",
    "f = plt.figure(figsize=(10,5))\n",
    "pass_count = train['passenger_count'].value_counts()\n",
    "sns.barplot(pass_count.index, pass_count.values, alpha=0.7)\n",
    "plt.xlabel('Number of passengers on a trip', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9 passengers on a trip. This may be the case where someone hired a taxi for many days. Does this belong to the same data points where our outliers for the trip duration exist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1040d64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's move to the store_and_fwd_flag column\n",
    "flags = train['store_and_fwd_flag'].value_counts()\n",
    "\n",
    "f = plt.figure(figsize=(10,5))\n",
    "sns.barplot(flags.index, flags.values, alpha=0.7)\n",
    "plt.xlabel('Flags', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look to the pickup and dropoff columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['pickup_datetime'] = pd.to_datetime(train['pickup_datetime'])\n",
    "train['dropoff_datetime'] = pd.to_datetime(train['dropoff_datetime'])\n",
    "\n",
    "train['pickup_day'] = train['pickup_datetime'].dt.day\n",
    "train['pickup_month'] = train['pickup_datetime'].dt.month\n",
    "train['pickup_weekday'] = train['pickup_datetime'].dt.weekday\n",
    "train['pickup_hour'] = train['pickup_datetime'].dt.hour\n",
    "\n",
    "train['drop_day'] = train['dropoff_datetime'].dt.day\n",
    "train['drop_month'] = train['dropoff_datetime'].dt.month\n",
    "train['drop_weekday'] = train['dropoff_datetime'].dt.weekday\n",
    "train['drop_hour'] = train['dropoff_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick-Ups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac9a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the number of pickups looks same for the whole month? Let's find out\n",
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='pickup_day', data=train)\n",
    "plt.xlabel('Day of month', fontsize=14)\n",
    "plt.ylabel('Pickup count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. No surprising thing here. Let's see how many pickups were there for each month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117892668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='pickup_month', data=train)\n",
    "plt.xlabel('Month', fontsize=14)\n",
    "plt.ylabel('Pickup count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expected because there is no major festive season around these months. Let's move on to the number of pickups on different days of the week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135eada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "days = [i for i in range(7)]\n",
    "sns.countplot(x='pickup_weekday', data=train)\n",
    "plt.xlabel('Day of the week', fontsize=14)\n",
    "plt.ylabel('Pickup count', fontsize=14)\n",
    "plt.xticks(days, ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about hour of the day? Let's check that too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad21a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='pickup_hour', data=train)\n",
    "plt.xlabel('Hour', fontsize=14)\n",
    "plt.ylabel('Pickup count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything looks good here too. As expected, the number of pickups gradually decreases after mid-night. The highest number of pickups are around 6pm and 7pm in the evening which makes sense as many people are on their way to home from office."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165e0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(10,8))\n",
    "days = [i for i in range(7)]\n",
    "sns.countplot(x='pickup_weekday', data=train, hue='pickup_hour', alpha=0.8)\n",
    "plt.xlabel('Day of the week', fontsize=14)\n",
    "plt.ylabel('Pickup count', fontsize=14)\n",
    "plt.xticks(days, ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))\n",
    "plt.legend(loc=(1.04,0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahh nice plot. Did you notice the peaks at mid-night on Saturday and Sunday? This should be fine because most of the people are doing parties on weekends.\n",
    "\n",
    "### Drop-Offs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127e46a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='drop_day', data=train)\n",
    "plt.xlabel('Day of month', fontsize=14)\n",
    "plt.ylabel('Dropoff count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb49eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='drop_month', data=train)\n",
    "plt.xlabel('Month', fontsize=14)\n",
    "plt.ylabel('Dropoff count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PdQxJkiRJ0ioatqfyNmDZBO3LgDumo5DWw7hS12lO9zGSHJ7k/CTnL1++fCZLkSRJkqR5YdhQ+QG66xfXGWtot9/QlvX18zZ0lfb7uta+DHj4wHqbtLap2jeZoH2qY9xLVX2kqnaoqh0WLVrU+0FJkiRJ0kIxbKjcmW7ynGVJzkpyFt0Q06cCOyY5ZexnJY9/CjA2g+vBwJcG2g9qs8DuDNzUhrAuAfZIsl6boGcPYElbdnOSndusrweN29dEx5AkSZIkraJhr6m8nu7ayUE/XpkDJfkUsDuwQZJr6GZxfRvw2SSHAVcB+7fVT6ULrEuB24FDAarqhiRHA+e19d5cVWOT/7yUbobZdYCvtB+mOIYkSZIkaRUNFSqr6tBVPVBVHTjJoqdMsG4BL5tkP4uBxRO0nw9sNUH7LyY6hiRJkiRp1Q3bUwlAkkcAW9JNdnN5VV05I1VJkiRJkkbCUKEyybrAscBfA7+7uzknA4dV1S0zVJ8kSZIkaQ4bdqKe9wFbA0+iu2ZxHbohpVsD752Z0iRJkiRJc92wofIZwAur6r+r6jft5yzgcGDfGatOkiRJkjSnDRsq1wF+MUH7DcDa01eOJEmSJGmUDBsqzwaOTnL/sYYkDwDeBJwzE4VJkiRJkua+YWd/fSXwVWBZkkta22PpvkNyz5koTJIkSZI09w37PZWXJtkCeC7wqNZ8InBSVd0xU8VJkiRJkua2FYbKJPcFPgG8tqo+OvMlSZIkSZJGxQqvqayq3wB7ADXz5UiSJEmSRsmwE/V8AXjWTBYiSZIkSRo9w07U81Pg9UmeCJwP3Da4sKrePd2FSZIkSZLmvmFD5SHAjcDW7WdQAYZKSZIkSVqAhp39dfOZLkSSJEmSNHqGvaZSkiRJkqR7WWGoTLJOkiOTXJLk1iS3JPlektcnWWd1FClJkiRJmpumHP6aZE3g68B2wFeB/wICbAm8Edg7yW5VdddMFypJkiRJmntWdE3l4cAjge2q6vuDC5JsBZwJvAg4ZmbKkyRJkiTNZSsa/rof8JbxgRKgqi4D3go8eyYKkyRJkiTNfSsKlY+hG/46ma8BW01fOZIkSZKkUbKiULkesHyK5cuBB09fOZIkSZKkUbKiULkGMNUkPL9r60iSJEmSFqAVTdQT4BNJfj3J8vtNcz2SJEmSpBGyolB5/BD7OGE6CpEkSZIkjZ4pQ2VVHbq6CpEkSZIkjZ4VXVMpSZIkSdKkDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3gyVkiRJkqTeDJWSJEmSpN4MlZIkSZKk3uZEqEzykySXJrk4yfmtbf0kpye5ov1er7UnyfuTLE1ySZLtBvZzcFv/iiQHD7Rv3/a/tG2b1f8oJUmSJGn+mROhsnlSVW1bVTu0+0cAZ1TVFsAZ7T7A3sAW7edw4BjoQihwJLATsCNw5FgQbeu8aGC7vWb+4UiSJEnS/DeXQuV4+wDHt9vHA/sOtJ9QnXOBByfZCNgTOL2qbqiqG4HTgb3asnWr6tyqKuCEgX1JkiRJklbBXAmVBZyW5IIkh7e2DavqZ+32tcCG7fbGwNUD217T2qZqv2aCdkmSJEnSKlpztgto/ryqliV5KHB6kh8OLqyqSlIzXUQLtIcDbLrppjN9OEmSJEkaeXOip7KqlrXf1wFfpLsm8udt6Crt93Vt9WXAwwc236S1TdW+yQTtE9Xxkaraoap2WLRo0ao+LEmSJEma92Y9VCZ5QJIHjd0G9gAuA04BxmZwPRj4Urt9CnBQmwV2Z+CmNkx2CbBHkvXaBD17AEvaspuT7NxmfT1oYF+SJEmSpFUwF4a/bgh8sX3Lx5rAJ6vqq0nOAz6b5DDgKmD/tv6pwFOBpcDtwKEAVXVDkqOB89p6b66qG9rtlwLHAesAX2k/kiRJkqRVNOuhsqquBLaZoP0XwFMmaC/gZZPsazGweIL284GtVrlYSZIkSdI9zPrwV0mSJEnS6DJUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSejNUSpIkSZJ6M1RKkiRJknozVEqSJEmSelswoTLJXkl+lGRpkiNmux5JkiRJmg8WRKhMsgbwIWBvYEvgwCRbzm5VkiRJkjT6FkSoBHYEllbVlVV1J/BpYJ9ZrkmSJEmSRt5CCZUbA1cP3L+mtUmSJEmSVkGqarZrmHFJ9gP2qqoXtvvPB3aqqpePW+9w4PB290+BH63WQmfXBsD1s12EZoTndn7z/M5fntv5zfM7v3l+56+Fdm7/qKoWrWilNVdHJXPAMuDhA/c3aW33UFUfAT6yuoqaS5KcX1U7zHYdmn6e2/nN8zt/eW7nN8/v/Ob5nb88txNbKMNfzwO2SLJ5krWAA4BTZrkmSZIkSRp5C6KnsqruSvJyYAmwBrC4qr4/y2VJkiRJ0shbEKESoKpOBU6d7TrmsAU57HeB8NzOb57f+ctzO795fuc3z+/85bmdwIKYqEeSJEmSNDMWyjWVkiRJkqQZYKiUJEmSJPVmqBxhSSrJJwbur5lkeZL/nKb9H5Xk1dOxLw0nyUOSXNx+rk2ybOD+WjNwvG8l2Xa697tQJXlPkr8buL8kyccG7r8rySuH3NeMPv+SHJLkgzO1/4ViiufsL5P8YDUc3/M4i5L8duD8X5xkswnWeViSz0+y/VlJ/GqCWZbkdUm+n+SSdh53mmLdQ5I8bBqO6bmfBStzrldin75fZgFN1DNP3QZslWSdqroD+Esm+P5NjY6q+gWwLXQvUsCtVfXOWS1KK+NsYH/gvUnuQ/cFyesOLP8z4O9nozDNjMmesy1c9P6AL8maVXXXdNSoGXVHVU36wVw7j/8H7Lcaa9JKSPIE4GnAdlX16yQbAFN9iHsIcBnwfytxDJ/Pc0CPc62VYE/l6DsV+Kt2+0DgU2MLkqyf5D/apzHnJtm6tR+VZHH7lOzKJP9vYJvXJfmfJN8C/nSg/UVJzkvyvSQnJ7l/kgcl+XGS+7Z11h28r+mT5JFJLh64f0SS17fbW7QesQuSfCPJn7T2A5Jc1s7Zma3t/kk+l+TyJCcDaw/s8yNJzm+f4L2xte0x+Al7kr2TfG41PexRdA7whHb7MXRvPG5Jsl6S+wGPBi5M8pr2fLokyZvGNp7i+XdWkrcn+W5b/sTWvkaSfx3Y14tb+0bt/8LF7f/A2PqHtu2/C+wysP+nJ/lOkouSfC3Jhknuk+SKJIvaOvdJsnTsvoayRpKPtufUaUnWgXv2UCTZIMlP2u1DkpyS5OvAGZ7H0TTBedwsyWVt2TpJPt1eg78IrDOw3TEDr8Fvam1PTvIfA+v8ZdtO02cj4Pqq+jVAVV1fVf+X5I3ttfWy9vcxSfYDdgBOas/LdZL8JF04IckOSc5qt49KcmKSs4ETPfdzwmTneqpz6PvlIRkqR9+ngQOSrA1sDXxnYNmbgIuqamvgtcAJA8seBewJ7AgcmeS+SbYHDqD71P2pwOMH1v9CVT2+qrYBLgcOq6pbgLO4O9Qe0Nb7zTQ/Rk3tI8BLq2p74J+AsaFwRwJPaefsma3t5cCNVfVo4J+Bxw3s54iq2gHYBvjLJFsCXwO2TvKQts6hwOIZfTQjrPVI3JVkU7peyW/TPSefQPdG5FJgd2ALuufetsD2SXZdwfMPYM2q2hH4O7pzC3AYcFNVPb6t/6IkmwPPAZa0HpRtgIuTbET3mrAL8OfAlgP7/hawc1U9ju415R+q6nfAJ4DntnX+AvheVS1ftX+lBWUL4ENV9Rjgl8BfD7HNdsB+VbUbnsdRsE7uHvo6+IZ/8DwO+hvg9vYafCSw/cCy17XX4K2B3dJ9EHwm8KiBDwF8DZ5+pwEPbwHhw0nGztkH2/ueregC4NOq6vPA+cBzq2rbNkpsKlsCf1FVB+K5nwsmO9dT8f3ykAyVI66qLgE2o+ulHP89nH8OnNjW+zrwkCRjQ/H+q6p+XVXXA9cBGwJPBL5YVbdX1c3AKQP72irJN5NcSvfm5DGt/WN0L3S03x+fzsenqSV5MLAzcHK6nswPAWPXepwNnJDkhdz9XN+V7g0mVXUR8P2B3R2Y5ELgQroetS3bG9KTgOckWZ/uj+BpM/uoRt45dIFyLFR+e+D+2cAe7eciun/rR9GFj6mefwBfaL8voHvO0/ZzUDv33wEe0vZ1HnBouuGYj21/0HYCzqqq5VV1J/CZgX1vAixpz+/XcPfzezFwULv9Anx+r6wfV9XYCIPB8zaV06vqhnbb8zj33dHCxbZV9cyB9sHzOGjwNfgS4JKBZfu31+CL6M7dltV979uJwPPa6/0TgK/MxANZqKrqVrq/bYcDy4HPJDkEeFLr+b8UeDJ3P59WxikDwdNzP8umONdT8f3ykLymcn44BXgnXQ/IQ6Ze9fd+PXD7t6z4/8JxwL5V9b32BNwdoKrObkN7dgfWqKrLhq5aK+Mu7vkh0NqtLXRDOSa6pudFdG9An0Y35PJxE6wDdENogVcAO1bVL9NNADU2NHYxcHK7/Zmq+u0qPZL572y6APlYuuGvVwOvAm6m+yOyG/DWqvr3wY0yMMHPJMaes4PP1wB/W1VLxq+cZFe6T0WPS/LudvzJfAB4d1Wd0p7LRwFU1dVJfp7kyXSf0j538l1oAuNfZ8eGuw0+n9fmnm4bu1FV3/A8jqzbVrzK3doIg1cDj6+qG5Mcx93/Nz4OfBn4FfA5r82bfu3v2lnAWS0MvJiu13CH9vw5ins/V8cM9XyejOd+9ZrgXB/M1OfQ98tDsqdyflgMvKmqLh3X/k3am4f2n/j69onKZL4B7NvG/T8IePrAsgcBP2vjv8e/ITkB+CTz8FOXOeRa4GHprs1bmzaEoqpupDsvz4TfXy+1TdvmEVV1LvAG4EZgY7pz/Jy27jbc/QnausAtwM1teN2eYweuqquB64Ej6F4sNbVz6IL8DVX129ZbMfYp8znAEuAFSR4IkGTjJA9l6uffZJYAfzNwncafJHlAkj8Cfl5VH6X7dHQ7up7M3dLNVnpf4NkD+/kD7p7k6+Bxx/gY3afrn/MDhWnzE+4e+jbpBC6ex3lp8DV4K7rgAt1r8G3ATUk2BPYe26ANq/8/4PX4d3baJfnT9sHqmG2BH7Xb17fX6sHn6S1074nG/IS7n89TDXH33M+ySc71VQx/Dsf4fnkC9lTOA1V1DfD+CRYdBSxOcglwO/d+kzF+Pxcm+QzwPbou/vMGFr+B7s3M8vZ78AX1JLrr8z6FZkRV/SrJv9Bdy7EMGPyqggOAY9onqWvRvXH8HvCe9glogNOq6rIkVwLHJ7mcbujrRW0fF7Z9/pDuBfbscSV8Eli3qv5nJh7fPHMp3ayvnxzX9sA2fOa0JI8Gvp0E4FbgeSt4/k3mY3RDKi9Mt7PlwL50n4y+Jslv2v4Pqqqftf8j36a7vu/igf0cBXwuyY3A14HNB5YTiL4xAAAHDklEQVSdQvcHcF7+EZwl7wQ+m+Rw4L+mWG93PI/zzTHAx9tr8OV0w6JpvRoX0b0GX829X4NPAhZV1eWrs9gF4oHAB9oQ07uApXTDI39JN9rkWu75enwc8G9J7qD7sPBNwLFJjqbrAZuM5372TXauH81w5xDw/fJk0g3ZlvpLNxvaPlX1/NmuRTMjyb8B366q42e7Fq1e6WYpfU9VPXG2a1F/nsfRlu67SC+qqmNnuxatXp77+WO+v1+2p1KrJMkH6IZqPHW2a9HMaJPA3Aj8vxWtq/klyRF0MxZ6Dd4I8zyOtiQX0A2PfNVs16LVy3M/fyyE98v2VEqSJEmSenOiHkmSJElSb4ZKSZIkSVJvhkpJkiRJUm+GSkmSZkiSP0xyWpLbkqzUJAZJqs0WONKS7N4eywazXYskaWYYKiVJIynJcS2sVJLfJLkuyZlJXta+eHoueDXwMLov2d5oohWSHJXkstValSRJ08hQKUkaZV+jC2ubAXsAX6b7MvJvJnnALNY15pHABVV1RVVdO9vFSJI0EwyVkqRR9uuquraqllXVxVX1bmB3YDvgH8ZWSvK8JOcluaX1aH4uycZtWZIsTfLqwR0n2aL1gm432cGTvLhte2f7/aKBZT8B9gEOavs5boLtDwGOBB4z0Ot6yMAq67dab0tyZZLnjdt+4ySfTnJj+/mvJFtMUe/bknx14P4L2zEPGGj7VpLXD9x/epILkvwqyY+TvCXJWgPL10ry9iTXJLm9/TvvOUUN90vyxSQXJnnoZOtJkkaHoVKSNK9U1WXAV4G/Hmheiy68bQM8DdgA+FRbv4BjgUPH7eoFwMVVdeFEx0nyTOCDwHuBrYD3AR9O8vS2yuPpelI/S9eb+ooJdvMZ4F3Aj9o6G7W2MW8EvtTq/gywOMmm7fj3B84EfgXsBjwB+BnwtbZsImcBuyRZs93fHbi+/R7b5+PberRweFJ7nI9p/yb7Af8ysM+Pt+M/p/07HA98Ock24w+eZF26c7M+sHtVXTdJnZKkEWKolCTNRz8AHjF2p6oWV9WpVXVlVX0X+BvgiUk2aat8HPiTJDsDJFkDOIgubE7m1cCJVfXBqvqfqvoAXQD7x3bM5cCvgTtab+pN43dQVXcAtwJ3tXWubW1jTqyqT1TVUuANwF3Arm3ZAUCAQ6vqkqr6IfBi4IF0wXki3wLWpguO0IXBdwJPavf/rB3ju+3+64B/raqPV9X/VtWZ7fG9pPXw/jFwILB/VX2j/ft+EDi11TLooXQh+BZgz6q6eZIaJUkjxlApSZqPAvx+ttUk2yX5UpKrktwCnN8WbQrQrnf8T7qeOIC96HrTTpriGI8Gzh7X9i1gy1Uv//cuGbtRVXcBy+nCGcD2wObALUluTXIrcBOwHvDHE+2sqm4FLgB2T/JI4A+ADwGbJtmIrsfy21V158AxXje2/3aMTwIPAP6QbphxgB+MW+evJqhhCXAN8Kyq+lXvfxFJ0pyz5opXkSRp5GwJXAnQJuxZQjcU9fnAdXTDX79JNyx2zMeATyb5O7pw+cWqurHHsVfqq0NW4DcT7HvsA+H7ABfT9ViOd8MU+zyLrmdyOfDNqro1yXda2+50w1PH3Idu4qPPTbCf5W150fV8jq/1jnH3/xPYn26I7MVT1CdJGjGGSknSvJJkK7qexn9uTY+iC5Gvraoft3WeNcGmXwVuBl4CPB146goOdTmwC/ccIvvndENvV8adwBoruQ3AhXRDT6+vql+uxHZnAX8L3Nhuj7X9FV04PGLcMR7Vht/eS5KL6Hoq/7ANjZ3KG+jC7hlJnlJVBktJmicc/ipJGmX3S/KHSR6WZJskr6QLSBfQXSsI8FO6axtfnuQRSf4KOHr8jqrqt8Bi4K3AMuCMFRz7X4Hnt+/F3CLJ3wLPBd6xko/hJ8AftSG6GyS535DbnQT8HPhSkt2SbJ5k1yTvmmoGWLohumsBz6K7xhG6f7P9uef1lABvBp6T5M1JtkryqCT7JXkHQFX9T6vjuNb+iCQ7JHn1RMG9ql4H/DvdZEL3mshHkjSaDJWSpFH2F3Qznv6ULgQ+AzgK2LWqboPfT5hzMLAvXS/ikcArJ9nfYrrA9fE2K+ykquo/6Hr8/r7t9xXAS6vqyyv5GE6mm9jmDLohpQcOs1FV3U43ac+VdMNTf0g38+p6dL2Qk203dl3lbcBFrflc4Lfc83pKqmoJXQ/mk+jC5nfpejJ/OrDLQ+kmOnpHq+E/W11XTXL81wIfpeuxNFhK0jyQFfzNlCRpwUiyE93kO4+oqp+uaH1JkmSolCSJNuR0EV1P5U1V9exZLkmSpJHh8FdJkrohp1fRTegz2dBYSZI0AXsqJUmSJEm92VMpSZIkSerNUClJkiRJ6s1QKUmSJEnqzVApSZIkSerNUClJkiRJ6u3/A1CrgjW8oI6nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e8ae320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "days = [i for i in range(7)]\n",
    "sns.countplot(x='drop_weekday', data=train)\n",
    "plt.xlabel('Day of the week', fontsize=14)\n",
    "plt.ylabel('Dropoff count', fontsize=14)\n",
    "plt.xticks(days, ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b70f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='drop_hour', data=train)\n",
    "plt.xlabel('Hour', fontsize=14)\n",
    "plt.ylabel('Dropoff count', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a614198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(10,8))\n",
    "days = [i for i in range(7)]\n",
    "sns.countplot(x='drop_weekday', data=train, hue='drop_hour', alpha=0.8)\n",
    "plt.xlabel('Day of the week', fontsize=14)\n",
    "plt.ylabel('Dropoff count', fontsize=14)\n",
    "plt.xticks(days, ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))\n",
    "plt.legend(loc=(1.04,0))\n",
    "plt.show()"
   ]
  }
 ],
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