01_data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Агрегация исходных данных  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "plt.rcParams[\"figure.figsize\"] = (15,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Загрузите данные за май 2016 года"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi = pd.read_csv(\"yellow_tripdata_2016-05.csv\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:17:31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.60</td>\n",
       "      <td>-73.985901</td>\n",
       "      <td>40.768040</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.983986</td>\n",
       "      <td>40.730099</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:07:31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-73.991577</td>\n",
       "      <td>40.744751</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.975700</td>\n",
       "      <td>40.765469</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:07:01</td>\n",
       "      <td>6</td>\n",
       "      <td>1.09</td>\n",
       "      <td>-73.993073</td>\n",
       "      <td>40.741573</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.980995</td>\n",
       "      <td>40.744633</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:19:47</td>\n",
       "      <td>1</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-73.991943</td>\n",
       "      <td>40.684601</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.002258</td>\n",
       "      <td>40.733002</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:06:39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-74.005280</td>\n",
       "      <td>40.740192</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.997498</td>\n",
       "      <td>40.737564</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:05:19</td>\n",
       "      <td>2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-73.979294</td>\n",
       "      <td>40.755764</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.988014</td>\n",
       "      <td>40.758469</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:15:43</td>\n",
       "      <td>1</td>\n",
       "      <td>6.68</td>\n",
       "      <td>-73.981544</td>\n",
       "      <td>40.780739</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.937805</td>\n",
       "      <td>40.855343</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:06:31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.48</td>\n",
       "      <td>-73.987198</td>\n",
       "      <td>40.750507</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.980797</td>\n",
       "      <td>40.767826</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>1.59</td>\n",
       "      <td>-74.001610</td>\n",
       "      <td>40.740990</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.981079</td>\n",
       "      <td>40.730675</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3.52</td>\n",
       "      <td>-74.013855</td>\n",
       "      <td>40.714119</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.994049</td>\n",
       "      <td>40.761452</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2016-05-01 00:00:00   2016-05-01 00:17:31                1   \n",
       "1         2  2016-05-01 00:00:00   2016-05-01 00:07:31                1   \n",
       "2         2  2016-05-01 00:00:00   2016-05-01 00:07:01                6   \n",
       "3         2  2016-05-01 00:00:00   2016-05-01 00:19:47                1   \n",
       "4         2  2016-05-01 00:00:00   2016-05-01 00:06:39                1   \n",
       "5         2  2016-05-01 00:00:00   2016-05-01 00:05:19                2   \n",
       "6         2  2016-05-01 00:00:00   2016-05-01 00:15:43                1   \n",
       "7         2  2016-05-01 00:00:00   2016-05-01 00:06:31                1   \n",
       "8         2  2016-05-01 00:00:00   2016-05-01 00:00:00                4   \n",
       "9         2  2016-05-01 00:00:00   2016-05-01 00:00:00                3   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  RatecodeID  \\\n",
       "0           3.60        -73.985901        40.768040           1   \n",
       "1           1.68        -73.991577        40.744751           1   \n",
       "2           1.09        -73.993073        40.741573           1   \n",
       "3           4.21        -73.991943        40.684601           1   \n",
       "4           0.56        -74.005280        40.740192           1   \n",
       "5           0.63        -73.979294        40.755764           1   \n",
       "6           6.68        -73.981544        40.780739           1   \n",
       "7           1.48        -73.987198        40.750507           1   \n",
       "8           1.59        -74.001610        40.740990           1   \n",
       "9           3.52        -74.013855        40.714119           1   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type  \\\n",
       "0                  N         -73.983986         40.730099             1   \n",
       "1                  N         -73.975700         40.765469             1   \n",
       "2                  N         -73.980995         40.744633             1   \n",
       "3                  N         -74.002258         40.733002             1   \n",
       "4                  N         -73.997498         40.737564             1   \n",
       "5                  N         -73.988014         40.758469             1   \n",
       "6                  N         -73.937805         40.855343             1   \n",
       "7                  N         -73.980797         40.767826             1   \n",
       "8                  N         -73.981079         40.730675             1   \n",
       "9                  N         -73.994049         40.761452             2   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0         15.0    0.5      0.5        1.50           0.0   \n",
       "1          7.5    0.5      0.5        0.88           0.0   \n",
       "2          6.5    0.5      0.5        1.56           0.0   \n",
       "3         17.0    0.5      0.5        3.66           0.0   \n",
       "4          6.0    0.5      0.5        1.46           0.0   \n",
       "5          5.0    0.5      0.5        0.00           0.0   \n",
       "6         20.5    0.5      0.5        2.00           0.0   \n",
       "7          7.0    0.5      0.5        1.66           0.0   \n",
       "8          8.0    0.5      0.5        0.00           0.0   \n",
       "9         13.0    0.5      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3         17.80  \n",
       "1                    0.3          9.68  \n",
       "2                    0.3          9.36  \n",
       "3                    0.3         21.96  \n",
       "4                    0.3          8.76  \n",
       "5                    0.3          6.30  \n",
       "6                    0.3         23.80  \n",
       "7                    0.3          9.96  \n",
       "8                    0.3          9.30  \n",
       "9                    0.3         14.30  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Проведите фильтрацию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалите поездки с нулевой длительностью, нулевым количеством пассажиров, нулевым расстоянием поездки по счётчику, координатами начала, не попадающими в прямоугольник Нью-Йорка (Нью-Йорк вписан в прямоугольник от -74.25559 до -73.70001 градусов долготы и от 40.49612 до 40.91553 широты)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon_west = -74.25559\n",
    "lon_east = -73.70001\n",
    "lat_south = 40.49612\n",
    "lat_north = 40.91553"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new = taxi.loc[(taxi['trip_distance']!=0)&(taxi['passenger_count']!=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new = taxi_new.loc[(taxi_new['tpep_pickup_datetime']!= taxi_new['tpep_dropoff_datetime'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(taxi_new['tpep_pickup_datetime']== taxi_new['tpep_dropoff_datetime']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pickup_longitude - долгота, pickup_latitude - широта "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new = taxi_new.loc[(taxi_new['pickup_longitude']>= lon_west)&(taxi_new['pickup_longitude']<= lon_east)&\n",
    "                        (taxi_new['pickup_latitude']>= lat_south)&(taxi_new['pickup_latitude']<= lat_north)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:17:31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.60</td>\n",
       "      <td>-73.985901</td>\n",
       "      <td>40.768040</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.983986</td>\n",
       "      <td>40.730099</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:07:31</td>\n",
       "      <td>1</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-73.991577</td>\n",
       "      <td>40.744751</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.975700</td>\n",
       "      <td>40.765469</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:07:01</td>\n",
       "      <td>6</td>\n",
       "      <td>1.09</td>\n",
       "      <td>-73.993073</td>\n",
       "      <td>40.741573</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.980995</td>\n",
       "      <td>40.744633</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:19:47</td>\n",
       "      <td>1</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-73.991943</td>\n",
       "      <td>40.684601</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.002258</td>\n",
       "      <td>40.733002</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-01 00:00:00</td>\n",
       "      <td>2016-05-01 00:06:39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-74.005280</td>\n",
       "      <td>40.740192</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.997498</td>\n",
       "      <td>40.737564</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11836837</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-31 23:53:01</td>\n",
       "      <td>2016-06-01 00:00:24</td>\n",
       "      <td>2</td>\n",
       "      <td>1.89</td>\n",
       "      <td>-73.947876</td>\n",
       "      <td>40.774818</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.974274</td>\n",
       "      <td>40.779797</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11836838</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-31 23:53:02</td>\n",
       "      <td>2016-06-01 00:19:36</td>\n",
       "      <td>1</td>\n",
       "      <td>15.21</td>\n",
       "      <td>-73.789467</td>\n",
       "      <td>40.646942</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.893929</td>\n",
       "      <td>40.731426</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>51.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11836839</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-31 23:53:02</td>\n",
       "      <td>2016-05-31 23:59:07</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>-74.008659</td>\n",
       "      <td>40.719723</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.999580</td>\n",
       "      <td>40.743649</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11836840</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-31 23:53:03</td>\n",
       "      <td>2016-06-01 00:05:47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.28</td>\n",
       "      <td>-73.981644</td>\n",
       "      <td>40.758862</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.952637</td>\n",
       "      <td>40.766491</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11836841</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-19 03:29:16</td>\n",
       "      <td>2016-05-19 03:31:24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-73.948959</td>\n",
       "      <td>40.586658</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>-73.948570</td>\n",
       "      <td>40.594837</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11626521 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0                1  2016-05-01 00:00:00   2016-05-01 00:17:31   \n",
       "1                2  2016-05-01 00:00:00   2016-05-01 00:07:31   \n",
       "2                2  2016-05-01 00:00:00   2016-05-01 00:07:01   \n",
       "3                2  2016-05-01 00:00:00   2016-05-01 00:19:47   \n",
       "4                2  2016-05-01 00:00:00   2016-05-01 00:06:39   \n",
       "...            ...                  ...                   ...   \n",
       "11836837         2  2016-05-31 23:53:01   2016-06-01 00:00:24   \n",
       "11836838         2  2016-05-31 23:53:02   2016-06-01 00:19:36   \n",
       "11836839         2  2016-05-31 23:53:02   2016-05-31 23:59:07   \n",
       "11836840         2  2016-05-31 23:53:03   2016-06-01 00:05:47   \n",
       "11836841         1  2016-05-19 03:29:16   2016-05-19 03:31:24   \n",
       "\n",
       "          passenger_count  trip_distance  pickup_longitude  pickup_latitude  \\\n",
       "0                       1           3.60        -73.985901        40.768040   \n",
       "1                       1           1.68        -73.991577        40.744751   \n",
       "2                       6           1.09        -73.993073        40.741573   \n",
       "3                       1           4.21        -73.991943        40.684601   \n",
       "4                       1           0.56        -74.005280        40.740192   \n",
       "...                   ...            ...               ...              ...   \n",
       "11836837                2           1.89        -73.947876        40.774818   \n",
       "11836838                1          15.21        -73.789467        40.646942   \n",
       "11836839                1           1.80        -74.008659        40.719723   \n",
       "11836840                1           2.28        -73.981644        40.758862   \n",
       "11836841                1           0.60        -73.948959        40.586658   \n",
       "\n",
       "          RatecodeID store_and_fwd_flag  dropoff_longitude  dropoff_latitude  \\\n",
       "0                  1                  N         -73.983986         40.730099   \n",
       "1                  1                  N         -73.975700         40.765469   \n",
       "2                  1                  N         -73.980995         40.744633   \n",
       "3                  1                  N         -74.002258         40.733002   \n",
       "4                  1                  N         -73.997498         40.737564   \n",
       "...              ...                ...                ...               ...   \n",
       "11836837           1                  N         -73.974274         40.779797   \n",
       "11836838           1                  N         -73.893929         40.731426   \n",
       "11836839           1                  N         -73.999580         40.743649   \n",
       "11836840           1                  N         -73.952637         40.766491   \n",
       "11836841           1                  Y         -73.948570         40.594837   \n",
       "\n",
       "          payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0                    1         15.0    0.5      0.5        1.50           0.0   \n",
       "1                    1          7.5    0.5      0.5        0.88           0.0   \n",
       "2                    1          6.5    0.5      0.5        1.56           0.0   \n",
       "3                    1         17.0    0.5      0.5        3.66           0.0   \n",
       "4                    1          6.0    0.5      0.5        1.46           0.0   \n",
       "...                ...          ...    ...      ...         ...           ...   \n",
       "11836837             1          8.0    0.5      0.5        1.86           0.0   \n",
       "11836838             1         42.0    0.5      0.5        8.66           0.0   \n",
       "11836839             1          7.5    0.5      0.5        1.76           0.0   \n",
       "11836840             1         11.0    0.5      0.5        2.46           0.0   \n",
       "11836841             2          4.0    0.5      0.5        0.00           0.0   \n",
       "\n",
       "          improvement_surcharge  total_amount  \n",
       "0                           0.3         17.80  \n",
       "1                           0.3          9.68  \n",
       "2                           0.3          9.36  \n",
       "3                           0.3         21.96  \n",
       "4                           0.3          8.76  \n",
       "...                         ...           ...  \n",
       "11836837                    0.3         11.16  \n",
       "11836838                    0.3         51.96  \n",
       "11836839                    0.3         10.56  \n",
       "11836840                    0.3         14.76  \n",
       "11836841                    0.3          5.30  \n",
       "\n",
       "[11626521 rows x 19 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Агрегируйте данные  по часам и областям "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>region</th>\n",
       "      <th>west</th>\n",
       "      <th>east</th>\n",
       "      <th>south</th>\n",
       "      <th>north</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-74.25559</td>\n",
       "      <td>-74.244478</td>\n",
       "      <td>40.496120</td>\n",
       "      <td>40.504508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-74.25559</td>\n",
       "      <td>-74.244478</td>\n",
       "      <td>40.504508</td>\n",
       "      <td>40.512896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-74.25559</td>\n",
       "      <td>-74.244478</td>\n",
       "      <td>40.512896</td>\n",
       "      <td>40.521285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-74.25559</td>\n",
       "      <td>-74.244478</td>\n",
       "      <td>40.521285</td>\n",
       "      <td>40.529673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-74.25559</td>\n",
       "      <td>-74.244478</td>\n",
       "      <td>40.529673</td>\n",
       "      <td>40.538061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region      west       east      south      north\n",
       "0       1 -74.25559 -74.244478  40.496120  40.504508\n",
       "1       2 -74.25559 -74.244478  40.504508  40.512896\n",
       "2       3 -74.25559 -74.244478  40.512896  40.521285\n",
       "3       4 -74.25559 -74.244478  40.521285  40.529673\n",
       "4       5 -74.25559 -74.244478  40.529673  40.538061"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = pd.read_csv(\"regions.csv\", sep=';')\n",
    "region.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regions_function(x):\n",
    "    d_lon = (lon_east - lon_west) / 50\n",
    "    d_lat = (lat_north - lat_south) / 50\n",
    "    return int(min((x[0] - lon_west) // d_lon, 49) * 50  + min((x[1] - lat_south) // d_lat, 49))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new['region'] = taxi_new[['pickup_longitude', 'pickup_latitude']].apply(regions_function, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new['tpep_pickup_datetime'] = pd.to_datetime(taxi_new['tpep_pickup_datetime']).dt.strftime('%Y-%m-%d %H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_new['trips_number'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_agg = taxi_new.groupby(['tpep_pickup_datetime', 'region'], as_index=False).agg({'trips_number':'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Выведите первые строки полученной таблицы "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "с указанием времени, идентификатора зоны и количества поездок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(taxi_agg['trips_number']==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>region</th>\n",
       "      <th>trips_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01 00</td>\n",
       "      <td>1014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-01 00</td>\n",
       "      <td>1016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-01 00</td>\n",
       "      <td>1028</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-01 00</td>\n",
       "      <td>1029</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 00</td>\n",
       "      <td>1032</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime  region  trips_number\n",
       "0        2016-05-01 00    1014             1\n",
       "1        2016-05-01 00    1016             1\n",
       "2        2016-05-01 00    1028             3\n",
       "3        2016-05-01 00    1029             1\n",
       "4        2016-05-01 00    1032             4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1217"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_agg['region'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_agg.to_csv('taxi_agg_2016.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Постройте график количества поездок жёлтого такси из ячейки, содержащей Empire State Building\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "esb_latitude = 40.748817\n",
    "esb_longitude = -73.985428"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1231.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(region.shape[0]):\n",
    "    reg, west, east, south, north = region.iloc[i]\n",
    "    if (east>=esb_longitude >= west)&(north>=esb_latitude>=south):\n",
    "        reg_esb = reg\n",
    "        print(reg_esb)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "esb_df = taxi_agg.loc[taxi_agg['region']== reg_esb].set_index('tpep_pickup_datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tpep_pickup_datetime'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "esb_df['trips_number'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Посчитайте, сколько  пар час-ячейка, для которых не было совершено ни одной поездки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество непредставленных пар: 1718238\n"
     ]
    }
   ],
   "source": [
    "# если бы поездки были совершены в каждый час в каждом районе, то их количество было бы:\n",
    "n = 50*50*24*31\n",
    "print(f\"Количество непредставленных пар: {n - taxi_agg.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}