MTA_EDA_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b813f370",
   "metadata": {},
   "source": [
    "# Data Science T5 MTA EDA Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721b55ef",
   "metadata": {},
   "source": [
    "![TLC logo](https://user-images.githubusercontent.com/87195262/134807871-6389592c-65f6-4fda-81be-86e3923bf1d9.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd6fc4c",
   "metadata": {},
   "source": [
    "## Needed Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "181ff49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy import inspect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd63424",
   "metadata": {},
   "source": [
    "## MTA Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e529f5f1",
   "metadata": {},
   "source": [
    "### 1. Load data from MTA website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd82570b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialise the number of weeks for the dataset\n",
    "num_weeks = 12\n",
    "\n",
    "#initialise the date for the first week of the dataset (week ending on start_date)\n",
    "start_date = filedate = pd.Timestamp('2019-10-12 00:00:00')\n",
    "\n",
    "#initialise the regex for the MTA turnstile url\n",
    "filename_regex = \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_{}.txt\"\n",
    "\n",
    "filelist = []\n",
    "\n",
    "for numfiles in range (num_weeks):\n",
    "\n",
    "    # create the appropriate filename for the week\n",
    "    filedate_str = str(filedate.year)[2:4] + str(filedate.month).zfill(2) + str(filedate.day).zfill(2)\n",
    "    filename = filename_regex.format(filedate_str)\n",
    "\n",
    "    # read the file and append it to the list of files to be concacated\n",
    "    df = pd.read_csv(filename, keep_date_col=True)\n",
    "    filelist.append(df)\n",
    "\n",
    "    # advance to the next week\n",
    "    filedate += pd.Timedelta(days=7)\n",
    "\n",
    "mta_test = pd.concat(filelist, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36efe2",
   "metadata": {},
   "source": [
    "### 2. Connect to sqlalchemy engine and store database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9e9ccde",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///MTA_data.db\")\n",
    "mta_test.to_sql(\"MTA_data\",engine, if_exists='replace', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece1e711",
   "metadata": {},
   "source": [
    "#### Check database tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3d2a4c95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MTA_data']\n"
     ]
    }
   ],
   "source": [
    "all_tables = inspect(engine).get_table_names()\n",
    "print(all_tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69973f26",
   "metadata": {},
   "source": [
    "### 2. Preveiw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c27e2a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
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       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224009</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
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       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224019</td>\n",
       "      <td>2447067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224033</td>\n",
       "      <td>2447074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  00:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  04:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  08:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  12:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  16:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  7224009   \n",
       "1  REGULAR  7224019   \n",
       "2  REGULAR  7224033   \n",
       "3  REGULAR  7224095   \n",
       "4  REGULAR  7224095   \n",
       "\n",
       "   EXITS                                                                 \n",
       "0                                            2447060                     \n",
       "1                                            2447067                     \n",
       "2                                            2447074                     \n",
       "3                                            2447082                     \n",
       "4                                            2447082                     "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data = pd.read_sql('SELECT * FROM MTA_data;', engine) # Reading table from sql database\n",
    "mta_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eec90cb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2474417 entries, 0 to 2474416\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                                                Dtype \n",
      "---  ------                                                                ----- \n",
      " 0   C/A                                                                   object\n",
      " 1   UNIT                                                                  object\n",
      " 2   SCP                                                                   object\n",
      " 3   STATION                                                               object\n",
      " 4   LINENAME                                                              object\n",
      " 5   DIVISION                                                              object\n",
      " 6   DATE                                                                  object\n",
      " 7   TIME                                                                  object\n",
      " 8   DESC                                                                  object\n",
      " 9   ENTRIES                                                               int64 \n",
      " 10  EXITS                                                                 int64 \n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 207.7+ MB\n"
     ]
    }
   ],
   "source": [
    "mta_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a2a23d0a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.474417e+06</td>\n",
       "      <td>2.474417e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.173024e+07</td>\n",
       "      <td>3.420674e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.144032e+08</td>\n",
       "      <td>1.965842e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.106830e+05</td>\n",
       "      <td>1.438140e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.981168e+06</td>\n",
       "      <td>1.169443e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.638394e+06</td>\n",
       "      <td>4.542663e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.129051e+09</td>\n",
       "      <td>2.123717e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            ENTRIES  \\\n",
       "count  2.474417e+06   \n",
       "mean   4.173024e+07   \n",
       "std    2.144032e+08   \n",
       "min    0.000000e+00   \n",
       "25%    3.106830e+05   \n",
       "50%    1.981168e+06   \n",
       "75%    6.638394e+06   \n",
       "max    2.129051e+09   \n",
       "\n",
       "       EXITS                                                                 \n",
       "count                                       2.474417e+06                     \n",
       "mean                                        3.420674e+07                     \n",
       "std                                         1.965842e+08                     \n",
       "min                                         0.000000e+00                     \n",
       "25%                                         1.438140e+05                     \n",
       "50%                                         1.169443e+06                     \n",
       "75%                                         4.542663e+06                     \n",
       "max                                         2.123717e+09                     "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fa61da",
   "metadata": {},
   "source": [
    "### 3. Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced5095d",
   "metadata": {},
   "source": [
    "#### 3.1 Check nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20585dcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C/A                                                                     0\n",
       "UNIT                                                                    0\n",
       "SCP                                                                     0\n",
       "STATION                                                                 0\n",
       "LINENAME                                                                0\n",
       "DIVISION                                                                0\n",
       "DATE                                                                    0\n",
       "TIME                                                                    0\n",
       "DESC                                                                    0\n",
       "ENTRIES                                                                 0\n",
       "EXITS                                                                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "mta_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318623df",
   "metadata": {},
   "source": [
    "#### 3.2 Strip columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b3def43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES',\n",
       "       'EXITS                                                               '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking columns names\n",
    "mta_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bde6031b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES', 'EXITS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix the columns names\n",
    "mta_data.columns = [column.strip() for column in mta_data.columns]\n",
    "mta_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5185234",
   "metadata": {},
   "source": [
    "#### 3.3 Concat Data and Time columns into a new datatime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a4f7795b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
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       "      <td>00:00:00</td>\n",
       "      <td>REGULAR</td>\n",
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       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224019</td>\n",
       "      <td>2447067</td>\n",
       "      <td>2019-10-05 04:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224033</td>\n",
       "      <td>2447074</td>\n",
       "      <td>2019-10-05 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 16:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  00:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  04:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  08:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  12:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  10/05/2019  16:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           DATE_TIME  \n",
       "0  REGULAR  7224009  2447060 2019-10-05 00:00:00  \n",
       "1  REGULAR  7224019  2447067 2019-10-05 04:00:00  \n",
       "2  REGULAR  7224033  2447074 2019-10-05 08:00:00  \n",
       "3  REGULAR  7224095  2447082 2019-10-05 12:00:00  \n",
       "4  REGULAR  7224095  2447082 2019-10-05 16:00:00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data[\"DATE_TIME\"] = pd.to_datetime(mta_data.DATE + \" \" + mta_data.TIME)\n",
    "mta_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31785517",
   "metadata": {},
   "source": [
    "#### 3.4 Check duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d9e9171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2030973</th>\n",
       "      <td>R285</td>\n",
       "      <td>R308</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>MT EDEN AV</td>\n",
       "      <td>2019-11-04 15:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030959</th>\n",
       "      <td>R285</td>\n",
       "      <td>R308</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>MT EDEN AV</td>\n",
       "      <td>2019-11-02 08:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225993</th>\n",
       "      <td>R514</td>\n",
       "      <td>R094</td>\n",
       "      <td>00-05-00</td>\n",
       "      <td>ASTORIA BLVD</td>\n",
       "      <td>2019-12-24 19:00:00</td>\n",
       "      <td>BMT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225737</th>\n",
       "      <td>R514</td>\n",
       "      <td>R094</td>\n",
       "      <td>00-03-00</td>\n",
       "      <td>ASTORIA BLVD</td>\n",
       "      <td>2019-12-17 03:00:00</td>\n",
       "      <td>BMT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225738</th>\n",
       "      <td>R514</td>\n",
       "      <td>R094</td>\n",
       "      <td>00-03-00</td>\n",
       "      <td>ASTORIA BLVD</td>\n",
       "      <td>2019-12-17 07:00:00</td>\n",
       "      <td>BMT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP       STATION           DATE_TIME DIVISION  \\\n",
       "2030973  R285  R308  00-00-00    MT EDEN AV 2019-11-04 15:00:00      IRT   \n",
       "2030959  R285  R308  00-00-00    MT EDEN AV 2019-11-02 08:00:00      IRT   \n",
       "2225993  R514  R094  00-05-00  ASTORIA BLVD 2019-12-24 19:00:00      BMT   \n",
       "2225737  R514  R094  00-03-00  ASTORIA BLVD 2019-12-17 03:00:00      BMT   \n",
       "2225738  R514  R094  00-03-00  ASTORIA BLVD 2019-12-17 07:00:00      BMT   \n",
       "\n",
       "         EXITS  \n",
       "2030973      2  \n",
       "2030959      2  \n",
       "2225993      2  \n",
       "2225737      2  \n",
       "2225738      2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicates\n",
    "(mta_data\n",
    " .groupby([\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\",\"DIVISION\"])\n",
    " .EXITS.count()\n",
    " .reset_index()\n",
    " .sort_values(\"EXITS\", ascending=False)).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c547406c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>EXITS</th>\n",
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       "      <th>0</th>\n",
       "      <td>A002</td>\n",
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       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>2019-10-05 00:00:00</td>\n",
       "      <td>BMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649555</th>\n",
       "      <td>R138</td>\n",
       "      <td>R293</td>\n",
       "      <td>00-02-02</td>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>2019-12-07 09:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649557</th>\n",
       "      <td>R138</td>\n",
       "      <td>R293</td>\n",
       "      <td>00-02-02</td>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>2019-12-07 17:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649558</th>\n",
       "      <td>R138</td>\n",
       "      <td>R293</td>\n",
       "      <td>00-02-02</td>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>2019-12-07 21:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649559</th>\n",
       "      <td>R138</td>\n",
       "      <td>R293</td>\n",
       "      <td>00-02-02</td>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>2019-12-08 01:00:00</td>\n",
       "      <td>IRT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP         STATION           DATE_TIME DIVISION  \\\n",
       "0        A002  R051  02-00-00           59 ST 2019-10-05 00:00:00      BMT   \n",
       "1649555  R138  R293  00-02-02  34 ST-PENN STA 2019-12-07 09:00:00      IRT   \n",
       "1649557  R138  R293  00-02-02  34 ST-PENN STA 2019-12-07 17:00:00      IRT   \n",
       "1649558  R138  R293  00-02-02  34 ST-PENN STA 2019-12-07 21:00:00      IRT   \n",
       "1649559  R138  R293  00-02-02  34 ST-PENN STA 2019-12-08 01:00:00      IRT   \n",
       "\n",
       "         EXITS  \n",
       "0            1  \n",
       "1649555      1  \n",
       "1649557      1  \n",
       "1649558      1  \n",
       "1649559      1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixing duplicets\n",
    "mta_data.drop_duplicates([\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\",\"DIVISION\"], inplace=True)\n",
    "\n",
    "# checking again\n",
    "(mta_data\n",
    " .groupby([\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\",\"DIVISION\"])\n",
    " .EXITS.count()\n",
    " .reset_index()\n",
    " .sort_values(\"EXITS\", ascending=False)).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32de5db2",
   "metadata": {},
   "source": [
    "#### 3.5 Remove unneeded columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2b605f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing unneeded columns\n",
    "mta_data = mta_data.drop([\"DESC\"], axis=1, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03c986b",
   "metadata": {},
   "source": [
    "### 4. Calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755d14f6",
   "metadata": {},
   "source": [
    "#### 4.1 Create a new column PARK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "33db9ed9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2019-10-05 04:00:00</td>\n",
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       "      <th>2</th>\n",
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       "      <td>7224033</td>\n",
       "      <td>2447074</td>\n",
       "      <td>2019-10-05 08:00:00</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
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       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 12:00:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 16:00:00</td>\n",
       "      <td>False</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474412</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 04:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474413</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 08:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474414</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 12:00:00</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2474415</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 16:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474416</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
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       "      <td>20:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2474340 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "0         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "1         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "2         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "3         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "4         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "...        ...   ...       ...            ...      ...      ...         ...   \n",
       "2474412  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474413  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474414  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474415  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474416  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME   PARK  \n",
       "0        00:00:00  7224009  2447060 2019-10-05 00:00:00  False  \n",
       "1        04:00:00  7224019  2447067 2019-10-05 04:00:00  False  \n",
       "2        08:00:00  7224033  2447074 2019-10-05 08:00:00  False  \n",
       "3        12:00:00  7224095  2447082 2019-10-05 12:00:00  False  \n",
       "4        16:00:00  7224095  2447082 2019-10-05 16:00:00  False  \n",
       "...           ...      ...      ...                 ...    ...  \n",
       "2474412  04:00:00     5554      420 2019-12-27 04:00:00  False  \n",
       "2474413  08:00:00     5554      420 2019-12-27 08:00:00  False  \n",
       "2474414  12:00:00     5554      420 2019-12-27 12:00:00  False  \n",
       "2474415  16:00:00     5554      420 2019-12-27 16:00:00  False  \n",
       "2474416  20:00:00     5554      420 2019-12-27 20:00:00  False  \n",
       "\n",
       "[2474340 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data['PARK'] = (mta_data.STATION.str.contains(' PK | PARK | PARK|PARK ')) | (\n",
    "    mta_data.STATION.str.endswith(' PK'))\n",
    "mta_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdbbcfd",
   "metadata": {},
   "source": [
    "#### 4.2 Create a new column TOTAL_EXITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e902526",
   "metadata": {
    "scrolled": false
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       "           C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "0         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "1         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "2         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "3         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "4         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "...        ...   ...       ...            ...      ...      ...         ...   \n",
       "2474412  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474413  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474414  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474415  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474416  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME   PARK  TOTAL_EXITS  \n",
       "0        00:00:00  7224009  2447060 2019-10-05 00:00:00  False          NaN  \n",
       "1        04:00:00  7224019  2447067 2019-10-05 04:00:00  False          7.0  \n",
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       "3        12:00:00  7224095  2447082 2019-10-05 12:00:00  False          8.0  \n",
       "4        16:00:00  7224095  2447082 2019-10-05 16:00:00  False          0.0  \n",
       "...           ...      ...      ...                 ...    ...          ...  \n",
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       "2474414  12:00:00     5554      420 2019-12-27 12:00:00  False          0.0  \n",
       "2474415  16:00:00     5554      420 2019-12-27 16:00:00  False          0.0  \n",
       "2474416  20:00:00     5554      420 2019-12-27 20:00:00  False          0.0  \n",
       "\n",
       "[2474340 rows x 13 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data['TOTAL_EXITS'] = mta_data.groupby(\n",
    "    ['STATION', 'UNIT', 'C/A', 'SCP','DIVISION','DATE'])['EXITS'].diff()\n",
    "mta_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15a11b8",
   "metadata": {},
   "source": [
    "#### 4.4 Remove `TOTAL_EXITS` negative values# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "510c2bcf",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2049346 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "1         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "2         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "3         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "4         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "5         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "...        ...   ...       ...            ...      ...      ...         ...   \n",
       "2474412  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474413  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474414  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474415  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474416  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME   PARK  TOTAL_EXITS  \n",
       "1        04:00:00  7224019  2447067 2019-10-05 04:00:00  False          7.0  \n",
       "2        08:00:00  7224033  2447074 2019-10-05 08:00:00  False          7.0  \n",
       "3        12:00:00  7224095  2447082 2019-10-05 12:00:00  False          8.0  \n",
       "4        16:00:00  7224095  2447082 2019-10-05 16:00:00  False          0.0  \n",
       "5        20:00:00  7224096  2447082 2019-10-05 20:00:00  False          0.0  \n",
       "...           ...      ...      ...                 ...    ...          ...  \n",
       "2474412  04:00:00     5554      420 2019-12-27 04:00:00  False          0.0  \n",
       "2474413  08:00:00     5554      420 2019-12-27 08:00:00  False          0.0  \n",
       "2474414  12:00:00     5554      420 2019-12-27 12:00:00  False          0.0  \n",
       "2474415  16:00:00     5554      420 2019-12-27 16:00:00  False          0.0  \n",
       "2474416  20:00:00     5554      420 2019-12-27 20:00:00  False          0.0  \n",
       "\n",
       "[2049346 rows x 13 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_data = mta_data[mta_data.TOTAL_EXITS >= 0]\n",
    "mta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ca4a64d2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rawabikhalaf/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/rawabikhalaf/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'cumulative probability distribution of exits'}, xlabel='TOTAL_EXITS', ylabel='Density'>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.title(\"Distribution of exits\", fontsize=20);\n",
    "plt.xlabel(\"Exits\", fontsize=15)\n",
    "plt.ylabel(\"Density\", fontsize=15)\n",
    "sns.distplot(mta_data.TOTAL_EXITS,color='#FFCE00')\n",
    "plt.figure()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title(\"cumulative probability distribution of exits\", fontsize=20);\n",
    "plt.xlabel(\"Exits\", fontsize=15)\n",
    "plt.ylabel(\"Density\", fontsize=15)\n",
    "sns.distplot( mta_data.TOTAL_EXITS,hist_kws={\"cumulative\":True},kde_kws={\"cumulative\":True},color='#FFCE00')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80b8882",
   "metadata": {},
   "source": [
    "#### Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7861f714",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = mta_data.TOTAL_EXITS\n",
    "removed_outliers = y.between(y.quantile(.05), y.quantile(.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e3fcb5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1946976\n",
       "False     102370\n",
       "Name: TOTAL_EXITS, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "removed_outliers.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "59e947a1",
   "metadata": {
    "scrolled": true
   },
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>PARK</th>\n",
       "      <th>TOTAL_EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>7224019</td>\n",
       "      <td>2447067</td>\n",
       "      <td>2019-10-05 04:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>7224033</td>\n",
       "      <td>2447074</td>\n",
       "      <td>2019-10-05 08:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 12:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>7224095</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 16:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>7224096</td>\n",
       "      <td>2447082</td>\n",
       "      <td>2019-10-05 20:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474412</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 04:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474413</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 08:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474414</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 12:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474415</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 16:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474416</th>\n",
       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
       "      <td>R</td>\n",
       "      <td>RIT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>5554</td>\n",
       "      <td>420</td>\n",
       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1946976 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "1         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "2         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "3         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "4         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "5         A002  R051  02-00-00          59 ST  NQR456W      BMT  10/05/2019   \n",
       "...        ...   ...       ...            ...      ...      ...         ...   \n",
       "2474412  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474413  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474414  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474415  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "2474416  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME   PARK  TOTAL_EXITS  \n",
       "1        04:00:00  7224019  2447067 2019-10-05 04:00:00  False          7.0  \n",
       "2        08:00:00  7224033  2447074 2019-10-05 08:00:00  False          7.0  \n",
       "3        12:00:00  7224095  2447082 2019-10-05 12:00:00  False          8.0  \n",
       "4        16:00:00  7224095  2447082 2019-10-05 16:00:00  False          0.0  \n",
       "5        20:00:00  7224096  2447082 2019-10-05 20:00:00  False          0.0  \n",
       "...           ...      ...      ...                 ...    ...          ...  \n",
       "2474412  04:00:00     5554      420 2019-12-27 04:00:00  False          0.0  \n",
       "2474413  08:00:00     5554      420 2019-12-27 08:00:00  False          0.0  \n",
       "2474414  12:00:00     5554      420 2019-12-27 12:00:00  False          0.0  \n",
       "2474415  16:00:00     5554      420 2019-12-27 16:00:00  False          0.0  \n",
       "2474416  20:00:00     5554      420 2019-12-27 20:00:00  False          0.0  \n",
       "\n",
       "[1946976 rows x 13 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_names = mta_data[~removed_outliers].index\n",
    "mta_data_clean = mta_data.drop(index_names, inplace=False)\n",
    "mta_data_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "861b95d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rawabikhalaf/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/rawabikhalaf/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'cumulative probability distribution of exits'}, xlabel='TOTAL_EXITS', ylabel='Density'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.title(\"Distribution of exits\", fontsize=20);\n",
    "plt.xlabel(\"Exits\", fontsize=15)\n",
    "plt.ylabel(\"Density\", fontsize=15)\n",
    "sns.distplot(mta_data_clean.TOTAL_EXITS,color='#FFCE00')\n",
    "plt.figure()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title(\"cumulative probability distribution of exits\", fontsize=20);\n",
    "plt.xlabel(\"Exits\", fontsize=15)\n",
    "plt.ylabel(\"Density\", fontsize=15)\n",
    "sns.distplot( mta_data_clean.TOTAL_EXITS,hist_kws={\"cumulative\":True},kde_kws={\"cumulative\":True})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "027db0bc",
   "metadata": {},
   "source": [
    "#### Filter the data based on stations that are near to any park"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96c71d39",
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>PARK</th>\n",
       "      <th>TOTAL_EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13539</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>6557665</td>\n",
       "      <td>5781951</td>\n",
       "      <td>2019-10-05 05:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13540</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>6557722</td>\n",
       "      <td>5781977</td>\n",
       "      <td>2019-10-05 09:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13541</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>6557832</td>\n",
       "      <td>5782046</td>\n",
       "      <td>2019-10-05 13:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>6557940</td>\n",
       "      <td>5782152</td>\n",
       "      <td>2019-10-05 17:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13543</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>6558041</td>\n",
       "      <td>5782281</td>\n",
       "      <td>2019-10-05 21:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>129.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472008</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>6290731</td>\n",
       "      <td>1502886</td>\n",
       "      <td>2019-12-27 04:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472009</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>6291041</td>\n",
       "      <td>1502892</td>\n",
       "      <td>2019-12-27 08:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472010</th>\n",
       "      <td>R726</td>\n",
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       "      <td>MORRIS PARK</td>\n",
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       "      <td>R329</td>\n",
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       "      <td>16:00:00</td>\n",
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       "      <td>1502951</td>\n",
       "      <td>2019-12-27 16:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472012</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>6291623</td>\n",
       "      <td>1503057</td>\n",
       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43555 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP      STATION LINENAME DIVISION        DATE  \\\n",
       "13539    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13540    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13541    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13542    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13543    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "...       ...   ...       ...          ...      ...      ...         ...   \n",
       "2472008  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472009  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472010  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472011  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472012  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME  PARK  TOTAL_EXITS  \n",
       "13539    05:00:00  6557665  5781951 2019-10-05 05:00:00  True         15.0  \n",
       "13540    09:00:00  6557722  5781977 2019-10-05 09:00:00  True         26.0  \n",
       "13541    13:00:00  6557832  5782046 2019-10-05 13:00:00  True         69.0  \n",
       "13542    17:00:00  6557940  5782152 2019-10-05 17:00:00  True        106.0  \n",
       "13543    21:00:00  6558041  5782281 2019-10-05 21:00:00  True        129.0  \n",
       "...           ...      ...      ...                 ...   ...          ...  \n",
       "2472008  04:00:00  6290731  1502886 2019-12-27 04:00:00  True          3.0  \n",
       "2472009  08:00:00  6291041  1502892 2019-12-27 08:00:00  True          6.0  \n",
       "2472010  12:00:00  6291322  1502908 2019-12-27 12:00:00  True         16.0  \n",
       "2472011  16:00:00  6291502  1502951 2019-12-27 16:00:00  True         43.0  \n",
       "2472012  20:00:00  6291623  1503057 2019-12-27 20:00:00  True        106.0  \n",
       "\n",
       "[43555 rows x 13 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_around_parks = mta_data_clean[mta_data_clean.PARK == True]\n",
    "stations_around_parks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3670694f",
   "metadata": {},
   "source": [
    "#### 4.5 Create a new column `MONTH`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a15c48e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-5b4e28ead936>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  stations_around_parks['MONTH'] = stations_around_parks['DATE_TIME'].dt.month_name()\n"
     ]
    },
    {
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       "      <td>2019-10-05 21:00:00</td>\n",
       "      <td>True</td>\n",
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       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43555 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP      STATION LINENAME DIVISION        DATE  \\\n",
       "13539    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13540    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13541    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13542    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13543    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "...       ...   ...       ...          ...      ...      ...         ...   \n",
       "2472008  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472009  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472010  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472011  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472012  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME  PARK  TOTAL_EXITS  \\\n",
       "13539    05:00:00  6557665  5781951 2019-10-05 05:00:00  True         15.0   \n",
       "13540    09:00:00  6557722  5781977 2019-10-05 09:00:00  True         26.0   \n",
       "13541    13:00:00  6557832  5782046 2019-10-05 13:00:00  True         69.0   \n",
       "13542    17:00:00  6557940  5782152 2019-10-05 17:00:00  True        106.0   \n",
       "13543    21:00:00  6558041  5782281 2019-10-05 21:00:00  True        129.0   \n",
       "...           ...      ...      ...                 ...   ...          ...   \n",
       "2472008  04:00:00  6290731  1502886 2019-12-27 04:00:00  True          3.0   \n",
       "2472009  08:00:00  6291041  1502892 2019-12-27 08:00:00  True          6.0   \n",
       "2472010  12:00:00  6291322  1502908 2019-12-27 12:00:00  True         16.0   \n",
       "2472011  16:00:00  6291502  1502951 2019-12-27 16:00:00  True         43.0   \n",
       "2472012  20:00:00  6291623  1503057 2019-12-27 20:00:00  True        106.0   \n",
       "\n",
       "            MONTH  \n",
       "13539     October  \n",
       "13540     October  \n",
       "13541     October  \n",
       "13542     October  \n",
       "13543     October  \n",
       "...           ...  \n",
       "2472008  December  \n",
       "2472009  December  \n",
       "2472010  December  \n",
       "2472011  December  \n",
       "2472012  December  \n",
       "\n",
       "[43555 rows x 14 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_around_parks['MONTH'] = stations_around_parks['DATE_TIME'].dt.month_name()\n",
    "stations_around_parks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "97d0d462",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-85-dc1a280e24de>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  stations_around_parks['MONTH_INDEX'] = stations_around_parks['DATE_TIME'].dt.month\n"
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       "      <td>09:00:00</td>\n",
       "      <td>6557722</td>\n",
       "      <td>5781977</td>\n",
       "      <td>2019-10-05 09:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13541</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>6557832</td>\n",
       "      <td>5782046</td>\n",
       "      <td>2019-10-05 13:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>69.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>6557940</td>\n",
       "      <td>5782152</td>\n",
       "      <td>2019-10-05 17:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13543</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>6558041</td>\n",
       "      <td>5782281</td>\n",
       "      <td>2019-10-05 21:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>129.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472008</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>6290731</td>\n",
       "      <td>1502886</td>\n",
       "      <td>2019-12-27 04:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472009</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>6291041</td>\n",
       "      <td>1502892</td>\n",
       "      <td>2019-12-27 08:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472010</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>6291322</td>\n",
       "      <td>1502908</td>\n",
       "      <td>2019-12-27 12:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>16.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472011</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>6291502</td>\n",
       "      <td>1502951</td>\n",
       "      <td>2019-12-27 16:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472012</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>6291623</td>\n",
       "      <td>1503057</td>\n",
       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43555 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP      STATION LINENAME DIVISION        DATE  \\\n",
       "13539    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13540    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13541    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13542    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13543    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "...       ...   ...       ...          ...      ...      ...         ...   \n",
       "2472008  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472009  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472010  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472011  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472012  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME  PARK  TOTAL_EXITS  \\\n",
       "13539    05:00:00  6557665  5781951 2019-10-05 05:00:00  True         15.0   \n",
       "13540    09:00:00  6557722  5781977 2019-10-05 09:00:00  True         26.0   \n",
       "13541    13:00:00  6557832  5782046 2019-10-05 13:00:00  True         69.0   \n",
       "13542    17:00:00  6557940  5782152 2019-10-05 17:00:00  True        106.0   \n",
       "13543    21:00:00  6558041  5782281 2019-10-05 21:00:00  True        129.0   \n",
       "...           ...      ...      ...                 ...   ...          ...   \n",
       "2472008  04:00:00  6290731  1502886 2019-12-27 04:00:00  True          3.0   \n",
       "2472009  08:00:00  6291041  1502892 2019-12-27 08:00:00  True          6.0   \n",
       "2472010  12:00:00  6291322  1502908 2019-12-27 12:00:00  True         16.0   \n",
       "2472011  16:00:00  6291502  1502951 2019-12-27 16:00:00  True         43.0   \n",
       "2472012  20:00:00  6291623  1503057 2019-12-27 20:00:00  True        106.0   \n",
       "\n",
       "            MONTH       Day  MONTH_INDEX  \n",
       "13539     October  Saturday           10  \n",
       "13540     October  Saturday           10  \n",
       "13541     October  Saturday           10  \n",
       "13542     October  Saturday           10  \n",
       "13543     October  Saturday           10  \n",
       "...           ...       ...          ...  \n",
       "2472008  December    Friday           12  \n",
       "2472009  December    Friday           12  \n",
       "2472010  December    Friday           12  \n",
       "2472011  December    Friday           12  \n",
       "2472012  December    Friday           12  \n",
       "\n",
       "[43555 rows x 16 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_around_parks['MONTH_INDEX'] = stations_around_parks['DATE_TIME'].dt.month\n",
    "stations_around_parks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e5bfdecf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-40c94c70cbf6>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  stations_around_parks['Day'] = stations_around_parks['DATE_TIME'].dt.day_name()\n"
     ]
    }
   ],
   "source": [
    "stations_around_parks['Day'] = stations_around_parks['DATE_TIME'].dt.day_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c02d59",
   "metadata": {},
   "source": [
    "### Calculate the average total number of Exits per Station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5ae4f2f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stations_parks = stations_around_parks.groupby(['STATION']).TOTAL_EXITS.mean().reset_index()\n",
    "stations_parks.sort_values(ascending=False,by='TOTAL_EXITS',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "22f49ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>PARK</th>\n",
       "      <th>TOTAL_EXITS</th>\n",
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       "      <th>Day</th>\n",
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       "      <th>13539</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
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       "      <td>5781951</td>\n",
       "      <td>2019-10-05 05:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>15.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13540</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>6557722</td>\n",
       "      <td>5781977</td>\n",
       "      <td>2019-10-05 09:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13541</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>6557832</td>\n",
       "      <td>5782046</td>\n",
       "      <td>2019-10-05 13:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>69.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>6557940</td>\n",
       "      <td>5782152</td>\n",
       "      <td>2019-10-05 17:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13543</th>\n",
       "      <td>B009</td>\n",
       "      <td>R411</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>S</td>\n",
       "      <td>BMT</td>\n",
       "      <td>10/05/2019</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>6558041</td>\n",
       "      <td>5782281</td>\n",
       "      <td>2019-10-05 21:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>129.0</td>\n",
       "      <td>October</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2472008</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>6290731</td>\n",
       "      <td>1502886</td>\n",
       "      <td>2019-12-27 04:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
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       "    <tr>\n",
       "      <th>2472009</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
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       "      <td>12/27/2019</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>6291041</td>\n",
       "      <td>1502892</td>\n",
       "      <td>2019-12-27 08:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
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       "    <tr>\n",
       "      <th>2472010</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>6291322</td>\n",
       "      <td>1502908</td>\n",
       "      <td>2019-12-27 12:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>16.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
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       "    <tr>\n",
       "      <th>2472011</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>6291502</td>\n",
       "      <td>1502951</td>\n",
       "      <td>2019-12-27 16:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472012</th>\n",
       "      <td>R726</td>\n",
       "      <td>R329</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>5</td>\n",
       "      <td>IRT</td>\n",
       "      <td>12/27/2019</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>6291623</td>\n",
       "      <td>1503057</td>\n",
       "      <td>2019-12-27 20:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>106.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43555 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP      STATION LINENAME DIVISION        DATE  \\\n",
       "13539    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13540    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13541    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13542    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "13543    B009  R411  00-00-00   PARK PLACE        S      BMT  10/05/2019   \n",
       "...       ...   ...       ...          ...      ...      ...         ...   \n",
       "2472008  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472009  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472010  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472011  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "2472012  R726  R329  00-00-02  MORRIS PARK        5      IRT  12/27/2019   \n",
       "\n",
       "             TIME  ENTRIES    EXITS           DATE_TIME  PARK  TOTAL_EXITS  \\\n",
       "13539    05:00:00  6557665  5781951 2019-10-05 05:00:00  True         15.0   \n",
       "13540    09:00:00  6557722  5781977 2019-10-05 09:00:00  True         26.0   \n",
       "13541    13:00:00  6557832  5782046 2019-10-05 13:00:00  True         69.0   \n",
       "13542    17:00:00  6557940  5782152 2019-10-05 17:00:00  True        106.0   \n",
       "13543    21:00:00  6558041  5782281 2019-10-05 21:00:00  True        129.0   \n",
       "...           ...      ...      ...                 ...   ...          ...   \n",
       "2472008  04:00:00  6290731  1502886 2019-12-27 04:00:00  True          3.0   \n",
       "2472009  08:00:00  6291041  1502892 2019-12-27 08:00:00  True          6.0   \n",
       "2472010  12:00:00  6291322  1502908 2019-12-27 12:00:00  True         16.0   \n",
       "2472011  16:00:00  6291502  1502951 2019-12-27 16:00:00  True         43.0   \n",
       "2472012  20:00:00  6291623  1503057 2019-12-27 20:00:00  True        106.0   \n",
       "\n",
       "            MONTH       Day  \n",
       "13539     October  Saturday  \n",
       "13540     October  Saturday  \n",
       "13541     October  Saturday  \n",
       "13542     October  Saturday  \n",
       "13543     October  Saturday  \n",
       "...           ...       ...  \n",
       "2472008  December    Friday  \n",
       "2472009  December    Friday  \n",
       "2472010  December    Friday  \n",
       "2472011  December    Friday  \n",
       "2472012  December    Friday  \n",
       "\n",
       "[43555 rows x 15 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_around_parks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1fa34d16",
   "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>STATION</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>MONTH_INDEX</th>\n",
       "      <th>TOTAL_EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42 ST-BRYANT PK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>191.956503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42 ST-BRYANT PK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>185.403795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42 ST-BRYANT PK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>194.549550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63 DR-REGO PARK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>66.018692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63 DR-REGO PARK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>63.627816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>63 DR-REGO PARK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>59.132939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85 ST-FOREST PK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>72.920223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>85 ST-FOREST PK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>77.218543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85 ST-FOREST PK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>66.769643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BEDFORD PK BLVD</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>92.078947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BEDFORD PK BLVD</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>93.499717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BEDFORD PK BLVD</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>102.008156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BRONX PARK EAST</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>51.632099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BRONX PARK EAST</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>50.557778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BRONX PARK EAST</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>57.459259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CENTRAL PK N110</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>194.636595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CENTRAL PK N110</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>195.579128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CENTRAL PK N110</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>197.135309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>71.989691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>77.403712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MORRIS PARK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>96.082474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>OZONE PK LEFFRT</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>80.922794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OZONE PK LEFFRT</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>92.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>OZONE PK LEFFRT</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>84.379671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>71.272218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>69.545657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PARK PLACE</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>78.200163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PELHAM BAY PARK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>72.788477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PELHAM BAY PARK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>74.020603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PELHAM BAY PARK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>64.051029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PROSPECT PARK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>129.660356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PROSPECT PARK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>122.315524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PROSPECT PARK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>112.400677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ROCKAWAY PARK B</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>22.137109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ROCKAWAY PARK B</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>23.475168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ROCKAWAY PARK B</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>24.951111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>V.CORTLANDT PK</td>\n",
       "      <td>December</td>\n",
       "      <td>12</td>\n",
       "      <td>119.105809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>V.CORTLANDT PK</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>120.179966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>V.CORTLANDT PK</td>\n",
       "      <td>October</td>\n",
       "      <td>10</td>\n",
       "      <td>113.302103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STATION     MONTH  MONTH_INDEX  TOTAL_EXITS\n",
       "0   42 ST-BRYANT PK  December           12   191.956503\n",
       "1   42 ST-BRYANT PK  November           11   185.403795\n",
       "2   42 ST-BRYANT PK   October           10   194.549550\n",
       "3   63 DR-REGO PARK  December           12    66.018692\n",
       "4   63 DR-REGO PARK  November           11    63.627816\n",
       "5   63 DR-REGO PARK   October           10    59.132939\n",
       "6   85 ST-FOREST PK  December           12    72.920223\n",
       "7   85 ST-FOREST PK  November           11    77.218543\n",
       "8   85 ST-FOREST PK   October           10    66.769643\n",
       "9   BEDFORD PK BLVD  December           12    92.078947\n",
       "10  BEDFORD PK BLVD  November           11    93.499717\n",
       "11  BEDFORD PK BLVD   October           10   102.008156\n",
       "12  BRONX PARK EAST  December           12    51.632099\n",
       "13  BRONX PARK EAST  November           11    50.557778\n",
       "14  BRONX PARK EAST   October           10    57.459259\n",
       "15  CENTRAL PK N110  December           12   194.636595\n",
       "16  CENTRAL PK N110  November           11   195.579128\n",
       "17  CENTRAL PK N110   October           10   197.135309\n",
       "18      MORRIS PARK  December           12    71.989691\n",
       "19      MORRIS PARK  November           11    77.403712\n",
       "20      MORRIS PARK   October           10    96.082474\n",
       "21  OZONE PK LEFFRT  December           12    80.922794\n",
       "22  OZONE PK LEFFRT  November           11    92.440000\n",
       "23  OZONE PK LEFFRT   October           10    84.379671\n",
       "24       PARK PLACE  December           12    71.272218\n",
       "25       PARK PLACE  November           11    69.545657\n",
       "26       PARK PLACE   October           10    78.200163\n",
       "27  PELHAM BAY PARK  December           12    72.788477\n",
       "28  PELHAM BAY PARK  November           11    74.020603\n",
       "29  PELHAM BAY PARK   October           10    64.051029\n",
       "30    PROSPECT PARK  December           12   129.660356\n",
       "31    PROSPECT PARK  November           11   122.315524\n",
       "32    PROSPECT PARK   October           10   112.400677\n",
       "33  ROCKAWAY PARK B  December           12    22.137109\n",
       "34  ROCKAWAY PARK B  November           11    23.475168\n",
       "35  ROCKAWAY PARK B   October           10    24.951111\n",
       "36   V.CORTLANDT PK  December           12   119.105809\n",
       "37   V.CORTLANDT PK  November           11   120.179966\n",
       "38   V.CORTLANDT PK   October           10   113.302103"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_parks_month = stations_around_parks.groupby(['STATION','MONTH','MONTH_INDEX']).TOTAL_EXITS.mean().reset_index()\n",
    "stations_parks_month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7c52d2",
   "metadata": {},
   "source": [
    "### Calculate the total number of Exits per Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3b886e4f",
   "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>Day</th>\n",
       "      <th>TOTAL_EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>117.404214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>115.676820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>82.927595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>67.264250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>114.254108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>117.553629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>114.823549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  TOTAL_EXITS\n",
       "0     Friday   117.404214\n",
       "1     Monday   115.676820\n",
       "2   Saturday    82.927595\n",
       "3     Sunday    67.264250\n",
       "4   Thursday   114.254108\n",
       "5    Tuesday   117.553629\n",
       "6  Wednesday   114.823549"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_day_exits = stations_around_parks.groupby(['Day']).agg({\"TOTAL_EXITS\": \"mean\"}).reset_index()\n",
    "mta_day_exits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "aab03f44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>TOTAL_EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2369.558815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5867.619100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>74.024368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>165.727505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>166.237692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>169.680586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15675.833462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TOTAL_EXITS\n",
       "count      7.000000\n",
       "mean    2369.558815\n",
       "std     5867.619100\n",
       "min       74.024368\n",
       "25%      165.727505\n",
       "50%      166.237692\n",
       "75%      169.680586\n",
       "max    15675.833462"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta_day_exits.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6e4298",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "dc56e6c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(context='notebook',style='white')\n",
    "plt.title(\"Total Exits per week day\\n\",size=20,family=\"Times\")\n",
    "sns.lineplot(palette = sns.color_palette(\"YlGnBu\", 3),\n",
    "             data = mta_day_exits,\n",
    "             x='TOTAL_EXITS',\n",
    "             y='Day',color='#FFCE00')\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f6deced4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rawabikhalaf/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = stations_parks.STATION\n",
    "x = stations_parks.TOTAL_EXITS\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.set_theme(context='notebook', style='white')\n",
    "\n",
    "plt.xlabel('Stations',size = 15)\n",
    "plt.ylabel('Number of Exits', size = 15)\n",
    "\n",
    "plt.title(\"Number of Exits per Station in NYC\\n\", size=20, family='Times')\n",
    "\n",
    "sns.barplot(x,y,color='#FFCE00');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "940b16e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stations_around_parks.groupby(['STATION','MONTH']).size().unstack().plot(\n",
    "    kind='bar',stacked=False,color=['#BA9603','#E6BA06','#FFCE00'])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}