LeenaAlqasem-BananAlhethlool-EDA_Code.ipynb

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   "source": [
    "![download.png](attachment:download.png)\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><H1>Exploratory Data Analysis (EDA) for Outfront on the MTA turnstiles</H1></center>\n",
    "                          \n",
    "                                 Leena AlQasem                   Banan Alhethlool \n",
    "                              Leenabdulh@gmail.com         banan.alhethlool@gmail.com\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of content: \n",
    "\n",
    "  * [Import Libraries](#first)\n",
    "  * [Extract Data](#second)\n",
    "  * [Exploring and Assessing dataset](#Third)\n",
    "  * [Create MTA DataBase](#Forth)\n",
    "  * [Data Cleaning and Filtering](#Fifth)\n",
    "  * [Visualizing and Analyzing Data](#sixth)\n",
    "  * [Conclution](#seventh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries <a class=\"anchor\" id=\"first\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from sqlalchemy import create_engine\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Data <a class=\"anchor\" id=\"second\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data will be extracted as below: \n",
    "> - Feb 23 to Mar 23 in 2019\n",
    "> - Feb 22 to Mar 21 in 2020\n",
    "> - Feb 27 to Mar 27 in 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(months):\n",
    "    url = \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_{}.txt\"\n",
    "    dfs = []\n",
    "    for week_num in months:\n",
    "        file_url = url.format(week_num)\n",
    "        dfs.append(pd.read_csv(file_url))\n",
    "    return pd.concat(dfs)\n",
    "        \n",
    "year_19 = get_data([190223,190302,190309,190316,190323])\n",
    "year_20 = get_data([200222,200229,200307,200314,200321])\n",
    "year_21 = get_data([210227,210306,210313,210320,210327])\n",
    "\n",
    "mta = pd.concat([year_19, year_20, year_21])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "          C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "0        A002  R051  02-00-00          59 ST  NQR456W      BMT  02/16/2019   \n",
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       "3        A002  R051  02-00-00          59 ST  NQR456W      BMT  02/16/2019   \n",
       "4        A002  R051  02-00-00          59 ST  NQR456W      BMT  02/16/2019   \n",
       "...       ...   ...       ...            ...      ...      ...         ...   \n",
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       "209466  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209467  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209468  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209469  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "\n",
       "            TIME     DESC  ENTRIES  \\\n",
       "0       03:00:00  REGULAR  6947666   \n",
       "1       07:00:00  REGULAR  6947678   \n",
       "2       11:00:00  REGULAR  6947737   \n",
       "3       15:00:00  REGULAR  6947883   \n",
       "4       19:00:00  REGULAR  6948186   \n",
       "...          ...      ...      ...   \n",
       "209465  05:00:00  REGULAR     5554   \n",
       "209466  09:00:00  REGULAR     5554   \n",
       "209467  13:00:00  REGULAR     5554   \n",
       "209468  17:00:00  REGULAR     5554   \n",
       "209469  21:00:00  REGULAR     5554   \n",
       "\n",
       "        EXITS                                                                 \n",
       "0                                                 2356412                     \n",
       "1                                                 2356425                     \n",
       "2                                                 2356508                     \n",
       "3                                                 2356549                     \n",
       "4                                                 2356599                     \n",
       "...                                                   ...                     \n",
       "209465                                                544                     \n",
       "209466                                                544                     \n",
       "209467                                                544                     \n",
       "209468                                                544                     \n",
       "209469                                                544                     \n",
       "\n",
       "[3092381 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA overall (All three years)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  6947666   \n",
       "1  REGULAR  6947678   \n",
       "2  REGULAR  6947737   \n",
       "3  REGULAR  6947883   \n",
       "4  REGULAR  6948186   \n",
       "\n",
       "   EXITS                                                                 \n",
       "0                                            2356412                     \n",
       "1                                            2356425                     \n",
       "2                                            2356508                     \n",
       "3                                            2356549                     \n",
       "4                                            2356599                     "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "mta.head()"
   ]
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   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>02-00-00</td>\n",
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       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  6947666   \n",
       "1  REGULAR  6947678   \n",
       "2  REGULAR  6947737   \n",
       "3  REGULAR  6947883   \n",
       "4  REGULAR  6948186   \n",
       "\n",
       "   EXITS                                                                 \n",
       "0                                            2356412                     \n",
       "1                                            2356425                     \n",
       "2                                            2356508                     \n",
       "3                                            2356549                     \n",
       "4                                            2356599                     "
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     "execution_count": 5,
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   "source": [
    "year_19.head()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>02/15/2020</td>\n",
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       "      <td>RECOVR AUD</td>\n",
       "      <td>7379676</td>\n",
       "      <td>2502916</td>\n",
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       "      <td>15:00:00</td>\n",
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       "      <td>7379928</td>\n",
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       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
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       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "\n",
       "         DESC  ENTRIES  \\\n",
       "0  RECOVR AUD  7379676   \n",
       "1  RECOVR AUD  7379687   \n",
       "2     REGULAR  7379740   \n",
       "3     REGULAR  7379928   \n",
       "4     REGULAR  7380236   \n",
       "\n",
       "   EXITS                                                                 \n",
       "0                                            2502916                     \n",
       "1                                            2502927                     \n",
       "2                                            2503012                     \n",
       "3                                            2503059                     \n",
       "4                                            2503132                     "
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "year_20.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
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       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
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       "      <td>02/20/2021</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533822</td>\n",
       "      <td>2568869</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
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       "      <td>02/20/2021</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533826</td>\n",
       "      <td>2568881</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>02/20/2021</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533854</td>\n",
       "      <td>2568925</td>\n",
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       "      <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>02/20/2021</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533940</td>\n",
       "      <td>2568965</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",
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       "      <td>02/20/2021</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534089</td>\n",
       "      <td>2568994</td>\n",
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       "  </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  02/20/2021  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  7533822   \n",
       "1  REGULAR  7533826   \n",
       "2  REGULAR  7533854   \n",
       "3  REGULAR  7533940   \n",
       "4  REGULAR  7534089   \n",
       "\n",
       "   EXITS                                                                 \n",
       "0                                            2568869                     \n",
       "1                                            2568881                     \n",
       "2                                            2568925                     \n",
       "3                                            2568965                     \n",
       "4                                            2568994                     "
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   "source": [
    "year_21.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring and Assessing dataset <a class=\"anchor\" id=\"Third\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA overall (All three years)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3092381 entries, 0 to 209469\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: 283.1+ MB\n"
     ]
    }
   ],
   "source": [
    "mta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.092381e+06</td>\n",
       "      <td>3.092381e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.183610e+07</td>\n",
       "      <td>3.415333e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.152451e+08</td>\n",
       "      <td>1.955477e+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>2.921930e+05</td>\n",
       "      <td>1.262800e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.879761e+06</td>\n",
       "      <td>1.096612e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.485677e+06</td>\n",
       "      <td>4.414620e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.129469e+09</td>\n",
       "      <td>2.124297e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTRIES  \\\n",
       "count  3.092381e+06   \n",
       "mean   4.183610e+07   \n",
       "std    2.152451e+08   \n",
       "min    0.000000e+00   \n",
       "25%    2.921930e+05   \n",
       "50%    1.879761e+06   \n",
       "75%    6.485677e+06   \n",
       "max    2.129469e+09   \n",
       "\n",
       "       EXITS                                                                 \n",
       "count                                       3.092381e+06                     \n",
       "mean                                        3.415333e+07                     \n",
       "std                                         1.955477e+08                     \n",
       "min                                         0.000000e+00                     \n",
       "25%                                         1.262800e+05                     \n",
       "50%                                         1.096612e+06                     \n",
       "75%                                         4.414620e+06                     \n",
       "max                                         2.124297e+09                     "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1015745 entries, 0 to 202721\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                                                Non-Null Count    Dtype \n",
      "---  ------                                                                --------------    ----- \n",
      " 0   C/A                                                                   1015745 non-null  object\n",
      " 1   UNIT                                                                  1015745 non-null  object\n",
      " 2   SCP                                                                   1015745 non-null  object\n",
      " 3   STATION                                                               1015745 non-null  object\n",
      " 4   LINENAME                                                              1015745 non-null  object\n",
      " 5   DIVISION                                                              1015745 non-null  object\n",
      " 6   DATE                                                                  1015745 non-null  object\n",
      " 7   TIME                                                                  1015745 non-null  object\n",
      " 8   DESC                                                                  1015745 non-null  object\n",
      " 9   ENTRIES                                                               1015745 non-null  int64 \n",
      " 10  EXITS                                                                 1015745 non-null  int64 \n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 93.0+ MB\n"
     ]
    }
   ],
   "source": [
    "year_19.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.015745e+06</td>\n",
       "      <td>1.015745e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.041366e+07</td>\n",
       "      <td>3.341496e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.077496e+08</td>\n",
       "      <td>1.938082e+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>4.037780e+05</td>\n",
       "      <td>1.682090e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.307482e+06</td>\n",
       "      <td>1.329979e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.820956e+06</td>\n",
       "      <td>4.735487e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.129469e+09</td>\n",
       "      <td>2.124297e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTRIES  \\\n",
       "count  1.015745e+06   \n",
       "mean   4.041366e+07   \n",
       "std    2.077496e+08   \n",
       "min    0.000000e+00   \n",
       "25%    4.037780e+05   \n",
       "50%    2.307482e+06   \n",
       "75%    6.820956e+06   \n",
       "max    2.129469e+09   \n",
       "\n",
       "       EXITS                                                                 \n",
       "count                                       1.015745e+06                     \n",
       "mean                                        3.341496e+07                     \n",
       "std                                         1.938082e+08                     \n",
       "min                                         0.000000e+00                     \n",
       "25%                                         1.682090e+05                     \n",
       "50%                                         1.329979e+06                     \n",
       "75%                                         4.735487e+06                     \n",
       "max                                         2.124297e+09                     "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1031793 entries, 0 to 206744\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                                                Non-Null Count    Dtype \n",
      "---  ------                                                                --------------    ----- \n",
      " 0   C/A                                                                   1031793 non-null  object\n",
      " 1   UNIT                                                                  1031793 non-null  object\n",
      " 2   SCP                                                                   1031793 non-null  object\n",
      " 3   STATION                                                               1031793 non-null  object\n",
      " 4   LINENAME                                                              1031793 non-null  object\n",
      " 5   DIVISION                                                              1031793 non-null  object\n",
      " 6   DATE                                                                  1031793 non-null  object\n",
      " 7   TIME                                                                  1031793 non-null  object\n",
      " 8   DESC                                                                  1031793 non-null  object\n",
      " 9   ENTRIES                                                               1031793 non-null  int64 \n",
      " 10  EXITS                                                                 1031793 non-null  int64 \n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 94.5+ MB\n"
     ]
    }
   ],
   "source": [
    "year_20.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.031793e+06</td>\n",
       "      <td>1.031793e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.296216e+07</td>\n",
       "      <td>3.525515e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.191006e+08</td>\n",
       "      <td>1.989931e+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>3.164780e+05</td>\n",
       "      <td>1.399490e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.910212e+06</td>\n",
       "      <td>1.104287e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.529658e+06</td>\n",
       "      <td>4.456098e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.128808e+09</td>\n",
       "      <td>2.123385e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTRIES  \\\n",
       "count  1.031793e+06   \n",
       "mean   4.296216e+07   \n",
       "std    2.191006e+08   \n",
       "min    0.000000e+00   \n",
       "25%    3.164780e+05   \n",
       "50%    1.910212e+06   \n",
       "75%    6.529658e+06   \n",
       "max    2.128808e+09   \n",
       "\n",
       "       EXITS                                                                 \n",
       "count                                       1.031793e+06                     \n",
       "mean                                        3.525515e+07                     \n",
       "std                                         1.989931e+08                     \n",
       "min                                         0.000000e+00                     \n",
       "25%                                         1.399490e+05                     \n",
       "50%                                         1.104287e+06                     \n",
       "75%                                         4.456098e+06                     \n",
       "max                                         2.123385e+09                     "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1044843 entries, 0 to 209469\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                                                Non-Null Count    Dtype \n",
      "---  ------                                                                --------------    ----- \n",
      " 0   C/A                                                                   1044843 non-null  object\n",
      " 1   UNIT                                                                  1044843 non-null  object\n",
      " 2   SCP                                                                   1044843 non-null  object\n",
      " 3   STATION                                                               1044843 non-null  object\n",
      " 4   LINENAME                                                              1044843 non-null  object\n",
      " 5   DIVISION                                                              1044843 non-null  object\n",
      " 6   DATE                                                                  1044843 non-null  object\n",
      " 7   TIME                                                                  1044843 non-null  object\n",
      " 8   DESC                                                                  1044843 non-null  object\n",
      " 9   ENTRIES                                                               1044843 non-null  int64 \n",
      " 10  EXITS                                                                 1044843 non-null  int64 \n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 95.7+ MB\n"
     ]
    }
   ],
   "source": [
    "year_21.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.044843e+06</td>\n",
       "      <td>1.044843e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.210694e+07</td>\n",
       "      <td>3.378309e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.185310e+08</td>\n",
       "      <td>1.937864e+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>2.227940e+05</td>\n",
       "      <td>9.385450e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.483456e+06</td>\n",
       "      <td>8.963570e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.162789e+06</td>\n",
       "      <td>4.049248e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.128709e+09</td>\n",
       "      <td>2.123017e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ENTRIES  \\\n",
       "count  1.044843e+06   \n",
       "mean   4.210694e+07   \n",
       "std    2.185310e+08   \n",
       "min    0.000000e+00   \n",
       "25%    2.227940e+05   \n",
       "50%    1.483456e+06   \n",
       "75%    6.162789e+06   \n",
       "max    2.128709e+09   \n",
       "\n",
       "       EXITS                                                                 \n",
       "count                                       1.044843e+06                     \n",
       "mean                                        3.378309e+07                     \n",
       "std                                         1.937864e+08                     \n",
       "min                                         0.000000e+00                     \n",
       "25%                                         9.385450e+04                     \n",
       "50%                                         8.963570e+05                     \n",
       "75%                                         4.049248e+06                     \n",
       "max                                         2.123017e+09                     "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create MTA DataBase <a class=\"anchor\" id=\"Forth\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#year_19.to_csv('MTA_19_dataset.csv', encoding = 'utf-8', index=False)\n",
    "#year_two.to_csv('MTA_20_dataset.csv', encoding = 'utf-8', index=False)\n",
    "#year_three.to_csv('MTA_21_dataset.csv', encoding = 'utf-8', index=False)\n",
    "#mta.to_csv('MTA_dataset.csv', encoding = 'utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///mta.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['turnstiles_19', 'turnstiles_20', 'turnstiles_21']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.table_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME', 'DESC', 'ENTRIES', 'EXITS')]\n"
     ]
    }
   ],
   "source": [
    "print(engine.execute(\"SELECT * FROM turnstiles_19 LIMIT 1 \").fetchall())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>CA</th>\n",
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       "      <th>0</th>\n",
       "      <td>C/A</td>\n",
       "      <td>UNIT</td>\n",
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       "      <td>EXITS                                         ...</td>\n",
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       "    <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>02/15/2020</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379676</td>\n",
       "      <td>2502916</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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CA  UNIT       SCP  STATION  LINENAME  DIVISION       DATE_     TIME_  \\\n",
       "0   C/A  UNIT       SCP  STATION  LINENAME  DIVISION        DATE      TIME   \n",
       "1  A002  R051  02-00-00    59 ST   NQR456W       BMT  02/15/2020  03:00:00   \n",
       "2  A002  R051  02-00-00    59 ST   NQR456W       BMT  02/15/2020  07:00:00   \n",
       "3  A002  R051  02-00-00    59 ST   NQR456W       BMT  02/15/2020  11:00:00   \n",
       "4  A002  R051  02-00-00    59 ST   NQR456W       BMT  02/15/2020  15:00:00   \n",
       "\n",
       "        DESC_  ENTRIES                                              EXITS  \n",
       "0        DESC  ENTRIES  EXITS                                         ...  \n",
       "1  RECOVR AUD  7379676                                            2502916  \n",
       "2  RECOVR AUD  7379687                                            2502927  \n",
       "3     REGULAR  7379740                                            2503012  \n",
       "4     REGULAR  7379928                                            2503059  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('SELECT * FROM turnstiles_20 limit 5;', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CA</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>DESC_</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </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>02/20/2021</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533822</td>\n",
       "      <td>2568869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A006</td>\n",
       "      <td>R079</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>5 AV/59 ST</td>\n",
       "      <td>NQRW</td>\n",
       "      <td>BMT</td>\n",
       "      <td>02/20/2021</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>4410904</td>\n",
       "      <td>7594572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     CA  UNIT       SCP     STATION LINENAME DIVISION       DATE_     TIME_  \\\n",
       "0  A002  R051  02-00-00       59 ST  NQR456W      BMT  02/20/2021  03:00:00   \n",
       "1  A006  R079  00-00-00  5 AV/59 ST     NQRW      BMT  02/20/2021  01:00:00   \n",
       "\n",
       "     DESC_  ENTRIES    EXITS  \n",
       "0  REGULAR  7533822  2568869  \n",
       "1  REGULAR  4410904  7594572  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"SELECT * FROM turnstiles_21 GROUP BY CA LIMIT 2;\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Filtering <a class=\"anchor\" id=\"Fifth\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting the date and time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_19[\"DATE_TIME\"] = pd.to_datetime(year_19.DATE + \" \" + year_19.TIME, format=\"%m/%d/%Y %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>STATION</th>\n",
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       "  </thead>\n",
       "  <tbody>\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>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>02/16/2019</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947666</td>\n",
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       "      <td>2019-02-16 03:00:00</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947678</td>\n",
       "      <td>2356425</td>\n",
       "      <td>2019-02-16 07:00:00</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>02/16/2019</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947737</td>\n",
       "      <td>2356508</td>\n",
       "      <td>2019-02-16 11:00:00</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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</td>\n",
       "      <td>2019-02-16 15:00:00</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "      <td>2019-02-16 19:00:00</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  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  6947666   \n",
       "1  REGULAR  6947678   \n",
       "2  REGULAR  6947737   \n",
       "3  REGULAR  6947883   \n",
       "4  REGULAR  6948186   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2356412                      \n",
       "1                                            2356425                      \n",
       "2                                            2356508                      \n",
       "3                                            2356549                      \n",
       "4                                            2356599                      \n",
       "\n",
       "            DATE_TIME  \n",
       "0 2019-02-16 03:00:00  \n",
       "1 2019-02-16 07:00:00  \n",
       "2 2019-02-16 11:00:00  \n",
       "3 2019-02-16 15:00:00  \n",
       "4 2019-02-16 19:00:00  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2019-02-16 07:00:00</td>\n",
       "      <td>Saturday</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>02/16/2019</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947737</td>\n",
       "      <td>2356508</td>\n",
       "      <td>2019-02-16 11:00:00</td>\n",
       "      <td>Saturday</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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</td>\n",
       "      <td>2019-02-16 15:00:00</td>\n",
       "      <td>Saturday</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "      <td>2019-02-16 19:00:00</td>\n",
       "      <td>Saturday</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  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  6947666   \n",
       "1  REGULAR  6947678   \n",
       "2  REGULAR  6947737   \n",
       "3  REGULAR  6947883   \n",
       "4  REGULAR  6948186   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2356412                      \n",
       "1                                            2356425                      \n",
       "2                                            2356508                      \n",
       "3                                            2356549                      \n",
       "4                                            2356599                      \n",
       "\n",
       "            DATE_TIME       DAY  \n",
       "0 2019-02-16 03:00:00  Saturday  \n",
       "1 2019-02-16 07:00:00  Saturday  \n",
       "2 2019-02-16 11:00:00  Saturday  \n",
       "3 2019-02-16 15:00:00  Saturday  \n",
       "4 2019-02-16 19:00:00  Saturday  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19['DAY'] = pd.to_datetime(year_19['DATE']).dt.day_name()\n",
    "year_19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "      <td>02/16/2019</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947666</td>\n",
       "      <td>2356412</td>\n",
       "      <td>2019-02-16 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 03:00:00</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\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>02/16/2019</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947678</td>\n",
       "      <td>2356425</td>\n",
       "      <td>2019-02-16 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</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>02/16/2019</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947737</td>\n",
       "      <td>2356508</td>\n",
       "      <td>2019-02-16 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</td>\n",
       "      <td>2019-02-16 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "      <td>2019-02-16 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</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  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  6947666   \n",
       "1  REGULAR  6947678   \n",
       "2  REGULAR  6947737   \n",
       "3  REGULAR  6947883   \n",
       "4  REGULAR  6948186   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2356412                      \n",
       "1                                            2356425                      \n",
       "2                                            2356508                      \n",
       "3                                            2356549                      \n",
       "4                                            2356599                      \n",
       "\n",
       "            DATE_TIME       DAY           TIME_conv     PERIOD  \n",
       "0 2019-02-16 03:00:00  Saturday 2021-10-07 03:00:00      Night  \n",
       "1 2019-02-16 07:00:00  Saturday 2021-10-07 07:00:00    Morning  \n",
       "2 2019-02-16 11:00:00  Saturday 2021-10-07 11:00:00  Afternoon  \n",
       "3 2019-02-16 15:00:00  Saturday 2021-10-07 15:00:00  Afternoon  \n",
       "4 2019-02-16 19:00:00  Saturday 2021-10-07 19:00:00    Evening  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_19['TIME_conv'] = pd.to_datetime(year_19[\"TIME\"])\n",
    "hours = year_19['TIME_conv'].dt.hour\n",
    "year_19['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "year_19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "    </tr>\n",
       "  </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>02/15/2020</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379676</td>\n",
       "      <td>2502916</td>\n",
       "      <td>2020-02-15 03:00:00</td>\n",
       "    </tr>\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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</td>\n",
       "      <td>2020-02-15 07:00:00</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</td>\n",
       "      <td>2020-02-15 11:00:00</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "      <td>2020-02-15 15:00:00</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>02/15/2020</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
       "      <td>2020-02-15 19:00:00</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  02/15/2020  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "\n",
       "         DESC  ENTRIES  \\\n",
       "0  RECOVR AUD  7379676   \n",
       "1  RECOVR AUD  7379687   \n",
       "2     REGULAR  7379740   \n",
       "3     REGULAR  7379928   \n",
       "4     REGULAR  7380236   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2502916                      \n",
       "1                                            2502927                      \n",
       "2                                            2503012                      \n",
       "3                                            2503059                      \n",
       "4                                            2503132                      \n",
       "\n",
       "            DATE_TIME  \n",
       "0 2020-02-15 03:00:00  \n",
       "1 2020-02-15 07:00:00  \n",
       "2 2020-02-15 11:00:00  \n",
       "3 2020-02-15 15:00:00  \n",
       "4 2020-02-15 19:00:00  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20[\"DATE_TIME\"] = pd.to_datetime(year_20.DATE + \" \" + year_20.TIME, format=\"%m/%d/%Y %H:%M:%S\")\n",
    "year_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </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>02/15/2020</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379676</td>\n",
       "      <td>2502916</td>\n",
       "      <td>2020-02-15 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</td>\n",
       "      <td>2020-02-15 07:00:00</td>\n",
       "      <td>Saturday</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</td>\n",
       "      <td>2020-02-15 11:00:00</td>\n",
       "      <td>Saturday</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "      <td>2020-02-15 15:00:00</td>\n",
       "      <td>Saturday</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>02/15/2020</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
       "      <td>2020-02-15 19:00:00</td>\n",
       "      <td>Saturday</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  02/15/2020  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "\n",
       "         DESC  ENTRIES  \\\n",
       "0  RECOVR AUD  7379676   \n",
       "1  RECOVR AUD  7379687   \n",
       "2     REGULAR  7379740   \n",
       "3     REGULAR  7379928   \n",
       "4     REGULAR  7380236   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2502916                      \n",
       "1                                            2502927                      \n",
       "2                                            2503012                      \n",
       "3                                            2503059                      \n",
       "4                                            2503132                      \n",
       "\n",
       "            DATE_TIME       DAY  \n",
       "0 2020-02-15 03:00:00  Saturday  \n",
       "1 2020-02-15 07:00:00  Saturday  \n",
       "2 2020-02-15 11:00:00  Saturday  \n",
       "3 2020-02-15 15:00:00  Saturday  \n",
       "4 2020-02-15 19:00:00  Saturday  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20['DAY'] = pd.to_datetime(year_20['DATE']).dt.day_name()\n",
    "year_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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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",
       "      <td>02/15/2020</td>\n",
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       "      <td>7379676</td>\n",
       "      <td>2502916</td>\n",
       "      <td>2020-02-15 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 03:00:00</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</td>\n",
       "      <td>2020-02-15 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</td>\n",
       "      <td>2020-02-15 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "      <td>2020-02-15 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</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>02/15/2020</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
       "      <td>2020-02-15 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</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  02/15/2020  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "\n",
       "         DESC  ENTRIES  \\\n",
       "0  RECOVR AUD  7379676   \n",
       "1  RECOVR AUD  7379687   \n",
       "2     REGULAR  7379740   \n",
       "3     REGULAR  7379928   \n",
       "4     REGULAR  7380236   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2502916                      \n",
       "1                                            2502927                      \n",
       "2                                            2503012                      \n",
       "3                                            2503059                      \n",
       "4                                            2503132                      \n",
       "\n",
       "            DATE_TIME       DAY           TIME_conv     PERIOD  \n",
       "0 2020-02-15 03:00:00  Saturday 2021-10-07 03:00:00      Night  \n",
       "1 2020-02-15 07:00:00  Saturday 2021-10-07 07:00:00    Morning  \n",
       "2 2020-02-15 11:00:00  Saturday 2021-10-07 11:00:00  Afternoon  \n",
       "3 2020-02-15 15:00:00  Saturday 2021-10-07 15:00:00  Afternoon  \n",
       "4 2020-02-15 19:00:00  Saturday 2021-10-07 19:00:00    Evening  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_20['TIME_conv'] = pd.to_datetime(year_20[\"TIME\"])\n",
    "hours = year_20['TIME_conv'].dt.hour\n",
    "year_20['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "year_20.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "    </tr>\n",
       "  </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>02/20/2021</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533822</td>\n",
       "      <td>2568869</td>\n",
       "      <td>2021-02-20 03:00:00</td>\n",
       "    </tr>\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>02/20/2021</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533826</td>\n",
       "      <td>2568881</td>\n",
       "      <td>2021-02-20 07:00:00</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>02/20/2021</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533854</td>\n",
       "      <td>2568925</td>\n",
       "      <td>2021-02-20 11:00:00</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>02/20/2021</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533940</td>\n",
       "      <td>2568965</td>\n",
       "      <td>2021-02-20 15:00:00</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>02/20/2021</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534089</td>\n",
       "      <td>2568994</td>\n",
       "      <td>2021-02-20 19:00:00</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  02/20/2021  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  7533822   \n",
       "1  REGULAR  7533826   \n",
       "2  REGULAR  7533854   \n",
       "3  REGULAR  7533940   \n",
       "4  REGULAR  7534089   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2568869                      \n",
       "1                                            2568881                      \n",
       "2                                            2568925                      \n",
       "3                                            2568965                      \n",
       "4                                            2568994                      \n",
       "\n",
       "            DATE_TIME  \n",
       "0 2021-02-20 03:00:00  \n",
       "1 2021-02-20 07:00:00  \n",
       "2 2021-02-20 11:00:00  \n",
       "3 2021-02-20 15:00:00  \n",
       "4 2021-02-20 19:00:00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21[\"DATE_TIME\"] = pd.to_datetime(year_21.DATE + \" \" + year_21.TIME, format=\"%m/%d/%Y %H:%M:%S\")\n",
    "year_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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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",
       "      <td>02/20/2021</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533822</td>\n",
       "      <td>2568869</td>\n",
       "      <td>2021-02-20 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\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>02/20/2021</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533826</td>\n",
       "      <td>2568881</td>\n",
       "      <td>2021-02-20 07:00:00</td>\n",
       "      <td>Saturday</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>02/20/2021</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533854</td>\n",
       "      <td>2568925</td>\n",
       "      <td>2021-02-20 11:00:00</td>\n",
       "      <td>Saturday</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>02/20/2021</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533940</td>\n",
       "      <td>2568965</td>\n",
       "      <td>2021-02-20 15:00:00</td>\n",
       "      <td>Saturday</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>02/20/2021</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534089</td>\n",
       "      <td>2568994</td>\n",
       "      <td>2021-02-20 19:00:00</td>\n",
       "      <td>Saturday</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  02/20/2021  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES  \\\n",
       "0  REGULAR  7533822   \n",
       "1  REGULAR  7533826   \n",
       "2  REGULAR  7533854   \n",
       "3  REGULAR  7533940   \n",
       "4  REGULAR  7534089   \n",
       "\n",
       "   EXITS                                                                 \\\n",
       "0                                            2568869                      \n",
       "1                                            2568881                      \n",
       "2                                            2568925                      \n",
       "3                                            2568965                      \n",
       "4                                            2568994                      \n",
       "\n",
       "            DATE_TIME       DAY  \n",
       "0 2021-02-20 03:00:00  Saturday  \n",
       "1 2021-02-20 07:00:00  Saturday  \n",
       "2 2021-02-20 11:00:00  Saturday  \n",
       "3 2021-02-20 15:00:00  Saturday  \n",
       "4 2021-02-20 19:00:00  Saturday  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21['DAY'] = pd.to_datetime(year_21['DATE']).dt.day_name()\n",
    "year_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>TRAM2</td>\n",
       "      <td>R469</td>\n",
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       "      <td>00-05-01</td>\n",
       "      <td>RIT-ROOSEVELT</td>\n",
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       "      <td>RIT</td>\n",
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       "      <td>5554</td>\n",
       "      <td>544</td>\n",
       "      <td>2021-03-26 09:00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2021-10-07 09:00:00</td>\n",
       "      <td>Morning</td>\n",
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       "      <th>209467</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>RIT</td>\n",
       "      <td>03/26/2021</td>\n",
       "      <td>13:00:00</td>\n",
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       "      <td>5554</td>\n",
       "      <td>544</td>\n",
       "      <td>2021-03-26 13:00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2021-10-07 13:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209468</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>03/26/2021</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5554</td>\n",
       "      <td>544</td>\n",
       "      <td>2021-03-26 17:00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2021-10-07 17:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209469</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>03/26/2021</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5554</td>\n",
       "      <td>544</td>\n",
       "      <td>2021-03-26 21:00:00</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2021-10-07 21:00:00</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C/A  UNIT       SCP        STATION LINENAME DIVISION        DATE  \\\n",
       "209465  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209466  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209467  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209468  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "209469  TRAM2  R469  00-05-01  RIT-ROOSEVELT        R      RIT  03/26/2021   \n",
       "\n",
       "            TIME     DESC  ENTRIES  \\\n",
       "209465  05:00:00  REGULAR     5554   \n",
       "209466  09:00:00  REGULAR     5554   \n",
       "209467  13:00:00  REGULAR     5554   \n",
       "209468  17:00:00  REGULAR     5554   \n",
       "209469  21:00:00  REGULAR     5554   \n",
       "\n",
       "        EXITS                                                                 \\\n",
       "209465                                                544                      \n",
       "209466                                                544                      \n",
       "209467                                                544                      \n",
       "209468                                                544                      \n",
       "209469                                                544                      \n",
       "\n",
       "                 DATE_TIME     DAY           TIME_conv     PERIOD  \n",
       "209465 2021-03-26 05:00:00  Friday 2021-10-07 05:00:00    Morning  \n",
       "209466 2021-03-26 09:00:00  Friday 2021-10-07 09:00:00    Morning  \n",
       "209467 2021-03-26 13:00:00  Friday 2021-10-07 13:00:00  Afternoon  \n",
       "209468 2021-03-26 17:00:00  Friday 2021-10-07 17:00:00  Afternoon  \n",
       "209469 2021-03-26 21:00:00  Friday 2021-10-07 21:00:00    Evening  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_21['TIME_conv'] = pd.to_datetime(year_21[\"TIME\"])\n",
    "hours = year_21['TIME_conv'].dt.hour\n",
    "year_21['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "year_21.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  *Removing whitespaces from all columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip(names):\n",
    "    stripped = []\n",
    "    for name in names:\n",
    "        stripped.append(name.strip())\n",
    "    return stripped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES', 'EXITS', 'DATE_TIME', 'DAY', 'TIME_conv', 'PERIOD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19.columns = strip(year_19.columns)\n",
    "year_19.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES', 'EXITS', 'DATE_TIME', 'DAY', 'TIME_conv', 'PERIOD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20.columns = strip(year_20.columns)\n",
    "year_20.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES', 'EXITS', 'DATE_TIME', 'DAY', 'TIME_conv', 'PERIOD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21.columns = strip(year_21.columns)\n",
    "year_21.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the Traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </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>02/16/2019</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947666</td>\n",
       "      <td>2356412</td>\n",
       "      <td>2019-02-16 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 03:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\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>02/16/2019</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947678</td>\n",
       "      <td>2356425</td>\n",
       "      <td>2019-02-16 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>25.0</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>02/16/2019</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947737</td>\n",
       "      <td>2356508</td>\n",
       "      <td>2019-02-16 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>142.0</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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</td>\n",
       "      <td>2019-02-16 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>187.0</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "      <td>2019-02-16 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>353.0</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  02/16/2019  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "0  REGULAR  6947666  2356412 2019-02-16 03:00:00  Saturday   \n",
       "1  REGULAR  6947678  2356425 2019-02-16 07:00:00  Saturday   \n",
       "2  REGULAR  6947737  2356508 2019-02-16 11:00:00  Saturday   \n",
       "3  REGULAR  6947883  2356549 2019-02-16 15:00:00  Saturday   \n",
       "4  REGULAR  6948186  2356599 2019-02-16 19:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "0 2021-10-07 03:00:00      Night    NaN  \n",
       "1 2021-10-07 07:00:00    Morning   25.0  \n",
       "2 2021-10-07 11:00:00  Afternoon  142.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  187.0  \n",
       "4 2021-10-07 19:00:00    Evening  353.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19['total'] = abs(year_19['ENTRIES'].diff()) + abs(year_19['EXITS'].diff())\n",
    "year_19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</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>02/16/2019</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947678</td>\n",
       "      <td>2356425</td>\n",
       "      <td>2019-02-16 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>25.0</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>02/16/2019</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947737</td>\n",
       "      <td>2356508</td>\n",
       "      <td>2019-02-16 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>142.0</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>02/16/2019</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6947883</td>\n",
       "      <td>2356549</td>\n",
       "      <td>2019-02-16 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>187.0</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>02/16/2019</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948186</td>\n",
       "      <td>2356599</td>\n",
       "      <td>2019-02-16 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>353.0</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>02/16/2019</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6948381</td>\n",
       "      <td>2356623</td>\n",
       "      <td>2019-02-16 23:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 23:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  19:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/16/2019  23:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "1  REGULAR  6947678  2356425 2019-02-16 07:00:00  Saturday   \n",
       "2  REGULAR  6947737  2356508 2019-02-16 11:00:00  Saturday   \n",
       "3  REGULAR  6947883  2356549 2019-02-16 15:00:00  Saturday   \n",
       "4  REGULAR  6948186  2356599 2019-02-16 19:00:00  Saturday   \n",
       "5  REGULAR  6948381  2356623 2019-02-16 23:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "1 2021-10-07 07:00:00    Morning   25.0  \n",
       "2 2021-10-07 11:00:00  Afternoon  142.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  187.0  \n",
       "4 2021-10-07 19:00:00    Evening  353.0  \n",
       "5 2021-10-07 23:00:00      Night  219.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19 = year_19.dropna() \n",
    "\n",
    "year_19.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </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>02/15/2020</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379676</td>\n",
       "      <td>2502916</td>\n",
       "      <td>2020-02-15 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 03:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</td>\n",
       "      <td>2020-02-15 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22.0</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</td>\n",
       "      <td>2020-02-15 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>138.0</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "      <td>2020-02-15 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>235.0</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>02/15/2020</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
       "      <td>2020-02-15 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>381.0</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  02/15/2020  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "\n",
       "         DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "0  RECOVR AUD  7379676  2502916 2020-02-15 03:00:00  Saturday   \n",
       "1  RECOVR AUD  7379687  2502927 2020-02-15 07:00:00  Saturday   \n",
       "2     REGULAR  7379740  2503012 2020-02-15 11:00:00  Saturday   \n",
       "3     REGULAR  7379928  2503059 2020-02-15 15:00:00  Saturday   \n",
       "4     REGULAR  7380236  2503132 2020-02-15 19:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "0 2021-10-07 03:00:00      Night    NaN  \n",
       "1 2021-10-07 07:00:00    Morning   22.0  \n",
       "2 2021-10-07 11:00:00  Afternoon  138.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  235.0  \n",
       "4 2021-10-07 19:00:00    Evening  381.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20['total'] = abs(year_20['ENTRIES'].diff()) + abs(year_20['EXITS'].diff())\n",
    "year_20.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</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>02/15/2020</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7379687</td>\n",
       "      <td>2502927</td>\n",
       "      <td>2020-02-15 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>22.0</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>02/15/2020</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379740</td>\n",
       "      <td>2503012</td>\n",
       "      <td>2020-02-15 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>138.0</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>02/15/2020</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7379928</td>\n",
       "      <td>2503059</td>\n",
       "      <td>2020-02-15 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>235.0</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>02/15/2020</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380236</td>\n",
       "      <td>2503132</td>\n",
       "      <td>2020-02-15 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>381.0</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>02/15/2020</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7380372</td>\n",
       "      <td>2503176</td>\n",
       "      <td>2020-02-15 23:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 23:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  19:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/15/2020  23:00:00   \n",
       "\n",
       "         DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "1  RECOVR AUD  7379687  2502927 2020-02-15 07:00:00  Saturday   \n",
       "2     REGULAR  7379740  2503012 2020-02-15 11:00:00  Saturday   \n",
       "3     REGULAR  7379928  2503059 2020-02-15 15:00:00  Saturday   \n",
       "4     REGULAR  7380236  2503132 2020-02-15 19:00:00  Saturday   \n",
       "5     REGULAR  7380372  2503176 2020-02-15 23:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "1 2021-10-07 07:00:00    Morning   22.0  \n",
       "2 2021-10-07 11:00:00  Afternoon  138.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  235.0  \n",
       "4 2021-10-07 19:00:00    Evening  381.0  \n",
       "5 2021-10-07 23:00:00      Night  180.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20 = year_20.dropna() \n",
    "\n",
    "year_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002</td>\n",
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       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>02/20/2021</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533822</td>\n",
       "      <td>2568869</td>\n",
       "      <td>2021-02-20 03:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 03:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
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       "      <td>02/20/2021</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533826</td>\n",
       "      <td>2568881</td>\n",
       "      <td>2021-02-20 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>16.0</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>02/20/2021</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533854</td>\n",
       "      <td>2568925</td>\n",
       "      <td>2021-02-20 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
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       "      <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>02/20/2021</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533940</td>\n",
       "      <td>2568965</td>\n",
       "      <td>2021-02-20 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
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       "      <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>02/20/2021</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534089</td>\n",
       "      <td>2568994</td>\n",
       "      <td>2021-02-20 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>178.0</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  02/20/2021  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "0  REGULAR  7533822  2568869 2021-02-20 03:00:00  Saturday   \n",
       "1  REGULAR  7533826  2568881 2021-02-20 07:00:00  Saturday   \n",
       "2  REGULAR  7533854  2568925 2021-02-20 11:00:00  Saturday   \n",
       "3  REGULAR  7533940  2568965 2021-02-20 15:00:00  Saturday   \n",
       "4  REGULAR  7534089  2568994 2021-02-20 19:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "0 2021-10-07 03:00:00      Night    NaN  \n",
       "1 2021-10-07 07:00:00    Morning   16.0  \n",
       "2 2021-10-07 11:00:00  Afternoon   72.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  126.0  \n",
       "4 2021-10-07 19:00:00    Evening  178.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21['total'] = abs(year_21['ENTRIES'].diff()) + abs(year_21['EXITS'].diff())\n",
    "year_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</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>02/20/2021</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533826</td>\n",
       "      <td>2568881</td>\n",
       "      <td>2021-02-20 07:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 07:00:00</td>\n",
       "      <td>Morning</td>\n",
       "      <td>16.0</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>02/20/2021</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533854</td>\n",
       "      <td>2568925</td>\n",
       "      <td>2021-02-20 11:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 11:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>72.0</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>02/20/2021</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7533940</td>\n",
       "      <td>2568965</td>\n",
       "      <td>2021-02-20 15:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 15:00:00</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>126.0</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>02/20/2021</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534089</td>\n",
       "      <td>2568994</td>\n",
       "      <td>2021-02-20 19:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 19:00:00</td>\n",
       "      <td>Evening</td>\n",
       "      <td>178.0</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>02/20/2021</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7534197</td>\n",
       "      <td>2569013</td>\n",
       "      <td>2021-02-20 23:00:00</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2021-10-07 23:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  19:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  02/20/2021  23:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           DATE_TIME       DAY  \\\n",
       "1  REGULAR  7533826  2568881 2021-02-20 07:00:00  Saturday   \n",
       "2  REGULAR  7533854  2568925 2021-02-20 11:00:00  Saturday   \n",
       "3  REGULAR  7533940  2568965 2021-02-20 15:00:00  Saturday   \n",
       "4  REGULAR  7534089  2568994 2021-02-20 19:00:00  Saturday   \n",
       "5  REGULAR  7534197  2569013 2021-02-20 23:00:00  Saturday   \n",
       "\n",
       "            TIME_conv     PERIOD  total  \n",
       "1 2021-10-07 07:00:00    Morning   16.0  \n",
       "2 2021-10-07 11:00:00  Afternoon   72.0  \n",
       "3 2021-10-07 15:00:00  Afternoon  126.0  \n",
       "4 2021-10-07 19:00:00    Evening  178.0  \n",
       "5 2021-10-07 23:00:00      Night  127.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21 = year_21.dropna() \n",
    "\n",
    "year_21.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Handling Missing values*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1015744 entries, 1 to 202721\n",
      "Data columns (total 16 columns):\n",
      " #   Column     Non-Null Count    Dtype         \n",
      "---  ------     --------------    -----         \n",
      " 0   C/A        1015744 non-null  object        \n",
      " 1   UNIT       1015744 non-null  object        \n",
      " 2   SCP        1015744 non-null  object        \n",
      " 3   STATION    1015744 non-null  object        \n",
      " 4   LINENAME   1015744 non-null  object        \n",
      " 5   DIVISION   1015744 non-null  object        \n",
      " 6   DATE       1015744 non-null  object        \n",
      " 7   TIME       1015744 non-null  object        \n",
      " 8   DESC       1015744 non-null  object        \n",
      " 9   ENTRIES    1015744 non-null  int64         \n",
      " 10  EXITS      1015744 non-null  int64         \n",
      " 11  DATE_TIME  1015744 non-null  datetime64[ns]\n",
      " 12  DAY        1015744 non-null  object        \n",
      " 13  TIME_conv  1015744 non-null  datetime64[ns]\n",
      " 14  PERIOD     1015744 non-null  category      \n",
      " 15  total      1015744 non-null  float64       \n",
      "dtypes: category(1), datetime64[ns](2), float64(1), int64(2), object(10)\n",
      "memory usage: 125.0+ MB\n"
     ]
    }
   ],
   "source": [
    "year_19.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C/A          False\n",
       "UNIT         False\n",
       "SCP          False\n",
       "STATION      False\n",
       "LINENAME     False\n",
       "DIVISION     False\n",
       "DATE         False\n",
       "TIME         False\n",
       "DESC         False\n",
       "ENTRIES      False\n",
       "EXITS        False\n",
       "DATE_TIME    False\n",
       "DAY          False\n",
       "TIME_conv    False\n",
       "PERIOD       False\n",
       "total        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1031792 entries, 1 to 206744\n",
      "Data columns (total 16 columns):\n",
      " #   Column     Non-Null Count    Dtype         \n",
      "---  ------     --------------    -----         \n",
      " 0   C/A        1031792 non-null  object        \n",
      " 1   UNIT       1031792 non-null  object        \n",
      " 2   SCP        1031792 non-null  object        \n",
      " 3   STATION    1031792 non-null  object        \n",
      " 4   LINENAME   1031792 non-null  object        \n",
      " 5   DIVISION   1031792 non-null  object        \n",
      " 6   DATE       1031792 non-null  object        \n",
      " 7   TIME       1031792 non-null  object        \n",
      " 8   DESC       1031792 non-null  object        \n",
      " 9   ENTRIES    1031792 non-null  int64         \n",
      " 10  EXITS      1031792 non-null  int64         \n",
      " 11  DATE_TIME  1031792 non-null  datetime64[ns]\n",
      " 12  DAY        1031792 non-null  object        \n",
      " 13  TIME_conv  1031792 non-null  datetime64[ns]\n",
      " 14  PERIOD     1031792 non-null  category      \n",
      " 15  total      1031792 non-null  float64       \n",
      "dtypes: category(1), datetime64[ns](2), float64(1), int64(2), object(10)\n",
      "memory usage: 126.9+ MB\n"
     ]
    }
   ],
   "source": [
    "year_20.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C/A          False\n",
       "UNIT         False\n",
       "SCP          False\n",
       "STATION      False\n",
       "LINENAME     False\n",
       "DIVISION     False\n",
       "DATE         False\n",
       "TIME         False\n",
       "DESC         False\n",
       "ENTRIES      False\n",
       "EXITS        False\n",
       "DATE_TIME    False\n",
       "DAY          False\n",
       "TIME_conv    False\n",
       "PERIOD       False\n",
       "total        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1044842 entries, 1 to 209469\n",
      "Data columns (total 16 columns):\n",
      " #   Column     Non-Null Count    Dtype         \n",
      "---  ------     --------------    -----         \n",
      " 0   C/A        1044842 non-null  object        \n",
      " 1   UNIT       1044842 non-null  object        \n",
      " 2   SCP        1044842 non-null  object        \n",
      " 3   STATION    1044842 non-null  object        \n",
      " 4   LINENAME   1044842 non-null  object        \n",
      " 5   DIVISION   1044842 non-null  object        \n",
      " 6   DATE       1044842 non-null  object        \n",
      " 7   TIME       1044842 non-null  object        \n",
      " 8   DESC       1044842 non-null  object        \n",
      " 9   ENTRIES    1044842 non-null  int64         \n",
      " 10  EXITS      1044842 non-null  int64         \n",
      " 11  DATE_TIME  1044842 non-null  datetime64[ns]\n",
      " 12  DAY        1044842 non-null  object        \n",
      " 13  TIME_conv  1044842 non-null  datetime64[ns]\n",
      " 14  PERIOD     1044842 non-null  category      \n",
      " 15  total      1044842 non-null  float64       \n",
      "dtypes: category(1), datetime64[ns](2), float64(1), int64(2), object(10)\n",
      "memory usage: 128.5+ MB\n"
     ]
    }
   ],
   "source": [
    "year_21.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C/A          False\n",
       "UNIT         False\n",
       "SCP          False\n",
       "STATION      False\n",
       "LINENAME     False\n",
       "DIVISION     False\n",
       "DATE         False\n",
       "TIME         False\n",
       "DESC         False\n",
       "ENTRIES      False\n",
       "EXITS        False\n",
       "DATE_TIME    False\n",
       "DAY          False\n",
       "TIME_conv    False\n",
       "PERIOD       False\n",
       "total        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Removing zeros values*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>total</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [C/A, UNIT, SCP, STATION, LINENAME, DIVISION, DATE, TIME, DESC, ENTRIES, EXITS, DATE_TIME, DAY, TIME_conv, PERIOD, total]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "year_19=year_19[year_19['ENTRIES'] !=0]\n",
    "year_19 =year_19[year_19['EXITS'] !=0]\n",
    "\n",
    "mask = (year_19.EXITS == 0) & (year_19.ENTRIES == 0)\n",
    "year_19[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [C/A, UNIT, SCP, STATION, LINENAME, DIVISION, DATE, TIME, DESC, ENTRIES, EXITS, DATE_TIME, DAY, TIME_conv, PERIOD, total]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "year_20=year_20[year_20['ENTRIES'] !=0]\n",
    "year_20 =year_20[year_20['EXITS'] !=0]\n",
    "\n",
    "mask = (year_20.EXITS == 0) & (year_20.ENTRIES == 0)\n",
    "year_20[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TIME_conv</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [C/A, UNIT, SCP, STATION, LINENAME, DIVISION, DATE, TIME, DESC, ENTRIES, EXITS, DATE_TIME, DAY, TIME_conv, PERIOD, total]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21=year_21[year_21['ENTRIES'] !=0]\n",
    "year_21 =year_21[year_21['EXITS'] !=0]\n",
    "\n",
    "mask = (year_21.EXITS == 0) & (year_21.ENTRIES == 0)\n",
    "year_21[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Handling Negative Numbers*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_19 = (year_19.groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE\", \"total\", \"TIME\"],as_index=False).ENTRIES.first())\n",
    "year_19[\"DAILY_ENTRIES\"] = (year_19.groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\"])[[\"ENTRIES\"]].diff())\n",
    "year_19.dropna(subset=[\"DAILY_ENTRIES\"], axis=0, inplace = True)\n",
    "\n",
    "year_19[\"DAILY_ENTRIES\"]=(np.where(((year_19.ENTRIES ==0) &\n",
    "                                             (year_19.DAILY_ENTRIES < 0)),0 # IN CASE OF COUNTER RESET RET 0  \n",
    "                                            ,np.where((year_19.ENTRIES !=0) & (year_19.DAILY_ENTRIES < 0)\n",
    "                                            , abs(year_19.DAILY_ENTRIES), # IN CASE OF COUNTER REVERSE RETURN ABS(DAILY_ENTRIES)\n",
    "                                                      year_19.DAILY_ENTRIES) # IN CASE NORMAL COUNTER RETURN SAME VALUE AS DAILY_ENTRIES\n",
    "                                           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    9.563340e+05\n",
      "mean     1.349690e+04\n",
      "std      3.585184e+06\n",
      "min      0.000000e+00\n",
      "25%      3.400000e+01\n",
      "50%      1.870000e+02\n",
      "75%      5.170000e+02\n",
      "max      2.088503e+09\n",
      "Name: DAILY_ENTRIES, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(year_19['DAILY_ENTRIES'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "718433    2.088503e+09\n",
       "147646    1.629649e+09\n",
       "605681    1.611465e+09\n",
       "669506    9.192697e+08\n",
       "341277    6.404931e+08\n",
       "740302    4.384978e+08\n",
       "769051    4.021022e+08\n",
       "769045    4.010876e+08\n",
       "769046    4.010873e+08\n",
       "769050    4.010863e+08\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_19[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11706     1241.0\n",
       "13787     1241.0\n",
       "16761     1241.0\n",
       "17679     1241.0\n",
       "25077     1241.0\n",
       "35983     1241.0\n",
       "70929     1241.0\n",
       "85903     1241.0\n",
       "107637    1241.0\n",
       "113757    1241.0\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = year_19['DAILY_ENTRIES'].quantile(0.25)\n",
    "Q3 = year_19['DAILY_ENTRIES'].quantile(0.75)\n",
    "\n",
    "IQR = (Q3 - Q1)\n",
    "high = (Q3 + 1.5 * IQR)\n",
    "low = (Q1 - 1.5 * IQR)\n",
    "\n",
    "year_19= year_19[~((year_19['DAILY_ENTRIES']<low) | (year_19['DAILY_ENTRIES']>high))]\n",
    "year_19['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_19[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_20 = (year_20.groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE\", \"total\", \"TIME\"],as_index=False).ENTRIES.first())\n",
    "year_20[\"DAILY_ENTRIES\"] = (year_20.groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\"])[[\"ENTRIES\"]].diff())\n",
    "year_20.dropna(subset=[\"DAILY_ENTRIES\"], axis=0, inplace = True)\n",
    "\n",
    "year_20[\"DAILY_ENTRIES\"]=(np.where(((year_20.ENTRIES ==0) &\n",
    "                                             (year_20.DAILY_ENTRIES < 0)),0 # IN CASE OF COUNTER RESET RET 0  \n",
    "                                            ,np.where((year_20.ENTRIES !=0) & (year_20.DAILY_ENTRIES < 0)\n",
    "                                            , abs(year_20.DAILY_ENTRIES), # IN CASE OF COUNTER REVERSE RETURN ABS(DAILY_ENTRIES)\n",
    "                                                      year_20.DAILY_ENTRIES) # IN CASE NORMAL COUNTER RETURN SAME VALUE AS DAILY_ENTRIES\n",
    "                                           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    9.719510e+05\n",
      "mean     8.409094e+03\n",
      "std      2.944944e+06\n",
      "min      0.000000e+00\n",
      "25%      2.500000e+01\n",
      "50%      1.380000e+02\n",
      "75%      4.070000e+02\n",
      "max      2.038597e+09\n",
      "Name: DAILY_ENTRIES, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(year_20['DAILY_ENTRIES'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500208    2.038597e+09\n",
       "594154    1.140869e+09\n",
       "686287    1.054866e+09\n",
       "794528    1.002753e+09\n",
       "618470    5.525366e+08\n",
       "145516    4.684764e+08\n",
       "500001    4.144281e+08\n",
       "250085    3.155379e+08\n",
       "232725    1.686280e+08\n",
       "774579    1.348585e+08\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#year_20_neg = year_20_copy.copy()\n",
    "year_20['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_20[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7364     980.0\n",
       "16978    980.0\n",
       "18833    980.0\n",
       "24614    980.0\n",
       "26143    980.0\n",
       "40981    980.0\n",
       "53476    980.0\n",
       "58512    980.0\n",
       "64304    980.0\n",
       "67341    980.0\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = year_20['DAILY_ENTRIES'].quantile(0.25)\n",
    "Q3 = year_20['DAILY_ENTRIES'].quantile(0.75)\n",
    "\n",
    "IQR = (Q3 - Q1)\n",
    "high = (Q3 + 1.5 * IQR)\n",
    "low = (Q1 - 1.5 * IQR)\n",
    "\n",
    "year_20 = year_20[~((year_20['DAILY_ENTRIES']<low) | (year_20['DAILY_ENTRIES']>high))]\n",
    "year_20['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_20[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_21 = (year_21.groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE\", \"total\", \"TIME\"],as_index=False).ENTRIES.first())\n",
    "year_21[\"DAILY_ENTRIES\"] = (year_21\n",
    "                            .groupby([\"DIVISION\",\"C/A\", \"UNIT\", \"SCP\", \"STATION\"])[[\"ENTRIES\"]].diff())\n",
    "year_21.dropna(subset=[\"DAILY_ENTRIES\"], axis=0, inplace = True)\n",
    "\n",
    "year_21[\"DAILY_ENTRIES\"]=(np.where(((year_21.ENTRIES ==0) &\n",
    "                                             (year_21.DAILY_ENTRIES < 0)),0 # IN CASE OF COUNTER RESET RET 0  \n",
    "                                            ,np.where((year_21.ENTRIES !=0) & (year_21.DAILY_ENTRIES < 0)\n",
    "                                            , abs(year_21.DAILY_ENTRIES), # IN CASE OF COUNTER REVERSE RETURN ABS(DAILY_ENTRIES)\n",
    "                                                      year_21.DAILY_ENTRIES) # IN CASE NORMAL COUNTER RETURN SAME VALUE AS DAILY_ENTRIES\n",
    "                                           ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    9.840410e+05\n",
      "mean     7.023498e+03\n",
      "std      2.786174e+06\n",
      "min      0.000000e+00\n",
      "25%      8.000000e+00\n",
      "50%      5.100000e+01\n",
      "75%      1.460000e+02\n",
      "max      1.568847e+09\n",
      "Name: DAILY_ENTRIES, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(year_21['DAILY_ENTRIES'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600609    1.568847e+09\n",
       "600610    1.568847e+09\n",
       "775748    1.487331e+09\n",
       "519449    5.370059e+08\n",
       "40814     1.852592e+08\n",
       "40815     1.852592e+08\n",
       "40811     1.852591e+08\n",
       "40813     1.852591e+08\n",
       "40810     1.852591e+08\n",
       "599767    9.664746e+07\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21 = year_21.copy()\n",
    "year_21['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_21[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16       353.0\n",
       "1803     353.0\n",
       "1998     353.0\n",
       "2209     353.0\n",
       "2238     353.0\n",
       "4858     353.0\n",
       "13893    353.0\n",
       "14943    353.0\n",
       "15343    353.0\n",
       "19917    353.0\n",
       "Name: DAILY_ENTRIES, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = year_21['DAILY_ENTRIES'].quantile(0.25)\n",
    "Q3 = year_21['DAILY_ENTRIES'].quantile(0.75)\n",
    "\n",
    "IQR = (Q3 - Q1)\n",
    "high = (Q3 + 1.5 * IQR)\n",
    "low = (Q1 - 1.5 * IQR)\n",
    "\n",
    "year_21 = year_21[~((year_21['DAILY_ENTRIES']<low) | (year_21['DAILY_ENTRIES']>high))]\n",
    "year_21['DAILY_ENTRIES'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(year_21[\"DAILY_ENTRIES\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Handling duplicates values*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior shape:  (889183, 10)\n",
      "post shape:  (159752, 10)\n"
     ]
    }
   ],
   "source": [
    "(year_19\n",
    " .groupby(['STATION', 'DATE','C/A','UNIT','SCP'])\n",
    " .ENTRIES.count()\n",
    " .reset_index()\n",
    " .sort_values(\"ENTRIES\", ascending=False)).head(5)\n",
    "\n",
    "# Remove duplicates\n",
    "print(\"prior shape: \", year_19.shape)\n",
    "year_19.drop_duplicates(subset=[\"STATION\", \"DATE\",'C/A','UNIT','SCP'], inplace=True)\n",
    "print(\"post shape: \", year_19.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior shape:  (894194, 10)\n",
      "post shape:  (162648, 10)\n"
     ]
    }
   ],
   "source": [
    "(year_20\n",
    " .groupby(['STATION', 'DATE','C/A','UNIT','SCP'])\n",
    " .ENTRIES.count()\n",
    " .reset_index()\n",
    " .sort_values(\"ENTRIES\", ascending=False)).head(5)\n",
    "\n",
    "# Remove duplicates\n",
    "print(\"prior shape: \", year_20.shape)\n",
    "year_20.drop_duplicates(subset=[\"STATION\", \"DATE\",'C/A','UNIT','SCP'], inplace=True)\n",
    "print(\"post shape: \", year_20.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior shape:  (909288, 10)\n",
      "post shape:  (165295, 10)\n"
     ]
    }
   ],
   "source": [
    "(year_21\n",
    " .groupby(['STATION', 'DATE','C/A','UNIT','SCP'])\n",
    " .ENTRIES.count()\n",
    " .reset_index()\n",
    " .sort_values(\"ENTRIES\", ascending=False)).head(5)\n",
    "\n",
    "# Remove duplicates\n",
    "print(\"prior shape: \", year_21.shape)\n",
    "year_21.drop_duplicates(subset=[\"STATION\", \"DATE\",'C/A','UNIT','SCP'], inplace=True)\n",
    "print(\"post shape: \", year_21.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing and Analyzing Data <a class=\"anchor\" id=\"sixth\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - Station & Traffic for each year\n",
    "> - Days & Traffic for each year\n",
    "> - Time Period & Traffic for each year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Active Days in NYC*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02/17/2019</td>\n",
       "      <td>1717369.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/24/2019</td>\n",
       "      <td>1675196.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/17/2019</td>\n",
       "      <td>1669047.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/03/2019</td>\n",
       "      <td>1666026.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03/10/2019</td>\n",
       "      <td>1658053.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>03/09/2019</td>\n",
       "      <td>1539156.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>03/02/2019</td>\n",
       "      <td>1500671.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  DAILY_ENTRIES       DAY\n",
       "0  02/17/2019      1717369.0    Sunday\n",
       "1  02/24/2019      1675196.0    Sunday\n",
       "2  03/17/2019      1669047.0    Sunday\n",
       "3  03/03/2019      1666026.0    Sunday\n",
       "4  03/10/2019      1658053.0    Sunday\n",
       "5  03/09/2019      1539156.0  Saturday\n",
       "6  03/02/2019      1500671.0  Saturday"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19_days = year_19.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "year_19_days['DAY'] = pd.to_datetime(year_19_days['DATE']).dt.day_name()\n",
    "year_19_days.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "ax.bar(year_19_days['DAY'], year_19_days['DAILY_ENTRIES'].sort_values(ascending=False), color='#8E44AD')\n",
    "ax.set_title('Active Days in NYC in year 2019', pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Traffic', labelpad=15, fontweight='bold')\n",
    "ax.set_xlabel('Days', labelpad=15, fontweight='bold')\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/08/2020</td>\n",
       "      <td>1395329.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/16/2020</td>\n",
       "      <td>1358560.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02/23/2020</td>\n",
       "      <td>1328026.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/01/2020</td>\n",
       "      <td>1320980.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03/07/2020</td>\n",
       "      <td>1307391.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>02/29/2020</td>\n",
       "      <td>1295613.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>03/15/2020</td>\n",
       "      <td>1292590.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  DAILY_ENTRIES       DAY\n",
       "0  03/08/2020      1395329.0    Sunday\n",
       "1  02/16/2020      1358560.0    Sunday\n",
       "2  02/23/2020      1328026.0    Sunday\n",
       "3  03/01/2020      1320980.0    Sunday\n",
       "4  03/07/2020      1307391.0  Saturday\n",
       "5  02/29/2020      1295613.0  Saturday\n",
       "6  03/15/2020      1292590.0    Sunday"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20_days = year_20.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "year_20_days['DAY'] = pd.to_datetime(year_20_days['DATE']).dt.day_name()\n",
    "year_20_days.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "ax.bar(year_20_days['DAY'], year_20_days['DAILY_ENTRIES'].sort_values(ascending=False), color='#8E44AD')\n",
    "ax.set_title('Active Days in NYC in year 2020', pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Traffic', labelpad=15, fontweight='bold')\n",
    "ax.set_xlabel('Days', labelpad=15, fontweight='bold')\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/14/2021</td>\n",
       "      <td>497673.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03/21/2021</td>\n",
       "      <td>489904.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02/28/2021</td>\n",
       "      <td>487235.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02/21/2021</td>\n",
       "      <td>482484.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03/07/2021</td>\n",
       "      <td>478704.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>03/20/2021</td>\n",
       "      <td>378716.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>03/06/2021</td>\n",
       "      <td>366416.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  DAILY_ENTRIES       DAY\n",
       "0  03/14/2021       497673.0    Sunday\n",
       "1  03/21/2021       489904.0    Sunday\n",
       "2  02/28/2021       487235.0    Sunday\n",
       "3  02/21/2021       482484.0    Sunday\n",
       "4  03/07/2021       478704.0    Sunday\n",
       "5  03/20/2021       378716.0  Saturday\n",
       "6  03/06/2021       366416.0  Saturday"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21_days = year_21.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "year_21_days['DAY'] = pd.to_datetime(year_21_days['DATE']).dt.day_name()\n",
    "year_21_days.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "ax.bar(year_21_days['DAY'], year_21_days['DAILY_ENTRIES'].sort_values(ascending=False), color='#8E44AD')\n",
    "ax.set_title('Active Days in NYC in year 2021', pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Traffic', labelpad=15, fontweight='bold')\n",
    "ax.set_xlabel('Days', labelpad=15, fontweight='bold')\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Active Period Times in NYC*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/24/2019</td>\n",
       "      <td>1675196.0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>03/09/2019</td>\n",
       "      <td>1539156.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>03/06/2019</td>\n",
       "      <td>1377575.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>02/26/2019</td>\n",
       "      <td>1339886.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>03/14/2019</td>\n",
       "      <td>1276407.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>02/19/2019</td>\n",
       "      <td>827609.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  DAILY_ENTRIES     PERIOD  MONTH\n",
       "1   02/24/2019      1675196.0  Afternoon      2\n",
       "5   03/09/2019      1539156.0    Morning      3\n",
       "11  03/06/2019      1377575.0      Night      3\n",
       "17  02/26/2019      1339886.0      Night      2\n",
       "23  03/14/2019      1276407.0      Night      3\n",
       "29  02/19/2019       827609.0      Night      2"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19_period = year_19.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "\n",
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_19['TIME_conv'] = pd.to_datetime(year_19[\"TIME\"])\n",
    "hours = year_19['TIME_conv'].dt.hour\n",
    "year_19_period['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "year_19_period['MONTH'] = pd.to_datetime(year_19_period[\"DATE\"]).dt.month\n",
    "\n",
    "year_19_period.dropna(subset=[\"PERIOD\"], axis=0, inplace = True)\n",
    "year_19_period.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heat_19 = year_19_period.pivot_table('DAILY_ENTRIES', 'MONTH', 'PERIOD')\n",
    "ax = sns.heatmap(heat_19, cmap=\"BuPu\")\n",
    "\n",
    "\n",
    "plt.title('Active Period Time in NYC in 2019', fontsize = 15) # title with fontsize 20\n",
    "plt.xlabel('Period', fontsize = 10) # x-axis label with fontsize 15\n",
    "plt.ylabel('Months (Mar-Feb)', fontsize = 10) # y-axis label with fontsize 15\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/16/2020</td>\n",
       "      <td>1358560.0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>02/29/2020</td>\n",
       "      <td>1295613.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>03/05/2020</td>\n",
       "      <td>1185077.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>02/19/2020</td>\n",
       "      <td>1151549.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>02/17/2020</td>\n",
       "      <td>733777.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>02/15/2020</td>\n",
       "      <td>649491.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  DAILY_ENTRIES     PERIOD  MONTH\n",
       "1   02/16/2020      1358560.0  Afternoon      2\n",
       "5   02/29/2020      1295613.0    Morning      2\n",
       "11  03/05/2020      1185077.0    Morning      3\n",
       "17  02/19/2020      1151549.0      Night      2\n",
       "25  02/17/2020       733777.0    Morning      2\n",
       "30  02/15/2020       649491.0      Night      2"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20_period = year_20.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "\n",
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_20['TIME_conv'] = pd.to_datetime(year_20[\"TIME\"])\n",
    "hours = year_20['TIME_conv'].dt.hour\n",
    "year_20_period['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "\n",
    "year_20_period['MONTH'] = pd.to_datetime(year_20_period[\"DATE\"]).dt.month\n",
    "\n",
    "year_20_period.dropna(subset=[\"PERIOD\"], axis=0, inplace = True)\n",
    "year_20_period.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heat_20 = year_20_period.pivot_table('DAILY_ENTRIES', 'MONTH', 'PERIOD')\n",
    "ax = sns.heatmap(heat_20, cmap=\"BuPu\")\n",
    "\n",
    "\n",
    "plt.title('Active Period Time in NYC in 2020', fontsize = 15) # title with fontsize 20\n",
    "plt.xlabel('Period', fontsize = 10) # x-axis label with fontsize 15\n",
    "plt.ylabel('Months (Mar-Feb)', fontsize = 10) # y-axis label with fontsize 15\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MTA 2021**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "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>DATE</th>\n",
       "      <th>DAILY_ENTRIES</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03/21/2021</td>\n",
       "      <td>489904.0</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>03/20/2021</td>\n",
       "      <td>378716.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>03/26/2021</td>\n",
       "      <td>340783.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>03/03/2021</td>\n",
       "      <td>332986.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>03/11/2021</td>\n",
       "      <td>326914.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>02/20/2021</td>\n",
       "      <td>301819.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  DAILY_ENTRIES     PERIOD  MONTH\n",
       "1   03/21/2021       489904.0  Afternoon      3\n",
       "5   03/20/2021       378716.0      Night      3\n",
       "11  03/26/2021       340783.0      Night      3\n",
       "17  03/03/2021       332986.0      Night      3\n",
       "23  03/11/2021       326914.0      Night      3\n",
       "29  02/20/2021       301819.0      Night      2"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21_period = year_21.groupby(['DATE'])['DAILY_ENTRIES'].sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "\n",
    "bins = [0, 5, 13, 17, 25]\n",
    "labels = ['Morning','Afternoon','Evening','Night']\n",
    "year_21['TIME_conv'] = pd.to_datetime(year_21[\"TIME\"])\n",
    "hours = year_21['TIME_conv'].dt.hour\n",
    "year_21_period['PERIOD'] = pd.cut(hours-5+24 *(hours<5),bins=bins,labels=labels,right=False)\n",
    "year_21_period['MONTH'] = pd.to_datetime(year_21_period[\"DATE\"]).dt.month\n",
    "\n",
    "\n",
    "year_21_period.dropna(subset=[\"PERIOD\"], axis=0, inplace = True)\n",
    "year_21_period.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heat_21 = year_21_period.pivot_table('DAILY_ENTRIES', 'MONTH', 'PERIOD')\n",
    "ax = sns.heatmap(heat_21, cmap=\"BuPu\")\n",
    "\n",
    "\n",
    "plt.title('Active Period Time in NYC in 2021', fontsize = 15) # title with fontsize 20\n",
    "plt.xlabel('Period', fontsize = 10) # x-axis label with fontsize 15\n",
    "plt.ylabel('Months (Mar-Feb)', fontsize = 10) # y-axis label with fontsize 15\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Top 10 Crowded Stations in NYC*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>DAILY_ENTRIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>1169534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FULTON ST</td>\n",
       "      <td>768782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23 ST</td>\n",
       "      <td>759650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>730976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34 ST-HERALD SQ</td>\n",
       "      <td>675683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42 ST-PORT AUTH</td>\n",
       "      <td>660204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86 ST</td>\n",
       "      <td>655278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>626742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>594942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>125 ST</td>\n",
       "      <td>527493.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATION  DAILY_ENTRIES\n",
       "0   34 ST-PENN STA      1169534.0\n",
       "1        FULTON ST       768782.0\n",
       "2            23 ST       759650.0\n",
       "3   TIMES SQ-42 ST       730976.0\n",
       "4  34 ST-HERALD SQ       675683.0\n",
       "5  42 ST-PORT AUTH       660204.0\n",
       "6            86 ST       655278.0\n",
       "7  GRD CNTRL-42 ST       626742.0\n",
       "8            59 ST       594942.0\n",
       "9           125 ST       527493.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_19_station = year_19.groupby(['STATION'])[['DAILY_ENTRIES']].sum().sort_values(by ='DAILY_ENTRIES', ascending = False).reset_index()\n",
    "year_19_station.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "ax.bar(year_19_station['STATION'][:10], year_19_station['DAILY_ENTRIES'][:10].sort_values(ascending=False), color = '#8E44AD')\n",
    "ax.set_title('Top 10 Active MTA Stations in the Year 2019',  pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Total Entries', labelpad=15, fontsize = 14)\n",
    "ax.set_xlabel('Station', fontsize = 14)\n",
    "ax.tick_params(labelrotation=45)\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>DAILY_ENTRIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>834828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23 ST</td>\n",
       "      <td>632214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FULTON ST</td>\n",
       "      <td>607870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42 ST-PORT AUTH</td>\n",
       "      <td>513201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>512473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86 ST</td>\n",
       "      <td>505852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34 ST-HERALD SQ</td>\n",
       "      <td>473081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>447952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>439457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CANAL ST</td>\n",
       "      <td>422873.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATION  DAILY_ENTRIES\n",
       "0   34 ST-PENN STA       834828.0\n",
       "1            23 ST       632214.0\n",
       "2        FULTON ST       607870.0\n",
       "3  42 ST-PORT AUTH       513201.0\n",
       "4   TIMES SQ-42 ST       512473.0\n",
       "5            86 ST       505852.0\n",
       "6  34 ST-HERALD SQ       473081.0\n",
       "7            14 ST       447952.0\n",
       "8  GRD CNTRL-42 ST       439457.0\n",
       "9         CANAL ST       422873.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_20_station = year_20.groupby(['STATION'])[['DAILY_ENTRIES']].sum().sort_values(by ='DAILY_ENTRIES', ascending = False).reset_index().head(10)\n",
    "year_20_station.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "\n",
    "ax.bar(year_20_station['STATION'][:10], year_20_station['DAILY_ENTRIES'][:10].sort_values(ascending=False), color ='#8E44AD')\n",
    "ax.set_title('Top 10 Active MTA Stations in the Year 2020',  pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Total Entries', labelpad=15, fontsize = 14)\n",
    "ax.set_xlabel('Station', fontsize = 14)\n",
    "ax.tick_params(labelrotation=45)\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>DAILY_ENTRIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34 ST-PENN STA</td>\n",
       "      <td>263522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23 ST</td>\n",
       "      <td>209607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FULTON ST</td>\n",
       "      <td>193397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86 ST</td>\n",
       "      <td>190598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>181437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34 ST-HERALD SQ</td>\n",
       "      <td>164453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42 ST-PORT AUTH</td>\n",
       "      <td>154320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>125 ST</td>\n",
       "      <td>152940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>149691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14 ST-UNION SQ</td>\n",
       "      <td>144482.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STATION  DAILY_ENTRIES\n",
       "0   34 ST-PENN STA       263522.0\n",
       "1            23 ST       209607.0\n",
       "2        FULTON ST       193397.0\n",
       "3            86 ST       190598.0\n",
       "4            59 ST       181437.0\n",
       "5  34 ST-HERALD SQ       164453.0\n",
       "6  42 ST-PORT AUTH       154320.0\n",
       "7           125 ST       152940.0\n",
       "8  GRD CNTRL-42 ST       149691.0\n",
       "9   14 ST-UNION SQ       144482.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_21_station = year_21.groupby(['STATION'])[['DAILY_ENTRIES']].sum().sort_values(by ='DAILY_ENTRIES', ascending = False).reset_index()\n",
    "year_21_station.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bwn0PS3J+kvMXLlw4zZVKkua6eRXAtOe7DkBV/Q44FrgUeESSRy3tjlV1QlVtU1XbbLzxxtNfqSRpTptXAVxVVwIfAw5JsllVXQ18GLgZ2LNrcZKkeWXOB3CSXZO8NMlzk9wROBUo4ICRED4G2C7JJl2LlSTNG3M6gJM8GngnbQ93K+CbtPD9FLAJ8JIkDwN2pA1I+0OfSiVJ881cHwW9LXBaVf0zQJLXA6cA+wP/DBw0XN8IPLeqrutVqCRpfpnTe8DAd4G1k9wJoKpeDXwV+CJw/RDMewJ7V9W3+5UpSZpv5lwAJ9k0yQbD5EXAlsCBE7dX1auAc4C9hulrqur3M16oJGlem1NN0En2Bl4GXJfkXFpf7+HA6UkAPj6MhL4GWL9boZKkeW/OBHCSjYDXA/8A3AA8DHgN8DZgH+B4YOskqwHbD/MkSepizgQwbaTzD4GLqmpRkp8CVwMvAo4CDgHuBzwYeHNV/ahXoZIkzZk+4OF43j/QTqxBVV1DG3D1aWD3qrqqqs6uqncbvpKk3mZ1ACfZeTjBxhHDrOcBVyY5Dv50usnzaCfZWLdXnZIkTTZrAzjJ9sBHaD+osF+S42kn23gX7dCjTyRZH3gAsDawerdiJUmaZNYGMPBw4B1VdSKwE3AtsDftxxZeQOv/fQdwJHDEsDcsSdJYmHUBnOF4IuA7wN8k2aKqFgGvo53R6uCq+n1VHQo8E3hMVV3Up1pJkhZvVgVwkkfQ+nMDXDJctk9yt6q6kRbC2yZ5BkBVLaqqG/pVLEnS4s2aAE6yK/BBYFE1vwS+DjwK2C3JlsOe8BnATR1LlSRpmWbFccDDgKv305qXL0iyXlVdW1WnJ1lIO6nGU5JcRDvt5I4dy5UkaZlmRQDTRjd/A7gqyT2Bo5PcANwReFFVvSjJDrQTbRxfVT/uWKskScs01gGc5D7AGsBpw/WzgScAb6X9tu8uwPuT7FdVZwFn9apVkqTlMbYBnGQP4E20w4l+CJwE3AJcWFUnDMtcAWxOG/0sSdKsMZaDsJJsB7wdeGpVPZoWvAdW1fG0gVgTHkML4LVnvkpJklbcWAbw4M1VdeHw96uBeyZZYzjciOFQo5cCzx3OAy1J0qwxrk3Q5wIXAyRZFVgT2BS4E7AwyebAA4EDquoH3aqUJGkFjWUAV9UttFNLAoTWD/zbqlqY5GDgQcBRVXXtktYhSdI4G8sAHlVVNwPXJ/lZkqNpI5+fZvhKkmazsQ/g4bSTqwM7DNc7+Xu+kqTZbuwDuKoK+GOS1wPnGb6SpLlg7AN4xAeHMJYkadYb58OQ/ozhK0maS2ZNAEuSNJcYwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHq/UuQOPl2J1Pm/HHPPJL+874Y0pSb+4BS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdTAvAzjJXZJs2rsOSdL8tVrvAmZakicBLwduTfIV4HNVdWbfqiRJ88282gNOsiHwfOAZwK7AIuAJSfabwn0PS3J+kvMXLlw4zZVKkua6eRXAtOe7DkBV/Q44FrgUeESSRy3tjlV1QlVtU1XbbLzxxtNfqSRpTptXAVxVVwIfAw5JsllVXQ18GLgZ2LNrcZKkeWXOB3CSXZO8NMlzk9wROBUo4ICRED4G2C7JJl2LlSTNG3M6gJM8GngnbQ93K+CbtPD9FLAJ8JIkDwN2pA1I+0OfSiVJ881cHwW9LXBaVf0zQJLXA6cA+wP/DBw0XN8IPLeqrutVqCRpfpnTe8DAd4G1k9wJoKpeDXwV+CJw/RDMewJ7V9W3+5UpSZpv5lwAJ9k0yQbD5EXAlsCBE7dX1auAc4C9hulrqur3M16oJGlem1NN0En2Bl4GXJfkXFpf7+HA6UkAPj6MhL4GWL9boZKkeW/OBHCSjYDXA/8A3AA8DHgN8DZgH+B4YOskqwHbD/MkSepizgQwbaTzD4GLqmpRkp8CVwMvAo4CDgHuBzwYeHNV/ahXoZIkzZk+4OF43j/QTqxBVV1DG3D1aWD3qrqqqs6uqncbvpKk3mZ1ACfZeTjBxhHDrOcBVyY5Dv50usnzaCfZWLdXnZIkTTZrm6CTbA98hPbLRk9Ocm/gP4B3Ac9L8gngUOABwNrA6p1K1e107M6nzfhjHvmlfWf8MSXNL7N5D/jhwDuq6kRgJ+BaYG/ajy28gNb/+w7gSOCIYW9YkqSxMJsD+HvAI5NsUVWLgNfRzmh1cFX9vqoOBZ4JPKaqLupYpyRJf2FWBfBwko01h/7cM2mjnndIcrequpEWwtsmeQZAVS2qqhv6VSxJ0uLNmgBOsjvwOdrxvO8D7gp8CNgB2D3JlsOe8BnATd0KlSRpCsZ+EFbaKaz+CngzbZTz94GnAmcDj6L18x5E+43fi2inndyxS7GaNxwYJun2GvsArqpKcgXt/M0/An5TVW9NchPwdWC7qnpRkh1oJ9o4vqp+3LFkSZKWaayboJPcJ8m2tPM23wn4+6oqgKo6lnbI0euTrF1VZ1XV+wxfSdJsMLZ7wEn2AN4E/I424vnDwHFJVq2qo4fFTgVeCSzqU6UkSStmLAM4yXbA24GDqurCJCfQjvvdDvhmklVp4bs9sDVtD9njfCVJs8Y4N0G/uaouHP5+FfDQqrqCNsBqc+CFtEFZT/MkG5Kk2WYs94CBc4GLAYa93TWBuw/H+16a5LXAL4A7DD+6IEnSrDKWe8BVdUtVXTtMhnZayd9W1S+THEzr913d8JUkzVbjugf8J1V1M3B9kp8lORrYBTi0qv7QuTRJklbY2AfwcCKO1WlnvFod2Mnf85UkzXZjH8DDcb9/TPJ64DzDV5I0F4x9AI/44MRJOCRJmu3GchDW4hi+kqS5ZNYEsCRJc4kBLElSBwawJEkdGMCSJHVgAEuS1MFsOgxJ0hIcu/NpXR73yC/t2+VxpbnAPWBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQOVutdgKS56didT5vxxzzyS/vO+GNKK8o9YEmSOjCAJUnqwACWJKkDA1iSpA4MYEmSOjCAJUnqwACWJKkDA1iSpA6mFMBJNk6y8cj0g5K8IclB01eaJElz11T3gP8DeAJAko2ArwH7AO9J8qJpqk2SpDlrqgG8FfDN4e8nAT+uqgcCTwGeNR2FSZI0l001gNcGrh/+fhzwqeHvbwObruyiJEma66YawD8C9k2yKbAL8MVh/l2Aq6ejMEmS5rKpBvBrgbcAlwPfrKpzh/m7AhdOQ12SJM1pU/o5wqo6LclmwN2B74zc9GXg49NRmCRJc9mUfw+4qn4N/DrJXZIsrKpbR/aEJUnScpjqccCrJ3lrkuuAXwALhvlvSfKcaaxPkqQ5aap9wK+hHQd8MHDjyPxvAYeu5JokSZrzptoEfRDw9Kr6apJbR+ZfDGyx8suSJGlum2oA3x34yRLuP+V+ZEnq6didT5vxxzzyS/vO+GNqdphqE/T/AI9ezPz9gQtWXjmSJM0PU917fS1w8nAijlWB/ZJsCTwZ2H26ipMkaa6a6nHAZyTZH3glcCttUNa3gSdU1ZensT5JmrN6NImDzeLjYnmOA/4C8IVprEWSpHljqn3AkiRpJVriHnCSa4HNq+rK4QQctaRlq2q96ShuuiVZpapuXfaSkiStXEtrgn4+cN3w9/NmoJYZkeShwD2A71bVT3vXI0man5YYwFX1QYAkqwELgXOr6qqZKmw6JNkdeD9wOvCaJCcD/1VVF0/hvocBhwFsttlm01qnJGnuW+YgrKq6OclpwJbArA3gJKsCj6Wd0eszSR4H7AQ8KcmtVXXJ0u5fVScAJwBss802S2yOl6TZzJHZM2eqg7C+A9xnOguZblV1C7A68MRh+svAacBawI7Q+oR71SdJml+mGjhHAcck2TvJpkk2HL1MY323W5J1kmSYfBtwY5IDAarqPOArwOFJ7u6ALEnSTJnqccCfGa5P489HQ2eYXnVlFrWyJNkTOAK4OMkvquotSb4NPDwJVXVqVX0hycHAfYEruhYsSZo3phrAfzutVUyDJH8NHA28GLgS+FCS9Wh7wU8CHptkL+As4HG0s3xJkjQjphrAlwE/q6o/G3w0NO1uutKrWjluBn4IfLmqbkrycIY9+ap6VZJPAC8E7gLsUlU/61eqJGm+WZ4Avhvwm0nzNxxuG8cm6GuB64GHAOdV1bVJdgPOT/LbqjoGeKUn45Ak9TDVQVgTfb2TrQssWnnl3D5Jtk6yV5LNquoK4Bu0wWMLAKrqOtqxvBtN3MfwlST1sNQ94CTHDX8WcHSS34/cvCrwcOCiaaptuQwDrt5Oq+eJSRYCrwDuBHwwyStpv138QGCrJKtX1U3dCpYkzWvLaoJ+0HAd4P7AH0du+yPtJwnfPg11rYi/A15SVZ9MsjVtoNX7gGfSTiDydFog3wN4quErSeppqQFcVX8LkOQDwAuq6toZqWo5DSfQWJX2JeGTVfXtYQ/4cOB1VfXyYdDVesCNQ/O0JGkW6HF2rpk4M9eU+oCr6mnjGL5J1kiy5tCP+y7ggCT7Dzf/HDgD2CzJXarqqqq6zPCVJI2DKY2CTrIW8ALauZM3YVJwV9VWK7+0Zdb0ROAgYL0k7wC+BLwKeE6SVNVHgW8meRWw1XC7JEljYaqHIb0L2Af4GHA2S/lt4JmQZAvgDbR+3QW0ft57Al8F3gm8Jcl9aYch3Qf4QZ9KJUlavKkG8N7AfsMPGIyDDYFfV9U5wDlJfgIcSvti8G5a8/PBwPrAgZ5kQ5I0bqYawL8HuodYknWq6vfAt4DLhv7e06rq7OH3Fl4NXFpVnwVe1rFUSZKWaqon4ngr8MKeP9eXZFfguUnWHmZdQDsOefvhmN6zgVOBQ5NM9YuFJEldTDWodgZ2AB6f5BLgz46hrao9V3Zho5L8HfBm2qFQfxjmnUT7oYW9aOej/hCtCXoRnfuoJUlalqkG8JXA6dNZyJIkeQBtENjRVXVmkjvTfkDh91V11PBTgo9P8gzgzsBTquqWHrVKkjRVUwrgqnradBeyFGsDnwNuTfJ42l7vVcDGSc6rqpcBJyd5EG1g1uQfjJAkaewstU83yRbDTw4u6fbVkzx25Zf1p0ONqKoLgA/TzuH8TuA/gQOBpwLbJHn0sNz3DF9J0myxrEFV3wc2nphI8tMk9xy5fUOm4QQXSfYALkpyKkBVfQM4BXhxVb2nmp/RRmb/cSmrkiRpLC0rgCfv/W7AX/727xL3kFdEkjsAzwOOABYl+QhAVX0L+PzIck+k7RX/cmU+viRJM2FlHFa0UkccV9UNtDNcfYTW37tGkg8Pt02MgH4q8HLgaVX1k5X5+JIkzYRux/UuTVVdUVXXV9WVwLOAtZKcDJDk/sAdgAOq6uKedUqStKKWNQq6gA2S3DwyvX6SDYfpDRd/t5Wnqq5K8izgbUl+SGvyfkxV2fQsSZq1lhXAAS6ZNH3epOlpP+lFVV2Z5LvA3wE7G76SpNluWQH8tzNSxTIk2QDYDdilqr7Xux5Jkm6vpQZwVX11pgpZmqr6XZInVNWi3rVIkrQyjOUgrMUxfCVJc8msCWBJkuYSA1iSpA4MYHhlKbMAACAASURBVEmSOjCAJUnqYImjoJMcN9WVVNU/rpxyJEmaH5Z2GNKDpriOaT8RhyRJc80SA7iqxuIkHJIkzUX2AUuS1MGyTkX5J0m2AJ4EbAasMXpbVT19JdclSdKcNqUATrI78HHgQuBhtB9kuDewJnDWtFUnSdIcNdUm6NcBr62qRwI3AocAC4AvA2dOS2WSJM1hUw3g+wEfHf6+CVhnODfz64AjpqMwSZLmsqkG8HXAWsPfvwTuM/y9GrDByi5KkqS5bqqDsM4FtgcuAT4DHJPkwcA+wDnTVJskSXPWVAP4hcC6w99HAXcEngj873CbJElaDlMK4Kq6dOTv3wPPnraKJEmaB6bUB5zk0iR3Xsz89ZNcurj7SJKkJZvqIKwFwKqLmb8mcI+VVo0kSfPEUpugk+w7Mrl7kmtGplcFdgIun4a6JEma05bVB/yfw3UBJ0667SZa+L5oJdckSdKct9QArqpVAJJcBmxbVVfOSFWSJM1xUx0Ffa/pLkSSpPlkyj9HmGT3JF9LcmWShUm+mmS36SxOkqS5aqqHIT0TOB34P+BlwMuBy4DTk/hThJIkLaepngnrZcALq+odI/NOTHIBLYzfv9IrkyRpDptqE/RmwOcXM/9zwD1XXjmSJM0PUw3gnwI7L2b+LsBPVl45kiTND8s6Ecf7gRcAbweOT7I1cDbtuODtgUOA5093kZIkzTXL6gN+KvDyqnpvkt/QTroxcXas7wP7V9Unp7NASZLmomUFcCb+qKrTaSOhJUnS7TSVPuCa9iokSZpnpnIY0q+SLHWBqlrcLyVJkqQlmEoAHwZcPd2FSJI0n0wlgM+oqt9MeyWSJM0jy+oDtv9XkqRpsKwAXnrnryRJWiFT+j1gSZK0chmwkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHRjAkiR1YABLktSBASxJUgcGsCRJHczrAE4yr5+/JKmf1XoXMNOSPBS4B/Ddqvpp73okSfPTvNoDTLI78HlgD+DjSV6Q5K+neN/Dkpyf5PyFCxdOa52SpLlv3gRwklWBxwJPr6rDgVcAdwWelOQBy7p/VZ1QVdtU1TYbb7zxNFcrSZrr5k0AV9UtwOrAE4fpLwOnAWsBO4J9wpKkmTPnAyfJOkkyTL4NuDHJgQBVdR7wFeDwJHevqlt71SlJml/mdAAn2RP4NPCvSV5WVT8Dvg08fCSEvwB8B7hvv0olSfPNnB0FPQyuOhp4MXAl8KEk69H2gp8EPDbJXsBZwOOAV/aqVZI0/8zZAAZuBn4IfLmqbkrycOAzAFX1qiSfAF4I3AXYZdg7liRpRszlAL4WuB54CHBeVV2bZDfg/CS/rapjgFcmWcW+X0nSTJtTfcBJtk6yV5LNquoK4BvAMUkWAFTVdcBhwEYT9zF8JUk9zJk94GHA1duBi4AnJllIO9b3TsAHk7wSuAB4ILBVktWr6qZuBUuS5rU5E8DA3wEvqapPJtmaNtDqfcAzgauAp9MC+R7AUw1fSVJPcyKAhxNorArcH/hkVX172AM+HHhdVb18GHS1HnDj0DwtSVI3s7oPOMkaSdYc+nHfBRyQZP/h5p8DZwCbJblLVV1VVZcZvpKkcTBrAzjJE4GPAGcM/b8/BF4FPCXJAdV8E7gjsFXHUiVJ+guzsgk6yRbAG2j9ugto/bz3BL4KvBN4S5L70g5Dug/wgz6VSpK0eLMygIENgF9X1TnAOUl+AhwKFPBuWvPzwcD6wIGeZEOSNG5mZQBX1blJfjr0955WVWcPv7fwauDSqvos8LKuRUqStBSzpg84ySOSPGY4pSTAmcB2wPbDMb1nA6cChyaZlV8sJEnzx6wIqiR/BxxH++nAuyT5SVX9Y5KXA3sDmwIfojVBLxquJUkaW2MfwElWBZ5KO5534heNvpDk/VX19CQHA49P8gzgzsBTquqWnjVLkrQsYx/AVXVLkgtHpq8FHpnk7CTHVtWRwMlJHkQbmPWbbsVKkjRFY9sHPBxqNOEXwMuSbDYyb0/aSTb+GqCqvmf4SpJmi7EM4CR7ABclORWgqk4GTge+MRHCVXUlcBNwh26FSpK0gsauCTrJHYDnAUcA2yU5paoOqqpXD4canZHkXbSfFHww4F6vJGnWGbs94Kq6gXaGq48ALwZWT3LKcNurgaNodd8L2K+qLutUqiRJK2zsAhigqq6oquuHZuZnAWtMhDDwv8Bnq+qZVXVxvyolSVpxYxnAo6rqKloIL0ryQ+CTgIcZSZJmtbEPYPjTgKvvAncC9qmqn3cuSZKk22VWBHCSDYDdgF2q6nu965Ek6fYau1HQi1NVv0vyhKpa1LsWSZJWhlmxBwxg+EqS5pJZE8CSJM0lBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUgQEsSVIHBrAkSR0YwJIkdWAAS5LUwbwP4CTzfhtIkmbear0LmGlJdgUeDFwPnFxV1yZJVVXn0iRJ88i82vtL8mjgncDNwFbAeUk2r6pKkr7VSZLmk3kVwMC2wGlV9c9VdThwKnBKknsOIbzE7ZHksCTnJzl/4cKFM1awJGlumm8B/F1g7SR3Aqiq1wBfAD6f5I5VdeuS7lhVJ1TVNlW1zcYbbzxD5UqS5qo5H8BJNk2ywTB5EbAlcODE7VX1T8BZwJ4dypMkzVNzehBWkr2BlwHXJTkX+BRwOHD60OX78aq6ErgW2GCJK5IkaSWbswGcZCPg9cA/ADcADwNeA7wN2Ac4Htg6yWrA9sM8SZJmxJwNYNpI5x8CF1XVoiQ/Ba4GXgQcBRwC3I92SNKbq+pHvQqVJM0/c7YPuKquBv4AfHiYvgb4KvBpYPequqqqzq6qdxu+kqSZNqcCOMnOSZ6b5Ihh1vOAK5McB1BVvwPOA7ZLsm6vOiVJmjMBnGR74CPAImC/JMfTTrbxLtqhR59Isj7wAGBtYPVuxUqS5r05E8DAw4F3VNWJwE60kc17A+sAL6D1/74DOBI4YtgbliSpi7kUwN8DHplki6paBLwOuBE4uKp+X1WHAs8EHlNVF3WsU5Kk2R3Aw0k21hz6c8+kjXreIcndqupGWghvm+QZAFW1qKpu6FexJEnNrA3gJLsDn6Mdz/s+4K7Ah4AdgN2TbDnsCZ8B3NStUEmSFmPWHQc8/GrRXwFvpo1y/j7wVOBs4FG0ft6DgEOSXEQ77eSOXYqVJGkJZl0AD7/b+7Mk5wD/C/ymqt6a5Cbg68B2VfWiJDvQTrRxfFX9uGPJkiT9hVnVBJ3kCUmOTLI6sB5w6BDIVNWxtEOOXp9k7ao6q6reZ/hKksbRrAngJLvQzu18SVXdBLwcODzJy0YWO5XW37uoQ4mSJE3ZrGiCTrIdbYDVE6rqW8MPLfycdpzvZ4bm508D2wFbA+sDHucrSRpbsyKAgatoe7Z3S3Jn4GO0H1v4H9oI6IcB9wW2AZ7mSTYkSeNuVgRwVf1wOOzodGAN4LXAibQTazwYeHlV/SzJBoavJGk2mDV9wFX1HWAP4Oiq+requrWqTgDuA2w8LHZ1twIlSVoOs2IPeEJVXQJcMjGd5InARsAvhturU2mSJC2XWRXAE4aTcTwNeDGwX1X9unNJkiQtl1kZwINLgX2r6ge9C5EkaXnNygAemprP7F2HJEkratYMwpIkaS4xgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjowgCVJ6sAAliSpAwNYkqQODGBJkjqYlwGc5H5JHplk9SSr9q5HkjT/rNa7gJmWZF/gTcAvhsv5SU6qqmv7ViZJmk/m1R5wktWBA4BnVNVOwCeBTYGXJlmva3GSpHklVdW7hhkzBPCngI9W1UlJVgF2AHYHLgXeW0vYIEkOAw4bJu8H/HAGSl6ajYArO9cw2bjVNG71gDVN1bjVNG71gDVNVe+a7llVGy/uhnkVwABJdgaeD7ytqs4a+oAPAHYDDllSAI+bJOdX1Ta96xg1bjWNWz1gTVM1bjWNWz1gTVM1jjVNmFdN0IOzgC8ChyR5dFXdUlUfAe4OPLhvaZKk+WLeDcKqqkVJPgwU8IokWwI3AncBftm1OEnSvDHvAhigqn6X5N+AS4BnAYuAg6vq130rWy4n9C5gMcatpnGrB6xpqsatpnGrB6xpqsaxJmAe9gFPNvQBV1Xd2rsWSdL8Me8DWJLmoiSruGMx3ubjICzNoCSLHX4/7pJsPRy2NhOPNXbbaBxr0tQl2R7YP0l61zIdkuyW5I2967i9DGAt0e198ybZA/hEkjuvpJJmRJJdgQ/Rjvee7scau200jjVNhyRjNwYmyUOTPPt2rmNX4F3AZbPlsMrlMTy/NwCf6V3L7TV2L0D1k+SxwObAKVV1Q1VVklWr6pYVWNeuwJuBp1bVVUkyGz4MhrrfARxeVRdPZzPeOG6jcaxpOiT5F+BOQyvHBcBJVfW7zjUFuCuwT5Jbq+q9K7COXYCPA4+rqnOTrFFVf1zZtfYyvD5PBt5QVWcP87q8RpO8lHY0zUbAu4FfVdWi5VmHe8ACIMmawBHAscALkrxpeGGvSPjuApwE/Ix2vu1ZYXhzvx+4DLgqydrTGL5jt43GsabpkOQk2hfNtwDnAvcGPt57j38Ika8Drwb2SPLk5bl/kt2Ao4EfA68b1vnHcdzTXxFJ9qR9OfwKsEmSxyRZrVP4fgR4LO3Q1fsBb6SdW+KOy7MeA1gkuTdwC/Bh4Le002zendYM+bQk912Ode0GHAO8Evg08E9JHjzue1FJHgS8BziQdo7wZwPbJ1ljGh5r7LbRONY0HZI8Etikqvasqh9U1fHAq4CLgXclWatDTVsM16mq64CrgHsBz0zynCmu497AU2gtNw8BbkrydYCqunm2h/Bwrv7HAs+pqv2BAE8CHjnTv2iX5B+ADarq8VV1clXtTftS8DDgUcMyU+q+M4DnueGD9z3AXavqo8CpwCpVdShtL+h1wOeSvCjJUvtEk2wArA08u6o+QDvr2BXAYUm2msancbskeSjtDb1zVZ1F6z/7KfBE4NErM4THcRuNY03TaD3gTnDbh2RVXQP8C3ADsM5MFpNkXeDUJMcPXT53p7XCfAB4BbDzcB76pa1jO+DRwKur6jyAqtoduHouhPDQMvVa4J+q6pxh9muBa4H9ge1mOIQL+PZQ2x0Bqup9tL3hQ4bpKX1xNYDnsaHJ8WjgyKr6edqPU/wSeFSSvwZ2Bg4eLg8AlthHlmR34HPAd6pq4k3/XeATtA/zZw17mWNl+ALyYeA+VfXjiSatqnojrSn2ScAOQxP97X2ssdtG41jTdBj5InEhcGmSBRMfksOe56XAAmDGRn8n2Qt4DrAX8DdJ3g2cQvuxmGOAi4D3AgcmedoS1vF42oDBK4DfDPPWBKiqPWghfOYwffO0PqFpMDy/Y4DzaK10DO/RG7kthJ8IPGa6Qzi3/WLe6sCWAFV1XW47WuIYWtP41Juhq8rLPLwAj6e9Ya+ZNH812hnCbgaeMDJ/jaWsa1fgu8AeS7h9K9q3+Q8CD+z93Efq+lvah9zfLGWZV9ACesfb+Vhjt43GsaZpep7/BhxP61ZZC/hP2h7vepOWO5M2BuLwGahpF9pe1KOH6U2Gx//vScutDewE/NVi1vFA4AeTX5vAWpOmvwZ8rvf/YQW20X2GbfSoYXrViW0yssyaw//yLaPzp6GW99DOmrgGrZXke7QWh9Fl1gVOp+2wPHhK6+29kb3M/AV4OPAd2l7tG4H/o/VpTNy+D/Ce4e/VGE7Yspj1ZHgxngLsPcy74/BB9zhgi5FlHwK8iNbU3X0bDDX9E/DS4e/1gG1p36q3B+4ystwLV7TucdxG41jTND7XE4D/HP5eZbi+M22v/520PdD70kYOfw04avT5T1NNu9J+Hu+9k+ZvRPtC+JYprmc74N9G7nso8D7a4TlbT1p2097/ixXYTpvQRhevNTy/l9JaZi4EDgLWHZZbA9h4Gus4kfYztusBqw/zHkH7UZ9/HWq7Iy18L6D1B09pe3smrHkmyQLaN+pvVdX3hnnvoO0Rb1NVVyd5AMNApKr68hTW+UbgetoL8AjaoRQPpn2j/1RVnT4sN1aHRCR5PnA32hvmH2h9OwtoA9E+XFUnr8THGrttNI41rUxJNgeOraq9hul70z7UNwC+QfsQfwzwB+CGqnr+DNS0B/B6WpCsBSwcapxoDt8EOAM4r6qet4x13YW2B3wSrTXnO7RgvxI4kvbl6Vc1S8+GNfSHn0gbIPdk4Mvc1hT9dOCgqvrxNNewB+1zcPdh+o6018+awB9pXxBupXXnXlFVz8xyHLo5KzvltWKGF9MLaMfQfW/ig7WqnpfkeOCCJNtU1SXDh/PPp7jqC2l7TufQmvfeRftgOBzYbGKhMfwQ/zxtBOxraB9ep1TV14fDP/ZPcspU30hTMI7baBxrWplWA+4zBNXjaWMaNqd90bioqvYF3pNk9aq6Cab39I1DH+I+tJG85yTZl7b9X0BrRqWqfjP0DZ+aZJOq+s0S1rVKVf067YxXz6YdG/sRhsAdBkyuPlvDF6CqrkjyElpL3Q9ofePXAyTZkfa/nNYAZmRg3vB/2QnYm9ZS8Y2q2i3J+sAdqmri0L2p79X2bmbwMjMX4O9oIfO4pSzzL8DVwPpTXOdEk94qtCaYhw7TEy0r/0RroglLaMae4W0w2nc00Z+0FnCnSXUfTBsNfrv7lEbW2XUbja57XGqaof/50bQvGpfT9poeNMz/LrDrkrbRNNRxP9rI5oeOzFtn+DB/J3DEpOVXWcw6Vpk0ndHrkfkH0/pOZ2W3wcRzWtL/A/h72l7xtDer0wblHUdrWr6U1rKwO62b6mOMdFWtyGuo+4b2Mv2X4cX8dmD3YXp92gCO/YBHTFr2rbQRwUta1xqTplddwnJPo31L3LL38x/q2YJ2SNV9RrbJ5A+uNYa6L5j4oF7Bx9oWeOTo9u+9jSb/n4DVetc0zc93lZG/7wVsOOn2zwA7zHBNxwEfBR7EyICiIYSPA16xlPvuBPy/xQXzyDL3pA2a+w7wgN7/g+XcNlsDBwB/zaTBcSPLbAE8D/gfpnlQ4Oj7hdbHu/PwOproA16f1iR+79vzOB6GNA9Ue8VsCOybdpL942iB/I/AF5M8cWTZl9YS+lWGw5Y+kuToJAcOy98yaZl7JHkqrU/x76vqB9PypJbfPWiDKJ6cZPMaTNw4nIDhQNqHwFNr6B9fXkn2pg38WXvkONMaPTB/prfR8H/7YJIXJDl4qOnm4bCzLjVNt2rNsKsNf19WVb9NsnaS1ZKcAvyy2jHf02r0GPKq+kfanvg/AQ8cDn/6A60r5OvAhkk2XMw6dqV9MT6rRpqUF3Oyh5uATWl9o5es7OcyXYausQ8Be9IGwU30t05+ftfSvjjvU1X/M021TLw/bhl5/VxZVV8aXkc3De+bdwI/rqr/u10P2Pubj5fpvXBbM9X6tDf5D2gDG3Ya5j+ZFhhLbTqh9aF9h3ag+T8Cx026faI5OsD9gbv3fu5DPeuO/L0d8Dbam/wvvrnSTtCw4e14rE2ArzK0KrCEEeQzuY1og4y+T/ti8Vja4RP/b9z/byvwPJ9NO4vX24BHMrTU8Od7wvelDX76wOTnP0013Zf2Rffuk+a/mdZ8edeReWuNvlZH5m9NG+Sz3TB95//f3nmH2VVVb/j9EiH0ECnSAkR6C01aIFIDJBJAQEAg0kIEKSIiCkgRpSMRKUIioZnQDAIiVSCU0ORHky5VpIMU6QTW749vn8lhmCHJZM69d+bu93nuM3PaPfvec+5Ze+/1rbWwD7tNNxHtzGw06guHuj0CrJqWh+ORZZvXpeLrNQfwJHBqaV15JPy19Bu5otU91GHXRR4Bd3MiWgoqvI0fxkMjYveIuDHtMj9++LZLUmbuAPw4Ii7ARnx5SdukER/hEUePMI9FxEvVfaqpIwXxXyfpZ0mUcj/2w82A87YuXtpXEfFORPx3Ok4ZWBl5v6SFgPOBP0k6pnSenjX+jhYA/hgRF0fETTj06BBJh0LLdVMjXbdpRdLZwFCsKO4HjAB+IalXlEaMEfEvnE1p13Rc1fVy58UP7X0lzVdqxy9wuslzS+s+iiQwSm0rfpP/wSr9Iel3eClwMnCXpEHF5yi9T1dLtvEy8NuI+D9oySj1Kb6OLZRmkyq7XhHxLjAQWDVFhhAeCfdI/08C3sHhY+V7qMOhRNkAd0MkDZA0tFgubqKI+Cw9hJA0U1L7bk8pDKItwkrMQyJigpyw/gisPuwDHKxUPq3ih1lHmBmPhvbHfrY7cQabAD7A09ELwtSnjvsqIuJ1PMJcG4ea3IlHQIMknZD26SxV9dQyK7CjJqchDODnwKaSvp3a1GVjEZPxnT0ihkTE6IjYBueyXhjYo3hwlx7gDxXLVd+vETERG0wBB0iav7R5b+AtSe2lviwe+q/hdIurAq8A4yNiB5x16bSklG60390UkdRP0nzpNzM2rftaMnazMTld6NKS5qjyHpV0kKQVJC0cEa8CW2EXwZnQ0kntmf5/KSKuTsdNdwcuG+DuSV/gFDnNIPBF4yinqtsF+Bmwa7TjL5K0uKTV00iiCEnqBRwRESMiYjRWTi9Y0eeYLsJxrEPwj/luYF8sVBmKp2SPBHbWdKSwk7SmpGGS1kyrXsC1Sj8Dzo6I+5n8g65JnmFJQyXtDy0jiseBiZLGYuXvqTjWt3ct2lMVKRSlPxY2lfkrjhddo3hwt36AV/VAl7S+pP6SeqfzTMQdggD2TzMjYCXvQsCX7j1JGwJjJB0m6bsR8SYutDAsIk5P7zsaK51rljqzs5C0HHZn7ZGM3sdpU8/0nHoNeFkO0zqeCsNl5VS0x+EQrssk/RSHPW0FLCuHY35hJFzQGR2fHAfcDYmIiyUFcFya8ryy1faP08P4ioh4ua33SMKIY/BU2SuSjo6Ih9MUZXmacmGc/7Tq6bypQtJA/ANaFPhTRFybRvpX4On3kemzL4tV4Jd1dFQql0f7DZ7a/q6kn6T3742n7NeQNJHkk8S+vEqRBVe/xp0rACJih9RB+ASH34B9joukY7pqzd97cfzrGnJ+4IuhpQTfX4HDJK0QHRTUTStykoYTcWjXVZKeiIhR4djy/+GR7HWSxuN44O3C1Y/K7zEkvcepuLO7dXqfR+USeMV+O+JIhjdr8dk6mWfxTNGMwHaSLo2I50qG+D84Jn1BYPfpdAtNiRuxdmAJ3KFZALsweqblwpVxYCXPt2gAR3x+Tf8Lp7c7F/eUZ0/rNsLxct9pte+UBFcD8KipiA89AxjTxn7D0k26TL0/f2rPFqndB+GUfH9P/8+Ytr0LbNxJ55oLuA5YPi2PwZmV5kjLe2Gl5AXAXUD/Gnz+AcCrwOppuTeudTsTJXEOsDvwPF8Rbtbor+LzYGP3Y5zDeRsmh4nMiv3dlaUobKddR+HZlrXSb+8Y3BkrxJCDsdq3LRHg3HjKuhBIfgO4BFi/tM+MePbmcbpYqFHpM/RK1+YQPIM2AmfyWjNtvxL7Wiu7P3Ge6f7p/x7YrXYcKTQN2AzHjT9OSstbSTvqfTHyq5MupDM6vYfDHK5KN/ZAnED8XmCDaXivAcAupeV5sHq0V2nd2jh3bkMk6cdZce4DViqtG4xHEgek5e3xKHTdTjhf7/T5t8HqyWfw1OfFuKB6Dxz6tRiwYI2+g6Xw6GEL3EG4Gbg6PdR3xTNe/bCIZ/l6X7MOfsaNSv+3NsK/AwandRdi8Vmt2lUY2Flxx2/dtDwKC41uTNdl/im8z7eTIS7U6Udj/UXLeXBMapfsPJU+137YfdAP5+H+L6n4C44FriwOHYsjr8J1z88nRQXgOOtROFd+0c5yTvhOT9SSfcBdHEnLywW9j8fimnG4B/43/ONdGN/o16dp5anhbuCy9P49cY91EWxokDRX2Le1RVQUj9cBPsM1fB8qfK0RcQ2Osdxc0mIRcRE2mK9M78nCNWR/jxMfXI/DEobi8nFLYAP334h4OianqKuUiHgCx1COxD62cbgnfw2uvtM7Ip7FD5yHa9GmzkRSX3wf7wuTa9yGp3HPwUVFBkl6IzNRNgAAG6xJREFUGHg3Ioan46aqOPp0tEsREckN8z4ewS0il3EchEes1+JR3pfcHWVtQETcGo47LaY7P8Bix8LlsVY4JrXqFIyV0OpzDcDTzGvgGtTflLRI2NVVSRy6pNF4lmozHBVyGi6/ekJE/AZ3YIcBG6Tr+Wo6rho3Tb17RPnV8RdOZfdLnDZxMTwq2wfH+S6PRzx9sGG+hA5MFaf3mA24MS3vSEo0Ue/Pn9qzIfan9ceq45mLdpf2uQD4WUXn74N9dpuV1o0HNq/jd7IssHerddfSqkJOV3oxeYS5Ig432re0rbjms+LZh8NK26qMG12VNuKm8QjqVZzWddPS+lna2Hco1hAUboNi5FVkytoeuzMGAfcwnZmX6v1qdR3H45mjIcCaOG3oXBWeu3BLzNZq/eJYI7IJHmycDuxUk++j3hckv6bzAvphezAeASyejOX+yQhvXNpvugL0sX/5WKYzTWMnf/aNce3iwnd0AZ5amiktF8kYjsfZnapqx+D0/W+M/Xv3AYvW+/sptW/rdN2+Ue+2dLD9rdOfroSnLPdttb4l3WpartL4boqV1m1O5eOO8Lj0f3vpWpfHPsYx6foURrict/s7uFrT3e2dqxFftDNdW+pg9MaVhModlC8lIunkNs0A3IFn7srre2Lf/eFpuWbJTOp+ofKrAxfNPextS8vz4JHwGJwvdbb0ALiIJOiYjnMJCz+exlO8S9T786d2bYKFGnuU1s2NxVfX4MxfMyfj83CV7U7n2g9nwbqOqSzGXYPvSFhI8igN4qvvwGc4B49O9mi1fuVkhIen5WuAy8ufvcI2bYL9h+u1dy6cweoGYOGveJ9vAD9I/++F3QaFEe5ZOtdrdDGfL6X64m1s2wiPMvuUP2uFbdkf2CH9/xtc37rQDxQdgqHYdfOloiWVtq3eFyq/pvGCecrzFSwm+h1wHg5HGIRr2o7BccBzYgHWV4o+puG8uzTKQxyPPv6J/a3jcB3jwuD0waP1W9KD+85aGUQsBmozkXydvicB69GFCyvgWO2bkoG9FoeOLZa2rYSno9/DIWctn7vC9gzGsd4P4c7uDF+x7+lAvym8X9lVsmcywkUq04XT3y41c4Hjm6+mjc5/ejbdDnyvhu0ZgTUx62FX3X2pw9OntM8VwJG1/q6K+fhMFyAJiZ5OKehG4x/4x9h/8nX8MFoIF+TeB3gtOukCN0KsaBLTLIAz5xwaERMl/QSLW34VKZ1d2ndR4CNgUkS8UYfmZjqBdK//HE/tD8GZzHbEZeFuwqPIn4ULHVRdz3c5rCjfEft/t8ZCxzvii4luOtwGSXsCO+OO42LYF/m/rz6qMSiJ0ebGIXkbASdHxC3Fdpz6tldYDFh1ew4Ero+IhyQNw+FgR+AO2xk4YkR45uyNiNi56jZ9qY3ZAHcNUlarA4HdIuJZOQfzSfim+gd+MA0DvoUThq8e7STZ6KqUfuDzF59NLrb+fVxooMUIN0pikMy0I2mGiPi0tDwGeCkifplUrOtin+n8wM8j4u60X5XGd14c5vVspKxwcrH49bERvrOzzi3pz/gzbhQRD3bGe9YCSXMXnd2UbW9XrMI/sTDCadu3gReqNMJJWX4QDpfcLyIek/QD/Lw8ErullsQdqbci4s/puNo+N2o95M6vaX9hcc/dpALiwKzp7/ewinDztFz4M2qafKBG30F/7FObJS2Xq5TMi/08l9GqvnF+da0X9tGdi5OFFElNVsFK8z8Dt6R1c1Aqbl9xm4bimO/HcA7nOUrbDsTTrWvRCaIvrOp/lAYROk5Duwdjt8/YdO1mTteoZfo37TcCZ+9apAZtKozwDaSkJThR0d9IMcet9q9MtNduG+t94fJrChfIgqvPmSzOWAwnxVgiLW+DS2htVTqmcvFAjb+DomNxBY6xnLm8Pv3fG4srxuHMT93qO2iGF1bFXoxHKQ/hNIRg3/oVeNT0hXsi/V+lz3dIasuy2Id4J7Blq31+CkzsjM4fHtUvUu9rMY1tHoqzva2PU6CeRQp5xNO7P0zX9fTUialMk4GzWf0CWAGrm3tid9zfS0Z4WGrvqnX/7urdgPyawgVyoPptWOX8dZxE/6BW+2wHPEAaGXenFxbanEoKUcDK7qtpNRLGU9Ab4mQTdW93fk3zdd4fiwuL6zwQZ/IqlLKLpJFLTUa96Zxz4lH3xaV1WwC/opUwCmfi6lvv77EO1202LEo7o7TuVOBHrfY7IO23UoVtWQJ4EY+wx6b7ZwesH9gTC/kWSfuuXe/vLiKLsBqWlEVnThwnWASHDwb2ioizC1+FpNWwP2zmcBaeboWkPvjH9DhOsPC+pAvxd7N1RHwgl0PcF6chfL6Ozc10kFRE47vAmxFxdLrut+Hrfgd+eA8GxsbkWtZVt0m4YzcYeC8ijpTLH66FR3bnAJ9HxMG1aE+jIdfbnhtnjzoe18kdI+kCHJnxOr5+l+Awxo/CdcmraMtqEfEPSQPwKPgU7JrqhUPx7sLFMJ7AseKFr7qu4tJsgBsQSYOBE4C3cN3dv+BsOecC90XEQWm/XbFicvuImO70io1KehiPxr3bQyPiPUmX4BJvt2JF7DYR8UAdm5npAJLOxwb2LDyDMQTn0V4bhx09jYWFKwFPRcQuNWjTojj2/X8R8XLq5O6IUye+jQ3yABxSsxsWRv676nY1EpI2wYZun3A0wvq4RvHbwPvYVz4QG+K1cfKLqozvOnja+byIuDSl3N0f+H1EXCnX/F4Kh1K+ExH7VtGOjpANcIORepWjcGjRazjLVb+I2C3dSGfjOLZ7sMBgRHTBvL5fhaRlgCER8dvSuj74e3kxIvZP6y7HP+4NIxVaz3QdJJ0GzBsR25bWDcAPz94RsUla1xM/qyal5cpGLZK2wKEqbwKTcCjbtrgDcBDweEQcVsW5uwrJ+J6Hi0SMKa1fHxc3+E1EnFWKWpg9Kg6lSoOR9YCrSkb4QKwruKT1/VLvkW9BLsbQePTF0yZzpAfOKKBvCr15ESsMN8I/gOHd0PjOiMMDlpO0X7E+It7CiUb6SzohrdsSp+fLxreLIel0nCBk27S8t6RdIuIO/NB8UNKRkr4eEZ/VyPiuh6dSh+NR7nexoO9v2M1zAjCzpJMlzVZFGxqdVBDiRCxMm1PSisW2iLgZq4x3k7Rf6Tq9V1FbFiqd+xysj9lc0vci4ipc9WtPYKcUFlUc1xDGF7IBbjgiYjQuLXixpP542uRtPBomGeEtcS3LR+vVziqQNBSrKG/CSu+lUqINANIU1t640kyvtO7VerQ103EkzYfj1ouKW3vhKd6b0y4TcErPRXE8bAsVPziXxNOW92Fx30cRMRhHIVwYEf/AIS0fYsPcVEjqjQ3sHljYtDSwddKrAC1G+HBgG0m9qzJ2aabwwlbnPgc/OzZPPuErcWbAhSLi49J+DWF8IU9BNwSS5omI11utG4ED/J+LiNXSup4R8aVyZt2BlPHoRODgiLgmjYQH4WT0T0bE79J+u+Ef//eilKwh0zVID847sLHbBxuypXFS/ldK+wn4ZkQ8XcO2HYvdPdun5Rki4lNJX8czTsOAd3Gxjw9q1a5GQNK3sNZiWKREFclXfjCu/HRpRPyztP8sVX5HkhbACue1cRGF8rmPwLkTBkaDJ+PJI+A6k3wVl0uaq7w+IkZhYcFMkpYtVte6fbVA0gZYaLZtMr79sOG9Jr2WlHS1XAd2PyzEysa3iyFpeZy17Ic4HnQkrlAzntI0paTrcfGFp9NyZfV8Ja0gaaCkObD/8p10P5KMb09cu3ZGYPaI+LyZjG/pu38KV1s7q9gWEc8Bx+B0oFtJWrm0rdLvKCJewtXPJgK/Lo+EU5uexNesockGuI6UlIT7RcSbknpI6pG2zYfl+yOB69KUSkP35qaDN3DmnEXT5x+L66x+jpWwh+EsO5Ow4vuRurU002GSXmEcjmf/EQ4JORqPhneQNJ+k84BnUge0OK4qn+/g1J6j8e9sXRx5sFESFJFmnIbiRC9NY3hL9IAW988gYPF0jUjrn8cGb0lg0zRzVQmSRiZFenHuV7ERvh04VtJaadP5wKcR8VFVbeks8hR0nZC0MZ7WegDYNSJe0eTY3vXx6HdERDwvaWfgtoh4pp5trpL0w7oe+AyHNlzUSGKJTMdRKiJSWj4TTx2ej5PiL4MTWawI3BsRu6X9qszt3Dra4BAcWz4K+zkXwakMH8RFF3aKLpSXuTOQtC6uuDYS+E9E3JSiEcbgEK0flPbti41eJeGQSbQ3X0RsnZa/VhLm9cVTzofheN8PSvdQQz9DsgGuA5KGYLXlyfhHvhwOYn8wTUWfD5wTKUF4s5CmkW7FyUYuStN/0Y1H/t2e9BC/GKdvvEuu9rMjrtu8LE7W8Ac8gvp2RJySjqs0Kb6kPXDe6XUj4nG5qMdYrC+YCZgV+xhfwJ2CJ6tqS6MiaW/g13iEuTR2E32In0+X4kpDlSchkTQKC083iIiHVSrWIekQ4J8R8ddkiD8q9DRV30OdQTbANSb1IDcAXo2I25PSeXOcA/bMiPinpPnSiLihe29VUBoJHxwRZ9a7PZnpJwkKh2Px1QrALhHxQuqIbgy8AxxbTBnW6sEpaTjOoDYMhx19C/h+MbJqViStgtPa3paiEL6BZwLewqk4ZwH6AesAR0eFcdHJ+PbD6ua5cd3n+9O2g3Go2NqtNSFd5dn5tXo3oJmQSwoehqezngII16r8HN/Ye0r6Q/KVNZRcvlaE08ltBPxD0scptCDThYmIUZIC+D2wYzK+PSLiakkzYHHTR6X9q5p2/kK0QUT8MWkObqRJog2mROoU/RY4V9KLOKXjr3BRmPERsVdSIC+FZwj+VGFb1gbmiohBkhbBroHhks4Kx/5PAE4qxHLla9ZVnp15BFwjkuDqRJw95qo2tvfHyt+lgROaXWiUFJUfRMQT9W5LZtqQNBIYF46bLa8fgRXQP46I29s4rsokG5vhkJnNI+LNVtt2x9m3touIR7vK6Kmzkev0/hGn1ry9tL4Hju2dH0/T3xMRH1XZUZF0Mi688mCkGN6kot8CWAAXf3gkre+yHaY8Aq6YJOOfGSfUODwirpI0Oy6xtizw74h4Mo2EewCf4DR4TU0xzZTpWpTEMv9Iy+WH43n4/j5d0r4RcWv52AqNbxFtsHMRbZDO93kp2iBwtMFWrTsOTcQK2LDdXlw3TY6FPgqn4vwh/q5uwwlKOh1Jo4GFgX/jyAfAKvp07TYDfiRpTET8X1c1vpANcOWkh8oHkp4BlpH0OO5tz4dVnxMkXRkRf4mIByQ9GhGf1LPNmUxHKItl0nJZLHMo8FBEnJt0EJtgwV3VbdoYFzF5ABfzAFqMbznaYIykz2juzm9PLIaDyTkHJqVBxIoRcZyknwL/gmo6TEmkN1+kPOBp3deBTyLivTRQCSzkWx6nCO2y5Djg2nE/Dm24E9/oZ+C4w5dxbw+AbHwzXZGSWGYksKuklUvG92A8dXgNQESMjIhDa9Cmwp95CHAVcLikFZPxnQsn6x8dqYRlRJwX3TjUbyr4NzB/Gv1+nvz0kQzt9pJWjYjfRrWV197HynMkbSrp17j4zEmSdgIIZ706NSLOa/9tugbZANeOy3Ce4w0iYg/ghnAJs4+AbypR1xZmMh2gLJbBiS3exmKZ/mmXCVipOimFlhXHVZnhqg92/eyVhHy3AS8BIyStkPzAu0fEn/PvzkTE5VjhXIjjAJC0I1aJv1aDZjyLs//dhMM0PwGOAh4BBkiaKbX1xdS2Ln3tsgirAiTNGRFvF2KO9kQdcgmtH+PsTo/XvqWZzPTRiGKZtqIN2mhXS7RBxlXIitk3SdfgeN8PgWeAbXDu9cq/r9RBWwlYA8cfPxcR78olSo/HuajfqbodtSIb4E5GTlo+AdgoIu5qZ58FcUnBA4Adml3xnOmalMQyOwOvl41rGv1uBiwIjImImvjqcrTBlEnGrA8wB/BYMQUvqVepE7URLo36GXBnRPyrwvZ85UAl7TMOeDMi9q2qHfUgi7A6n9mxj/dUST9pK9wCT4XdAwwOJxXPZLoUjSaWydEGU0eaHTgWFzEYADws6d7kl/+4EM5FxN9r0JZRwNiIuAW+LOqSS44uhuswv1oY3+4UJpYNcOdzO57+ehc4L/XI342IFv9Junkeq1P7MpnO4AtiGZzbeRhwraTbI+JP4axupxb+uirJ0QZTJonSjsR+8Yly+s21gd2STftd1KjKmKSFcY7t1STtGRF3t7HbJzgt6Pjkx+8S6SWnhSzC6kRSz3o2HIZxFZ5ivgV4VlK/ri4YyGRKNKpYJkcbtIGkhbAi/NqImAgt1YSuw/HZK0uat1btSQLUs3Ao2jhJq7exT0TEfd3V+EIeAU83kpbDOUofA96IiLckXYHLl92HHwL/BXp1l2mTTAYbuA9pWywzCOiFFf5AZTGjLVORpf8vw0blrIi4v+RfbIk2qKo9Dc4nwOnAEpK2jojxABHxvqS7cKKSpamB0rkkxuuJKyvdApwv6Vk82v1jW8d1N+MLeQQ8Xcj1RC8EfgKcg9WVAB/jaiG3ANvhOMSxkmapRzszmc6gMF7JqH0WzkJ0RkQ8FBHvpt0OA56vkVK1ZVSdjGzPiPg8Iv4XKZNaWr8rsBVWPkczGV9J8yS/7ms4ROwRXLf3u8U+EfECcDMeKFTdnh4lsd5ErIO5DHgFWA94qr1juyPZAHcQSevhROXDI2JL3MNcJm0ei2+uAyLiloi4AKuim7Ggd6aLI2mUpHULw9WWWEbSspKuAj4si2UqbNOGwDmSflkYk9YhTpIWlGtp74+LQDRVqF8SXF0K3JBied8HrsQC0CHF95a2rQy8UWFbDpcTeXyuybHgL2B//Sn4+XkA8Jc0i9IU5DCkDpJukvki4mY5n+x9+MZ+HRvfy8OxwEVO1W6j3Ms0D0kscz/OktSmWCYZ2pVxusLK/XVJTHQicCqe6l4Nl8V7rNV+wtOq7zRbtEEyvkcCI7DaeX3gV0kYtyAOxVoOJ97oj5OSVBLnK+lMXDbwZZz2857StiuAeXBd5k8lbRgRN1bRjkYkj4A7SEQ8FhE3p8XdccKBLXHN002xeq+lV56Nb6Yr0mhiGUnzALsC+4XrRV8EzIDVzuX9lNr1WBMa3zmBI4AnI+L+iDgd+CewmaSZkzDufCykW5FqjW9v4H/AmjhN6ehW99APgXVicknBG9NxTWGb8gi4AiRdDfwyIu6rd1symY4i6Wvh9JHHY//hYsAx+MHdrlimBu1aHxuU/6YpzaOB9yPimHq0p5EoOj/JRbY78Ei4iMK5OB57VmA88CBwA0BEvF1xm2YFJoXjjPcE9gJ+VKixu6O6eWppil5GlbT2c0naGvgGTraRyXQ5ku9UEVGUgruDOotlJK2T/LlExM0R8Ubpof0BzuyEpKHJ+DQdktbBhTBWiIgJwChgRUl3k6Z5gV/jqeAjgJ5VG1+w0jpShq00azEKOEXSKpKOAb40q9IsZAM8nZTCIHrJhb2PwnVHq6wYkslUQvLX/QEnkSnEMP8Blq2HWEZSD0mz4Wnwn6cRVLFtpvTv08AzkgZhFfYLVber0ZCToZyK6+fOnVbfg+OgPwPuSIZwXJqSXiVckKLW7eyRzn8mcC+wdLSTsrcZyFPQnYRcPWQQ8HREPFHv9mQy04qkMTiRzE44vO62iPhd2nYJTmQxsB5iGUkHYUOyInB/RIwsbRsKXAI8RIX+zEZF0rrA2VjpfXdp/VIR8UQaGe8NPBsRh6RtdRWFSvoLVszv0AjtqRd5BNxJhPOnXp2Nb6YrIqkvjqvdIZwl6glgoKTj0sjyFxGxZh3FMpNwcYDzgNUlnSzpuLStB/AONkBNZXwTK+P6uGXjeyJwh5zm8XZgNK71OxfUVxQqaW7g1pLx7dGMxheyAc5kMrQkY9g9ia42BobjJPhv4Zy980KLMOuz0nG1Es9cAbySDP+9wJ64mg8RcQWwcpRKDzYDJf3JYtjHW6wfnJaHAgdL+k5E3ISFT3UvQJH89yOhuQVYkA1wJpNJFA/CiLgeWDYi7o6I43GVoeXStklf8RZV8iGwlKQ9sPE9Dugrae+0vek0F6VR4+XAGpJWSct/x/G2d+CRb++0/4e1b+VX08zGF3Iu6EwmU6LkiyuPlGbH/te6EREvSXoBi6z2joi/pnCkp9L2ppzCTNyFk/9sn2Yo7gGQ9H1gMK5SlWlAsggrk8m0SYrfvBB4PSJ2b4D29AXmjYj/S8tNPX1ZJmW32h1XYrsfzxhsA2wZEY/Ws22Z9skGOJPJfImk6l8fx//+JK1rCIPXrIrZKSFpZmAVHI3xIjAhIv5V31ZlvopsgDOZTJuUDW6jGN9MpjuRDXAmk8lkMnUgq6AzmUwmk6kD2QBnMplMJlMHsgHOZDKZTKYOZAOcyWQymUwdyAY4k8lkMpk6kA1wJpOpBEmLSgpJ36p3WzKZRiQb4EymiZA0j6QzJD0n6WNJr0q6MVU8Iq0/sAPvO0HSaa1WvwDMDzzQCU3PZLodORd0JtNcjAdmwWkLn8JVjtYF5ursE6WqSU1XJCGTmVpyIo5MpkmQNCcuLzgoIv7exvYJ2Bi3EBFKNWRPAwZiQ/0McFJEnJOOOxfYudXb9Ut/nwVWi4h7077fBk4EVsQ1fMcBP081iIs2PAq8DYwAPgfOBw7Kmbgy3Y08BZ3JNA/vpdfmkmZqY/tWwH+Ao/DU8fxp/UzAfcBmuCzhKcBZkjZM238M3AmcUzruhdZvngoGXIOLBayMR+HfB45tteuOwCRgALAPsD+w3TR/2kymwckj4EymiZC0Na4ROws2hBOBSyPi7rT9OeC0iDhpCu9zEfBeRAxPyxOAhyNin9I+i1IaAUs6GhvSJUs5pncBzgL6RMQH6X16RcRapfe5AXi+OFcm013II+BMpomIiPHAAsBQPBodANwl6ZD2jpHUU9Khkh6S9Kak9/BoeeFpPP0ywJ2tppJvB2YEFi+te6jVcS9hX3Um063IBjiTaTIi4qOIuCEijoqIAcDZwJGSZmznkAOBn2Lf7YbASsDl2HBOCwLam3Irr/+0jW35WZXpdmQVdCaTeRQ/C2YCPgF6ttq+DvDXiLgAXI8XWBILpQraOq6t82zbqrThOunYp6frE2QyXZDcq8xkmgRJc0m6SdJOkvpL6ifpe8BBwI0R8S7wHDBQ0oKS5k6HPglsKGkdSUtjRXS/Vm//HLB6Sr4xt6S2ni1n4OnvMyQtI+k7wHHY5/xBp3/gTKbByQY4k2ke3gPuwqrlW4BHgGNwKFChMj4c6ItHpK+ndb8B7sE+41uB94Gxrd77JDySfTQd9yX/cES8CAzGCugHgDHAhUC7/udMpjuTVdCZTCaTydSBPALOZDKZTKYOZAOcyWQymUwdyAY4k8lkMpk6kA1wJpPJZDJ1IBvgTCaTyWTqQDbAmUwmk8nUgWyAM5lMJpOpA9kAZzKZTCZTB7IBzmQymUymDvw/rKJq4yept1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,0.5,0.5])\n",
    "\n",
    "fig.set_figwidth(11)\n",
    "fig.set_figheight(15)\n",
    "\n",
    "ax.bar(year_21_station['STATION'][:10], year_21_station['DAILY_ENTRIES'][:10].sort_values(ascending=False), color ='#8E44AD')\n",
    "ax.set_title('Top 10 Active MTA Stations in the Year 2021',  pad=30, fontweight='bold')\n",
    "ax.set_ylabel('Total Entries', labelpad=15, fontsize = 14)\n",
    "ax.set_xlabel('Station', fontsize = 14)\n",
    "ax.tick_params(labelrotation=45)\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.yaxis.get_major_formatter().set_useOffset(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}