Project_Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2111fdde-e8c2-4912-9598-a0b529f6c228",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03977a4c-09ec-43a2-915a-2a97fd67692a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an engine to connect SQL database to Python\n",
    "engine = create_engine(\"sqlite:///mta_data.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52baa783-3885-4e38-82cb-50a6c367262c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine(sqlite:///mta_data.db)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine #check to see if the method worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30fd4bab-653e-4ebe-a81d-be5d1aa89be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#assign database to new value\n",
    "MTA_data = pd.read_sql('SELECT * FROM mta_data;', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22016c9a-e37f-4ea3-9045-4cdc16c9780a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5458607 entries, 0 to 5458606\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: 458.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5458607, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTA_data.info() #get info on database content and type\n",
    "MTA_data.columns#get name of columns\n",
    "MTA_data.shape #get shape of data in (x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05ea7adc-4763-48f8-b8c5-0cc29d0d2ca3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
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       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </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>01/22/2022</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680606</td>\n",
       "      <td>2656954</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>01/22/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680608</td>\n",
       "      <td>2656967</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>01/22/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680620</td>\n",
       "      <td>2657068</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>01/22/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680671</td>\n",
       "      <td>2657118</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>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680734</td>\n",
       "      <td>2657163</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>01/22/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680769</td>\n",
       "      <td>2657179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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>01/23/2022</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680780</td>\n",
       "      <td>2657185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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>01/23/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680784</td>\n",
       "      <td>2657190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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>01/23/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680805</td>\n",
       "      <td>2657231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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>01/23/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680828</td>\n",
       "      <td>2657272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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>01/23/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680889</td>\n",
       "      <td>2657314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</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>01/23/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7680917</td>\n",
       "      <td>2657330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</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>01/24/2022</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7680928</td>\n",
       "      <td>2657333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</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>01/24/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>RECOVR AUD</td>\n",
       "      <td>7680932</td>\n",
       "      <td>2657374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</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>01/24/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680962</td>\n",
       "      <td>2657636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</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>01/24/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7681018</td>\n",
       "      <td>2657702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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>01/24/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7681173</td>\n",
       "      <td>2657759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</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>01/24/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7681217</td>\n",
       "      <td>2657775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</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>01/25/2022</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7681223</td>\n",
       "      <td>2657777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</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>01/25/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7681225</td>\n",
       "      <td>2657820</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  03:00:00   \n",
       "1   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  07:00:00   \n",
       "2   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  11:00:00   \n",
       "3   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  15:00:00   \n",
       "4   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  19:00:00   \n",
       "5   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  23:00:00   \n",
       "6   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  03:00:00   \n",
       "7   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  07:00:00   \n",
       "8   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  11:00:00   \n",
       "9   A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  15:00:00   \n",
       "10  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  19:00:00   \n",
       "11  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/23/2022  23:00:00   \n",
       "12  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  03:00:00   \n",
       "13  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  07:00:00   \n",
       "14  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  11:00:00   \n",
       "15  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  15:00:00   \n",
       "16  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  19:00:00   \n",
       "17  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/24/2022  23:00:00   \n",
       "18  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/25/2022  03:00:00   \n",
       "19  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/25/2022  07:00:00   \n",
       "\n",
       "          DESC  ENTRIES    EXITS  \n",
       "0      REGULAR  7680606  2656954  \n",
       "1      REGULAR  7680608  2656967  \n",
       "2      REGULAR  7680620  2657068  \n",
       "3      REGULAR  7680671  2657118  \n",
       "4      REGULAR  7680734  2657163  \n",
       "5      REGULAR  7680769  2657179  \n",
       "6      REGULAR  7680780  2657185  \n",
       "7      REGULAR  7680784  2657190  \n",
       "8      REGULAR  7680805  2657231  \n",
       "9      REGULAR  7680828  2657272  \n",
       "10     REGULAR  7680889  2657314  \n",
       "11  RECOVR AUD  7680917  2657330  \n",
       "12  RECOVR AUD  7680928  2657333  \n",
       "13  RECOVR AUD  7680932  2657374  \n",
       "14     REGULAR  7680962  2657636  \n",
       "15     REGULAR  7681018  2657702  \n",
       "16     REGULAR  7681173  2657759  \n",
       "17     REGULAR  7681217  2657775  \n",
       "18     REGULAR  7681223  2657777  \n",
       "19     REGULAR  7681225  2657820  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTAClean = MTA_data.copy() \n",
    "MTAClean.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60d47034-5e0d-41de-b436-74c69af90f71",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680608</td>\n",
       "      <td>2656967</td>\n",
       "      <td>01/22/2022 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>01/22/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680620</td>\n",
       "      <td>2657068</td>\n",
       "      <td>01/22/2022 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>01/22/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680671</td>\n",
       "      <td>2657118</td>\n",
       "      <td>01/22/2022 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>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680734</td>\n",
       "      <td>2657163</td>\n",
       "      <td>01/22/2022 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  01/22/2022  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS            Date_Time  \n",
       "0  REGULAR  7680606  2656954  01/22/2022 03:00:00  \n",
       "1  REGULAR  7680608  2656967  01/22/2022 07:00:00  \n",
       "2  REGULAR  7680620  2657068  01/22/2022 11:00:00  \n",
       "3  REGULAR  7680671  2657118  01/22/2022 15:00:00  \n",
       "4  REGULAR  7680734  2657163  01/22/2022 19:00:00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concat date & time into one column, dropping the redundant columns\n",
    "MTAClean['Date_Time'] = MTAClean.DATE + ' ' + MTAClean.TIME\n",
    "MTAClean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24cdb3de-e171-46c1-8b66-2058ceb4293e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5458607 entries, 0 to 5458606\n",
      "Data columns (total 12 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",
      " 11  Date_Time  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(9)\n",
      "memory usage: 499.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#convert Date_Time data type from object into datetime\n",
    "MTAClean['Date_Time'] = pd.to_datetime(MTAClean.Date_Time)\n",
    "MTAClean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f45b9863-8ff1-4793-a50e-2a4fe203f805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-01-28 23:57:18')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTAClean['Date_Time'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f97fbcf-e9cb-4cbe-b182-d8f6dc5d1824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-07-31 00:00:00')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTAClean['Date_Time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3687baf9-f915-4b6d-ab21-71b4e944153b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\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",
       "      <th>Prev_Date</th>\n",
       "      <th>Prev_Entries</th>\n",
       "      <th>Prev_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",
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       "    </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>01/22/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680608</td>\n",
       "      <td>2656967</td>\n",
       "      <td>2022-01-22 07:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680606.0</td>\n",
       "      <td>2656954.0</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>01/22/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680620</td>\n",
       "      <td>2657068</td>\n",
       "      <td>2022-01-22 11:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680608.0</td>\n",
       "      <td>2656967.0</td>\n",
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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>01/22/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680671</td>\n",
       "      <td>2657118</td>\n",
       "      <td>2022-01-22 15:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680620.0</td>\n",
       "      <td>2657068.0</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>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680734</td>\n",
       "      <td>2657163</td>\n",
       "      <td>2022-01-22 19:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680671.0</td>\n",
       "      <td>2657118.0</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  01/22/2022  03:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  19:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           Date_Time   Prev_Date  Prev_Entries  \\\n",
       "0  REGULAR  7680606  2656954 2022-01-22 03:00:00         NaN           NaN   \n",
       "1  REGULAR  7680608  2656967 2022-01-22 07:00:00  01/22/2022     7680606.0   \n",
       "2  REGULAR  7680620  2657068 2022-01-22 11:00:00  01/22/2022     7680608.0   \n",
       "3  REGULAR  7680671  2657118 2022-01-22 15:00:00  01/22/2022     7680620.0   \n",
       "4  REGULAR  7680734  2657163 2022-01-22 19:00:00  01/22/2022     7680671.0   \n",
       "\n",
       "   Prev_Exits  \n",
       "0         NaN  \n",
       "1   2656954.0  \n",
       "2   2656967.0  \n",
       "3   2657068.0  \n",
       "4   2657118.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by C/A UNIT SCP and STATION to represent a unique turnstile\n",
    "#shift date, entries, and exits in prep to represent daily traffic flow/turnstile\n",
    "MTAClean[[\"Prev_Date\", \n",
    "          \"Prev_Entries\",\n",
    "          \"Prev_Exits\"]] = (MTAClean.groupby([\"C/A\", \"UNIT\", \"SCP\", \"STATION\"])[[\"DATE\", \"ENTRIES\",\"EXITS\"]]\n",
    "                                                       .apply(lambda x: x.shift(1))) \n",
    "MTAClean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fb10b8e-8ac7-4c82-a1d8-490d2bdac131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5453558 entries, 1 to 5458606\n",
      "Data columns (total 15 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",
      " 11  Date_Time     datetime64[ns]\n",
      " 12  Prev_Date     object        \n",
      " 13  Prev_Entries  float64       \n",
      " 14  Prev_Exits    float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(2), object(10)\n",
      "memory usage: 665.7+ MB\n"
     ]
    }
   ],
   "source": [
    "#Dropped rows that have NaN value\n",
    "#Dropped total of 5049 rows of data. Now at 5453558 rows. \n",
    "\n",
    "MTAClean.dropna(subset=['Prev_Date'], axis=0, inplace=True)\n",
    "MTAClean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19d4979f-c393-45b1-a3f4-e7d7c398ec1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>01-03-00</td>\n",
       "      <td>57 ST-7 AV</td>\n",
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       "      <td>01-03-00</td>\n",
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       "      <td>01/22/2022</td>\n",
       "      <td>885528309.0</td>\n",
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       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>A011</td>\n",
       "      <td>R080</td>\n",
       "      <td>01-03-00</td>\n",
       "      <td>57 ST-7 AV</td>\n",
       "      <td>NQRW</td>\n",
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       "      <td>01/22/2022</td>\n",
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       "      <td>2022-01-22 15:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>885528295.0</td>\n",
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       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>A011</td>\n",
       "      <td>R080</td>\n",
       "      <td>01-03-00</td>\n",
       "      <td>57 ST-7 AV</td>\n",
       "      <td>NQRW</td>\n",
       "      <td>BMT</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>885528207</td>\n",
       "      <td>489321157</td>\n",
       "      <td>2022-01-22 19:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>885528263.0</td>\n",
       "      <td>489321412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>A011</td>\n",
       "      <td>R080</td>\n",
       "      <td>01-03-00</td>\n",
       "      <td>57 ST-7 AV</td>\n",
       "      <td>NQRW</td>\n",
       "      <td>BMT</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>885528170</td>\n",
       "      <td>489321060</td>\n",
       "      <td>2022-01-22 23:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
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       "      <th>5166775</th>\n",
       "      <td>PTH18</td>\n",
       "      <td>R549</td>\n",
       "      <td>01-02-08</td>\n",
       "      <td>NEWARK BM BW</td>\n",
       "      <td>1</td>\n",
       "      <td>PTH</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>09:10:30</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>30310</td>\n",
       "      <td>960</td>\n",
       "      <td>2021-08-09 09:10:30</td>\n",
       "      <td>12/30/2021</td>\n",
       "      <td>95046.0</td>\n",
       "      <td>19835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5166776</th>\n",
       "      <td>PTH18</td>\n",
       "      <td>R549</td>\n",
       "      <td>01-02-08</td>\n",
       "      <td>NEWARK BM BW</td>\n",
       "      <td>1</td>\n",
       "      <td>PTH</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>10:49:37</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>10576</td>\n",
       "      <td>441</td>\n",
       "      <td>2021-08-09 10:49:37</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>30310.0</td>\n",
       "      <td>960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5167298</th>\n",
       "      <td>PTH19</td>\n",
       "      <td>R549</td>\n",
       "      <td>02-02-00</td>\n",
       "      <td>NEWARK C</td>\n",
       "      <td>1</td>\n",
       "      <td>PTH</td>\n",
       "      <td>08/07/2021</td>\n",
       "      <td>03:25:07</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>14767</td>\n",
       "      <td>3388</td>\n",
       "      <td>2021-08-07 03:25:07</td>\n",
       "      <td>08/20/2021</td>\n",
       "      <td>16210.0</td>\n",
       "      <td>3556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5366326</th>\n",
       "      <td>OB01</td>\n",
       "      <td>R459</td>\n",
       "      <td>00-00-02</td>\n",
       "      <td>ORCHARD BEACH</td>\n",
       "      <td>6</td>\n",
       "      <td>IND</td>\n",
       "      <td>07/31/2021</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>1693</td>\n",
       "      <td>488</td>\n",
       "      <td>2021-07-31 00:00:00</td>\n",
       "      <td>08/13/2021</td>\n",
       "      <td>1732.0</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5372814</th>\n",
       "      <td>PTH12</td>\n",
       "      <td>R542</td>\n",
       "      <td>00-04-01</td>\n",
       "      <td>TWENTY THIRD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>PTH</td>\n",
       "      <td>07/31/2021</td>\n",
       "      <td>00:59:22</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>631</td>\n",
       "      <td>17949</td>\n",
       "      <td>2021-07-31 00:59:22</td>\n",
       "      <td>08/11/2021</td>\n",
       "      <td>838.0</td>\n",
       "      <td>25543.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23919 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           C/A  UNIT       SCP          STATION LINENAME DIVISION        DATE  \\\n",
       "1645      A011  R080  01-03-00       57 ST-7 AV     NQRW      BMT  01/22/2022   \n",
       "1646      A011  R080  01-03-00       57 ST-7 AV     NQRW      BMT  01/22/2022   \n",
       "1647      A011  R080  01-03-00       57 ST-7 AV     NQRW      BMT  01/22/2022   \n",
       "1648      A011  R080  01-03-00       57 ST-7 AV     NQRW      BMT  01/22/2022   \n",
       "1649      A011  R080  01-03-00       57 ST-7 AV     NQRW      BMT  01/22/2022   \n",
       "...        ...   ...       ...              ...      ...      ...         ...   \n",
       "5166775  PTH18  R549  01-02-08     NEWARK BM BW        1      PTH  08/09/2021   \n",
       "5166776  PTH18  R549  01-02-08     NEWARK BM BW        1      PTH  08/09/2021   \n",
       "5167298  PTH19  R549  02-02-00         NEWARK C        1      PTH  08/07/2021   \n",
       "5366326   OB01  R459  00-00-02    ORCHARD BEACH        6      IND  07/31/2021   \n",
       "5372814  PTH12  R542  00-04-01  TWENTY THIRD ST        1      PTH  07/31/2021   \n",
       "\n",
       "             TIME     DESC    ENTRIES      EXITS           Date_Time  \\\n",
       "1645     07:00:00  REGULAR  885528309  489321828 2022-01-22 07:00:00   \n",
       "1646     11:00:00  REGULAR  885528295  489321673 2022-01-22 11:00:00   \n",
       "1647     15:00:00  REGULAR  885528263  489321412 2022-01-22 15:00:00   \n",
       "1648     19:00:00  REGULAR  885528207  489321157 2022-01-22 19:00:00   \n",
       "1649     23:00:00  REGULAR  885528170  489321060 2022-01-22 23:00:00   \n",
       "...           ...      ...        ...        ...                 ...   \n",
       "5166775  09:10:30  REGULAR      30310        960 2021-08-09 09:10:30   \n",
       "5166776  10:49:37  REGULAR      10576        441 2021-08-09 10:49:37   \n",
       "5167298  03:25:07  REGULAR      14767       3388 2021-08-07 03:25:07   \n",
       "5366326  00:00:00  REGULAR       1693        488 2021-07-31 00:00:00   \n",
       "5372814  00:59:22  REGULAR        631      17949 2021-07-31 00:59:22   \n",
       "\n",
       "          Prev_Date  Prev_Entries   Prev_Exits  \n",
       "1645     01/22/2022   885528313.0  489321867.0  \n",
       "1646     01/22/2022   885528309.0  489321828.0  \n",
       "1647     01/22/2022   885528295.0  489321673.0  \n",
       "1648     01/22/2022   885528263.0  489321412.0  \n",
       "1649     01/22/2022   885528207.0  489321157.0  \n",
       "...             ...           ...          ...  \n",
       "5166775  12/30/2021       95046.0      19835.0  \n",
       "5166776  08/09/2021       30310.0        960.0  \n",
       "5167298  08/20/2021       16210.0       3556.0  \n",
       "5366326  08/13/2021        1732.0        501.0  \n",
       "5372814  08/11/2021         838.0      25543.0  \n",
       "\n",
       "[23919 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#code shows entries are decreasing in number/day as time moves foward, which is wrong.\n",
    "#code shows same phenomenon with exits count\n",
    "check = (MTAClean[MTAClean['ENTRIES'] < MTAClean['Prev_Entries']]).groupby([\"C/A\", \"UNIT\", \"SCP\", \"STATION\"])\n",
    "check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22828188-d75d-4086-b0c2-9b8be9c77243",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
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       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>Prev_Date</th>\n",
       "      <th>Prev_Entries</th>\n",
       "      <th>Prev_Exits</th>\n",
       "      <th>Daily_Exits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680608</td>\n",
       "      <td>2656967</td>\n",
       "      <td>2022-01-22 07:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680606.0</td>\n",
       "      <td>2656954.0</td>\n",
       "      <td>13.0</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>01/22/2022</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680620</td>\n",
       "      <td>2657068</td>\n",
       "      <td>2022-01-22 11:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680608.0</td>\n",
       "      <td>2656967.0</td>\n",
       "      <td>101.0</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>01/22/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680671</td>\n",
       "      <td>2657118</td>\n",
       "      <td>2022-01-22 15:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680620.0</td>\n",
       "      <td>2657068.0</td>\n",
       "      <td>50.0</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>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680734</td>\n",
       "      <td>2657163</td>\n",
       "      <td>2022-01-22 19:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680671.0</td>\n",
       "      <td>2657118.0</td>\n",
       "      <td>45.0</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>01/22/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680769</td>\n",
       "      <td>2657179</td>\n",
       "      <td>2022-01-22 23:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680734.0</td>\n",
       "      <td>2657163.0</td>\n",
       "      <td>16.0</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  01/22/2022  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  19:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  23:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           Date_Time   Prev_Date  Prev_Entries  \\\n",
       "1  REGULAR  7680608  2656967 2022-01-22 07:00:00  01/22/2022     7680606.0   \n",
       "2  REGULAR  7680620  2657068 2022-01-22 11:00:00  01/22/2022     7680608.0   \n",
       "3  REGULAR  7680671  2657118 2022-01-22 15:00:00  01/22/2022     7680620.0   \n",
       "4  REGULAR  7680734  2657163 2022-01-22 19:00:00  01/22/2022     7680671.0   \n",
       "5  REGULAR  7680769  2657179 2022-01-22 23:00:00  01/22/2022     7680734.0   \n",
       "\n",
       "   Prev_Exits  Daily_Exits  \n",
       "1   2656954.0         13.0  \n",
       "2   2656967.0        101.0  \n",
       "3   2657068.0         50.0  \n",
       "4   2657118.0         45.0  \n",
       "5   2657163.0         16.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#write a function based on a MTA Exercise encountered during the course\n",
    "#get the difference of current exit count and prior day exit count for a daily count value\n",
    "def Daily_Exits(row, max_count):\n",
    "    count = row['EXITS']-row['Prev_Exits']\n",
    "    if count < 0:\n",
    "        count = -count\n",
    "    if count > max_count:\n",
    "        return 0\n",
    "    return count\n",
    "\n",
    "MTAClean['Daily_Exits'] = MTAClean.apply(Daily_Exits, axis=1, max_count = 200000)\n",
    "MTAClean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5dc62f5-951e-48f4-98d5-7ba4ea0f8cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Daily_Entries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
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       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
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       "      <td>2656954.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</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",
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       "      <td>11:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680620</td>\n",
       "      <td>2657068</td>\n",
       "      <td>2022-01-22 11:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680608.0</td>\n",
       "      <td>2656967.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>12.0</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>01/22/2022</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680671</td>\n",
       "      <td>2657118</td>\n",
       "      <td>2022-01-22 15:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680620.0</td>\n",
       "      <td>2657068.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</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>01/22/2022</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680734</td>\n",
       "      <td>2657163</td>\n",
       "      <td>2022-01-22 19:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680671.0</td>\n",
       "      <td>2657118.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>63.0</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>01/22/2022</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>7680769</td>\n",
       "      <td>2657179</td>\n",
       "      <td>2022-01-22 23:00:00</td>\n",
       "      <td>01/22/2022</td>\n",
       "      <td>7680734.0</td>\n",
       "      <td>2657163.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>35.0</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  01/22/2022  07:00:00   \n",
       "2  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  11:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  15:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  19:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  01/22/2022  23:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS           Date_Time   Prev_Date  Prev_Entries  \\\n",
       "1  REGULAR  7680608  2656967 2022-01-22 07:00:00  01/22/2022     7680606.0   \n",
       "2  REGULAR  7680620  2657068 2022-01-22 11:00:00  01/22/2022     7680608.0   \n",
       "3  REGULAR  7680671  2657118 2022-01-22 15:00:00  01/22/2022     7680620.0   \n",
       "4  REGULAR  7680734  2657163 2022-01-22 19:00:00  01/22/2022     7680671.0   \n",
       "5  REGULAR  7680769  2657179 2022-01-22 23:00:00  01/22/2022     7680734.0   \n",
       "\n",
       "   Prev_Exits  Daily_Exits  Daily_Entries  \n",
       "1   2656954.0         13.0            2.0  \n",
       "2   2656967.0        101.0           12.0  \n",
       "3   2657068.0         50.0           51.0  \n",
       "4   2657118.0         45.0           63.0  \n",
       "5   2657163.0         16.0           35.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#write a function based on a MTA Exercise encountered during the course\n",
    "#get the difference of current entries count and prior day entries count for a daily count value\n",
    "def Daily_Entries(row, max_count):\n",
    "    count = row[\"ENTRIES\"] - row[\"Prev_Entries\"]\n",
    "    if count < 0:\n",
    "        count = -count\n",
    "    if count > max_count:\n",
    "        return 0\n",
    "    return count\n",
    "\n",
    "#set max_count to be 200,000 because based on MTA data from (http://web.mta.info/nyct/facts/ffsubway.htm)\n",
    "#avg daily ridership at the busiest station is 179,254.187\n",
    "MTAClean[\"Daily_Entries\"] = MTAClean.apply(Daily_Entries, axis=1, max_count=200000)\n",
    "MTAClean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "26e8d8d9-ab0e-4e42-a112-16dfc4371ba4",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Daily_Entries</th>\n",
       "      <th>Daily_Exits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-00-00</th>\n",
       "      <th>2021-07-31 08:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01 08:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02 08:00:00</th>\n",
       "      <td>22.0</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03 08:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04 08:00:00</th>\n",
       "      <td>20.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-05-01</th>\n",
       "      <th>2022-01-24 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-27 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-28 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>863305 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Daily_Entries  Daily_Exits\n",
       "STATION   SCP      Date_Time                                      \n",
       "1 AV      00-00-00 2021-07-31 08:00:00           16.0        159.0\n",
       "                   2021-08-01 08:00:00            9.0        124.0\n",
       "                   2021-08-02 08:00:00           22.0        409.0\n",
       "                   2021-08-03 08:00:00           26.0        451.0\n",
       "                   2021-08-04 08:00:00           20.0        455.0\n",
       "...                                               ...          ...\n",
       "ZEREGA AV 00-05-01 2022-01-24 08:00:00            0.0          0.0\n",
       "                   2022-01-25 08:00:00            0.0          0.0\n",
       "                   2022-01-26 08:00:00            0.0          0.0\n",
       "                   2022-01-27 08:00:00            0.0          0.0\n",
       "                   2022-01-28 08:00:00            0.0          0.0\n",
       "\n",
       "[863305 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#manually turning Date_Time column into an Datetimeindex\n",
    "#using the Datetimeindex to call the between_time function\n",
    "#Looking at activity between 6 and 10 am\n",
    "\n",
    "index = pd.DatetimeIndex(MTAClean['Date_Time']) \n",
    "target_time_morning = MTAClean.iloc[index.indexer_between_time('06:00:00','10:00:00')]\n",
    "\n",
    "activity__morn = target_time_morning.groupby(['STATION','SCP','Date_Time'])[['Daily_Entries','Daily_Exits']].sum()\n",
    "\n",
    "display(activity__morn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b8207567-9d6b-40e2-bb6e-6bd3ce0a0d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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></th>\n",
       "      <th></th>\n",
       "      <th>Daily_Entries</th>\n",
       "      <th>Daily_Exits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-00-00</th>\n",
       "      <th>2021-07-31 08:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01 08:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02 08:00:00</th>\n",
       "      <td>22.0</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03 08:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04 08:00:00</th>\n",
       "      <td>20.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-05-01</th>\n",
       "      <th>2021-12-02 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-03 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-08 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03 08:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>758107 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Daily_Entries  Daily_Exits\n",
       "STATION   SCP      Date_Time                                      \n",
       "1 AV      00-00-00 2021-07-31 08:00:00           16.0        159.0\n",
       "                   2021-08-01 08:00:00            9.0        124.0\n",
       "                   2021-08-02 08:00:00           22.0        409.0\n",
       "                   2021-08-03 08:00:00           26.0        451.0\n",
       "                   2021-08-04 08:00:00           20.0        455.0\n",
       "...                                               ...          ...\n",
       "ZEREGA AV 00-05-01 2021-12-02 08:00:00            0.0          1.0\n",
       "                   2021-12-03 08:00:00            0.0          1.0\n",
       "                   2021-12-08 08:00:00            0.0          1.0\n",
       "                   2021-12-14 08:00:00            0.0          3.0\n",
       "                   2022-01-03 08:00:00            0.0          1.0\n",
       "\n",
       "[758107 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping entry & exits data points that = 0 to prevent it from influencing the mean\n",
    "#There was a total of 105,198 hits\n",
    "\n",
    "activity__morn.drop(activity__morn[(activity__morn['Daily_Entries']==0)&(activity__morn['Daily_Exits']==0)].index,inplace=True)\n",
    "activity__morn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed0bbdc1-48a9-41b5-a7f9-27e0082435a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily_Entries    43921\n",
       "Daily_Exits      43921\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity__morn[activity__morn['Daily_Entries']==0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c294634d-f027-4c66-b92e-c3820a8b9b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily_Entries    28071\n",
       "Daily_Exits      28071\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity__morn[activity__morn['Daily_Exits']==0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "04262292-0f32-425f-a39f-697428aead83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily_Entries    0\n",
       "Daily_Exits      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity__morn[(activity__morn['Daily_Exits']==0)&(activity__morn['Daily_Entries']==0)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eda7bf13-41a5-4562-ab01-e883d7999396",
   "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></th>\n",
       "      <th>Tot_Entries_Morn</th>\n",
       "      <th>Tot_Exits_Morn</th>\n",
       "      <th>Tot_Activity_Morn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th>00-00-00</th>\n",
       "      <td>3611.0</td>\n",
       "      <td>54273.0</td>\n",
       "      <td>57884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-00-01</th>\n",
       "      <td>7152.0</td>\n",
       "      <td>41189.0</td>\n",
       "      <td>48341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-00</th>\n",
       "      <td>942.0</td>\n",
       "      <td>9739.0</td>\n",
       "      <td>10681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-01</th>\n",
       "      <td>640.0</td>\n",
       "      <td>3338.0</td>\n",
       "      <td>3978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-02</th>\n",
       "      <td>1527.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>2982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th>00-00-01</th>\n",
       "      <td>7628.0</td>\n",
       "      <td>7140.0</td>\n",
       "      <td>14768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-00</th>\n",
       "      <td>15882.0</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>19427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-01</th>\n",
       "      <td>37017.0</td>\n",
       "      <td>3452.0</td>\n",
       "      <td>40469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-05-00</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-05-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4413 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Tot_Entries_Morn  Tot_Exits_Morn  Tot_Activity_Morn\n",
       "STATION   SCP                                                          \n",
       "1 AV      00-00-00            3611.0         54273.0            57884.0\n",
       "          00-00-01            7152.0         41189.0            48341.0\n",
       "          00-03-00             942.0          9739.0            10681.0\n",
       "          00-03-01             640.0          3338.0             3978.0\n",
       "          00-03-02            1527.0          1455.0             2982.0\n",
       "...                              ...             ...                ...\n",
       "ZEREGA AV 00-00-01            7628.0          7140.0            14768.0\n",
       "          00-03-00           15882.0          3545.0            19427.0\n",
       "          00-03-01           37017.0          3452.0            40469.0\n",
       "          00-05-00              56.0             0.0               56.0\n",
       "          00-05-01               0.0            17.0               17.0\n",
       "\n",
       "[4413 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filtering to find daily traffic mean, grouping by station and scp\n",
    "\n",
    "per_scp_morn = activity__morn.reset_index().groupby(['STATION','SCP'])[['Daily_Entries','Daily_Exits']].sum()\n",
    "\n",
    "per_scp_morn = per_scp_morn.rename({'Daily_Entries':'Tot_Entries_Morn','Daily_Exits':'Tot_Exits_Morn'},axis=1)\n",
    "\n",
    "per_scp_morn['Tot_Activity_Morn'] = per_scp_morn['Tot_Entries_Morn']+per_scp_morn['Tot_Exits_Morn']\n",
    "\n",
    "display(per_scp_morn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "80627875-5898-40be-9bd4-30d417081d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tot_Entries_Morn</th>\n",
       "      <th>Tot_Exits_Morn</th>\n",
       "      <th>Tot_Activity_Morn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 AV</th>\n",
       "      <td>2744.166667</td>\n",
       "      <td>9523.833333</td>\n",
       "      <td>12268.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103 ST</th>\n",
       "      <td>56241.166667</td>\n",
       "      <td>60958.000000</td>\n",
       "      <td>117199.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103 ST-CORONA</th>\n",
       "      <td>54735.555556</td>\n",
       "      <td>10106.000000</td>\n",
       "      <td>64841.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104 ST</th>\n",
       "      <td>18226.000000</td>\n",
       "      <td>1647.166667</td>\n",
       "      <td>19873.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110 ST</th>\n",
       "      <td>15174.166667</td>\n",
       "      <td>17433.000000</td>\n",
       "      <td>32607.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOODLAWN</th>\n",
       "      <td>26607.800000</td>\n",
       "      <td>3269.400000</td>\n",
       "      <td>29877.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLD TRADE CTR</th>\n",
       "      <td>3084.388889</td>\n",
       "      <td>5473.555556</td>\n",
       "      <td>8557.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTC-CORTLANDT</th>\n",
       "      <td>2401.687500</td>\n",
       "      <td>2908.531250</td>\n",
       "      <td>5310.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK ST</th>\n",
       "      <td>16353.000000</td>\n",
       "      <td>42868.000000</td>\n",
       "      <td>59221.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEREGA AV</th>\n",
       "      <td>11730.500000</td>\n",
       "      <td>5709.500000</td>\n",
       "      <td>17440.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>378 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tot_Entries_Morn  Tot_Exits_Morn  Tot_Activity_Morn\n",
       "STATION                                                             \n",
       "1 AV                  2744.166667     9523.833333       12268.000000\n",
       "103 ST               56241.166667    60958.000000      117199.166667\n",
       "103 ST-CORONA        54735.555556    10106.000000       64841.555556\n",
       "104 ST               18226.000000     1647.166667       19873.166667\n",
       "110 ST               15174.166667    17433.000000       32607.166667\n",
       "...                           ...             ...                ...\n",
       "WOODLAWN             26607.800000     3269.400000       29877.200000\n",
       "WORLD TRADE CTR       3084.388889     5473.555556        8557.944444\n",
       "WTC-CORTLANDT         2401.687500     2908.531250        5310.218750\n",
       "YORK ST              16353.000000    42868.000000       59221.000000\n",
       "ZEREGA AV            11730.500000     5709.500000       17440.000000\n",
       "\n",
       "[378 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finding total mean entries, exits, total activity per station during 6-10am slot \n",
    "\n",
    "tot_mean_morn = per_scp_morn.reset_index().groupby(['STATION'])[['Tot_Entries_Morn','Tot_Exits_Morn','Tot_Activity_Morn']].mean()\n",
    "display(tot_mean_morn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "814dfdb0-4ab5-43ec-b765-75350a3b5072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION\n",
       "ST. GEORGE        210810.000000\n",
       "JOURNAL SQUARE    174872.960000\n",
       "103 ST            117199.166667\n",
       "125 ST             71745.608696\n",
       "9TH STREET         70888.600000\n",
       "116 ST             68901.818182\n",
       "MYRTLE AV          66598.250000\n",
       "3 AV 138 ST        66228.750000\n",
       "103 ST-CORONA      64841.555556\n",
       "111 ST             64119.000000\n",
       "NEWKIRK PLAZA      61247.200000\n",
       "SUTTER AV          60453.000000\n",
       "7 AV               59357.769231\n",
       "167 ST             59228.571429\n",
       "YORK ST            59221.000000\n",
       "Name: Tot_Activity_Morn, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ST. GEORGE': 210810.0,\n",
       " 'JOURNAL SQUARE': 174872.96,\n",
       " '103 ST': 117199.16666666667,\n",
       " '125 ST': 71745.60869565218,\n",
       " '9TH STREET': 70888.6,\n",
       " '116 ST': 68901.81818181818,\n",
       " 'MYRTLE AV': 66598.25,\n",
       " '3 AV 138 ST': 66228.75,\n",
       " '103 ST-CORONA': 64841.555555555555,\n",
       " '111 ST': 64119.0,\n",
       " 'NEWKIRK PLAZA': 61247.2,\n",
       " 'SUTTER AV': 60453.0,\n",
       " '7 AV': 59357.769230769234,\n",
       " '167 ST': 59228.57142857143,\n",
       " 'YORK ST': 59221.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highest avg traffic per station\n",
    "mean_graph_am = tot_mean_morn['Tot_Activity_Morn'].sort_values(ascending=False).head(15)\n",
    "display(mean_graph_am)\n",
    "graph_dict_act = mean_graph_am.to_dict()\n",
    "display(graph_dict_act)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d656e05-4ad3-4410-a19f-618e9ef4c875",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative methods to try\n",
    "#df.datetime.dt.time\n",
    "#.dt.date\n",
    "\n",
    "#timestamp = '2016-02-22 14:59:44.561776'\n",
    "#dt = datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S.%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "672eeb7e-23a0-4a6b-9dad-91ad04c23477",
   "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": [
    "#AM AVG Graph\n",
    "import numpy as np\n",
    "\n",
    "x = graph_dict_act.keys()\n",
    "y = graph_dict_act.values()\n",
    "plt.bar(x,y, color = 'Crimson')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Daily Activity 6-10am 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Entries + Exits')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Daily Activity AM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "28c6bbc8-6e37-4b5b-92b4-69a0b8e26b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ST. GEORGE': 210603.8,\n",
       " 'JOURNAL SQUARE': 115088.8,\n",
       " '103 ST': 56241.166666666664,\n",
       " '111 ST': 55598.0,\n",
       " '103 ST-CORONA': 54735.555555555555,\n",
       " 'NEWKIRK PLAZA': 47749.0,\n",
       " 'ALLERTON AV': 47148.333333333336,\n",
       " 'JUNCTION BLVD': 46127.833333333336,\n",
       " '25 AV': 45808.0,\n",
       " '167 ST': 44885.57142857143,\n",
       " 'FOREST AVE': 44683.333333333336,\n",
       " 'CRESCENT ST': 43850.333333333336,\n",
       " 'MYRTLE AV': 42217.75,\n",
       " '20 AV': 41993.8,\n",
       " '3 AV 138 ST': 41787.25}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entry_graph = tot_mean_morn['Tot_Entries_Morn'].sort_values(ascending=False).head(15)\n",
    "graph_dict_entry_am = entry_graph.to_dict()\n",
    "display(graph_dict_entry_am)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "edcbb0d8-384c-42fe-9456-a0b50882ea8e",
   "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": [
    "#AM ENTRY GRAPH\n",
    "x1 = graph_dict_entry_am.keys()\n",
    "y2 = graph_dict_entry_am.values()\n",
    "plt.bar(x1,y2, color = 'Gold')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Entries 6-10am 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Entries')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Entries AM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b6a6f627-4f4e-4053-9e72-4dafaf35aff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'103 ST': 60958.0,\n",
       " 'JOURNAL SQUARE': 59784.16,\n",
       " 'YORK ST': 42868.0,\n",
       " '125 ST': 42172.913043478264,\n",
       " '9TH STREET': 42017.8,\n",
       " '77 ST': 40650.333333333336,\n",
       " '7 AV': 39431.46153846154,\n",
       " 'PATH WTC 2': 35997.0,\n",
       " '5 AV/53 ST': 35628.19047619047,\n",
       " '72 ST-2 AVE': 34989.23529411765,\n",
       " '96 ST': 31494.48,\n",
       " '68ST-HUNTER CO': 31470.785714285714,\n",
       " '14 ST-UNION SQ': 29751.60975609756,\n",
       " '116 ST': 29727.090909090908,\n",
       " '138/GRAND CONC': 29212.333333333332}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exit_graph = tot_mean_morn['Tot_Exits_Morn'].sort_values(ascending=False).head(15)\n",
    "graph_dict_exit_am = exit_graph.to_dict()\n",
    "display(graph_dict_exit_am)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9a9adcbb-ed72-4ae5-9777-2770c0f83edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpW0c8126wBdWT2P2LZ6Hma29PAdiZmZVeJAYmZmlbhqyxaranVZo6qyduRpZp3hOxIzM6vEgcTMzCpxIDEzs0raHkgkDZZ0k6QL8/MRki6VdHf+uUrhtUdJminpLkk7FdK3lHRr3neCJOX0oZLOzunXShrT7vMxM7OF9Udj+2HAncDw/PxI4LKIOFbSkfn5FyRtBEwENgZeC/xZ0usjYgFwEnAQcA3wJ2ACMA04EHgiIjaQNBE4DtinH87JuozHz5h1TlvvSCSNBv4X+GUheXdgct6eDOxRSD8rIl6MiFnATGBrSSOB4RFxdUQEcGrdMbW8zgHG1+5WzMysf7S7auvHwBHAy4W0NSPiQYD8c42cPgq4v/C6OTltVN6uT1/omIiYDzwFrFpfCEkHSZouafrcuXMrnpKZmRW1LZBI2hV4JCJu6OshDdKil/Tejlk4IeLkiBgXEeNWX331PhbHzMz6op1tJG8H3iNpF2A5YLik3wIPSxoZEQ/maqtH8uvnAGsXjh8NPJDTRzdILx4zR9IQYCXg8XadkC1dPGjSrG/adkcSEUdFxOiIGENqRL88IvYHpgKT8ssmAefn7anAxNwTaz1gLHBdrv6aJ2mb3P5xQN0xtbz2zO+xyB2JmZm1TyemSDkWmCLpQGA2sBdARNwuaQpwBzAfOCT32AI4GDgFGEbqrTUtp/8KOE3STNKdyMT+OgkzM0v6JZBExJXAlXn7MWB8D687BjimQfp0YJMG6S+QA5GZmXWGR7abmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlXRiZLvZUslrptiSyoHEbADzxJLWDVy1ZWZmlfiOxMxe4eo3K6NPdySS3i5p+by9v6QfSlq3vUUzM7OBoK9VWycBz0najLR07n2ktdPNzGwp19dAMj8vGLU7cHxEHA+s2L5imZnZQNHXNpJ5ko4C9gfeJWkwsEz7imVmZgNFX+9I9gFeBA6MiIeAUcD32lYqMzMbMPp6R/LZiPhC7UlEzJa0cZvKZGZmA0hf70j+p0Hazq0siJmZDUy93pFIOhj4JLC+pFsKu1YE/tHOgpmZ2cCwuKqtM4BpwHeAIwvp8yLi8baVyszMBozFBZKIiHslHVK/Q9IIBxMzM+vLHcmuwA1AACrsC2D9NpXLzMwGiF4DSUTsmn+u1z/FMbMlTTtmKPasx92lr3NtHVj3fLCkr7WnSGZmNpD0tfvveEl/kjRS0puAa1jMFCmSlpN0naSbJd0u6es5fYSkSyXdnX+uUjjmKEkzJd0laadC+paSbs37TpCknD5U0tk5/VpJY5r9BZiZWTV9CiQRsR8wGbgV+CPwmYj4/GIOexHYPiI2AzYHJkjahtT767KIGAtclp8jaSNgIrAxMAE4MU/FAmnSyIOAsfkxIacfCDwRERsAPwKO68v5mJlZ6/S1amsscBhwLnAv8EFJr+ntmEieyU+XyY/axI+Tc/pkYI+8vTtwVkS8GBGzgJnA1pJGAsMj4uo8ceSpdcfU8jqHdOdU7BBgZmZt1teqrQuAr0TEx4F3A3cD1y/uoNyWMgN4BLg0Iq4F1oyIBwHyzzXyy0cB9xcOn5PTRuXt+vSFjomI+cBTwKoNynGQpOmSps+dO7dPJ2xmZn3T17m2to6IpyHdaQA/kDR1cQdFxAJgc0krA+dJ2qSXlze6k6jvclxM7+2Y+nKcDJwMMG7cuEX2m9nSrR0rQy5NPct6vSORdARARDwtaa+63R/u65tExJPAlaS2jYdzdRX55yP5ZXOAtQuHjQYeyOmjG6QvdIykIcBKgAdJmpn1o8XdkUwEvpu3jwJ+V9g3AfhiTwdKWh14KSKelDQM2IHUGD4VmAQcm3+enw+ZCpwh6YfAa0mN6tdFxAJJ83JD/bXAAcBPCsdMAq4G9gQuz3dMZmZLlHbcNbXK4gKJethu9LzeSGBy7nk1CJgSERdKuhqYksemzAb2AoiI2yVNAe4A5gOH5KoxgIOBU4BhpLm/puX0XwGnSZpJuhOZuJgymZlZiy12rq0eths9X3hnxC3AmxukPwaM7+GYY4BjGqRPBxZpX4mIF8iByMzMOmNxgWQzSU+T7j6G5W3y8+XaWjIzMxsQFjfX1uDe9puZmfV1HImZmVlDDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV0rZAImltSVdIulPS7ZIOy+kjJF0q6e78c5XCMUdJminpLkk7FdK3lHRr3neCJOX0oZLOzunXShrTrvMxM7PG2nlHMh84PCLeCGwDHCJpI+BI4LKIGAtclp+T900ENgYmACdKGpzzOgk4CBibHxNy+oHAExGxAfAj4Lg2no+ZmTXQtkASEQ9GxI15ex5wJzAK2B2YnF82Gdgjb+8OnBURL0bELGAmsLWkkcDwiLg6IgI4te6YWl7nAONrdytmZtY/+qWNJFc5vRm4FlgzIh6EFGyANfLLRgH3Fw6bk9NG5e369IWOiYj5wFPAqg3e/yBJ0yVNnzt3bovOyszMoB8CiaQVgHOBz0TE0729tEFa9JLe2zELJ0ScHBHjImLc6quvvrgim5lZE9oaSCQtQwoip0fE73Pyw7m6ivzzkZw+B1i7cPho4IGcPrpB+kLHSBoCrAQ83vozMTOznrSz15aAXwF3RsQPC7umApPy9iTg/EL6xNwTaz1So/p1ufprnqRtcp4H1B1Ty2tP4PLcjmJmZv1kSBvzfjvwQeBWSTNy2heBY4Epkg4EZgN7AUTE7ZKmAHeQenwdEhEL8nEHA6cAw4Bp+QEpUJ0maSbpTmRiG8/HzMwaaFsgiYi/0bgNA2B8D8ccAxzTIH06sEmD9BfIgcjMzDrDI9vNzKwSBxIzM6vEgcTMzCpxIDEzs0ocSMzMrBIHEjMzq8SBxMzMKnEgMTOzShxIzMysEgcSMzOrxIHEzMwqcSAxM7NKHEjMzKwSBxIzM6vEgcTMzCpxIDEzs0ocSMzMrBIHEjMzq8SBxMzMKnEgMTOzShxIzMysEgcSMzOrxIHEzMwqcSAxM7NKHEjMzKyStgUSSb+W9Iik2wppIyRdKunu/HOVwr6jJM2UdJeknQrpW0q6Ne87QZJy+lBJZ+f0ayWNade5mJlZz9p5R3IKMKEu7UjgsogYC1yWnyNpI2AisHE+5kRJg/MxJwEHAWPzo5bngcATEbEB8CPguLadiZmZ9ahtgSQi/gI8Xpe8OzA5b08G9iiknxURL0bELGAmsLWkkcDwiLg6IgI4te6YWl7nAONrdytmZtZ/+ruNZM2IeBAg/1wjp48C7i+8bk5OG5W369MXOiYi5gNPAas2elNJB0maLmn63LlzW3QqZmYG3dPY3uhOInpJ7+2YRRMjTo6IcRExbvXVVy9ZRDMza6S/A8nDubqK/PORnD4HWLvwutHAAzl9dIP0hY6RNARYiUWr0szMrM36O5BMBSbl7UnA+YX0ibkn1nqkRvXrcvXXPEnb5PaPA+qOqeW1J3B5bkcxM7N+NKRdGUs6E9gWWE3SHOBrwLHAFEkHArOBvQAi4nZJU4A7gPnAIRGxIGd1MKkH2DBgWn4A/Ao4TdJM0p3IxHadi5mZ9axtgSQi9u1h1/geXn8McEyD9OnAJg3SXyAHIjMz65xuaWw3M7MByoHEzMwqcSAxM7NKHEjMzKwSBxIzM6vEgcTMzCpxIDEzs0ocSMzMrBIHEjMzq8SBxMzMKnEgMTOzShxIzMysEgcSMzOrxIHEzMwqcSAxM7NKHEjMzKwSBxIzM6vEgcTMzCpxIDEzs0ocSMzMrBIHEjMzq8SBxMzMKnEgMTOzShxIzMysEgcSMzOrZMAHEkkTJN0laaakIztdHjOzpc2ADiSSBgM/A3YGNgL2lbRRZ0tlZrZ0GdCBBNgamBkR90TEf4GzgN07XCYzs6XKkE4XoKJRwP2F53OAt9S/SNJBwEH56TOS7mpjmVYDHu1ppzqfXzvyHPBlbEeeS2gZ25HngC9jO/LskjIWrdvTjoEeSBr9XmKRhIiTgZPbXxyQND0ixnVrfu3I02XszvwGSp4uY3fm14yBXrU1B1i78Hw08ECHymJmtlQa6IHkemCspPUkLQtMBKZ2uExmZkuVAV21FRHzJX0KuBgYDPw6Im7vcLFaXYXWjio5l7E78xwIZWxHni5jd+bXZ4pYpEnBzMyszwZ61ZaZmXWYA4mZmVXiQFJSbpvpapJa2gbWjnOW9O0W57dOK/PLee4pablW59tKbTrv4a18v3aUcSAYCN8VVTmQlPeRVmYm6YjC9l51+8p+2V5XqVCLauk5ZxNanN8fWpwfwAeA2ZJOlbRznpqnNEnDJX1H0mmS9qvbd2LJbP9QpUw9uLK2IemyFrxfmWP6RNJ2kj4l6RBJ27Ugvx0kHZofb6uYXau/K7aStHOD9PdI2rKV79VXDiTdY2Jh+6i6fWW/bCsOZO0XgyWtImlEo0eJ/Fp+zhHxXmAD4DLgUOB+SSdJelfJLH9DKue5wERJ50oamvdtUzLPdvyti3nW/y3KvF/LyyhplKRrgaOB9Ul/p6MlXSdpVIn81pZ0I/AVYAywHvAdSRdJGirpo60rfWnfA+5skH5H3tfvBnT33w7bVNLTDdIFRET0WC3QA/Ww3eh5X60u6XM97YyIHzaZX6vPGWBD4AZ6nqVg/SbzGyXphJ52RsShTeZXO+5pYDIwWdKqwJ7ATySNiIi1ez96Ea+LiPfn7T9I+hJwuaT3lClb1o7zjh62Gz3vi3aU8afASRFxSjFR0gHAiTQ/997PgBN6yO/q/PSXTebZ6s/NqhFxb31iRMzM/5v9zoGkvFsj4s0tzK/VH1pIY2tWoHVXgq0+Z4A7Wpzn86TA1BaSVgHeB+xDuko/t0Q2QyUNioiXASLiGElzgL+Q/l5ltOO818gXIipsk5+vXiK/dpRxo3zHuJCIODUH6GZtWB9ECvl9G9iiRJ6t/twM62Xf8i18nz5zIOkem+WrFgHDClcwAso29D4YEd9oSekGjsciYnIrM5S0IrAHsC/pi2Qq8C3giig3EOsCYHvgz7WEiJgs6WHgJyWL2fLzBn4BrNhgG5q/Kof2lLFhe5WkQT3tW4yG1f05v+cj4pESebbanyUdA3y5+P8n6evA5Z0okANJeb9rZWYRUakBtwetrpNu6Tlnx/e0Q9KQiJjfZH7/rVieRmaRZk84CbgoIl6q7ZC0ZkQ83ExmEXFED+kXAWNLlrEd5/3HiJjewvzaUcYLJP0C+ExEPAsgaXngR8CfuiA/aP3n5nBSIJ8paUZO2wyYDnSkDceN7eXNlTQWQMlvJD0t6RZJZW5/G5K0cslbdIDdJS1TyOsNkj4r6X0l82vHOb/yjy/ptLp9ZXqdTZS0UiHP7SQdL+lzSvOxlbFORHwgIi6IiJckrSTpI5L+DNxYMs9Wa8d5/0LS3ZK+odYsGNeOMh4BPAXcJ+kGSdOBe4Gngc+3IL8bCvn9X8kytvRzExHPRsS+wP8Ap+THjhExMSKeKVnGaiLCjxIP4DZgmby9H6nud1VgB+CvJfJbmzRXzoWkL9fXAD8A5gLHlyzjX4CxeXsD4HFS1cllwHc6fc45n5sK2zf2tK+J/K4FXpu3Nyetz3A4qaH8lxX+3sNI7SLnk9bAeRLYFhjU6f/FNp/3G4CvkXoEzQC+AKzbTWUs/H3eBGwKvKYFv8+W5deG74p1ent05P+vE2+6JDyAGYXtM4DDCs9vLJHfFaQujDuRbqNvAc4E1qpQxlsL298Efpa3ly3u69Q51x/XIJCU+T3eUtj+PvDdvD2ouK/JPE/PweNXpKvAwcCsTv3v9dd5N3iPzYDvAP8G/t4NZQT2Bz7YIP1jwH4l8tuq+JkDDiBdPJwAjChZxhmF7VZ8V9yavx9uLTxuAR4EFnTi/89VW+W9LGmk0ojn8RQaTum9V0VPRkTE0RFxcUR8FlgT+FBEPFShjMWG4O2BSwEiLUv8con8Wn3OACtLeq+k9+ft9+XH+4GVFndwA8V2oe1Jd19E7iFV0ibAE6S++/+MiAWU70mHpP0lfbBB+sdUN0CxmWwL260671czT43Na5D+L5cn3Sk3nU1hu1VlPJzGAx3Pzvua9XNyW47SOKFjgVNJ1V1lZ9dt6ecmIt4UEZvmn28CdgP+DjwDfKZkGStxY3t5XyU1bg0Gpkaevl7Su4F7ymSYu5bWPmwPAa/JDX1ExOMlsrxF0veB/5Cqti7J77NymfLRhnMmVb/Vxk9cRfpQFPc163JJU0hXZ6uQe7FIGknJxt6I2EzShqRqiT9LegRYUdJaJQP94UCjwYxnkUaTn1Eiz5afdz7+naTeanuQqmjOAj4bEU91SRkHR8S8+sSIeLrYPthkfrXP2j7AyRFxLnBuoWG7We343JDbXb5EWl78B8ChUegI0p88jXwFSnNZrRgRTxTSlif9Xptq9JJ0L+kuoeHAvIhodmAekoYBhwEjSWu13JzT30YaFFffuN2XPFt2zu0gSaQvgJHAlIj4T05/M7BGRFzcgvcYR/py3QuYExFNTaEh6ZaI2LTZfYvJs+XnLel+YDYpeEyJQu80Scs0+6XVpjLeCYyL3MOqkL4icH1EbNhkfrcBm0da6+ifwEER8ZfavojYpNky5mNb+V2xCSmAbAx8Fzgz3yV3jAOJdZSkH0fEZ/L2YRFxfGHfKRHxoU6VbXHyF+O7IuKqJo9r6Zdfu0haNyLuKzwXsB3pzmy3iFizY4XLJH2eVF10cOTR3pLGkEaoXxkRTU0ZotRDchdSR4B1gC0iIiRtAEyOiLe3sPilSFpAarP7I7BIAImSszdUKpMDSfeS9DrSHFz7lr0S6naSboyILeq3Gz1fUrT6y6/dJL2FFDzeSxrNfwipiuaJXg/sJ5I+QZqfbgVS29WzwLERcVLJ/LYh3TVdEq+OJXk9sEJEdLy7t6RJve2P1g/6XCwHki6T64v3IX1wNyX1kvl9RNza0YK1iaSbIk8fUdzOz5fUQDKE1MW7ZV9+7aA0enpvUvXWmcB5wPSIWK+jBeuBpBVI32mLtJksqfI5R/3dbX9zr60KJA3KvVmQtKykLVRuxtpaj53LSQ3Oq5G+aB6MiK+XDSKSNutl38Fl8qzLY4V8zitXyGaQ0uy/qxa2azP/tmO0fze4LiL+X0SsC6wLrBcR63ZTEMkOAh4mjej/bUQ8RoXeao2ohZMMRsQz3RhE8neDCs+3k3S4GkwF30SeB0uaDdxHWuLgPkmfbEV5y3AgKUnSHqTeJ/+RtDvwV1Lf+Fsk7dbbsT34GemLc7+I+HJE3EL1D+15arA+gdKcPB9rNjMV1sqQ9A7SILUfALdK2qVkGVciDdCaDgwnjRS/IT+ank1Y0o2SvpyrBVtCaU2KZmf47TXL2karvvwkraU0tf3PJK0q6WhJt0qaku9yy1gLOIbUq26m0swDw1RywTRJx0paLW+Pk3QPcG3+Enx3yTIOBNcDKwNI+j/S73QY8DlJ32k2M0lfJvVu3DYiVo2IVUltVzvnff3OVVslSboJ2Jn0D3EzsFVE3CVpXeDciBjXZH6rkXoB7Uvqqz+FNI6k9BdYDiK/Az4QEVfnq6KTgNcDe0SaGr2Z/IrtGVcAh0fEjZLWJ/XCaeqc20HSLNKMvHuTulCfCZwdEQ9UyPMpUtXTv3N+v4uIMuMoavnNAXqcwj+an94fSReRGl+XJ1WLnk4q6+7ADhHR7HTq9fkvB+xK+v98B3BZRDQ15kXSrXncQ+3/54iIuD63P5zRDf8/7VDs7aU0hcs7I+L5HJBvbLaXnqS7gM0i4oW69GHAzRHx+laVva98R1JBRDwUEbOA2RFxV067jxK/14h4NCJOioh3kRpinwIekXSnSq6QGBE3kPr//1bSBOAc0vTfE5oNIg0MrzU8RsQ9tLgaSmlesF+UOPSJiPh8RKxDGq8xFrhR0hWSDipZnHuA0aTZAbYE7lBa6GhS7mnVrNr0/iv28ChjzYj4SUQcC6wcEcdFxOyI+Amp+qySiHghIs6JtI7KWNIkls1apnA3Mywirs95/wsY2vNhzZP0P5IuLXHcLEn3FB7F5/8uWZynlbrsQuoNVpvNewglv4Prg0hOe55yA42riw4Mp18SHsBN5HmWgK0L6YOB21r4Pm8Avlby2BH58Q7SP/BZpPaXEZSY7gF4jlenZpgHrJLTB5U9Z1KHgktIg92+RbobOxeYQxr41mx+i0w5kf8mE4DflCxj/dQty5Cqe84E5pb532nD/+PNhe1v1e1ryRQpLSjjp/PfenvSdEA/Jg3M/DpwWsk8twf+RRrV/VtgI1I16Q3A+0rkt2rdY3VSL7VZpJqGsv/jN5NGyJ9KurP9dS5nmWlcLgPG9/C7uKITf1tXbZUkaSvSfFX1t5djgHdExG+bzK9+Rt4gffnPiJJ16Lmap/YHrtXLR96OaHKQY662K3og0my4q5HGU/y+RBmvJVW3XU36sj+CNLL7K/W/2z7md1ZETFz8K5vK86boYWEiScMiXQm2JL+yJH2DNHfVM3XpG5B6g+3ZyvcrS9K2wMGk6tUhpAuG80hBvulR2bmK+bOk/5+dSV/UX4nCeKSS5RwEfJA04+8M4NsRcUeF/AYDO7LweV8cEU+WyGtj0vxffyMFzCDNEfZ2YPfII+f7kwNJl5D0mwbJI0hXMwdGREcWrGk3STMiYvPC8/uBMdHhkbpFkl4fqfqlVfmdQOqc8Y/Io7utHC069ujfEVG6o4XStCofIQWnv5FmyS5bpdU2uc1qP9LodgG3A6eXufhqBc+1VZLSugpHkdogasuOPkK6Uji22SuNiPhwD++zLqnh/S1ly9ogzzcAn4+IpnpuSRpOOufRwLSIOKOw78SIKNP9cDmlKTJqd0zPkNa4FkA0OQBM0qHAeRFxf4myNNTKIJL9izS473v5NP9BmnTvH6Qqqsr13LlX3dakKsdLKuQzHFi9/stU0qaRehY2m9/rSOe+NjAfuJs0xUeZubsgT/S58Fu8+rzEXfKsXK4fk8bPbKZCN/oyd929kTQtIprqBpzvMteMiF/Xpb9T0gOdCHy+IylJ0sWkSecmR564T9JawCRSL5n/aeF7lRqYJ2lTUpfk15JmSP0JcCJ5kreI+FGT+Z1L+uBfQ7pqe4lUx/tihTJe0cvuiIjtm8yvpT2s2i13zX078DZSu8saEVGm2/N1EbF13v4YqV7/PFJ1ygWRGuGbzXNv0hfqI6R2oQ9FbiAv8/fOQX430lipXUhVRk+QAssnI+LKEmU8hZ67yUdEfKST+eU8e/o9CbgwIprqni3pQuCL9YFcaQ64r0VEmeEH1XSiYWZJeAB3ldlX4n3eAFxd8thrgQ/lPA4jzQL8PWC5kvnNqHv+JdKV9KqUX49kmxb/XW4iNf7vSFo/ZC5wESnAr9jp/5tCOUWqtvwEcBqprvsiynesuKmwfT3pLgJSd+Cm156p/b2BkXl7a+Cf5AZsyi06ditpdl1IC7ddmbfXKZPfQHmQ5sO6nLTmUP3j+RL59dixpezfuurDVVvl3SfpCNIdycMAktYkfXE3Xa0i6QIWvRIaQZrzZ/+SZRwaEafk7buU5ng6Msq3PwyVNChy1UtEHJPHRPyF1J21jBOBVk6DErl8lwCX5DrvnUnjH77Pq9WQHZO7pQ4nfVFfQ2rIvbNitoOUliEYRKppmAtpWVZJza57XzM4Ih7M+VwnaTvgQkmjKT9Ydgjpi3UouatzRMxWuSnfURsm/cxddf+P1P4QpIG334/y0xTdCXw8Iu5u8F5lqmCX62Vf2XWBKnEgKW8f4EjgKklr5LSHgamkwXDN+n7d8wAeA+6OtBBVGS1tfwAuIHUxfGVhnoiYLOlhUrVZGY2mza9iofwi9QSaCkzNA7a6wT2k1QbHkv7Gj0qaGxGPVsizNkOAgFBeK0V5/qmSec6T9LrIde4R8WAOJueRvmSb9UvgeknXkLr9HgcgaXXSMtBlFNd1mQQUe2uVmY5/d9Jn8TukWRtEGjv0e0mfj4jzS5TxaHoeL/LpEvldL+ljEbHQOCtJB5L+B/qd20i6jNJiNW/MT2+MiDkV8mpp+0M7SHqSXhawioj39LSvh/xa2sOqnXJD9jak9pFtSHdLt0VEr7O7NvkeryE1zM4qcexmwLMRMbMufRlg74g4vUSeG5P+v2+LiH82e3yD/G6KFk76KelmUhfae+vSxwDnR0SP89f1l1zzcR5pMbBa4BhHWkL7vVFtVdVSfEfSBpI+HBGNuvP2dszKwCmkq6gZpCuhH0n6M6kefaeIuKiZPCNiu2ZeX0WZc87mkq78WmKgBJHsRdIgz+fz9mjSl0HTlKbe+DswjdT28AJARDxH6onUtMgLodW9z64RcSFpCpYyed5O6qraKsUqvdp27Q6szGwLy9QHEYCIuLds9VtvynxuclX62/LdYW3E/B+jg0MEfEfSBpJmR5qio5ljTiP1NPpGrQ0iV0F9mdTL6g0RMbZEWdYg9eAp1vf+LCIeaTavxbxP0+ecj1voKnJpIOlHpLuQsaSLhn/UHlFigFrOcwhpBoMJpAn8HiNNYzKtlcG1bO+8dlGLVxbNdyS7RcTsuvR1Sb3fmq4uW8z7lfrcdBvfkZQkqac+9CJN89GsbSLig8WESFH+m0prhDe9Mpukt5NGiZ9CGvErUsP2dZI+EBF/bzK/Vp8zlLxaHuBmka7ob6rQ8WEhETGftN77lfBKt+KdgW/lcQfXRJPjfCStU/+FSuvbtKp6dxRWcWyBrwF/Vprfrjhq/EjgC2UybNPnpqv4jqSk3MC8E6kf/EK7SFeWr20yv5kRsUEP++4ueTdyDWkVvpvq0jcHfh4RTQ1ybPU55zy3Au6PV8fiHAC8n7TOwtERUbYRdqkiaU/SmIRFRjYrTffx1hIXDovcfUjaOiKuq1ba1mnHHVJuGzqchUeNf79RVV8f82v556bb+I6kvAtJS2/OqN8h6coS+f1d0leBb0YhuiutL3B1yTIOrw8iABExQ+VmrW31OQP8HNgh5/Eu4FhST5bNgZOBrpgjagD4AHCi0nTyZ5KWiV0AkKtKmwoi2SJ3H90URLKW3yHlgHFAC7Nsx+em1q5au8D8V5SfHaAy35F0idyD51ekqqcZpFvqLUgLPX0kSkz7LulO4G1Rt7a20uqD/4iIDauWuypJN9d6wkj6GWk23aPz8xlRmIfLepf/h94LTCR1Lz6fNP1Ij73iFpPfI6QZoxuKiEPL5NtKrS5jD+O5ivk11YuwHSQtS7rI2oNUTSrSUgHnAZ+oMFygNN+RdIkcKPZSmotoI9I/xxei2rw5PyQNyvs8KSBB6hN/HNDU9ChtNFjSkFzHP560vGvNEvn/qcUsx1y2Oi//D00GJistYbsn8BNJI6LcAmnP06FxCU1odRnrx3N1oy+TpqxZO/LM4LmG4WfAV/KjX/mOpEvkXiFP1m5Pc9e+PUhtBT8tc5UhaRrwJ9LKi7UBZLcD34uIC1pR7qokfYk079KjpKkytoiIyA3EkyOi6U4G3U6vTu8v0swFD/BqFU3TPY0a5L8KKYjsS6r6ODfy6O8m8+mqHlqNDIQytpqk20hrID1Xl74CqVPFJo2PbJ8l8opvgJpCqpZ4KjeG/440unYz0jQiHy2R529Ii0VNJi2E0/R6D+0WaZqVy0hfqJcU2ocGUW7Ub9eLiPVq263q/pyvSPcgBY8tSKP5v0Va6Kjs1WK/V5GU0NIy5kG8vU3aOL6V71fSy/VBBCAinpHUkTsD35F0CUm31PqoS/o+6Z/liNzjZkbZ/ut5ZPPXSOMLTqOwFGeUWBvcWqtVV9SSHiWNGzkLuKh40SBpzTyIrdk8x5CWLm7ZXXKrSep1DEaD7suLy2/LBsnbkBZceyQitmomv3bIY122pXFHgyuiA6PvfUfSPYr/FNuT1v0gIl5O4xJLm0+aVr02SV5n1nS2dluneJWqtF7O+0mLH70RGFUiz7Np/V1yq/2RV6sJa4I03cwaNDm6PSJeaW+R9G5Se8NQUiP2tMqlbY3ivGr1OnJn4EDSPS6XNAV4EFiFNO10bWBZqSs/SRNIDe5TSW0Pi9wOW/+T9LnC0zXqnpe6U4yI55QmpXwPKXhsQbpw2INe5jJbjGER8UDe3h/4dUT8oHaXXDLPloqINxWf57uoL5C6lH+7TJ6SdiIFkBeAYyKitznr+l1EjOl0Geo5kHSPz5BmFB5JWvO9VjWxFmndjzK+BOwVHVjD2XpVHMPzi7rnpUg6nTQT7iXAT0kXIjOjxGJRxWwL2628S245pclOv0RetA04tEyboKTrSXcz3yOP31JhYapofsbslmt1dV4rOJB0idwgukh/+EYDCpvI852VCtUPJM2j8e24SL+WplcLHAAei4iftjjPTUgjp+8E/hkRC1rQ8Nryu+RWU1o75EukXonfBQ6sOO3Ms6TlFvZk0cGwQQqondbS6rxWcGO7dY2lZQLHdnVZlbQhqVprH9LyuBsCb4qS04rnSUNrd8lTIuI/Of3NpCWBL25JwSuQtIC0kNwfSQtmLaQbBk22W1113gkRUXZtoPJlcCCxbrG0jAnoj/NUWr97X9IYojkR8bZ2vl+nSPoQvY9En9xkfu+rz4I0xmlGbfBft2hQnTe5U138HUgGAElnR8Q+nS5Huy1FgWQ+aR2SRXbR4uq8fFfxroi4qlV5LskkNVobZARpnaADo4NrftQ0qM47s2J1XvUyOZB0Py0haxY0UncF+H3g88X9EfH7/i1R+y0tVXj9ocHcWLU7iCsi4rctfJ91SdV7Tc2Y3Q7dWJ3nxnbrtN0K21fVPQ9giQskvZG0TDfOQNDFGs2NNQLYX9ImEXFkK94kIu5TG1ZILOkjnS5APQeSLlHsYli/izRB25LqgiXxrmMxfld8kquftiM1lO9Glyx2lGcTPoq0BPC0iDijsO/EaHKhrHboqcpO0lTSoL2WBBJJbyAth9xxPbX7SFqOhS/E+o2rtrpEnuOnR9GP66/3p6WlXaQRSW8hBY/3kq6iDwGmRt20/yXzfgewNXBbRFxSMo9zgbuBa0hXwS8B+0XEiwPh71ZmGYIeppEfQeq5tn9ElF0bqC0kDQZ2JHWs2An4a0T0+xo+DiQDwJJc3TEQvpBaTdIxwN7AbNIiVOcB04uTOZbI87qI2Dpvf4wUlM4jfclcEBHHlshzoS/iwkzN7wEu7Ya/Ww9T8q9CWphqg4j4QJP5vbsuKYDHgLu7YW6xGqVF4PYD/he4jrQU9/qdmr3CgaRL1Vd3RERXVHe0mqTngJmNdpF6MJWarLKbSZoL3AX8mLw8rqR7osL08cUG/Dw6e5eImCtpedLU4m/qPYeGed4JbBxphcVa2iTSBIYrRMS6ZcvbKnVT8sOrX/xXAN+KEgvCNXiPXSPiwqr5tIqkOaSLkJOAP0TEPEmzqlyIVOU2ki7TQ3XH/3W0UO01iw7V63bQWrxaHfHjXK05TK8u8FXGoLwOySDSBeJcgIh4Nnc3LuMC0kjuP9cSImKy0hrk/T7orZF++vL8Bmm53G5xLmkOtX2ABZLOp0OTNdb4jqRLtKO6YyBY2rvC5gbSXUlB5R3AZRGxX4l87iXN7CzSl8rbIuIhpcWO/tZsW8FA0WAA4UKa7cghaZ36uaq68X+0UGOxL6m6cThwIPCniHim38vjQNId2lHdMRBI+mlEfKrT5ehPOXh8AtgAuIU0q+783Evqvc2Oxl7Me70GWDMiZpU49lDg9xExp1XlabW6AYS7ke6iaiIimuoq26jNTtLWEXFdhWK2Ve6WvDMwEdgxIlbr9zI4kHSHut4X25PqeHcgrctctmpiQFBap/69wNqk9VPuJo3WfaqjBWsTSWeTekD9lfQFcG+UWAq33SQ9RZrE8N+ku+Tf1arMulEr7hy68e6jr/Jca0c0GzxbwW0kXSJPcTANmFao7ngN8B9Jpao7BoJ81bsbaTDiVqR1LtYGrpb0yag2DXq32qjW+C3pV6ReN93oHmBL0gXNPsDXJd1ACiq/77a5p2hNO8EoSSf0+AZdMAmkpE1JAzFfC/yB1F51Iq/OudXvHEi6UES8AJwDnKO0Fnev9cAD3MeAzfO05z8k1fFuK+nnwPnAgLw6XIxXunLnKq1OlqU3kXtsXQJcUqhC2Zf0RbZ6JwvXJs+TBjJ2s1+QemxdTVpC+0bgDOAD+buj3zmQdAlJB/Sye0mvfxxCmjOothwwETG7i6akaLXNJNW6pYrUY+tpum8NloUiXB7LNBWYqrQaY8fVDSBcP49of0VEvKfJLB9rZRtVmwyNiFPy9l2SPg8c2cmJGx1IusdWDdJEqvYZBZzav8XpN78Erpd0DWmFv+MAJK0OPN7JgrVLRPT7wkMl9TjjdEQ8358F6UVxrq1WVOt0zaDDXiyX14SpBfpngE1zT66OrOLoxvYulP8hPkBarOYO0rrRt3S2VO0jaWPgjaTpPP7Z6fLYwCZpi7JfpkqLRD1R6+ghaTvSmI37gJ92w+j2xUynFBHR76s4OpB0EUlDgA8BhwPXAt+JiLs6WiizAabKtDuSriV1wX5A0uakwZjfIa1H8lJEfLR1JV1yuGqrS0g6BDgMuAyYEBH3dbhIZl2vh9kAqvReGBYRD+Tt/UljfH4gaRCpR2HH5fFGa0bE3fn5XkCtzeriiHi438vkO5LuIOll0jrbc1m4cX2JnXPKrKoeBhDuERF/KJnfrYWu2TcCR0Vem17SLd3wOZR0MvCPWoO7pJmkoQPDgPkR8Yn+LpPvSLrHEj0VilmbLHL3UTaIZJdLmgI8SJpF+HIASSPpnob4rYCPF57Pi4hPA0j6WycK5EDSJXqqysoj3ieSGvvMbGGrS/pcTzsj4odN5vcZUm+1kcA7Css3rEVaJ70bDImFq5I+WNheuZ/LAjiQdI1c73kIqavvVOBS4FOkNcxnAKd3rHBm3WswsALV2kVekb+gz2qQflMr8m+RlyWtFREPAUTEbQCSRpEm7ux3biPpEnkq6CdIo1XHk26rlwUOi4gZHSyaWddaShdG25/UMedwoBbgtiCNqTkhIk7r9zI5kHSHuka+wcCjwDpdOJ+RWdcYyJMsViFpAvBFYGNS55zbgWMjYlonyuOqre5RnH9pQV7xzEHErHe7F5eilvQG0voc9zW7FklPJK0REY+0Iq9WkLQvcElEvKvTZanxHUmXkLSANGU35PmXgOfovvmXzLqGpL8AB0bE3ZI2IM2kfDqwEXBdRBzVZH71a8CLNInjm0nflx2ftkfSkaQlJ5YhjTubRjrXjn2ZO5CY2YBVVyX8TWBERBwiaVnghmhyrfo8nqu+h+RoYA7pgq5rFprLM4PvQJoBeGvgTuAiOjAo0VVbXaLBlVAAT3byKsNsACh+PrYHvgcQEf/NQaFZR5C+nP8vIm4FyNXMXTfOK1d9n5cfSNqINM3/qcBO/VkW35F0CUmzSB+KYjfGFUldfz8aEfd2oFhmXU3Sb4GHgP8ARwLrRcRzklYGroqIzUrkORr4EXA/8DXg5m66E4G0tjzwdEQ8mSeaHAf8s9YVuL8N6sSb2qIiYr2IWD//rD1WI6189v86XT6zLvUxUg/HMaT1yp/L6Rux8BTzfRYRcyJiL9Jy15eSVirtGrmN5CrgGkkfJVVn7Qyc3dvgzLaWyXck3W9p7Ctv1g3yAl6v69SVfiOSbifdgbwGuBdYPyLmSloeuDYiNunvMvmOpMtJWgH/ncwakjRO0hWSfitpbUmXSnpK0vV5GvhKIuL5wsjxD1cucGssyAuLPUlaGvgxgIh4treD2sl3JF2ih1vSVYD3kBbU+UU/F8ms60m6jtSOsTLwXeCzEXGOpPHAtyLirS18r9kRsU6r8qtQjlNIs14sTxoiMJ9UvbU9sGJE7N3vZXIg6Q6SvlaXFKQrjb/Ueo+Y2cKKI9vrv+jLjHqX1NNKpAJeHxFDy5e2NfICeHuRviPOIXX93Q+YDfysE3cm7v7bJSLi67XtXJ0VnbxVNRsgXpC0I7ASELW1SCS9G1hQIr81SV1nn6hLF/CPakVtjbyQ15mFpH/Q4bK57r2LSDpY0mzSgKjZku6T9MlOl8usi32CNHnhR0gBYDtJT5J6Ox5WIr8LgRUi4r66x73Ala0pcjWS1pJ0kqSfSVpV0tGSbpE0Ja+b0v9lctVWd5D0ZeBtwKci4p6ctj5wPKknxrc6WT4z6w6SLgL+SGoj2Y80JcyZwO7ADhGxe7+XyYGkO0i6C9gsIl6oSx9GGhD1+s6UzKx7SXoLcGdEPJ0/K0eSplS/A/h2RDzV0QK2wWLahWZExOb9XSZXbXWR+iCS056nQ4vVmA0Avyb1XIJ0974ScFxO+02nCtVmxe/tU3vZ12/c2N495kgaHxGXFRMlbU9aP9rMFjUoNz4DjCsM3P2bpBkdKlO7nS9phYh4JiK+XEvMsx//qxMFctVWl5C0MXA+8DfStNUBbAW8Hdijm0bWmnULSb8D/hQRv5H0G1L31+mSXg+cHhFbdbiISwUHki4iaTlS49lGpO6Gt5M+DC92tGBmXUrSSqQqrXeS5tzagjTZ4v3AoRFxcweL1+8kfTgi+r1Kz4GkS0iax6Kz/5LTXgT+DXypvurLzF5Zm2N9UnX9nP5ej6NbdGr0vQPJAJDXcN+EdHfS7xOymVn36MbR925sHwAiYgFws6SfdLosZtZxXTf63oFkAImIn3e6DGbWcbXR9zPqd0i6st9Lg6u2zMysIg9INDOzShxIzMysEgcSMzOrxIHELJP0JUm35ym5Z+QJAZH0GUmv6cPxC71O0p8krdyCch0t6TlJaxTSnqmar1mrOJCYAZLeCuwKbBERmwI7kEZHA3wGWGwgqX9dROwSEU+2qIiPktbdMOs6DiRmyUjg0dp0NBHxaEQ8IOlQ4LXAFZKuAMiLCk3Pdy9fz2mNXnevpNXy9uck3ZYfn8lpYyTdKekXOa9L8lTojfwa2EfSiPodkv4g6Yacx0GF9GckHZf3/VnS1pKulHSPpPfk1wyW9D1J1+c7sY+34pdpSxd3/zXjleWN/0a6o/gzcHZEXJX33UuaWfbR/HxERDyeZxy4jDSn0y0NXncvMA5YFzgF2IY0aOxaYH/SgLKZ+ZgZkqYAUyPit3VlOxp4JpdtcER8TdIzEbFCXXmGAdcD746IxyQFsEtETJN0HmkhpP8lzeU2OSI2z4FnjYj4lqShwN+BvSJiVgt/vbaE8x2JGRARzwBbAgcBc4GzJX2oh5fvLelG4CZgY9IXc2/eAZwXEc/m9/k9aZJBgFmFgWU3AGN6yecEYJKk4XXph0q6GbgGWBsYm9P/C1yUt28FroqIl/J27X12BA7IU65fC6xaON6sTzyy3SzLU9FcCVwp6VZgEulO4hWS1gM+D2wVEU9IOgVYbjFZ10/EWVSc2XkB0FPVFhHxpKQzgE8WyrMtqT3nrRHxXB7ZXCvPS/FqlcPLtfeKiJcl1T77Aj4dERcv5hzMeuQ7EjNA0hskFa/ENwfuy9vzgBXz9nDgWeApSWsCOxeOKb6u6C/AHpJeI2l54L3AX0sW9YfAx3n1InAl4IkcRDYkVZ8142LgYEnLAEh6fS6jWZ/5jsQsWQH4Se6uO5/UdlFruD4ZmCbpwYjYTtJNpLVi7iG1KdDodbXEiLgx37lcl5N+GRE3SRrTbCEj4tHc3vHZnHQR8Ik8I+xdpOqtZvySVM11oySRqvX2aLZctnRzY7uZmVXiqi0zM6vEgcTMzCpxIDEzs0ocSMzMrBIHEjMzq8SBxMzMKnEgMTOzSv4/f0HKswXkXl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AM EXIT GRAPH\n",
    "x3 = graph_dict_exit_am.keys()\n",
    "y3 = graph_dict_exit_am.values()\n",
    "plt.bar(x3,y3, color = 'DeepSkyBlue')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Exits 6-10am 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Exits')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Exits AM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d047b26f-3919-4c53-86ed-bd0f3f33d9d0",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Daily_Entries</th>\n",
       "      <th>Daily_Exits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-00-00</th>\n",
       "      <th>2021-07-31 16:00:00</th>\n",
       "      <td>111.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01 16:00:00</th>\n",
       "      <td>92.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02 16:00:00</th>\n",
       "      <td>111.0</td>\n",
       "      <td>402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03 16:00:00</th>\n",
       "      <td>135.0</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04 16:00:00</th>\n",
       "      <td>133.0</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-05-01</th>\n",
       "      <th>2022-01-24 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-25 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-26 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-27 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-28 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>828633 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Daily_Entries  Daily_Exits\n",
       "STATION   SCP      Date_Time                                      \n",
       "1 AV      00-00-00 2021-07-31 16:00:00          111.0        552.0\n",
       "                   2021-08-01 16:00:00           92.0        455.0\n",
       "                   2021-08-02 16:00:00          111.0        402.0\n",
       "                   2021-08-03 16:00:00          135.0        465.0\n",
       "                   2021-08-04 16:00:00          133.0        489.0\n",
       "...                                               ...          ...\n",
       "ZEREGA AV 00-05-01 2022-01-24 16:00:00            0.0          0.0\n",
       "                   2022-01-25 16:00:00            0.0          0.0\n",
       "                   2022-01-26 16:00:00            0.0          0.0\n",
       "                   2022-01-27 16:00:00            0.0          0.0\n",
       "                   2022-01-28 16:00:00            0.0          0.0\n",
       "\n",
       "[828633 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Repeating steps for the evening 4:00-7:00pm slot\n",
    "\n",
    "target_time_evening = MTAClean.iloc[index.indexer_between_time('16:00:00','19:00:00')]\n",
    "activity__eve = target_time_evening.groupby(['STATION','SCP','Date_Time'])[['Daily_Entries','Daily_Exits']].sum()\n",
    "display(activity__eve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "dfd28eda-db6a-4f01-bac5-2eb11b9a1153",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Daily_Entries</th>\n",
       "      <th>Daily_Exits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-00-00</th>\n",
       "      <th>2021-07-31 16:00:00</th>\n",
       "      <td>111.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01 16:00:00</th>\n",
       "      <td>92.0</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02 16:00:00</th>\n",
       "      <td>111.0</td>\n",
       "      <td>402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03 16:00:00</th>\n",
       "      <td>135.0</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04 16:00:00</th>\n",
       "      <td>133.0</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">00-05-01</th>\n",
       "      <th>2021-12-12 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-15 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-21 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03 16:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>748753 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Daily_Entries  Daily_Exits\n",
       "STATION   SCP      Date_Time                                      \n",
       "1 AV      00-00-00 2021-07-31 16:00:00          111.0        552.0\n",
       "                   2021-08-01 16:00:00           92.0        455.0\n",
       "                   2021-08-02 16:00:00          111.0        402.0\n",
       "                   2021-08-03 16:00:00          135.0        465.0\n",
       "                   2021-08-04 16:00:00          133.0        489.0\n",
       "...                                               ...          ...\n",
       "ZEREGA AV 00-05-01 2021-12-12 16:00:00            0.0          1.0\n",
       "                   2021-12-14 16:00:00            0.0          1.0\n",
       "                   2021-12-15 16:00:00            0.0          3.0\n",
       "                   2021-12-21 16:00:00            0.0          1.0\n",
       "                   2022-01-03 16:00:00            0.0          1.0\n",
       "\n",
       "[748753 rows x 2 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop evening entry & exit activiy that = 0, 79,880 hits\n",
    "activity__eve.drop(activity__eve[(activity__eve['Daily_Entries']==0)&(activity__eve['Daily_Exits']==0)].index,inplace=True)\n",
    "activity__eve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8f5113cf-8f1c-4fb3-aa95-d5db91c71757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily_Entries    21959\n",
       "Daily_Exits      21959\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity__eve[activity__eve['Daily_Entries']==0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b5f9bded-0df7-4600-a2b5-e7ee8b738010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daily_Entries    26412\n",
       "Daily_Exits      26412\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity__eve[activity__eve['Daily_Exits']==0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "892564da-e33c-49d0-9c76-47f3ca7bd02c",
   "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></th>\n",
       "      <th>Tot_Entries_Eve</th>\n",
       "      <th>Tot_Exits_Eve</th>\n",
       "      <th>Tot_Activity_Eve</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th>SCP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1 AV</th>\n",
       "      <th>00-00-00</th>\n",
       "      <td>40238.0</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>130052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-00-01</th>\n",
       "      <td>55460.0</td>\n",
       "      <td>48251.0</td>\n",
       "      <td>103711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-00</th>\n",
       "      <td>8214.0</td>\n",
       "      <td>22044.0</td>\n",
       "      <td>30258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-01</th>\n",
       "      <td>5223.0</td>\n",
       "      <td>6668.0</td>\n",
       "      <td>11891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-02</th>\n",
       "      <td>10255.0</td>\n",
       "      <td>2227.0</td>\n",
       "      <td>12482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZEREGA AV</th>\n",
       "      <th>00-00-01</th>\n",
       "      <td>5911.0</td>\n",
       "      <td>12607.0</td>\n",
       "      <td>18518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-00</th>\n",
       "      <td>9344.0</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>16168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-03-01</th>\n",
       "      <td>17775.0</td>\n",
       "      <td>6777.0</td>\n",
       "      <td>24552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-05-00</th>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00-05-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4435 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Tot_Entries_Eve  Tot_Exits_Eve  Tot_Activity_Eve\n",
       "STATION   SCP                                                       \n",
       "1 AV      00-00-00          40238.0        89814.0          130052.0\n",
       "          00-00-01          55460.0        48251.0          103711.0\n",
       "          00-03-00           8214.0        22044.0           30258.0\n",
       "          00-03-01           5223.0         6668.0           11891.0\n",
       "          00-03-02          10255.0         2227.0           12482.0\n",
       "...                             ...            ...               ...\n",
       "ZEREGA AV 00-00-01           5911.0        12607.0           18518.0\n",
       "          00-03-00           9344.0         6824.0           16168.0\n",
       "          00-03-01          17775.0         6777.0           24552.0\n",
       "          00-05-00             78.0            0.0              78.0\n",
       "          00-05-01              0.0           18.0              18.0\n",
       "\n",
       "[4435 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filtering to find daily traffic mean, grouping by station and scp in the evening\n",
    "\n",
    "per_scp_eve = activity__eve.reset_index().groupby(['STATION','SCP'])[['Daily_Entries','Daily_Exits']].sum()\n",
    "\n",
    "per_scp_eve = per_scp_eve.rename({'Daily_Entries':'Tot_Entries_Eve','Daily_Exits':'Tot_Exits_Eve'},axis=1)\n",
    "\n",
    "per_scp_eve['Tot_Activity_Eve'] = per_scp_eve['Tot_Entries_Eve']+per_scp_eve['Tot_Exits_Eve']\n",
    "\n",
    "display(per_scp_eve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0390c3a2-eeef-4148-9782-869bd652f1bd",
   "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>Tot_Entries_Eve</th>\n",
       "      <th>Tot_Exits_Eve</th>\n",
       "      <th>Tot_Activity_Eve</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 AV</th>\n",
       "      <td>18731.375000</td>\n",
       "      <td>27238.833333</td>\n",
       "      <td>45970.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103 ST</th>\n",
       "      <td>106749.166667</td>\n",
       "      <td>92736.166667</td>\n",
       "      <td>199485.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103 ST-CORONA</th>\n",
       "      <td>38860.111111</td>\n",
       "      <td>70494.666667</td>\n",
       "      <td>109354.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104 ST</th>\n",
       "      <td>9411.666667</td>\n",
       "      <td>11504.333333</td>\n",
       "      <td>20916.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110 ST</th>\n",
       "      <td>36750.500000</td>\n",
       "      <td>45824.333333</td>\n",
       "      <td>82574.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOODLAWN</th>\n",
       "      <td>22274.600000</td>\n",
       "      <td>12234.200000</td>\n",
       "      <td>34508.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLD TRADE CTR</th>\n",
       "      <td>16568.166667</td>\n",
       "      <td>13846.055556</td>\n",
       "      <td>30414.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTC-CORTLANDT</th>\n",
       "      <td>6267.121212</td>\n",
       "      <td>6851.848485</td>\n",
       "      <td>13118.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK ST</th>\n",
       "      <td>68505.666667</td>\n",
       "      <td>61873.000000</td>\n",
       "      <td>130378.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEREGA AV</th>\n",
       "      <td>6802.666667</td>\n",
       "      <td>9604.500000</td>\n",
       "      <td>16407.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tot_Entries_Eve  Tot_Exits_Eve  Tot_Activity_Eve\n",
       "STATION                                                          \n",
       "1 AV                18731.375000   27238.833333      45970.208333\n",
       "103 ST             106749.166667   92736.166667     199485.333333\n",
       "103 ST-CORONA       38860.111111   70494.666667     109354.777778\n",
       "104 ST               9411.666667   11504.333333      20916.000000\n",
       "110 ST              36750.500000   45824.333333      82574.833333\n",
       "...                          ...            ...               ...\n",
       "WOODLAWN            22274.600000   12234.200000      34508.800000\n",
       "WORLD TRADE CTR     16568.166667   13846.055556      30414.222222\n",
       "WTC-CORTLANDT        6267.121212    6851.848485      13118.969697\n",
       "YORK ST             68505.666667   61873.000000     130378.666667\n",
       "ZEREGA AV            6802.666667    9604.500000      16407.166667\n",
       "\n",
       "[379 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finding total mean entries, exits, total activity per station during 4-7pm slot \n",
    "\n",
    "tot_mean_eve = per_scp_eve.reset_index().groupby(['STATION'])[['Tot_Entries_Eve','Tot_Exits_Eve','Tot_Activity_Eve']].mean()\n",
    "display(tot_mean_eve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4fda18e0-6381-489d-9661-8c1e0cb1b129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION\n",
       "ST. GEORGE         322257.880000\n",
       "103 ST             199485.333333\n",
       "145 ST             152752.545455\n",
       "125 ST             138840.304348\n",
       "YORK ST            130378.666667\n",
       "GRAND ST           127623.727273\n",
       "191 ST             122262.500000\n",
       "116 ST             114665.727273\n",
       "82 ST-JACKSON H    111817.700000\n",
       "34 ST-HERALD SQ    111698.951220\n",
       "103 ST-CORONA      109354.777778\n",
       "HOYT-SCHER         105445.200000\n",
       "167 ST             104611.285714\n",
       "86 ST              103694.052632\n",
       "7 AV               101515.692308\n",
       "Name: Tot_Activity_Eve, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'ST. GEORGE': 322257.88,\n",
       " '103 ST': 199485.33333333334,\n",
       " '145 ST': 152752.54545454544,\n",
       " '125 ST': 138840.30434782608,\n",
       " 'YORK ST': 130378.66666666667,\n",
       " 'GRAND ST': 127623.72727272728,\n",
       " '191 ST': 122262.5,\n",
       " '116 ST': 114665.72727272728,\n",
       " '82 ST-JACKSON H': 111817.7,\n",
       " '34 ST-HERALD SQ': 111698.9512195122,\n",
       " '103 ST-CORONA': 109354.77777777778,\n",
       " 'HOYT-SCHER': 105445.2,\n",
       " '167 ST': 104611.28571428571,\n",
       " '86 ST': 103694.05263157895,\n",
       " '7 AV': 101515.69230769231}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Highest evening avg traffic per station\n",
    "mean_graph_pm = tot_mean_eve['Tot_Activity_Eve'].sort_values(ascending=False).head(15)\n",
    "display(mean_graph_pm)\n",
    "graph_dict_actpm = mean_graph_pm.to_dict()\n",
    "display(graph_dict_actpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3a8c4eb0-03bd-429d-8223-1a45a0f82465",
   "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": [
    "pmx = graph_dict_actpm.keys()\n",
    "pmy = graph_dict_actpm.values()\n",
    "plt.bar(pmx,pmy,color='Crimson')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Daily Activity 4-7pm 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Entries + Exits')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Daily Activity PM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a22fe08f-ce21-41f7-8955-877b94aea694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ST. GEORGE': 321940.72,\n",
       " '103 ST': 106749.16666666667,\n",
       " '145 ST': 72306.27272727272,\n",
       " 'GRAND ST': 69742.81818181818,\n",
       " 'YORK ST': 68505.66666666667,\n",
       " '125 ST': 64445.69565217391,\n",
       " '116 ST': 61109.72727272727,\n",
       " 'CATHEDRAL PKWY': 57849.57142857143,\n",
       " '96 ST': 52319.8,\n",
       " 'QUEENSBORO PLZ': 51722.5,\n",
       " '23 ST': 50733.07692307692,\n",
       " '34 ST-HERALD SQ': 49460.90243902439,\n",
       " '86 ST': 49268.10526315789,\n",
       " '7 AV': 48905.307692307695,\n",
       " 'HOYT-SCHER': 48480.6}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entry_graph_pm = tot_mean_eve['Tot_Entries_Eve'].sort_values(ascending=False).head(15)\n",
    "graph_dict_entry_pm = entry_graph_pm.to_dict()\n",
    "display(graph_dict_entry_pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e08e94cc-0a54-4c6b-90c5-2b807f04978e",
   "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": [
    "#PM ENTRY GRAPH\n",
    "pmx2 = graph_dict_entry_pm.keys()\n",
    "pmy2 = graph_dict_entry_pm.values()\n",
    "plt.bar(pmx2,pmy2,color='Gold')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Entries 4-7pm 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Entries')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Entries PM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "26eeec80-99ed-47ca-b611-c1a31f7053e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'103 ST': 92736.16666666667,\n",
       " '145 ST': 80446.27272727272,\n",
       " '191 ST': 79081.75,\n",
       " '125 ST': 74394.60869565218,\n",
       " '82 ST-JACKSON H': 72645.9,\n",
       " '103 ST-CORONA': 70494.66666666667,\n",
       " '34 ST-PENN STA': 63458.53968253968,\n",
       " '34 ST-HERALD SQ': 62238.04878048781,\n",
       " 'YORK ST': 61873.0,\n",
       " 'JUNCTION BLVD': 61429.083333333336,\n",
       " '167 ST': 61038.28571428572,\n",
       " 'TIMES SQ-42 ST': 60045.27027027027,\n",
       " '42 ST-PORT AUTH': 59825.2972972973,\n",
       " 'JKSN HT-ROOSVLT': 58763.318181818184,\n",
       " 'BROADWAY': 58145.71428571428}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exit_graph_pm = tot_mean_eve['Tot_Exits_Eve'].sort_values(ascending=False).head(15)\n",
    "graph_dict_exit_pm = exit_graph_pm.to_dict()\n",
    "display(graph_dict_exit_pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e99ef4be-31c0-4030-9156-4dcc9db5c3a7",
   "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": [
    "#PM EXIT GRAPH\n",
    "pmx3 = graph_dict_exit_pm.keys()\n",
    "pmy3 = graph_dict_exit_pm.values()\n",
    "plt.bar(pmx3,pmy3,color='DeepSkyBlue')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title('AVG Exits 4-7pm 7/31/21 - 1/28/22')\n",
    "plt.ylabel('Exits')\n",
    "plt.xlabel('Station Name')\n",
    "plt.savefig('AVG Exits PM', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fcd782-465b-47af-8cfb-3e941394732b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}