fds-project-option-3-visualistion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FDS Final - Just Eat Cycle Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil import parser\n",
    "import datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import gmaps\n",
    "import gmaps.datasets\n",
    "gmaps.configure(api_key='')\n #insert api key",
    "mpl.rcParams['font.size'] = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Just_Eat_Data/04:20.csv', 'Just_Eat_Data/08:20.csv', 'Just_Eat_Data/06:20.csv', 'Just_Eat_Data/02:20.csv', 'Just_Eat_Data/12:19.csv', 'Just_Eat_Data/10:20.csv', 'Just_Eat_Data/02:21.csv', 'Just_Eat_Data/12:20.csv', 'Just_Eat_Data/10:19.csv', 'Just_Eat_Data/05:20.csv', 'Just_Eat_Data/09:20.csv', 'Just_Eat_Data/09:19.csv', 'Just_Eat_Data/07:20.csv', 'Just_Eat_Data/03:20.csv', 'Just_Eat_Data/11:20.csv', 'Just_Eat_Data/01:21.csv', 'Just_Eat_Data/01:20.csv', 'Just_Eat_Data/11:19.csv']\n"
     ]
    },
    {
     "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>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_description</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_description</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01 01:41:11.814000+00:00</td>\n",
       "      <td>2020-04-01 02:28:08.128000+00:00</td>\n",
       "      <td>2816</td>\n",
       "      <td>1808</td>\n",
       "      <td>Gorgie Road</td>\n",
       "      <td>Corner with McLeod Street</td>\n",
       "      <td>55.938741</td>\n",
       "      <td>-3.229909</td>\n",
       "      <td>1808</td>\n",
       "      <td>Gorgie Road</td>\n",
       "      <td>Corner with McLeod Street</td>\n",
       "      <td>55.938741</td>\n",
       "      <td>-3.229909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-01 04:52:10.586000+00:00</td>\n",
       "      <td>2020-04-01 05:45:32.129000+00:00</td>\n",
       "      <td>3201</td>\n",
       "      <td>1752</td>\n",
       "      <td>IGMM - Western General</td>\n",
       "      <td>The Institute of Genetics and Molecular Medicine</td>\n",
       "      <td>55.962642</td>\n",
       "      <td>-3.231916</td>\n",
       "      <td>1752</td>\n",
       "      <td>IGMM - Western General</td>\n",
       "      <td>The Institute of Genetics and Molecular Medicine</td>\n",
       "      <td>55.962642</td>\n",
       "      <td>-3.231916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-01 05:45:52.745000+00:00</td>\n",
       "      <td>2020-04-01 06:24:32.266000+00:00</td>\n",
       "      <td>2319</td>\n",
       "      <td>1752</td>\n",
       "      <td>IGMM - Western General</td>\n",
       "      <td>The Institute of Genetics and Molecular Medicine</td>\n",
       "      <td>55.962642</td>\n",
       "      <td>-3.231916</td>\n",
       "      <td>1753</td>\n",
       "      <td>Waitrose Comely Bank</td>\n",
       "      <td>On Fettes Avenue next to Waitrose</td>\n",
       "      <td>55.959536</td>\n",
       "      <td>-3.223434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01 06:24:58.055000+00:00</td>\n",
       "      <td>2020-04-01 07:13:07.359000+00:00</td>\n",
       "      <td>2889</td>\n",
       "      <td>1753</td>\n",
       "      <td>Waitrose Comely Bank</td>\n",
       "      <td>On Fettes Avenue next to Waitrose</td>\n",
       "      <td>55.959536</td>\n",
       "      <td>-3.223434</td>\n",
       "      <td>1752</td>\n",
       "      <td>IGMM - Western General</td>\n",
       "      <td>The Institute of Genetics and Molecular Medicine</td>\n",
       "      <td>55.962642</td>\n",
       "      <td>-3.231916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-01 06:50:15.745000+00:00</td>\n",
       "      <td>2020-04-01 07:05:20.319000+00:00</td>\n",
       "      <td>904</td>\n",
       "      <td>1769</td>\n",
       "      <td>Brunswick Place</td>\n",
       "      <td>Corner of Elm Row/Brunswick Street</td>\n",
       "      <td>55.960852</td>\n",
       "      <td>-3.180986</td>\n",
       "      <td>1752</td>\n",
       "      <td>IGMM - Western General</td>\n",
       "      <td>The Institute of Genetics and Molecular Medicine</td>\n",
       "      <td>55.962642</td>\n",
       "      <td>-3.231916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9744</th>\n",
       "      <td>2019-11-29 23:19:36.946000+00:00</td>\n",
       "      <td>2019-11-29 23:27:43.990000+00:00</td>\n",
       "      <td>487</td>\n",
       "      <td>247</td>\n",
       "      <td>Charlotte Square</td>\n",
       "      <td>North Corner of Charlotte Square</td>\n",
       "      <td>55.952335</td>\n",
       "      <td>-3.207101</td>\n",
       "      <td>1720</td>\n",
       "      <td>Dundas Street</td>\n",
       "      <td>On corner of Henderson Row</td>\n",
       "      <td>55.960762</td>\n",
       "      <td>-3.201278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9745</th>\n",
       "      <td>2019-11-29 23:22:43.246000+00:00</td>\n",
       "      <td>2019-11-29 23:29:29.738000+00:00</td>\n",
       "      <td>406</td>\n",
       "      <td>259</td>\n",
       "      <td>St Andrew Square</td>\n",
       "      <td>North East corner</td>\n",
       "      <td>55.954906</td>\n",
       "      <td>-3.192444</td>\n",
       "      <td>1743</td>\n",
       "      <td>Logie Green Road</td>\n",
       "      <td>Outside Lidl</td>\n",
       "      <td>55.964058</td>\n",
       "      <td>-3.195700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9746</th>\n",
       "      <td>2019-11-29 23:25:03.496000+00:00</td>\n",
       "      <td>2019-11-29 23:43:20.440000+00:00</td>\n",
       "      <td>1096</td>\n",
       "      <td>1097</td>\n",
       "      <td>Gladstone Terrace</td>\n",
       "      <td>Corner of Gladstone Terrace and Sciennes Road</td>\n",
       "      <td>55.937963</td>\n",
       "      <td>-3.185021</td>\n",
       "      <td>1091</td>\n",
       "      <td>Holyrood Road</td>\n",
       "      <td>Opposite St Leonards Land</td>\n",
       "      <td>55.949560</td>\n",
       "      <td>-3.180413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9747</th>\n",
       "      <td>2019-11-29 23:29:57.437000+00:00</td>\n",
       "      <td>2019-11-29 23:47:18.314000+00:00</td>\n",
       "      <td>1040</td>\n",
       "      <td>250</td>\n",
       "      <td>Victoria Quay</td>\n",
       "      <td>Entrance to Scottish Government Office</td>\n",
       "      <td>55.977617</td>\n",
       "      <td>-3.174126</td>\n",
       "      <td>1728</td>\n",
       "      <td>Portobello - Kings Road</td>\n",
       "      <td>Foot of Kings Road next to the promenade</td>\n",
       "      <td>55.957915</td>\n",
       "      <td>-3.118332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9748</th>\n",
       "      <td>2019-11-29 23:39:15.440000+00:00</td>\n",
       "      <td>2019-11-29 23:45:55.315000+00:00</td>\n",
       "      <td>399</td>\n",
       "      <td>249</td>\n",
       "      <td>Fountainbridge</td>\n",
       "      <td>Fountainbridge by Gardner's Crescent</td>\n",
       "      <td>55.943357</td>\n",
       "      <td>-3.209248</td>\n",
       "      <td>248</td>\n",
       "      <td>Bristo Square</td>\n",
       "      <td>Bristo Square, near Potterrow</td>\n",
       "      <td>55.945834</td>\n",
       "      <td>-3.189053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280376 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            started_at                          ended_at  \\\n",
       "0     2020-04-01 01:41:11.814000+00:00  2020-04-01 02:28:08.128000+00:00   \n",
       "1     2020-04-01 04:52:10.586000+00:00  2020-04-01 05:45:32.129000+00:00   \n",
       "2     2020-04-01 05:45:52.745000+00:00  2020-04-01 06:24:32.266000+00:00   \n",
       "3     2020-04-01 06:24:58.055000+00:00  2020-04-01 07:13:07.359000+00:00   \n",
       "4     2020-04-01 06:50:15.745000+00:00  2020-04-01 07:05:20.319000+00:00   \n",
       "...                                ...                               ...   \n",
       "9744  2019-11-29 23:19:36.946000+00:00  2019-11-29 23:27:43.990000+00:00   \n",
       "9745  2019-11-29 23:22:43.246000+00:00  2019-11-29 23:29:29.738000+00:00   \n",
       "9746  2019-11-29 23:25:03.496000+00:00  2019-11-29 23:43:20.440000+00:00   \n",
       "9747  2019-11-29 23:29:57.437000+00:00  2019-11-29 23:47:18.314000+00:00   \n",
       "9748  2019-11-29 23:39:15.440000+00:00  2019-11-29 23:45:55.315000+00:00   \n",
       "\n",
       "      duration  start_station_id      start_station_name  \\\n",
       "0         2816              1808             Gorgie Road   \n",
       "1         3201              1752  IGMM - Western General   \n",
       "2         2319              1752  IGMM - Western General   \n",
       "3         2889              1753    Waitrose Comely Bank   \n",
       "4          904              1769         Brunswick Place   \n",
       "...        ...               ...                     ...   \n",
       "9744       487               247        Charlotte Square   \n",
       "9745       406               259        St Andrew Square   \n",
       "9746      1096              1097       Gladstone Terrace   \n",
       "9747      1040               250           Victoria Quay   \n",
       "9748       399               249          Fountainbridge   \n",
       "\n",
       "                             start_station_description  \\\n",
       "0                            Corner with McLeod Street   \n",
       "1     The Institute of Genetics and Molecular Medicine   \n",
       "2     The Institute of Genetics and Molecular Medicine   \n",
       "3                    On Fettes Avenue next to Waitrose   \n",
       "4                   Corner of Elm Row/Brunswick Street   \n",
       "...                                                ...   \n",
       "9744                  North Corner of Charlotte Square   \n",
       "9745                                 North East corner   \n",
       "9746     Corner of Gladstone Terrace and Sciennes Road   \n",
       "9747            Entrance to Scottish Government Office   \n",
       "9748              Fountainbridge by Gardner's Crescent   \n",
       "\n",
       "      start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0                  55.938741                -3.229909            1808   \n",
       "1                  55.962642                -3.231916            1752   \n",
       "2                  55.962642                -3.231916            1753   \n",
       "3                  55.959536                -3.223434            1752   \n",
       "4                  55.960852                -3.180986            1752   \n",
       "...                      ...                      ...             ...   \n",
       "9744               55.952335                -3.207101            1720   \n",
       "9745               55.954906                -3.192444            1743   \n",
       "9746               55.937963                -3.185021            1091   \n",
       "9747               55.977617                -3.174126            1728   \n",
       "9748               55.943357                -3.209248             248   \n",
       "\n",
       "             end_station_name  \\\n",
       "0                 Gorgie Road   \n",
       "1      IGMM - Western General   \n",
       "2        Waitrose Comely Bank   \n",
       "3      IGMM - Western General   \n",
       "4      IGMM - Western General   \n",
       "...                       ...   \n",
       "9744            Dundas Street   \n",
       "9745         Logie Green Road   \n",
       "9746            Holyrood Road   \n",
       "9747  Portobello - Kings Road   \n",
       "9748            Bristo Square   \n",
       "\n",
       "                               end_station_description  end_station_latitude  \\\n",
       "0                            Corner with McLeod Street             55.938741   \n",
       "1     The Institute of Genetics and Molecular Medicine             55.962642   \n",
       "2                    On Fettes Avenue next to Waitrose             55.959536   \n",
       "3     The Institute of Genetics and Molecular Medicine             55.962642   \n",
       "4     The Institute of Genetics and Molecular Medicine             55.962642   \n",
       "...                                                ...                   ...   \n",
       "9744                        On corner of Henderson Row             55.960762   \n",
       "9745                                      Outside Lidl             55.964058   \n",
       "9746                         Opposite St Leonards Land             55.949560   \n",
       "9747          Foot of Kings Road next to the promenade             55.957915   \n",
       "9748                     Bristo Square, near Potterrow             55.945834   \n",
       "\n",
       "      end_station_longitude  \n",
       "0                 -3.229909  \n",
       "1                 -3.231916  \n",
       "2                 -3.223434  \n",
       "3                 -3.231916  \n",
       "4                 -3.231916  \n",
       "...                     ...  \n",
       "9744              -3.201278  \n",
       "9745              -3.195700  \n",
       "9746              -3.180413  \n",
       "9747              -3.118332  \n",
       "9748              -3.189053  \n",
       "\n",
       "[280376 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dataframe\n",
    "\n",
    "from os import listdir\n",
    "filepaths = [('Just_Eat_Data/') + f for f in listdir(\"./Just_Eat_Data\") if f.endswith('.csv')]\n",
    "print(filepaths)\n",
    "data_set = pd.concat(map(pd.read_csv, filepaths))\n",
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dictionary of average durations for each month and list of months used\n",
    "data_set.sort_values(by='started_at')\n",
    "\n",
    "duration_months = {}\n",
    "for row in data_set.iterrows():\n",
    "    if row[0] == 0:\n",
    "        current_month_datetime = parser.parse(row[1]['started_at'])\n",
    "        current_month = float(str(current_month_datetime.year) + '.' + str(current_month_datetime.month).zfill(2))\n",
    "    \n",
    "    try:\n",
    "        duration_months[current_month].append((row[1]['duration']))\n",
    "    except:\n",
    "        duration_months[current_month] = [(row[1]['duration'])]\n",
    "        \n",
    "#dictionary of average duration time for each month in mins\n",
    "for key in duration_months:\n",
    "    durations = duration_months[key]\n",
    "    durations = np.mean(durations) / 60\n",
    "    duration_months[key] = durations\n",
    "\n",
    "duration_months_keys_sorted = sorted(duration_months)\n",
    "\n",
    "\n",
    "months_used = ['Sep 19', 'Oct 19', 'Nov 19', 'Dec 19', 'Jan 20', 'Feb 20', 'Mar 20', 'Apr 20', 'May 20',\n",
    "               'Jun 20', 'Jul 20', 'Aug 20', 'Sep 20', 'Oct 20', 'Nov 20', 'Dec 20', 'Jan 21', 'Feb 21' ]\n",
    "\n",
    "durations = []\n",
    "for key in duration_months_keys_sorted:\n",
    "    duration = round(duration_months[key], 1)\n",
    "    durations.append(duration)\n",
    "    \n",
    "durations\n",
    "\n",
    "\n",
    "plt.figure(figsize = (6,3))\n",
    "plt.bar(months_used, durations)\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Trip Average')\n",
    "plt.title('Average Trip Time')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.savefig('average-trip-time.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dictionary of average durations for each month and list of months used\n",
    "data_set.sort_values(by='started_at')\n",
    "\n",
    "duration_months = {}\n",
    "for row in data_set.iterrows():\n",
    "    if row[0] == 0:\n",
    "        current_month_datetime = parser.parse(row[1]['started_at'])\n",
    "        current_month = float(str(current_month_datetime.year) + '.' + str(current_month_datetime.month).zfill(2))\n",
    "    \n",
    "    try:\n",
    "        duration_months[current_month].append((row[1]['duration']))\n",
    "    except:\n",
    "        duration_months[current_month] = [(row[1]['duration'])]\n",
    "        \n",
    "#dictionary of total time hours for each trip for each month\n",
    "for key in duration_months:\n",
    "    durations = duration_months[key]\n",
    "    durations = ((sum(durations))/60)/60\n",
    "    duration_months[key] = durations\n",
    "\n",
    "duration_months_keys_sorted = sorted(duration_months)\n",
    "\n",
    "\n",
    "months_used = ['Sep 19', 'Oct 19', 'Nov 19', 'Dec 19', 'Jan 20', 'Feb 20', 'Mar 20', 'Apr 20', 'May 20',\n",
    "               'Jun 20', 'Jul 20', 'Aug 20', 'Sep 20', 'Oct 20', 'Nov 20', 'Dec 20', 'Jan 21', 'Feb 21' ]\n",
    "\n",
    "durations = []\n",
    "for key in duration_months_keys_sorted:\n",
    "    duration = round(duration_months[key], 1)\n",
    "    durations.append(duration)\n",
    "    \n",
    "durations\n",
    "\n",
    "\n",
    "plt.figure(figsize = (6,3))\n",
    "plt.bar(months_used, durations)\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Trip Average')\n",
    "plt.title('Total Trip Time')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.savefig('total-trip-time.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.946071</td>\n",
       "      <td>-3.210396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.927985</td>\n",
       "      <td>-3.209750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.927985</td>\n",
       "      <td>-3.209750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.953164</td>\n",
       "      <td>-3.181682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.945569</td>\n",
       "      <td>-3.218185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149769</th>\n",
       "      <td>55.940238</td>\n",
       "      <td>-3.194640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149770</th>\n",
       "      <td>55.946071</td>\n",
       "      <td>-3.210396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149771</th>\n",
       "      <td>55.947409</td>\n",
       "      <td>-3.205765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149772</th>\n",
       "      <td>55.940238</td>\n",
       "      <td>-3.194640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149773</th>\n",
       "      <td>55.937123</td>\n",
       "      <td>-3.206432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149774 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              lat      long\n",
       "0       55.946071 -3.210396\n",
       "1       55.927985 -3.209750\n",
       "2       55.927985 -3.209750\n",
       "3       55.953164 -3.181682\n",
       "4       55.945569 -3.218185\n",
       "...           ...       ...\n",
       "149769  55.940238 -3.194640\n",
       "149770  55.946071 -3.210396\n",
       "149771  55.947409 -3.205765\n",
       "149772  55.940238 -3.194640\n",
       "149773  55.937123 -3.206432\n",
       "\n",
       "[149774 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe of all gps points\n",
    "data_set['started_at'] = pd.to_datetime(data_set['started_at'])\n",
    "data_set['ended_at'] = pd.to_datetime(data_set['ended_at'])\n",
    "data_set = data_set.reset_index(drop=True)\n",
    "\n",
    "\n",
    "#prepandemic\n",
    "mask = (data_set['started_at'] < '2020-3-1')\n",
    "data_set_prepandemic = data_set.loc[mask]\n",
    "data_set_prepandemic.sort_values(by='started_at')\n",
    "data_set_prepandemic = data_set_prepandemic.reset_index(drop=True)\n",
    "\n",
    "gps_data1 = data_set_prepandemic.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_prepandemic.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#6 months into pandemic\n",
    "mask = (data_set['started_at'] > '2020-03-1') &  (data_set['started_at'] < '2020-9-1')\n",
    "data_set_6mpandemic = data_set.loc[mask]\n",
    "data_set_6mpandemic.sort_values(by='started_at')\n",
    "data_set_6mpandemic = data_set_6mpandemic.reset_index(drop=True)\n",
    "\n",
    "gps_data1 = data_set_6mpandemic.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_6mpandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_6mpandemic = gps_data_6mpandemic.reset_index(drop=True)\n",
    "gps_data_6mpandemic\n",
    "\n",
    "#6-12 months pandemic\n",
    "mask = (data_set['started_at'] > '2020-09-1')\n",
    "data_set_612mpandemic = data_set.loc[mask]\n",
    "data_set_612mpandemic.sort_values(by='started_at')\n",
    "data_set_612mpandemic = data_set_612mpandemic.reset_index(drop=True)\n",
    "\n",
    "gps_data1 = data_set_612mpandemic.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_612mpandemic.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_612mpandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_612mpandemic = gps_data_612mpandemic.reset_index(drop=True)\n",
    "gps_data_612mpandemic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d50efdff67ce44b493f7cef3ce238317",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fb1162941ed4d9a8d5481c9d1f632ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_6mpandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b990389d7bd446378b1821d53d5fa388",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_612mpandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mask = (data_set['started_at'].between_time('00:00:00', '13:00'))\n",
    "\n",
    "trips_time_prepandemic = []\n",
    "\n",
    "for x in range(0,24):\n",
    "    y = x + 1\n",
    "    mask = (data_set_prepandemic['started_at'].dt.hour >= x) & (data_set_prepandemic['started_at'].dt.hour < y)\n",
    "    timed = data_set_prepandemic.loc[mask]\n",
    "    trips_time_prepandemic.append(len(timed))\n",
    "\n",
    "trips_time_612pandemic = []\n",
    "\n",
    "for x in range(0,24):\n",
    "    y = x + 1\n",
    "    mask = (data_set_612mpandemic['started_at'].dt.hour >= x) & (data_set_612mpandemic['started_at'].dt.hour < y)\n",
    "    timed = data_set_612mpandemic.loc[mask]\n",
    "    trips_time_612pandemic.append(len(timed))    \n",
    "\n",
    "trips_time_6mpandemic = []\n",
    "\n",
    "for x in range(0,24):\n",
    "    y = x + 1\n",
    "    mask = (data_set_6mpandemic['started_at'].dt.hour >= x) & (data_set_6mpandemic['started_at'].dt.hour < y)\n",
    "    timed = data_set_6mpandemic.loc[mask]\n",
    "    trips_time_6mpandemic.append(len(timed))    \n",
    "    \n",
    "hours = ['00:00',\n",
    "'01:00',\n",
    "'02:00',\n",
    "'03:00',\n",
    "'04:00',\n",
    "'05:00',\n",
    "'06:00',\n",
    "'07:00',\n",
    "'08:00',\n",
    "'09:00',\n",
    "'10:00',\n",
    "'11:00',\n",
    "'12:00',\n",
    "'13:00',\n",
    "'14:00',\n",
    "'15:00',\n",
    "'16:00',\n",
    "'17:00',\n",
    "'18:00',\n",
    "'19:00',\n",
    "'20:00',\n",
    "'21:00',\n",
    "'22:00',\n",
    "'23:00']\n",
    "\n",
    "datetime_hours = [datetime.datetime.now() + datetime.timedelta(hours=i) for i in range(24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = (6,6))\n",
    "\n",
    "fig,(plt1, plt2, plt3) = plt.subplots(1, 3,  figsize=(20, 4))\n",
    "fig.suptitle('Total Trips Taken at Each Hour')\n",
    "\n",
    "plt1.bar(hours, trips_time_prepandemic)\n",
    "plt1.tick_params(labelrotation=45)\n",
    "plt1.set(title = 'Prepandemic')\n",
    "plt1.set_xlabel('Hour (Beginning)')\n",
    "plt1.set_ylabel('Total Trips Taken')\n",
    "\n",
    "plt2.bar(hours, trips_time_612pandemic )\n",
    "plt2.tick_params(labelrotation=45)\n",
    "plt2.set(title = '6 Months Into Pandemic')\n",
    "plt2.set_xlabel('Hour (Beginning)')\n",
    "plt2.set_ylabel('Total Trips Taken')\n",
    "\n",
    "plt3.bar(hours, trips_time_6mpandemic)\n",
    "plt3.tick_params(labelrotation=45)\n",
    "plt3.set(title = '6 - 12 Months into Pandemic')\n",
    "plt3.set_xlabel('Hour (Beginning)')\n",
    "plt3.set_ylabel('Total Trips Taken')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('total-trips-hour.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83bf5b13c813476e9c8678a0001e2cdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6months into pandemic 00:00 - 06:00\n",
    "mask = (data_set_6mpandemic['started_at'].dt.hour >= 0) & (data_set_6mpandemic['started_at'].dt.hour < 6)\n",
    "data_set_6mpandemic_timed = data_set_6mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_6mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1db18daeafc40a08c4c24da65226252",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap prepandemic 00:00 - 06:00\n",
    "mask = (data_set_prepandemic['started_at'].dt.hour >= 0) & (data_set_prepandemic['started_at'].dt.hour < 6)\n",
    "data_set_prepandemic_timed = data_set_prepandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_prepandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_prepandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8025d8799967413a9f3069353dc8cf32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap prepandemic 06:00 - 12:00\n",
    "mask = (data_set_prepandemic['started_at'].dt.hour >= 6) & (data_set_prepandemic['started_at'].dt.hour < 12)\n",
    "data_set_prepandemic_timed = data_set_prepandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_prepandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_prepandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e150c900ab1542bab68406c935697e7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap prepandemic 12:00 - 18:00\n",
    "mask = (data_set_prepandemic['started_at'].dt.hour >= 12) & (data_set_prepandemic['started_at'].dt.hour < 18)\n",
    "data_set_prepandemic_timed = data_set_prepandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_prepandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_prepandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e6d6fb18f0c4cfd98ae4a772a7c0caa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap prepandemic 18:00 - 24:00\n",
    "mask = (data_set_prepandemic['started_at'].dt.hour >= 18) & (data_set_prepandemic['started_at'].dt.hour < 24)\n",
    "data_set_prepandemic_timed = data_set_prepandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_prepandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_prepandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d77c3eddd0294a2b81057da0220c1966",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6months into pandemic 00:00 - 06:00\n",
    "mask = (data_set_6mpandemic['started_at'].dt.hour >= 0) & (data_set_6mpandemic['started_at'].dt.hour < 6)\n",
    "data_set_6mpandemic_timed = data_set_6mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_6mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c72f47d37c59496c98ae410350c44e2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6months into pandemic 06:00 - 12:00\n",
    "mask = (data_set_6mpandemic['started_at'].dt.hour >= 6) & (data_set_6mpandemic['started_at'].dt.hour < 12)\n",
    "data_set_6mpandemic_timed = data_set_6mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_6mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5240c168aa834d82a29b8083535c3531",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6months into pandemic 12:00 - 18:00\n",
    "mask = (data_set_6mpandemic['started_at'].dt.hour >= 12) & (data_set_6mpandemic['started_at'].dt.hour < 18)\n",
    "data_set_6mpandemic_timed = data_set_6mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_6mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0b5030e66ef46acad6bdaffc140a7de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6months into pandemic 18:00 - 24:00\n",
    "mask = (data_set_6mpandemic['started_at'].dt.hour >= 18) & (data_set_6mpandemic['started_at'].dt.hour < 24)\n",
    "data_set_6mpandemic_timed = data_set_6mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_6mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_6mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acce2e133f5c44e2b8a4ca5f6fa541bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6-12 months into pandemic 00:00 - 06:00\n",
    "mask = (data_set_612mpandemic['started_at'].dt.hour >= 0) & (data_set_612mpandemic['started_at'].dt.hour < 6)\n",
    "data_set_612mpandemic_timed = data_set_612mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_612mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_612mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91a0ca16d36c43c19c526824ab7ad5c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6-12 months into pandemic 06:00 - 12:00\n",
    "mask = (data_set_612mpandemic['started_at'].dt.hour >= 6) & (data_set_612mpandemic['started_at'].dt.hour < 12)\n",
    "data_set_612mpandemic_timed = data_set_612mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_612mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_612mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4eb199b20f14e58aeebe1bc17ed8798",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6-12 months into pandemic 12:00 - 18:00\n",
    "mask = (data_set_612mpandemic['started_at'].dt.hour >= 12) & (data_set_612mpandemic['started_at'].dt.hour < 18)\n",
    "data_set_612mpandemic_timed = data_set_612mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_612mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_612mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46eac16252ae4bbb96d5b2a470991841",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 6-12 months into pandemic 18:00 - 24:00\n",
    "mask = (data_set_612mpandemic['started_at'].dt.hour >= 18) & (data_set_612mpandemic['started_at'].dt.hour < 24)\n",
    "data_set_612mpandemic_timed = data_set_612mpandemic.loc[mask]\n",
    "\n",
    "gps_data1 = data_set_612mpandemic_timed.loc[:,['start_station_latitude', 'start_station_longitude']]\n",
    "gps_data1.columns = ['lat', 'long']\n",
    "gps_data2 = data_set_612mpandemic_timed.loc[:,['end_station_latitude', 'end_station_longitude']]\n",
    "gps_data2.columns = ['lat', 'long']\n",
    "gps_data_prepandemic = pd.concat([gps_data1, gps_data2])\n",
    "gps_data_prepandemic = gps_data_prepandemic.reset_index(drop=True)\n",
    "gps_data_prepandemic\n",
    "\n",
    "#heatmap creation\n",
    "fig= gmaps.figure(map_type='ROADMAP')\n",
    "heatmap_layer = gmaps.heatmap_layer(gps_data_prepandemic)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}