Mobility Reports AGILE 2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc0de90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import numpy as np\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1daa28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arunsharma/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (27,28) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "#Download Files \"Weekly_Patterns.csv\" and \"core_places.csv\" by copy and pasting Google Drive link:\n",
    "#https://drive.google.com/drive/folders/1gE-yFdIgpPjkAIj_EtumlrRvpQHs0514?usp=sharing\n",
    "#Change the path variable as per the local machine address:\n",
    "path = \"/Users/arunsharma/Desktop/\"\n",
    "weekly_patterns = pd.read_csv(path+\"Weekly_Patterns.csv\")\n",
    "weekly_patterns = weekly_patterns.drop(['Unnamed: 0'], axis = 1)\n",
    "core_pls = pd.read_csv(path+\"core_places.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a602b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge Core_Places and Weekly_Patterns Data\n",
    "core_pls = core_pls[['safegraph_place_id','top_category','sub_category', 'naics_code']]\n",
    "merged_data = pd.merge(weekly_patterns,core_pls,left_on='safegraph_place_id',right_on='safegraph_place_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "045e9855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "#Grouped Merged Data by Week from 2020-03-02T00:00:00-06:00 to 2021-06-21T00:00:00-04:00\n",
    "weeks = []\n",
    "\n",
    "week1 = merged_data.loc[(merged_data['date_range_start'] == \"2020-03-02T00:00:00-06:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-02T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-02T00:00:00-07:00\")]\n",
    "weeks.append(week1)\n",
    "\n",
    "week2 = merged_data.loc[(merged_data['date_range_start'] == \"2020-03-09T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-09T00:00:00-06:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-09T00:00:00-04:00\")]\n",
    "weeks.append(week2)\n",
    "\n",
    "week3 = merged_data.loc[(merged_data['date_range_start'] == \"2020-03-16T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-16T00:00:00-06:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-16T00:00:00-04:00\")]\n",
    "weeks.append(week3)\n",
    "\n",
    "week4 = merged_data.loc[(merged_data['date_range_start'] == \"2020-03-23T00:00:00-06:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-23T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-23T00:00:00-04:00\")]\n",
    "weeks.append(week4)\n",
    "\n",
    "week5 = merged_data.loc[(merged_data['date_range_start'] == \"2020-03-30T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-03-30T00:00:00-06:00\")]\n",
    "weeks.append(week5)\n",
    "\n",
    "week6 = merged_data.loc[(merged_data['date_range_start'] == \"2020-04-06T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-06T00:00:00-06:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-06T00:00:00-04:00\")]\n",
    "weeks.append(week6)\n",
    "\n",
    "week7 = merged_data.loc[(merged_data['date_range_start'] == \"2020-04-13T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-13T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-13T00:00:00-06:00\")]\n",
    "weeks.append(week7)\n",
    "\n",
    "week8 = merged_data.loc[(merged_data['date_range_start'] == \"2020-04-20T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-20T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-20T00:00:00-06:00\")]\n",
    "weeks.append(week8)\n",
    "\n",
    "week9 = merged_data.loc[(merged_data['date_range_start'] == \"2020-04-27T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-27T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-04-27T00:00:00-06:00\")]\n",
    "weeks.append(week9)\n",
    "\n",
    "week10 = merged_data.loc[(merged_data['date_range_start'] == \"2020-05-04T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-05-04T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-05-04T00:00:00-06:00\")]\n",
    "weeks.append(week10)\n",
    "\n",
    "week11 = merged_data.loc[(merged_data['date_range_start'] == \"2020-05-11T00:00:00-05:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-05-11T00:00:00-06:00\")]\n",
    "weeks.append(week11)\n",
    "\n",
    "week12 = merged_data.loc[(merged_data['date_range_start'] == \"2020-05-18T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-05-18T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-05-18T00:00:00-06:00\")]\n",
    "weeks.append(week12)\n",
    "\n",
    "week13 = merged_data.loc[(merged_data['date_range_start'] == \"2020-05-25T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-05-25T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-05-25T00:00:00-06:00\")]\n",
    "weeks.append(week13)\n",
    "\n",
    "week14 = merged_data.loc[(merged_data['date_range_start'] == \"2020-06-01T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-06-01T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-06-01T00:00:00-06:00\")]\n",
    "weeks.append(week14)\n",
    "\n",
    "week15 = merged_data.loc[(merged_data['date_range_start'] == \"2020-06-08T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-06-08T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-06-08T00:00:00-06:00\")]\n",
    "weeks.append(week15)\n",
    "\n",
    "week16 = merged_data.loc[(merged_data['date_range_start'] == \"2020-06-15T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-06-15T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-06-15T00:00:00-06:00\")]\n",
    "weeks.append(week16)\n",
    "\n",
    "week17 = merged_data.loc[(merged_data['date_range_start'] == \"2020-06-22T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-06-22T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-06-22T00:00:00-06:00\")]\n",
    "weeks.append(week17)\n",
    "\n",
    "week18 = merged_data.loc[(merged_data['date_range_start'] == \"2020-06-29T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-06-29T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-06-29T00:00:00-06:00\")]\n",
    "weeks.append(week18)\n",
    "\n",
    "week19 = merged_data.loc[(merged_data['date_range_start'] == \"2020-07-06T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-07-06T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-07-06T00:00:00-06:00\")]\n",
    "weeks.append(week19)\n",
    "\n",
    "week20 = merged_data.loc[(merged_data['date_range_start'] == \"2020-07-13T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-07-13T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-07-13T00:00:00-06:00\")]\n",
    "weeks.append(week20)\n",
    "\n",
    "week21 = merged_data.loc[(merged_data['date_range_start'] == \"2020-07-20T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-07-20T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-07-20T00:00:00-06:00\")]\n",
    "weeks.append(week21)\n",
    "\n",
    "week22 = merged_data.loc[(merged_data['date_range_start'] == \"2020-07-27T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-07-27T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-07-27T00:00:00-06:00\")]\n",
    "weeks.append(week22)\n",
    "\n",
    "week23 = merged_data.loc[(merged_data['date_range_start'] == \"2020-08-03T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-08-03T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-08-03T00:00:00-06:00\")]\n",
    "weeks.append(week23)\n",
    "\n",
    "week24 = merged_data.loc[(merged_data['date_range_start'] == \"2020-08-10T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-08-10T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-08-10T00:00:00-06:00\")]\n",
    "weeks.append(week24)\n",
    "\n",
    "week25 = merged_data.loc[(merged_data['date_range_start'] == \"2020-08-17T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-08-17T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-08-17T00:00:00-06:00\")]\n",
    "weeks.append(week25)\n",
    "\n",
    "week26 = merged_data.loc[(merged_data['date_range_start'] == \"2020-08-24T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-08-24T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-08-24T00:00:00-06:00\")]\n",
    "weeks.append(week26)\n",
    "\n",
    "week27 = merged_data.loc[(merged_data['date_range_start'] == \"2020-08-31T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-08-31T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-08-31T00:00:00-06:00\")]\n",
    "weeks.append(week27)\n",
    "\n",
    "week28 = merged_data.loc[(merged_data['date_range_start'] == \"2020-09-07T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-09-07T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-09-07T00:00:00-06:00\")]\n",
    "weeks.append(week28)\n",
    "\n",
    "week29 = merged_data.loc[(merged_data['date_range_start'] == \"2020-09-14T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-09-14T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-09-14T00:00:00-06:00\")]\n",
    "weeks.append(week29)\n",
    "\n",
    "week30 = merged_data.loc[(merged_data['date_range_start'] == \"2020-09-21T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-09-21T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-09-21T00:00:00-06:00\")]\n",
    "weeks.append(week30)\n",
    "\n",
    "week31 = merged_data.loc[(merged_data['date_range_start'] == \"2020-09-28T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-09-28T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-09-28T00:00:00-06:00\")]\n",
    "weeks.append(week31)\n",
    "\n",
    "week32 = merged_data.loc[(merged_data['date_range_start'] == \"2020-10-05T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-10-05T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-10-05T00:00:00-06:00\")]\n",
    "weeks.append(week32)\n",
    "\n",
    "week33 = merged_data.loc[(merged_data['date_range_start'] == \"2020-10-12T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-10-12T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-10-12T00:00:00-06:00\")]\n",
    "weeks.append(week33)\n",
    "\n",
    "week34 = merged_data.loc[(merged_data['date_range_start'] == \"2020-10-19T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-10-19T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-10-19T00:00:00-06:00\")]\n",
    "weeks.append(week34)\n",
    "\n",
    "week35 = merged_data.loc[(merged_data['date_range_start'] == \"2020-10-26T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-10-26T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-10-26T00:00:00-06:00\")]\n",
    "weeks.append(week35)\n",
    "\n",
    "week36 = merged_data.loc[(merged_data['date_range_start'] == \"2020-11-02T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-11-02T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-11-02T00:00:00-06:00\")]\n",
    "weeks.append(week36)\n",
    "\n",
    "week37 = merged_data.loc[(merged_data['date_range_start'] == \"2020-11-09T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-11-09T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-11-09T00:00:00-06:00\")]\n",
    "weeks.append(week37)\n",
    "\n",
    "week38 = merged_data.loc[(merged_data['date_range_start'] == \"2020-11-16T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-11-16T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-11-16T00:00:00-06:00\")]\n",
    "weeks.append(week38)\n",
    "\n",
    "week39 = merged_data.loc[(merged_data['date_range_start'] == \"2020-11-23T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-11-23T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-11-23T00:00:00-06:00\")]\n",
    "weeks.append(week39)\n",
    "\n",
    "week40 = merged_data.loc[(merged_data['date_range_start'] == \"2020-11-30T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-11-30T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-11-30T00:00:00-06:00\")]\n",
    "weeks.append(week40)\n",
    "\n",
    "week41 = merged_data.loc[(merged_data['date_range_start'] == \"2020-12-07T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-12-07T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-12-07T00:00:00-06:00\")]\n",
    "weeks.append(week41)\n",
    "\n",
    "week42 = merged_data.loc[(merged_data['date_range_start'] == \"2020-12-14T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-12-14T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-12-14T00:00:00-06:00\")]\n",
    "weeks.append(week42)\n",
    "\n",
    "week43 = merged_data.loc[(merged_data['date_range_start'] == \"2020-12-21T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-12-21T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-12-21T00:00:00-06:00\")]\n",
    "weeks.append(week43)\n",
    "\n",
    "week44 = merged_data.loc[(merged_data['date_range_start'] == \"2020-12-28T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2020-12-28T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2020-12-28T00:00:00-06:00\")]\n",
    "weeks.append(week44)\n",
    "\n",
    "week45 = merged_data.loc[(merged_data['date_range_start'] == \"2021-01-04T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-01-04T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-01-04T00:00:00-06:00\")]\n",
    "weeks.append(week45)\n",
    "\n",
    "week46 = merged_data.loc[(merged_data['date_range_start'] == \"2021-01-11T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-01-11T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-01-11T00:00:00-06:00\")]\n",
    "weeks.append(week46)\n",
    "\n",
    "week47 = merged_data.loc[(merged_data['date_range_start'] == \"2021-01-18T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-01-18T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-01-18T00:00:00-06:00\")]\n",
    "weeks.append(week47)\n",
    "\n",
    "week48 = merged_data.loc[(merged_data['date_range_start'] == \"2021-01-25T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-01-25T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-01-25T00:00:00-06:00\")]\n",
    "weeks.append(week48)\n",
    "\n",
    "week49 = merged_data.loc[(merged_data['date_range_start'] == \"2021-02-01T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-02-01T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-02-01T00:00:00-06:00\")]\n",
    "weeks.append(week49)\n",
    "\n",
    "week50 = merged_data.loc[(merged_data['date_range_start'] == \"2021-02-08T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-02-08T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-02-08T00:00:00-06:00\")]\n",
    "weeks.append(week50)\n",
    "\n",
    "week51 = merged_data.loc[(merged_data['date_range_start'] == \"2021-02-15T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-02-15T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-02-15T00:00:00-06:00\")]\n",
    "weeks.append(week51)\n",
    "\n",
    "week52 = merged_data.loc[(merged_data['date_range_start'] == \"2021-02-22T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-02-22T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-02-22T00:00:00-06:00\")]\n",
    "weeks.append(week52)\n",
    "\n",
    "week53 = merged_data.loc[(merged_data['date_range_start'] == \"2021-03-01T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-03-01T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-03-01T00:00:00-06:00\")]\n",
    "weeks.append(week53)\n",
    "\n",
    "week54 = merged_data.loc[(merged_data['date_range_start'] == \"2021-03-08T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-03-08T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-03-08T00:00:00-06:00\")]\n",
    "weeks.append(week54)\n",
    "\n",
    "week55 = merged_data.loc[(merged_data['date_range_start'] == \"2021-03-15T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-03-15T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-03-15T00:00:00-06:00\")]\n",
    "weeks.append(week55)\n",
    "\n",
    "week56 = merged_data.loc[(merged_data['date_range_start'] == \"2021-03-22T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-03-22T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-03-22T00:00:00-06:00\")]\n",
    "weeks.append(week56)\n",
    "\n",
    "week57 = merged_data.loc[(merged_data['date_range_start'] == \"2021-03-29T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-03-29T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-03-29T00:00:00-06:00\")]\n",
    "weeks.append(week57)\n",
    "\n",
    "week58 = merged_data.loc[(merged_data['date_range_start'] == \"2021-04-05T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-04-05T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-04-05T00:00:00-06:00\")]\n",
    "weeks.append(week58)\n",
    "\n",
    "week59 = merged_data.loc[(merged_data['date_range_start'] == \"2021-04-12T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-04-12T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-04-12T00:00:00-06:00\")]\n",
    "weeks.append(week59)\n",
    "\n",
    "week60 = merged_data.loc[(merged_data['date_range_start'] == \"2021-04-19T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-04-19T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-04-19T00:00:00-06:00\")]\n",
    "weeks.append(week60)\n",
    "\n",
    "week61 = merged_data.loc[(merged_data['date_range_start'] == \"2021-04-26T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-04-26T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-04-26T00:00:00-06:00\")]\n",
    "weeks.append(week61)\n",
    "\n",
    "week62 = merged_data.loc[(merged_data['date_range_start'] == \"2021-05-03T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-05-03T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-05-03T00:00:00-06:00\")]\n",
    "weeks.append(week62)\n",
    "\n",
    "week63 = merged_data.loc[(merged_data['date_range_start'] == \"2021-05-10T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-05-10T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-05-10T00:00:00-06:00\")]\n",
    "weeks.append(week63)\n",
    "                        \n",
    "week64 = merged_data.loc[(merged_data['date_range_start'] == \"2021-05-17T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-05-17T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-05-17T00:00:00-06:00\")]\n",
    "weeks.append(week64)\n",
    "\n",
    "week65 = merged_data.loc[(merged_data['date_range_start'] == \"2021-05-24T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-05-24T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-05-24T00:00:00-06:00\")]\n",
    "weeks.append(week65)\n",
    "\n",
    "week66 = merged_data.loc[(merged_data['date_range_start'] == \"2021-05-31T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-05-31T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-05-31T00:00:00-06:00\")]\n",
    "weeks.append(week66)\n",
    "\n",
    "week67 = merged_data.loc[(merged_data['date_range_start'] == \"2021-06-07T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-06-07T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-06-07T00:00:00-06:00\")]\n",
    "weeks.append(week67)\n",
    "                        \n",
    "week68 = merged_data.loc[(merged_data['date_range_start'] == \"2021-06-14T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-06-14T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-06-14T00:00:00-06:00\")]\n",
    "weeks.append(week68)\n",
    "\n",
    "week69 = merged_data.loc[(merged_data['date_range_start'] == \"2021-06-21T00:00:00-04:00\") \n",
    "                    | (merged_data['date_range_start'] == \"2021-06-21T00:00:00-05:00\")\n",
    "                    | (merged_data['date_range_start'] == \"2021-06-21T00:00:00-06:00\")]\n",
    "weeks.append(week69)  \n",
    "\n",
    "time = ['03/02-03/09','03/09-3/16','03/16-03/23','03/23-03/30','03/30-04/06','04/06-04/13','04/13-04/20','04/20-04/27',\n",
    "        '04/27-05/04','05/04-05/11','05/11-05/18','05/18-05/25','05/25-06/01','06/01-06/08','06/08-06/15','06/15-06/22',\n",
    "        '06/22-06/29','06/29-07/06','07/06-07/13','07/13-07/20','07/20-07/27','07/27-08/03','08/03-08/10','08/10-08/17',\n",
    "        '08/17-08/24','08/24-08/31','08/31-09/07','09/07-09/14','09/14-09/21','09/21-09/28','09/28-10/05','10/05-10/12',\n",
    "        '10/12-10/19','10/19-10/26','10/26-11/02','11/02-11/09','11/09-11/16','11/16-11/23','11/23-11/30','11/30-12/07',\n",
    "        '12/07-12/14','12/14-12/21','12/21-12/28','12/28-01/04','01/04-01/11','01/11-01/18','01/18-01/25','01/25-02/01',\n",
    "        '02/01-02/08','02/08-02/15','02/15-02/22','02/15-03/01','03/01-03/08','03/08-03/15','03/15-03/22','03/22-03/29',\n",
    "        '03/29-04/05','04/05-04/12','04/12-04/19','04/19-04/26','04/26-05/02', '05/03-05/10','05/10-05/17','05/17-05/24',\n",
    "        '05/24-05/31','05/31-06/07','06/07-06/14','06/14-06/21','06/21-06/28']\n",
    "print (len(time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c93f5a8",
   "metadata": {},
   "source": [
    "### Code for generating Figure 8, Figure 9 and Figure 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31320c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gathering Visits by Duration for Figure 8-10\n",
    "#Variable sum(global_dic['<5'] provides total aggregated visits by dwell time\n",
    "def get_overall_dwell_time(merged_data,weeks,code):\n",
    "    list_1 = []\n",
    "    list_2 = []\n",
    "    list_3 = []\n",
    "    list_4 = []\n",
    "    list_5 = []\n",
    "    global_dic = {}\n",
    "    count = 0\n",
    "    for week in weeks:\n",
    "        count = count + 1\n",
    "        naics_table = week.loc[merged_data['naics_code'] == code]\n",
    "        dwell_list = naics_table[['bucketed_dwell_times']]['bucketed_dwell_times'].tolist()\n",
    "        list_1.append(sum([ast.literal_eval(dwell_time).get('<5') for dwell_time in dwell_list]))\n",
    "        list_3.append(sum([ast.literal_eval(dwell_time).get('21-60') for dwell_time in dwell_list]))\n",
    "        list_5.append(sum([ast.literal_eval(dwell_time).get('>240') for dwell_time in dwell_list]))\n",
    "        if count > 39:   \n",
    "            list_2.append(sum([ast.literal_eval(dwell_time).get('5-10') for dwell_time in dwell_list])+sum([ast.literal_eval(dwell_time).get('11-20') for dwell_time in dwell_list])) \n",
    "            list_4.append(sum([ast.literal_eval(dwell_time).get('61-120') for dwell_time in dwell_list])+sum([ast.literal_eval(dwell_time).get('121-240') for dwell_time in dwell_list]))\n",
    "        else :\n",
    "            list_2.append(sum([ast.literal_eval(dwell_time).get('5-20') for dwell_time in dwell_list]))  \n",
    "            list_4.append(sum([ast.literal_eval(dwell_time).get('61-240') for dwell_time in dwell_list]))\n",
    "\n",
    "    global_dic['<5'] = list_1\n",
    "    global_dic['5-20'] = list_2\n",
    "    global_dic['21-60'] = list_3\n",
    "    global_dic['61-240'] = list_4\n",
    "    global_dic['>240'] = list_5\n",
    "\n",
    "    print (sum(global_dic['<5']))\n",
    "    print (sum(global_dic['5-20']))\n",
    "    print (sum(global_dic['21-60']))\n",
    "    print (sum(global_dic['61-240']))\n",
    "    print (sum(global_dic['>240']))\n",
    "        \n",
    "    return global_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1be10634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "416949\n",
      "5446795\n",
      "4416678\n",
      "3867499\n",
      "1467323\n",
      "592995\n",
      "7262497\n",
      "1522801\n",
      "1000987\n",
      "590133\n",
      "5646\n",
      "73095\n",
      "74254\n",
      "81226\n",
      "25724\n"
     ]
    }
   ],
   "source": [
    "#Gathering Visits by Duration excel sheet for Figure 8-10\n",
    "#Figure 8-10 can be reproduced from the excel sheet \"restaurant_bucket_dwell.xlsx\", \"limited_restaurant_bucket_dwell.xlsx\" and \"bars_bucket_dwell.xlsx\" for Figures 8,9 and 10 respectively.  \n",
    "get_median_buckets_restaurant = get_overall_dwell_time(merged_data,weeks,722511.0)\n",
    "restaurant_bucket = pd.DataFrame.from_dict(get_median_buckets_restaurant)\n",
    "restaurant_bucket.insert(0, \"Date\", time, True)\n",
    "restaurant_bucket.to_excel(path+\"restaurant_bucket_dwell.xlsx\")\n",
    "\n",
    "get_median_buckets_restaurant = get_overall_dwell_time(merged_data,weeks,722513.0)\n",
    "restaurant_bucket = pd.DataFrame.from_dict(get_median_buckets_restaurant)\n",
    "restaurant_bucket.insert(0, \"Date\", time, True)\n",
    "restaurant_bucket.to_excel(path+\"limited_restaurant_bucket_dwell.xlsx\")\n",
    "\n",
    "get_median_buckets_hardware = get_overall_dwell_time(merged_data,weeks,722410.0)\n",
    "hardware_bucket = pd.DataFrame.from_dict(get_median_buckets_hardware)\n",
    "hardware_bucket.insert(0, \"Date\", time, True)\n",
    "hardware_bucket.to_excel(path+\"bars_bucket_dwell.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a65c93f",
   "metadata": {},
   "source": [
    "### Code for generating Bar Charts in Figure 7 and Figure 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49b5bdb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_sorted_by_buckets(data,group,values):\n",
    "    grouped_naics_code = data[[group,values]]\n",
    "    get_groups = grouped_naics_code.groupby([group], as_index = False)\n",
    "    return get_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9434f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_length(bucket_list):\n",
    "    list_1 = []\n",
    "    list_2 = []\n",
    "    list_3 = []\n",
    "    list_4 = []\n",
    "    list_5 = []\n",
    "    for dwell_time in bucket_list:\n",
    "        list_1.append(ast.literal_eval(dwell_time).get('<5'))\n",
    "        list_3.append(ast.literal_eval(dwell_time).get('21-60'))\n",
    "        list_5.append(ast.literal_eval(dwell_time).get('>240'))\n",
    "        if len(ast.literal_eval(dwell_time)) == 7:\n",
    "            list_2.append(ast.literal_eval(dwell_time).get('5-10') + ast.literal_eval(dwell_time).get('11-20'))\n",
    "            list_4.append(ast.literal_eval(dwell_time).get('61-120') + ast.literal_eval(dwell_time).get('121-240'))\n",
    "        elif len(ast.literal_eval(dwell_time)) == 5:\n",
    "            list_2.append(ast.literal_eval(dwell_time).get('5-20'))\n",
    "            list_4.append(ast.literal_eval(dwell_time).get('61-240'))\n",
    "    \n",
    "    return (sum(list_1),sum(list_2),sum(list_3),sum(list_4),sum(list_5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6aa5447",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_names(merged_data,code_list,category):\n",
    "    name_list = []\n",
    "    for code in code_list:\n",
    "        name_list.append(merged_data.loc[merged_data[category] == code]['location_name'].iloc[0])\n",
    "    return name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a584f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_visits(merged_data,category):\n",
    "    x = top_sorted_by_buckets(merged_data,category,'bucketed_dwell_times')\n",
    "    bucket_sum_table = x.first()[category]\n",
    "    bucket_sum_table = pd.DataFrame(bucket_sum_table)\n",
    "    safegraph_list = x.first()[category].tolist()\n",
    "\n",
    "    list_1 = []\n",
    "    list_2 = []\n",
    "    list_3 = []\n",
    "    list_4 = []\n",
    "    list_5 = []\n",
    "\n",
    "    for code in safegraph_list:\n",
    "        bucket_list = x.get_group(code)['bucketed_dwell_times'].tolist()\n",
    "#         print (bucket_list)\n",
    "        sum_list_1,sum_list_2,sum_list_3,sum_list_4,sum_list_5 = check_length(bucket_list)\n",
    "        list_1.append(sum_list_1)\n",
    "        list_2.append(sum_list_2)\n",
    "        list_3.append(sum_list_3)\n",
    "        list_4.append(sum_list_4)\n",
    "        list_5.append(sum_list_5)\n",
    "\n",
    "    bucket_sum_table['<5'] = list_1\n",
    "    bucket_sum_table['5-20'] = list_2\n",
    "    bucket_sum_table['21-60'] = list_3\n",
    "    bucket_sum_table['61-240'] = list_4\n",
    "    bucket_sum_table['>240'] = list_5\n",
    "    \n",
    "    bucket_sum_table['Total_Long_Visits'] = bucket_sum_table['21-60'] + bucket_sum_table['61-240'] + bucket_sum_table['>240']\n",
    "#     bucket_sum_table['Total_Overall_Visits'] = bucket_sum_table['<5'] + bucket_sum_table['5-20'] + bucket_sum_table['21-60'] + bucket_sum_table['61-240'] + bucket_sum_table['>240']\n",
    "    \n",
    "    long_sorted = bucket_sum_table.sort_values(by='Total_Long_Visits',ascending=False).reset_index(drop=True)[0:10]\n",
    "#     overall_sorted = bucket_sum_table.sort_values(by='Total_Overall_Visits',ascending=False).reset_index(drop=True)[0:10]\n",
    "    \n",
    "    long_visit_frame = long_sorted[[category,'Total_Long_Visits']]\n",
    "#     overall_sorted_frame = overall_sorted[[category,'Total_Overall_Visits']]\n",
    "    \n",
    "    return long_visit_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c36141c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          sub_category     <5    5-20   21-60  \\\n",
      "0                  Administration of Veterans' Affairs     13     264     352   \n",
      "1        All Other Amusement and Recreation Industries   2539   37135   33857   \n",
      "2          All Other Automotive Repair and Maintenance    582   12079    8160   \n",
      "3                  All Other Business Support Services     92     832     630   \n",
      "4                      All Other Consumer Goods Rental    396    5122    2805   \n",
      "..                                                 ...    ...     ...     ...   \n",
      "255                     Wind Electric Power Generation    226    3101    2453   \n",
      "256                                           Wineries    777    9977   10863   \n",
      "257  Wireless Telecommunications Carriers (except S...   5502   77872   53651   \n",
      "258                            Women's Clothing Stores  14451  231887  162187   \n",
      "259            Women's Handbag and Purse Manufacturing    433    7345    5507   \n",
      "\n",
      "     61-240   >240  \n",
      "0       696    471  \n",
      "1     47122  14555  \n",
      "2      4676   4990  \n",
      "3      1323   2319  \n",
      "4      3564   4330  \n",
      "..      ...    ...  \n",
      "255    2720   1512  \n",
      "256   16621   5262  \n",
      "257   59475  42042  \n",
      "258  104086  57410  \n",
      "259    3101   4452  \n",
      "\n",
      "[260 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4c3663340>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# long_visits_name_frame = get_visits(merged_data,'safegraph_place_id')\n",
    "long_visits_name_frame_1 = get_visits(merged_data,'sub_category')\n",
    "\n",
    "naics_category_long = long_visits_name_frame_1['sub_category'].tolist()\n",
    "# naics_category_overall = overall_visits_name_frame_1['sub_category'].tolist()\n",
    "\n",
    "visits_long = long_visits_name_frame_1['Total_Long_Visits'].tolist()\n",
    "visits_long_float = [float(i) for i in visits_long]\n",
    "# visits_overall = overall_visits_name_frame_1['Total_Overall_Visits'].tolist()\n",
    "# visits_overall_float = [float(i) for i in visits_overall]\n",
    "\n",
    "df_bar_long2 = pd.DataFrame({'>20': visits_long_float, 'Category' : naics_category_long})\n",
    "df_bar_long2.plot.barh(x = 'Category', y = '>20')\n",
    "# df_bar_overall2 = pd.DataFrame({'Total Visits': visits_overall_float, 'Category' : naics_category_overall})\n",
    "# df_bar_overall2.plot.barh(x = 'Category', y = 'Total Visits')\n",
    "\n",
    "# print (df_bar_long2)\n",
    "# print (df_bar_overall2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44ffcb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb23f7e8f70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "poi_category_long = long_visits_name_frame['safegraph_place_id'].tolist()\n",
    "poi_long_visits_name_list = get_names(merged_data,poi_category_long,'safegraph_place_id')\n",
    "# poi_category_overall = overall_visits_name_frame['safegraph_place_id'].tolist()\n",
    "# poi_overall_visits_name_list = get_names(merged_data,poi_category_overall,'safegraph_place_id')\n",
    "\n",
    "visits_long_poi = long_visits_name_frame['Total_Long_Visits'].tolist()\n",
    "visits_long_poi_float = [float(i) for i in visits_long]\n",
    "# visits_poi_overall = overall_visits_name_frame['Total_Overall_Visits'].tolist()\n",
    "# visits_poi_overall_float = [float(i) for i in visits_overall]\n",
    "\n",
    "df_bar_long1 = pd.DataFrame({'>20': visits_long_poi_float, 'Category' : poi_long_visits_name_list})\n",
    "df_bar_long1.plot.barh(x = 'Category', y = '>20')\n",
    "# df_bar_overall1 = pd.DataFrame({'Total Visits': visits_overall_float, 'Category' : poi_overall_visits_name_list})\n",
    "# df_bar_overall1.plot.barh(x = 'Category', y = 'Total Visits')\n",
    "\n",
    "# print (df_bar_long1)\n",
    "# print (df_bar_overall1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4434f3",
   "metadata": {},
   "source": [
    "### Code for generating Time Series Plots in Figure 7 and Figure 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2474f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weekly_long_data(merged_data,category,name_list):\n",
    "    \n",
    "    global_dic = {}\n",
    "    for code in name_list:\n",
    "        weekly_visits = []\n",
    "        count = 0\n",
    "        for week in weeks:\n",
    "            list_1 = []\n",
    "            list_2 = []\n",
    "            list_3 = []\n",
    "            list_4 = []\n",
    "            list_5 = []\n",
    "            count = count + 1        \n",
    "            naics_table = week.loc[merged_data[category] == code]\n",
    "            dwell_list = naics_table[['bucketed_dwell_times']]['bucketed_dwell_times'].tolist()\n",
    "            list_1.append(sum([ast.literal_eval(dwell_time).get('<5') for dwell_time in dwell_list]))\n",
    "            list_3.append(sum([ast.literal_eval(dwell_time).get('21-60') for dwell_time in dwell_list]))\n",
    "            list_5.append(sum([ast.literal_eval(dwell_time).get('>240') for dwell_time in dwell_list]))\n",
    "            if count > 39:\n",
    "                list_2.append(sum([ast.literal_eval(dwell_time).get('5-10') for dwell_time in dwell_list])+sum([ast.literal_eval(dwell_time).get('11-20') for dwell_time in dwell_list])) \n",
    "                list_4.append(sum([ast.literal_eval(dwell_time).get('61-120') for dwell_time in dwell_list])+sum([ast.literal_eval(dwell_time).get('121-240') for dwell_time in dwell_list]))\n",
    "            else :\n",
    "                list_2.append(sum([ast.literal_eval(dwell_time).get('5-20') for dwell_time in dwell_list]))  \n",
    "                list_4.append(sum([ast.literal_eval(dwell_time).get('61-240') for dwell_time in dwell_list]))\n",
    "            weekly_visits.append(sum(list_3 + list_4 + list_5))            \n",
    "        if category == 'safegraph_place_id':\n",
    "            poi_name = merged_data.loc[merged_data[category] == code]['location_name'].iloc[0]\n",
    "            global_dic[poi_name] = weekly_visits\n",
    "        else: \n",
    "            global_dic[code] = weekly_visits\n",
    "    \n",
    "    return global_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "da9c1899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_excel(dataframe_dict,time,filename):\n",
    "    visits = pd.DataFrame.from_dict(dataframe_dict)\n",
    "    visits.insert(0, \"Date\", time, True)\n",
    "    visits.to_excel(filename+\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0cd13df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gathering Time Series plots via excel sheet for Figure 7 and Figure 16\n",
    "#Figure 7 and 16 can be reproduced from the excel sheet  from naics_long_visits.xlsx\" and \"poi_long_visits\" respectively.  \n",
    "poi_category_long = long_visits_name_frame['safegraph_place_id'].tolist()\n",
    "naics_category_long = long_visits_name_frame_1['sub_category'].tolist()\n",
    "weekly_long_dict = get_weekly_long_data(merged_data,'sub_category',naics_category_long)\n",
    "weekly_long_dict_poi = get_weekly_long_data(merged_data,'safegraph_place_id',poi_category_long)\n",
    "write_excel(weekly_long_dict,time,\"naics_long_visits\")\n",
    "write_excel(weekly_long_dict_poi,time,\"poi_long_visits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8e29ce",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}