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alexiswl/Active Supermarket Exposure Sites (VIC).ipynb

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Active Supermarket Exposure Sites (VIC)
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Active Supermarket Exposure Sites (VIC).ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "bf13d225",
"metadata": {},
"source": [
"# Supermarket exposure sites"
]
},
{
"cell_type": "markdown",
"id": "5bffcbbd",
"metadata": {},
"source": [
"A quick pipeline looking at the supermarket exposure sites in Victoria,\n",
"\n",
"Is there a better time to go to the supermarket?"
]
},
{
"cell_type": "markdown",
"id": "b8a4c20a",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7280f7f5",
"metadata": {},
"outputs": [],
"source": [
"# ! conda update -n base -c defaults conda -y\n",
"# ! conda install -c conda-forge pandas numpy seaborn matplotlib -y"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "68c98f67",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from typing import List\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "55571020",
"metadata": {},
"outputs": [],
"source": [
"ACTIVE_CASES_DATASET=\"https://drive.google.com/uc?export=download&id=1hULHQeuuMQwndvKy1_ScqObgX0NRUv1A\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "33215f0a",
"metadata": {},
"outputs": [],
"source": [
"PLACEHOLDER_DATE = \"1970-01-01\" # Placeholder as we map all exposure sites over a period of one day\n",
"\n",
"PLOTS_CAPTION = \"Source: Alexis Lucattini - https://gist.github.com/alexiswl/f07af4e6e30ad63c1af11de4a8c34f49\""
]
},
{
"cell_type": "markdown",
"id": "008bdc32",
"metadata": {},
"source": [
"## Step 1: Read in DataSet"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4dac641a",
"metadata": {},
"outputs": [],
"source": [
"active_cases = pd.read_csv(ACTIVE_CASES_DATASET)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "be3da7ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 823 entries, 0 to 822\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Suburb 823 non-null object \n",
" 1 Site_title 823 non-null object \n",
" 2 Site_streetaddress 764 non-null object \n",
" 3 Site_state 823 non-null object \n",
" 4 Site_postcode 765 non-null float64\n",
" 5 Exposure_date_dtm 823 non-null object \n",
" 6 Exposure_date 823 non-null object \n",
" 7 Exposure_time 823 non-null object \n",
" 8 Notes 823 non-null object \n",
" 9 Added_date_dtm 823 non-null object \n",
" 10 Added_date 823 non-null object \n",
" 11 Added_time 822 non-null object \n",
" 12 Advice_title 822 non-null object \n",
" 13 Advice_instruction 823 non-null object \n",
" 14 Exposure_time_start_24 823 non-null object \n",
" 15 Exposure_time_end_24 823 non-null object \n",
"dtypes: float64(1), object(15)\n",
"memory usage: 103.0+ KB\n"
]
}
],
"source": [
"active_cases.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9ea7816e",
"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>Suburb</th>\n",
" <th>Site_title</th>\n",
" <th>Site_streetaddress</th>\n",
" <th>Site_state</th>\n",
" <th>Site_postcode</th>\n",
" <th>Exposure_date_dtm</th>\n",
" <th>Exposure_date</th>\n",
" <th>Exposure_time</th>\n",
" <th>Notes</th>\n",
" <th>Added_date_dtm</th>\n",
" <th>Added_date</th>\n",
" <th>Added_time</th>\n",
" <th>Advice_title</th>\n",
" <th>Advice_instruction</th>\n",
" <th>Exposure_time_start_24</th>\n",
" <th>Exposure_time_end_24</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Northcote</td>\n",
" <td>Northcote Primary School OSH Club</td>\n",
" <td>42-44 Henry Street</td>\n",
" <td>VIC</td>\n",
" <td>3070.0</td>\n",
" <td>2021-08-18</td>\n",
" <td>18/08/2021</td>\n",
" <td>7:15am - 9:45am</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-26</td>\n",
" <td>26/08/2021</td>\n",
" <td>11:00:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>07:15:00</td>\n",
" <td>09:45:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Northcote</td>\n",
" <td>Northcote Primary School OSH Club</td>\n",
" <td>42-44 Henry Street</td>\n",
" <td>VIC</td>\n",
" <td>3070.0</td>\n",
" <td>2021-08-18</td>\n",
" <td>18/08/2021</td>\n",
" <td>2:45pm - 6:45pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-26</td>\n",
" <td>26/08/2021</td>\n",
" <td>11:00:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>14:45:00</td>\n",
" <td>18:45:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Northcote</td>\n",
" <td>Northcote Primary School OSH Club</td>\n",
" <td>42-44 Henry Street</td>\n",
" <td>VIC</td>\n",
" <td>3070.0</td>\n",
" <td>2021-08-19</td>\n",
" <td>19/08/2021</td>\n",
" <td>7:15am - 9:45am</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-26</td>\n",
" <td>26/08/2021</td>\n",
" <td>11:00:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>07:15:00</td>\n",
" <td>09:45:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Northcote</td>\n",
" <td>Northcote Primary School OSH Club</td>\n",
" <td>42-44 Henry Street</td>\n",
" <td>VIC</td>\n",
" <td>3070.0</td>\n",
" <td>2021-08-19</td>\n",
" <td>19/08/2021</td>\n",
" <td>2:45pm - 6:45pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-26</td>\n",
" <td>26/08/2021</td>\n",
" <td>11:00:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>14:45:00</td>\n",
" <td>18:45:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Northcote</td>\n",
" <td>Northcote Primary School OSH Club</td>\n",
" <td>42-44 Henry Street</td>\n",
" <td>VIC</td>\n",
" <td>3070.0</td>\n",
" <td>2021-08-20</td>\n",
" <td>20/08/2021</td>\n",
" <td>7:15am - 9:30am</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-26</td>\n",
" <td>26/08/2021</td>\n",
" <td>11:00:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>07:15:00</td>\n",
" <td>09:30:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Suburb Site_title Site_streetaddress \\\n",
"0 Northcote Northcote Primary School OSH Club 42-44 Henry Street \n",
"1 Northcote Northcote Primary School OSH Club 42-44 Henry Street \n",
"2 Northcote Northcote Primary School OSH Club 42-44 Henry Street \n",
"3 Northcote Northcote Primary School OSH Club 42-44 Henry Street \n",
"4 Northcote Northcote Primary School OSH Club 42-44 Henry Street \n",
"\n",
" Site_state Site_postcode Exposure_date_dtm Exposure_date Exposure_time \\\n",
"0 VIC 3070.0 2021-08-18 18/08/2021 7:15am - 9:45am \n",
"1 VIC 3070.0 2021-08-18 18/08/2021 2:45pm - 6:45pm \n",
"2 VIC 3070.0 2021-08-19 19/08/2021 7:15am - 9:45am \n",
"3 VIC 3070.0 2021-08-19 19/08/2021 2:45pm - 6:45pm \n",
"4 VIC 3070.0 2021-08-20 20/08/2021 7:15am - 9:30am \n",
"\n",
" Notes Added_date_dtm Added_date Added_time \\\n",
"0 Case attended venue\\r 2021-08-26 26/08/2021 11:00:00 \n",
"1 Case attended venue\\r 2021-08-26 26/08/2021 11:00:00 \n",
"2 Case attended venue\\r 2021-08-26 26/08/2021 11:00:00 \n",
"3 Case attended venue\\r 2021-08-26 26/08/2021 11:00:00 \n",
"4 Case attended venue\\r 2021-08-26 26/08/2021 11:00:00 \n",
"\n",
" Advice_title \\\n",
"0 Tier 1 - Get tested immediately and quarantine... \n",
"1 Tier 1 - Get tested immediately and quarantine... \n",
"2 Tier 1 - Get tested immediately and quarantine... \n",
"3 Tier 1 - Get tested immediately and quarantine... \n",
"4 Tier 1 - Get tested immediately and quarantine... \n",
"\n",
" Advice_instruction Exposure_time_start_24 \\\n",
"0 Anyone who has visited this location during th... 07:15:00 \n",
"1 Anyone who has visited this location during th... 14:45:00 \n",
"2 Anyone who has visited this location during th... 07:15:00 \n",
"3 Anyone who has visited this location during th... 14:45:00 \n",
"4 Anyone who has visited this location during th... 07:15:00 \n",
"\n",
" Exposure_time_end_24 \n",
"0 09:45:00 \n",
"1 18:45:00 \n",
"2 09:45:00 \n",
"3 18:45:00 \n",
"4 09:30:00 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_cases.head()"
]
},
{
"cell_type": "markdown",
"id": "cb6c777f",
"metadata": {},
"source": [
"## Coercing date types\n",
"Let's put the following columns in 'datetime' order"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c7a3fe55",
"metadata": {},
"outputs": [],
"source": [
"active_cases[\"Exposure_date_dtm\"] = pd.to_datetime(active_cases[\"Exposure_date_dtm\"])\n",
"for column in [\"Exposure_time_start_24\", \"Exposure_time_end_24\"]:\n",
" active_cases[column] = pd.to_datetime(active_cases[column].apply(lambda x: f\"{PLACEHOLDER_DATE}T{x}\"))\n",
" \n",
"# Let's also create a time delta object of the exposure time\n",
"active_cases[\"exposure_time_td\"] = active_cases[\"Exposure_time_end_24\"] - active_cases[\"Exposure_time_start_24\"]"
]
},
{
"cell_type": "markdown",
"id": "2c03afe7",
"metadata": {},
"source": [
"It's always very important to sanity check data, let's make sure that the exposure_times make sense"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c5ba3125",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 823\n",
"mean 0 days 05:40:13.341433778\n",
"std 0 days 08:53:05.350363064\n",
"min -1 days +00:12:00\n",
"25% 0 days 00:37:30\n",
"50% 0 days 01:00:00\n",
"75% 0 days 07:05:00\n",
"max 0 days 23:59:00\n",
"Name: exposure_time_td, dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_cases[\"exposure_time_td\"].describe()"
]
},
{
"cell_type": "markdown",
"id": "5506bc1c",
"metadata": {},
"source": [
"Hmmm, if we have a negative exposure time, that's going to throw off some data, let's remove all rows where the exposure time is less than zero"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1fbeb986",
"metadata": {},
"outputs": [],
"source": [
"active_cases = active_cases.query(\"exposure_time_td.dt.total_seconds() > 0\", engine=\"python\")"
]
},
{
"cell_type": "markdown",
"id": "8848380c",
"metadata": {},
"source": [
"## Getting Supermarket Site title lists\n",
"\n",
"Look through complete list of key words in the site titles to see which ones match supermarkets"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f738da6a",
"metadata": {},
"outputs": [],
"source": [
"# For each cell in the Site_title column, split by either ' ', '/' and filter out non words like \"\", \"-\" and \"&\"\n",
"# And strip off any non-characters like \"()\", \"+\", tabs (\\t) and line endings (\\r, \\n), and put to lowercase\n",
"# Then use 'set' to get the unique list across all rows in the Site_title column\n",
"site_title_words = sorted(list(set([word.strip(\"()+\\t\\r\\n\").lower()\n",
" for word_list in active_cases[\"Site_title\"].apply(lambda x: re.split(\" |/\", x))\n",
" for word in word_list\n",
" if not word in [\"\", \"-\", \"&\"]])))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9cd5581d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"815"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(site_title_words)"
]
},
{
"cell_type": "markdown",
"id": "b6ccb503",
"metadata": {},
"source": [
"Ooohkay, a few to filter through"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a3b0617c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['\"next',\n",
" '1',\n",
" '10',\n",
" '100-102',\n",
" '11',\n",
" '115',\n",
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" '12,',\n",
" '122',\n",
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" '58',\n",
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" '7-eleven',\n",
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" '903',\n",
" '96',\n",
" '996',\n",
" 'a',\n",
" 'a1',\n",
" 'advantage',\n",
" 'afghan',\n",
" 'airport',\n",
" 'albans',\n",
" 'aldi',\n",
" 'alexander',\n",
" 'alfa',\n",
" 'alfred',\n",
" 'all',\n",
" 'alma',\n",
" 'altona',\n",
" 'amaroo',\n",
" 'amcal',\n",
" 'and',\n",
" 'animal',\n",
" 'anz',\n",
" 'apartment',\n",
" 'apex',\n",
" 'apple',\n",
" 'apples',\n",
" 'aquamoves',\n",
" 'archer',\n",
" 'ardeer',\n",
" 'area',\n",
" 'areas',\n",
" 'army',\n",
" 'ascot',\n",
" 'ashburton',\n",
" 'ashwood',\n",
" 'asian',\n",
" 'at',\n",
" 'atlantis',\n",
" 'atm',\n",
" 'atm\"',\n",
" 'ato',\n",
" 'augustus',\n",
" 'australia',\n",
" 'autism',\n",
" 'autistic',\n",
" 'auto',\n",
" 'automotives',\n",
" 'avenue',\n",
" 'awm',\n",
" 'baguette',\n",
" 'bakehouse',\n",
" 'bakehouses',\n",
" 'bakers',\n",
" 'bakery',\n",
" 'balaclava',\n",
" 'bank',\n",
" 'bar',\n",
" 'barakat',\n",
" 'barkly',\n",
" 'basement',\n",
" 'batman',\n",
" 'beach',\n",
" 'beans',\n",
" 'beau',\n",
" 'bellagio',\n",
" 'bend',\n",
" 'best',\n",
" 'beverages',\n",
" 'big',\n",
" 'biryani',\n",
" 'bistro',\n",
" 'black',\n",
" 'blackburn',\n",
" 'blackwood',\n",
" 'blooms',\n",
" 'blue',\n",
" 'bmw',\n",
" 'bob',\n",
" 'bonnie',\n",
" 'borrack',\n",
" 'bosisto',\n",
" 'bottle-o',\n",
" 'bourke',\n",
" 'boy',\n",
" 'bp',\n",
" 'branch',\n",
" 'braybrook',\n",
" 'bread',\n",
" 'brendans',\n",
" 'bridge',\n",
" 'brighton',\n",
" 'broadmeadows',\n",
" 'brooks',\n",
" 'brunswick',\n",
" 'building',\n",
" 'bunnings',\n",
" 'bus',\n",
" 'business',\n",
" 'butcher',\n",
" 'butchers',\n",
" 'butter',\n",
" 'bws',\n",
" 'c',\n",
" 'cafe',\n",
" 'caltex',\n",
" 'cammaroto',\n",
" 'campus',\n",
" 'car',\n",
" 'care',\n",
" \"carl's\",\n",
" 'carlton',\n",
" 'carnovale',\n",
" 'carpark',\n",
" 'carrum',\n",
" 'casino',\n",
" 'cat',\n",
" 'catch',\n",
" 'catfish',\n",
" 'caulfield',\n",
" 'cbd',\n",
" 'cellars',\n",
" 'central',\n",
" 'centre',\n",
" 'centrelink',\n",
" 'charcoal',\n",
" 'chatime',\n",
" 'check',\n",
" 'cheesesteaks',\n",
" 'chemist',\n",
" 'chemmart',\n",
" 'chene',\n",
" 'chevron',\n",
" 'chicken',\n",
" 'child',\n",
" \"children's\",\n",
" 'chips',\n",
" 'circle',\n",
" 'citiport',\n",
" 'city',\n",
" 'clarendon',\n",
" 'class',\n",
" 'clayton',\n",
" 'cleaners',\n",
" 'cleaning',\n",
" 'clearance',\n",
" 'click',\n",
" 'clinic',\n",
" 'club',\n",
" 'co-living',\n",
" 'co.',\n",
" 'cobble',\n",
" 'cobblebank',\n",
" 'coburg',\n",
" 'coffee',\n",
" 'coffees',\n",
" 'cohealth',\n",
" 'cold',\n",
" 'coles',\n",
" 'collect',\n",
" 'college',\n",
" 'collingwood',\n",
" 'collins',\n",
" 'colombo',\n",
" 'combi',\n",
" 'comfort',\n",
" 'commonwealth',\n",
" 'community',\n",
" 'complex',\n",
" 'confectionery',\n",
" 'construction',\n",
" 'cook',\n",
" 'cooking',\n",
" 'coolaroo',\n",
" 'corio',\n",
" 'costco',\n",
" 'court',\n",
" 'courtney',\n",
" 'craigieburn',\n",
" 'cranbourne',\n",
" 'creamery',\n",
" 'creative',\n",
" 'crisis',\n",
" 'cross',\n",
" 'crossing',\n",
" 'cuts',\n",
" 'd',\n",
" 'd2',\n",
" 'd5',\n",
" 'dallas',\n",
" 'dame',\n",
" 'dance',\n",
" 'dandenong',\n",
" 'dandy',\n",
" \"daneli's\",\n",
" 'darul',\n",
" 'dash',\n",
" 'de',\n",
" 'deer',\n",
" \"delecca's\",\n",
" 'deli',\n",
" 'delight',\n",
" 'delta',\n",
" 'dentist',\n",
" 'department',\n",
" 'department,',\n",
" 'departures',\n",
" 'derrimut',\n",
" 'dfo',\n",
" 'dining',\n",
" 'discount',\n",
" 'distribution',\n",
" 'dock',\n",
" 'docklands',\n",
" \"domino's\",\n",
" 'doms',\n",
" 'doon',\n",
" 'dory',\n",
" 'dough',\n",
" 'downs',\n",
" 'dr',\n",
" 'drive',\n",
" 'drive-through',\n",
" 'drummond',\n",
" 'dry',\n",
" 'dysons',\n",
" 'early',\n",
" 'east',\n",
" 'eatery',\n",
" 'eglinton',\n",
" 'electrical',\n",
" 'elephant',\n",
" 'elizabeth',\n",
" 'elsternwick',\n",
" 'elwood',\n",
" 'emergency',\n",
" 'emmanuel',\n",
" 'emmaus',\n",
" 'employment',\n",
" 'entrance',\n",
" 'episode',\n",
" 'epping',\n",
" 'esplanade',\n",
" 'espresso',\n",
" 'essendon',\n",
" 'evolution',\n",
" 'express',\n",
" 'ezymart',\n",
" 'face',\n",
" 'factory',\n",
" 'fair',\n",
" 'fairleys',\n",
" 'family',\n",
" 'farm',\n",
" 'fast',\n",
" 'fawkner',\n",
" 'female',\n",
" 'ferguson',\n",
" 'fernwood',\n",
" 'fine',\n",
" 'fish',\n",
" 'fishermans',\n",
" 'fitness',\n",
" 'fitzroy',\n",
" 'flagstaff',\n",
" 'flemington',\n",
" 'flinders',\n",
" 'floor',\n",
" 'food',\n",
" 'foodhouse',\n",
" 'foods',\n",
" 'foodworks',\n",
" 'footscray',\n",
" 'ford',\n",
" 'fort',\n",
" 'fountain',\n",
" 'france',\n",
" 'frankston',\n",
" 'french',\n",
" 'fresh',\n",
" 'friendly',\n",
" 'from',\n",
" 'fruit',\n",
" 'future',\n",
" 'gai',\n",
" 'garam',\n",
" 'garden',\n",
" 'gardens',\n",
" 'gate',\n",
" 'gc',\n",
" 'gea',\n",
" 'gecko',\n",
" 'geelong',\n",
" 'gelateria',\n",
" 'gelatery',\n",
" 'gelato',\n",
" 'gladstone',\n",
" 'glenroy',\n",
" 'gloria',\n",
" 'golden',\n",
" 'golistan',\n",
" 'good',\n",
" 'goods',\n",
" 'goodstart',\n",
" 'goose',\n",
" 'gouge',\n",
" 'greater',\n",
" 'green',\n",
" 'greenvale',\n",
" 'grill',\n",
" 'grocer',\n",
" 'groceries',\n",
" 'grocery',\n",
" 'ground',\n",
" 'group',\n",
" 'gum',\n",
" 'gym',\n",
" 'hairdressing',\n",
" 'halal',\n",
" 'hall',\n",
" 'happy',\n",
" 'harar',\n",
" 'harbour',\n",
" \"hatch'd\",\n",
" 'havenlea',\n",
" 'hawthorn',\n",
" 'haymisha',\n",
" 'health',\n",
" 'healthcare',\n",
" 'heart',\n",
" 'highett',\n",
" 'hill',\n",
" 'home',\n",
" 'homeco',\n",
" 'hong',\n",
" 'hoppers',\n",
" 'hospital',\n",
" 'hot',\n",
" 'hotel',\n",
" 'house',\n",
" 'hub',\n",
" 'hub,',\n",
" 'hunky',\n",
" 'hunter',\n",
" 'hwy',\n",
" 'ice',\n",
" 'iga',\n",
" 'ikea',\n",
" 'impex',\n",
" 'in',\n",
" 'inbound',\n",
" 'indoor',\n",
" 'inn',\n",
" 'international',\n",
" 'invergordon',\n",
" 'irabina',\n",
" 'jacana',\n",
" 'jack',\n",
" 'jamz',\n",
" 'janus',\n",
" \"jean's\",\n",
" 'jeffcott',\n",
" 'jimmi',\n",
" 'jiro',\n",
" 'jr.',\n",
" 'juliette',\n",
" 'junction',\n",
" 'junior',\n",
" 'kebab',\n",
" 'kebabs',\n",
" 'keilor',\n",
" 'kennards',\n",
" 'kennedy',\n",
" 'keysborough',\n",
" 'kfc',\n",
" 'kfl',\n",
" 'kilda',\n",
" 'kindergarten',\n",
" 'king',\n",
" 'kmart',\n",
" 'knox',\n",
" 'kong',\n",
" \"kuz's\",\n",
" 'kyo',\n",
" 'la',\n",
" 'lahinch',\n",
" 'lake',\n",
" 'lakes',\n",
" 'lakeside',\n",
" 'laksa',\n",
" 'lara',\n",
" 'latrobe',\n",
" 'laundry',\n",
" 'laverton',\n",
" 'league',\n",
" 'learning',\n",
" 'lebanese',\n",
" \"leo's\",\n",
" 'less',\n",
" 'level',\n",
" 'liberty',\n",
" \"lichtenstein's\",\n",
" 'lift',\n",
" 'lilydale',\n",
" 'line',\n",
" 'lion',\n",
" 'lipari',\n",
" 'liquor',\n",
" 'liquorland',\n",
" 'little',\n",
" 'loading',\n",
" 'lpo',\n",
" 'ltd',\n",
" 'luna',\n",
" 'lygon',\n",
" 'madina',\n",
" 'mainview',\n",
" 'major',\n",
" 'male',\n",
" 'malvern',\n",
" 'mansfield',\n",
" 'mansions',\n",
" 'maribyrnong',\n",
" 'market',\n",
" 'marketplace',\n",
" 'marlin',\n",
" 'mart',\n",
" 'mary',\n",
" 'massarany',\n",
" 'master',\n",
" \"mcdonald's\",\n",
" 'mcdonalds',\n",
" 'mcec',\n",
" 'mcguire',\n",
" 'meadow',\n",
" 'meats',\n",
" 'meatsmith',\n",
" 'medical',\n",
" 'mega',\n",
" \"mel's\",\n",
" 'melbourne',\n",
" 'melton',\n",
" 'merchant',\n",
" 'mercy',\n",
" 'merica',\n",
" 'mernda',\n",
" 'metro',\n",
" 'michel’s',\n",
" 'middle',\n",
" 'midnight',\n",
" 'miele',\n",
" 'migrant',\n",
" 'milk',\n",
" 'milkbar',\n",
" 'mill',\n",
" 'millers',\n",
" 'mission',\n",
" 'mitre',\n",
" 'monash',\n",
" \"montano's\",\n",
" 'moonee',\n",
" 'moorabbin',\n",
" 'mooroopna',\n",
" 'morang',\n",
" 'more',\n",
" 'moreland',\n",
" 'motors',\n",
" 'mount',\n",
" 'mt',\n",
" 'my',\n",
" 'mycentre',\n",
" 'n',\n",
" \"nando's\",\n",
" 'national',\n",
" 'near',\n",
" 'network',\n",
" 'newmarket',\n",
" 'newport',\n",
" 'next',\n",
" 'nextra',\n",
" 'north',\n",
" 'northcote',\n",
" 'notre',\n",
" 'nuts',\n",
" 'oakbank',\n",
" 'oakleigh',\n",
" 'oakwood',\n",
" 'of',\n",
" 'offices',\n",
" 'officeworks',\n",
" 'oh',\n",
" 'old',\n",
" 'on',\n",
" 'ootoro',\n",
" 'opposite',\n",
" 'origin',\n",
" 'orrvale',\n",
" 'osh',\n",
" 'other',\n",
" 'outlet',\n",
" 'outside',\n",
" 'oval',\n",
" 'oven',\n",
" 'pachamama',\n",
" 'pacific',\n",
" 'pakenham',\n",
" 'parade',\n",
" 'park',\n",
" 'parliament',\n",
" 'parts',\n",
" 'pascoe',\n",
" 'pathology',\n",
" 'patisserie',\n",
" 'pepper',\n",
" 'peppermill',\n",
" 'pet',\n",
" 'petrol',\n",
" 'petroleum',\n",
" 'pharmacy',\n",
" 'philly',\n",
" 'pho',\n",
" 'physiotherapy',\n",
" 'pie',\n",
" 'pier',\n",
" 'pines',\n",
" 'pint',\n",
" 'pizza',\n",
" 'plarre',\n",
" 'playground',\n",
" 'plaza',\n",
" 'plumbing',\n",
" 'plus',\n",
" 'point',\n",
" 'ponds',\n",
" 'port',\n",
" 'post',\n",
" 'poultry',\n",
" 'prahran',\n",
" 'pratt',\n",
" 'precinct',\n",
" 'premium',\n",
" 'priceline',\n",
" 'primary',\n",
" 'princes',\n",
" 'private',\n",
" 'produce',\n",
" 'pty',\n",
" 'public',\n",
" 'quality',\n",
" 'queen',\n",
" 'queens',\n",
" 'raftery',\n",
" 'railway',\n",
" 'ramsay',\n",
" 'rebel',\n",
" 'reject',\n",
" 'rentals',\n",
" 'repairs',\n",
" 'replacement',\n",
" 'reserve',\n",
" 'reservoir',\n",
" 'residential',\n",
" 'residents',\n",
" 'restaurant',\n",
" 'retail',\n",
" 'revival',\n",
" 'rex',\n",
" 'richmond',\n",
" 'rise',\n",
" 'ritchies',\n",
" 'ritz',\n",
" 'river',\n",
" 'road',\n",
" 'roadhouse',\n",
" 'roasters',\n",
" 'rock',\n",
" 'rockbank',\n",
" 'room',\n",
" 'roper',\n",
" 'ross',\n",
" 'route',\n",
" 'roxburgh',\n",
" 'royal',\n",
" 'rupert',\n",
" 'rush',\n",
" 'russell',\n",
" \"ryan's\",\n",
" 'rye',\n",
" \"sacca's\",\n",
" 'saccas',\n",
" 'sacred',\n",
" 'sales',\n",
" 'salvation',\n",
" 'samios',\n",
" 'sanctuary',\n",
" 'sandringham',\n",
" 'schnitz',\n",
" 'school',\n",
" 'sea',\n",
" 'secondary',\n",
" 'seddon',\n",
" 'self',\n",
" 'service',\n",
" 'services',\n",
" 'shell',\n",
" 'shepparton',\n",
" 'shop',\n",
" 'shopping',\n",
" 'showgrounds',\n",
" 'side',\n",
" 'signature',\n",
" 'sirius',\n",
" 'site',\n",
" 'sk',\n",
" 'skatepark',\n",
" 'skysalon',\n",
" 'smartline',\n",
" 'smash',\n",
" 'smith',\n",
" 'somerton',\n",
" 'soul',\n",
" 'south',\n",
" 'southbank',\n",
" 'southern',\n",
" 'southland',\n",
" \"sparrow's\",\n",
" 'spc',\n",
" 'specialty',\n",
" 'spencer',\n",
" 'spices',\n",
" 'splash',\n",
" 'sport',\n",
" 'sport,',\n",
" 'sports',\n",
" 'spot',\n",
" 'spotswood',\n",
" 'springvale',\n",
" 'square',\n",
" 'st',\n",
" 'st.',\n",
" 'stadium',\n",
" 'standard',\n",
" 'starbucks',\n",
" 'station',\n",
" 'stella',\n",
" 'stockland',\n",
" 'stonegrill',\n",
" 'stop',\n",
" 'storage',\n",
" 'store',\n",
" 'street',\n",
" 'street-',\n",
" 'subway',\n",
" 'sunbury',\n",
" 'sunshine',\n",
" 'supa',\n",
" 'supercare',\n",
" 'superclinic',\n",
" 'supermarket',\n",
" 'superpharmacy',\n",
" 'supplies',\n",
" 'sureway',\n",
" 'surgery',\n",
" 'sushi',\n",
" 'sussex',\n",
" 'sydney',\n",
" 'takeaway',\n",
" 'tarneit',\n",
" 'taylors',\n",
" 'tempo',\n",
" 'terry',\n",
" 'terrywhite',\n",
" 'testing',\n",
" 'the',\n",
" 'through',\n",
" 'to',\n",
" 'together',\n",
" 'toilet',\n",
" 'toilets',\n",
" 'toilets-',\n",
" 'tooronga',\n",
" 'towards',\n",
" 'tower',\n",
" 'towerhill',\n",
" 'towers',\n",
" 'track',\n",
" 'trail',\n",
" 'train',\n",
" 'training',\n",
" 'trains',\n",
" 'tram',\n",
" 'transport',\n",
" 'treat',\n",
" 'tree',\n",
" 'triage',\n",
" 'trobe',\n",
" 'truck',\n",
" 'truckstop',\n",
" 'truganina',\n",
" 'tullamarine',\n",
" 'turf',\n",
" 'tutoring',\n",
" 'tyreplus',\n",
" 'ulum',\n",
" 'united',\n",
" 'university',\n",
" 'upfield',\n",
" 'urban',\n",
" 'v',\n",
" 'v-line',\n",
" 'vaccination',\n",
" 'vale',\n",
" 'valley',\n",
" 'veg',\n",
" 'vegetable',\n",
" 'vegetables',\n",
" 'vegtables',\n",
" 'vermont',\n",
" 'victoria',\n",
" 'village',\n",
" 'villiers',\n",
" \"vincent's\",\n",
" 'vincents',\n",
" 'visitors',\n",
" 'vorno',\n",
" 'w',\n",
" 'waiting',\n",
" 'wanganui',\n",
" 'warehouse',\n",
" 'warhouse',\n",
" 'wash',\n",
" 'watergardens',\n",
" 'waverly',\n",
" 'way',\n",
" 'wb',\n",
" 'wedge',\n",
" 'wellington',\n",
" 'werribee',\n",
" 'west',\n",
" 'westbound',\n",
" 'western',\n",
" 'westfield',\n",
" 'westgate',\n",
" 'westpac',\n",
" 'wharf',\n",
" 'white',\n",
" 'wholefoods',\n",
" 'wholesale',\n",
" 'william',\n",
" 'williamstown',\n",
" 'windsor',\n",
" 'wine',\n",
" 'wing',\n",
" 'wingate',\n",
" 'wolf',\n",
" 'womens',\n",
" 'wong',\n",
" 'woodgrove',\n",
" 'woolworths',\n",
" 'wrap',\n",
" 'wreckyn',\n",
" 'wurundjeri',\n",
" 'wyndham',\n",
" 'xpress',\n",
" 'yarraville',\n",
" 'york',\n",
" 'your',\n",
" 'zaatar',\n",
" 'zambrero',\n",
" 'zet&zaatar',\n",
" '–']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"site_title_words"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ef62a563",
"metadata": {},
"outputs": [],
"source": [
"# We will also match on partial words so does not need to be full word\n",
"SUPERMARKET_LIST = [\"aldi\", \"amcal\", \n",
" \"baker\", # Covers 'bakers' and 'bakery'\n",
" \"barkly\", # For barkly square \n",
" \"chemist\", \"coles\", \"costco\", \n",
" \"food\", # Covers foods, foodworks etc\n",
" \"fresh\", # Likely associated with grocery\n",
" \"fruit\", \"grocer\", # Covers \"grocery too\"\n",
" \"iga\", \"market\", # Covers marketplace\n",
" \"mart\", \"meats\", \n",
" \"pharmacy\", \"plaza\", \"produce\", \n",
" \"supa\", \"super\", \"vegetable\", \"whole\", \"woolworths\"]"
]
},
{
"cell_type": "markdown",
"id": "8db6f805",
"metadata": {},
"source": [
"## Filtering Dataset to supermarkets"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bba0b0f6",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def is_supermarket(site_title) -> bool:\n",
" \"\"\"\n",
" Site_title.lower contains one of the values in the supermarket list\n",
" \"\"\"\n",
" for supermarket_item in SUPERMARKET_LIST:\n",
" if supermarket_item.lower() in site_title.lower():\n",
" return True\n",
" return False\n",
" \n",
"active_cases[\"is_supermarket\"] = active_cases[\"Site_title\"].apply(is_supermarket)\n",
"active_supermarket_cases = active_cases.query(\"is_supermarket\").drop(columns=\"is_supermarket\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c245f251",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 236 entries, 16 to 812\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Suburb 236 non-null object \n",
" 1 Site_title 236 non-null object \n",
" 2 Site_streetaddress 229 non-null object \n",
" 3 Site_state 236 non-null object \n",
" 4 Site_postcode 229 non-null float64 \n",
" 5 Exposure_date_dtm 236 non-null datetime64[ns] \n",
" 6 Exposure_date 236 non-null object \n",
" 7 Exposure_time 236 non-null object \n",
" 8 Notes 236 non-null object \n",
" 9 Added_date_dtm 236 non-null object \n",
" 10 Added_date 236 non-null object \n",
" 11 Added_time 235 non-null object \n",
" 12 Advice_title 236 non-null object \n",
" 13 Advice_instruction 236 non-null object \n",
" 14 Exposure_time_start_24 236 non-null datetime64[ns] \n",
" 15 Exposure_time_end_24 236 non-null datetime64[ns] \n",
" 16 exposure_time_td 236 non-null timedelta64[ns]\n",
"dtypes: datetime64[ns](3), float64(1), object(12), timedelta64[ns](1)\n",
"memory usage: 33.2+ KB\n"
]
}
],
"source": [
"active_supermarket_cases.info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1021d026",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Suburb</th>\n",
" <th>Site_title</th>\n",
" <th>Site_streetaddress</th>\n",
" <th>Site_state</th>\n",
" <th>Site_postcode</th>\n",
" <th>Exposure_date_dtm</th>\n",
" <th>Exposure_date</th>\n",
" <th>Exposure_time</th>\n",
" <th>Notes</th>\n",
" <th>Added_date_dtm</th>\n",
" <th>Added_date</th>\n",
" <th>Added_time</th>\n",
" <th>Advice_title</th>\n",
" <th>Advice_instruction</th>\n",
" <th>Exposure_time_start_24</th>\n",
" <th>Exposure_time_end_24</th>\n",
" <th>exposure_time_td</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Hoppers Crossing</td>\n",
" <td>Advantage Pharmacy - Hoppers Crossing Shopping...</td>\n",
" <td>24-48 Old Geelong Road</td>\n",
" <td>VIC</td>\n",
" <td>3029.0</td>\n",
" <td>2021-08-21</td>\n",
" <td>21/08/2021</td>\n",
" <td>11:00am - 7:30pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>21:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 11:00:00</td>\n",
" <td>1970-01-01 19:30:00</td>\n",
" <td>0 days 08:30:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Hoppers Crossing</td>\n",
" <td>Bakers Delight - Hoppers Crossing Shopping Centre</td>\n",
" <td>Shop 17, 50 Old Geelong Road</td>\n",
" <td>VIC</td>\n",
" <td>3029.0</td>\n",
" <td>2021-08-21</td>\n",
" <td>21/08/2021</td>\n",
" <td>2:15pm - 2:50pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>21:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 14:15:00</td>\n",
" <td>1970-01-01 14:50:00</td>\n",
" <td>0 days 00:35:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Altona North</td>\n",
" <td>Woolworths Millers Junction (Altona North)</td>\n",
" <td>302-330 Millers Road</td>\n",
" <td>VIC</td>\n",
" <td>3025.0</td>\n",
" <td>2021-08-21</td>\n",
" <td>21/08/2021</td>\n",
" <td>7:30pm - 8:50pm</td>\n",
" <td>Case attended venue. Some individuals will be ...</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>21:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 19:30:00</td>\n",
" <td>1970-01-01 20:50:00</td>\n",
" <td>0 days 01:20:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Brighton</td>\n",
" <td>Chemist Warehouse - Brighton</td>\n",
" <td>363 Bay Street</td>\n",
" <td>VIC</td>\n",
" <td>3186.0</td>\n",
" <td>2021-08-24</td>\n",
" <td>24/08/2021</td>\n",
" <td>12:37pm - 1:00pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>21:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 12:37:00</td>\n",
" <td>1970-01-01 13:00:00</td>\n",
" <td>0 days 00:23:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Brunswick East</td>\n",
" <td>IGA - Princes Hill</td>\n",
" <td>1-9 Lygon Street</td>\n",
" <td>VIC</td>\n",
" <td>3054.0</td>\n",
" <td>2021-08-22</td>\n",
" <td>22/08/2021</td>\n",
" <td>8:25pm - 9:20pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>21:00:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 20:25:00</td>\n",
" <td>1970-01-01 21:20:00</td>\n",
" <td>0 days 00:55:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>794</th>\n",
" <td>Glenroy</td>\n",
" <td>Coles Glenroy</td>\n",
" <td>Corner Glenroy Road and Morgan Court\\t</td>\n",
" <td>VIC</td>\n",
" <td>3046.0</td>\n",
" <td>2021-08-11</td>\n",
" <td>11/08/2021</td>\n",
" <td>2:40pm - 3:40pm</td>\n",
" <td>Case attended venue</td>\n",
" <td>2021-08-13</td>\n",
" <td>13/08/2021</td>\n",
" <td>22:25:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 14:40:00</td>\n",
" <td>1970-01-01 15:40:00</td>\n",
" <td>0 days 01:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>795</th>\n",
" <td>Glenroy</td>\n",
" <td>Coles Glenroy</td>\n",
" <td>Corner Glenroy Road and Morgan Court\\t</td>\n",
" <td>VIC</td>\n",
" <td>3046.0</td>\n",
" <td>2021-08-12</td>\n",
" <td>12/08/2021</td>\n",
" <td>9:40am - 10:40am</td>\n",
" <td>Case attended venue</td>\n",
" <td>2021-08-13</td>\n",
" <td>13/08/2021</td>\n",
" <td>22:25:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 09:40:00</td>\n",
" <td>1970-01-01 10:40:00</td>\n",
" <td>0 days 01:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>804</th>\n",
" <td>Middle Park</td>\n",
" <td>The Village Grocer - IGA Middle Park</td>\n",
" <td>19-21 Armstrong Street</td>\n",
" <td>VIC</td>\n",
" <td>3206.0</td>\n",
" <td>2021-08-11</td>\n",
" <td>11/08/2021</td>\n",
" <td>6:05pm - 6:20pm</td>\n",
" <td>Case attended</td>\n",
" <td>2021-08-13</td>\n",
" <td>13/08/2021</td>\n",
" <td>10:30:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 18:05:00</td>\n",
" <td>1970-01-01 18:20:00</td>\n",
" <td>0 days 00:15:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>811</th>\n",
" <td>Melton West</td>\n",
" <td>Coles - Woodgrove Shopping Centre</td>\n",
" <td>533-555 High Street</td>\n",
" <td>VIC</td>\n",
" <td>3337.0</td>\n",
" <td>2021-08-09</td>\n",
" <td>09/08/2021</td>\n",
" <td>12:00pm - 1:00pm</td>\n",
" <td>Case attended</td>\n",
" <td>2021-08-12</td>\n",
" <td>12/08/2021</td>\n",
" <td>17:15:00</td>\n",
" <td>Tier 1 - Get tested immediately and quarantine...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 12:00:00</td>\n",
" <td>1970-01-01 13:00:00</td>\n",
" <td>0 days 01:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>812</th>\n",
" <td>Melton West</td>\n",
" <td>Bakers Delight - Woodgrove Shopping Centre</td>\n",
" <td>533-555 High Street</td>\n",
" <td>VIC</td>\n",
" <td>3337.0</td>\n",
" <td>2021-08-09</td>\n",
" <td>09/08/2021</td>\n",
" <td>12:20pm - 1:06pm</td>\n",
" <td>Case attended</td>\n",
" <td>2021-08-12</td>\n",
" <td>12/08/2021</td>\n",
" <td>17:15:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 12:20:00</td>\n",
" <td>1970-01-01 13:06:00</td>\n",
" <td>0 days 00:46:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>236 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" Suburb Site_title \\\n",
"16 Hoppers Crossing Advantage Pharmacy - Hoppers Crossing Shopping... \n",
"17 Hoppers Crossing Bakers Delight - Hoppers Crossing Shopping Centre \n",
"19 Altona North Woolworths Millers Junction (Altona North) \n",
"20 Brighton Chemist Warehouse - Brighton \n",
"21 Brunswick East IGA - Princes Hill \n",
".. ... ... \n",
"794 Glenroy Coles Glenroy \n",
"795 Glenroy Coles Glenroy \n",
"804 Middle Park The Village Grocer - IGA Middle Park \n",
"811 Melton West Coles - Woodgrove Shopping Centre \n",
"812 Melton West Bakers Delight - Woodgrove Shopping Centre \n",
"\n",
" Site_streetaddress Site_state Site_postcode \\\n",
"16 24-48 Old Geelong Road VIC 3029.0 \n",
"17 Shop 17, 50 Old Geelong Road VIC 3029.0 \n",
"19 302-330 Millers Road VIC 3025.0 \n",
"20 363 Bay Street VIC 3186.0 \n",
"21 1-9 Lygon Street VIC 3054.0 \n",
".. ... ... ... \n",
"794 Corner Glenroy Road and Morgan Court\\t VIC 3046.0 \n",
"795 Corner Glenroy Road and Morgan Court\\t VIC 3046.0 \n",
"804 19-21 Armstrong Street VIC 3206.0 \n",
"811 533-555 High Street VIC 3337.0 \n",
"812 533-555 High Street VIC 3337.0 \n",
"\n",
" Exposure_date_dtm Exposure_date Exposure_time \\\n",
"16 2021-08-21 21/08/2021 11:00am - 7:30pm \n",
"17 2021-08-21 21/08/2021 2:15pm - 2:50pm \n",
"19 2021-08-21 21/08/2021 7:30pm - 8:50pm \n",
"20 2021-08-24 24/08/2021 12:37pm - 1:00pm \n",
"21 2021-08-22 22/08/2021 8:25pm - 9:20pm \n",
".. ... ... ... \n",
"794 2021-08-11 11/08/2021 2:40pm - 3:40pm \n",
"795 2021-08-12 12/08/2021 9:40am - 10:40am \n",
"804 2021-08-11 11/08/2021 6:05pm - 6:20pm \n",
"811 2021-08-09 09/08/2021 12:00pm - 1:00pm \n",
"812 2021-08-09 09/08/2021 12:20pm - 1:06pm \n",
"\n",
" Notes Added_date_dtm \\\n",
"16 Case attended venue\\r 2021-08-25 \n",
"17 Case attended venue\\r 2021-08-25 \n",
"19 Case attended venue. Some individuals will be ... 2021-08-25 \n",
"20 Case attended venue\\r 2021-08-25 \n",
"21 Case attended venue\\r 2021-08-25 \n",
".. ... ... \n",
"794 Case attended venue 2021-08-13 \n",
"795 Case attended venue 2021-08-13 \n",
"804 Case attended 2021-08-13 \n",
"811 Case attended 2021-08-12 \n",
"812 Case attended 2021-08-12 \n",
"\n",
" Added_date Added_time Advice_title \\\n",
"16 25/08/2021 21:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"17 25/08/2021 21:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"19 25/08/2021 21:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"20 25/08/2021 21:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"21 25/08/2021 21:00:00 Tier 2 - Get tested urgently and isolate until... \n",
".. ... ... ... \n",
"794 13/08/2021 22:25:00 Tier 2 - Get tested urgently and isolate until... \n",
"795 13/08/2021 22:25:00 Tier 2 - Get tested urgently and isolate until... \n",
"804 13/08/2021 10:30:00 Tier 2 - Get tested urgently and isolate until... \n",
"811 12/08/2021 17:15:00 Tier 1 - Get tested immediately and quarantine... \n",
"812 12/08/2021 17:15:00 Tier 2 - Get tested urgently and isolate until... \n",
"\n",
" Advice_instruction Exposure_time_start_24 \\\n",
"16 Anyone who has visited this location during th... 1970-01-01 11:00:00 \n",
"17 Anyone who has visited this location during th... 1970-01-01 14:15:00 \n",
"19 Anyone who has visited this location during th... 1970-01-01 19:30:00 \n",
"20 Anyone who has visited this location during th... 1970-01-01 12:37:00 \n",
"21 Anyone who has visited this location during th... 1970-01-01 20:25:00 \n",
".. ... ... \n",
"794 Anyone who has visited this location during th... 1970-01-01 14:40:00 \n",
"795 Anyone who has visited this location during th... 1970-01-01 09:40:00 \n",
"804 Anyone who has visited this location during th... 1970-01-01 18:05:00 \n",
"811 Anyone who has visited this location during th... 1970-01-01 12:00:00 \n",
"812 Anyone who has visited this location during th... 1970-01-01 12:20:00 \n",
"\n",
" Exposure_time_end_24 exposure_time_td \n",
"16 1970-01-01 19:30:00 0 days 08:30:00 \n",
"17 1970-01-01 14:50:00 0 days 00:35:00 \n",
"19 1970-01-01 20:50:00 0 days 01:20:00 \n",
"20 1970-01-01 13:00:00 0 days 00:23:00 \n",
"21 1970-01-01 21:20:00 0 days 00:55:00 \n",
".. ... ... \n",
"794 1970-01-01 15:40:00 0 days 01:00:00 \n",
"795 1970-01-01 10:40:00 0 days 01:00:00 \n",
"804 1970-01-01 18:20:00 0 days 00:15:00 \n",
"811 1970-01-01 13:00:00 0 days 01:00:00 \n",
"812 1970-01-01 13:06:00 0 days 00:46:00 \n",
"\n",
"[236 rows x 17 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_supermarket_cases"
]
},
{
"cell_type": "markdown",
"id": "dcd5a763",
"metadata": {},
"source": [
"Great! We've narrowed down from 700+ exposure sites to < 200!"
]
},
{
"cell_type": "markdown",
"id": "f55083d7",
"metadata": {},
"source": [
"## Aggregating exposure sites over time\n",
"\n",
"Let's create an array of a 'date_range' again using today's date with ten minute intervals"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e44b1d62",
"metadata": {},
"outputs": [],
"source": [
"ten_min_ints = pd.date_range(start=f\"{PLACEHOLDER_DATE}T00:00:00\", end=f\"{PLACEHOLDER_DATE}T23:59:59\", freq=\"10Min\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6c6434c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['1970-01-01 00:00:00', '1970-01-01 00:10:00',\n",
" '1970-01-01 00:20:00', '1970-01-01 00:30:00',\n",
" '1970-01-01 00:40:00', '1970-01-01 00:50:00',\n",
" '1970-01-01 01:00:00', '1970-01-01 01:10:00',\n",
" '1970-01-01 01:20:00', '1970-01-01 01:30:00',\n",
" ...\n",
" '1970-01-01 22:20:00', '1970-01-01 22:30:00',\n",
" '1970-01-01 22:40:00', '1970-01-01 22:50:00',\n",
" '1970-01-01 23:00:00', '1970-01-01 23:10:00',\n",
" '1970-01-01 23:20:00', '1970-01-01 23:30:00',\n",
" '1970-01-01 23:40:00', '1970-01-01 23:50:00'],\n",
" dtype='datetime64[ns]', length=144, freq='10T')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints"
]
},
{
"cell_type": "markdown",
"id": "070fd50b",
"metadata": {},
"source": [
"We should expect there to be 114 intervals"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a0069d47",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"144"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(ten_min_ints)"
]
},
{
"cell_type": "markdown",
"id": "fa422b52",
"metadata": {},
"source": [
"Now let's create a function that given a dataframe of cases info can map over ten minute intervals to aggregate the number of exposures that span that time point in a day"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "2b552d96",
"metadata": {},
"outputs": [],
"source": [
"def get_ten_min_ints_df_from_case_df(cases_df: pd.DataFrame) -> pd.DataFrame:\n",
" # Get the base line ten minute intervals\n",
" ten_min_ints = pd.date_range(start=f\"{PLACEHOLDER_DATE}T00:00:00\", end=f\"{PLACEHOLDER_DATE}T23:59:59\", freq=\"10Min\") \n",
" \n",
" # Get an iterable of key value pairs for each interval\n",
" # Where the time is the key and the value is the number of exposure sites that span over that interval\n",
" ten_min_ints_dict = {\n",
" # Get the time as the key, .shape[0], counts the number of rows (exposure sites) that match the query\n",
" ten_min_int: cases_df.query(\"Exposure_time_start_24 <= @ten_min_int & \"\n",
" \"@ten_min_int < Exposure_time_end_24\").shape[0]\n",
" for ten_min_int in ten_min_ints\n",
" }\n",
"\n",
" # Convert to a dataframe and transpose so ExposureCount is the column\n",
" # Then migrate the times from the index to their own column\n",
" ten_min_ints_df = pd.DataFrame(ten_min_ints_dict, index=[\"exposure_count\"]).transpose().\\\n",
" reset_index().\\\n",
" rename(columns={\"index\": \"time\"})\n",
" \n",
" # We add in a proportion value which represents the proportion of cases that span this timepoint\n",
" ten_min_ints_df[\"exposure_count_proc\"] = ten_min_ints_df[\"exposure_count\"] / cases_df.shape[0]\n",
" \n",
" # Let's also add in the time_in_seconds (since the start of the day) for plotting convenience\n",
" ten_min_ints_df[\"time_in_seconds\"] = ten_min_ints_df[\"time\"].apply(lambda x: (x - ten_min_ints_df[\"time\"].min()).total_seconds())\n",
" \n",
" # We can also re set the time to the time of day for printing purposes\n",
" ten_min_ints_df[\"time_of_day\"] = ten_min_ints_df[\"time\"].apply(lambda x: f\"{x.hour:02}:{x.minute:02}\")\n",
" \n",
" return ten_min_ints_df"
]
},
{
"cell_type": "markdown",
"id": "41eddf35",
"metadata": {},
"source": [
"Now lets iterate over the intervals and for each interval count how many exposure sites have a start time less than this interval AND an end time greater than this interval. Let's then collect this in a dictionary comprehension and convert to a dateframe"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "8808444a",
"metadata": {},
"outputs": [],
"source": [
"ten_min_ints_df = get_ten_min_ints_df_from_case_df(active_supermarket_cases)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "93777e57",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 144 entries, 0 to 143\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 time 144 non-null datetime64[ns]\n",
" 1 exposure_count 144 non-null int64 \n",
" 2 exposure_count_proc 144 non-null float64 \n",
" 3 time_in_seconds 144 non-null float64 \n",
" 4 time_of_day 144 non-null object \n",
"dtypes: datetime64[ns](1), float64(2), int64(1), object(1)\n",
"memory usage: 5.8+ KB\n"
]
}
],
"source": [
"ten_min_ints_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0da8d6a5",
"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>time</th>\n",
" <th>exposure_count</th>\n",
" <th>exposure_count_proc</th>\n",
" <th>time_in_seconds</th>\n",
" <th>time_of_day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1970-01-01 00:00:00</td>\n",
" <td>7</td>\n",
" <td>0.029661</td>\n",
" <td>0.0</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1970-01-01 00:10:00</td>\n",
" <td>7</td>\n",
" <td>0.029661</td>\n",
" <td>600.0</td>\n",
" <td>00:10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1970-01-01 00:20:00</td>\n",
" <td>7</td>\n",
" <td>0.029661</td>\n",
" <td>1200.0</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1970-01-01 00:30:00</td>\n",
" <td>7</td>\n",
" <td>0.029661</td>\n",
" <td>1800.0</td>\n",
" <td>00:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1970-01-01 00:40:00</td>\n",
" <td>7</td>\n",
" <td>0.029661</td>\n",
" <td>2400.0</td>\n",
" <td>00:40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time exposure_count exposure_count_proc time_in_seconds \\\n",
"0 1970-01-01 00:00:00 7 0.029661 0.0 \n",
"1 1970-01-01 00:10:00 7 0.029661 600.0 \n",
"2 1970-01-01 00:20:00 7 0.029661 1200.0 \n",
"3 1970-01-01 00:30:00 7 0.029661 1800.0 \n",
"4 1970-01-01 00:40:00 7 0.029661 2400.0 \n",
"\n",
" time_of_day \n",
"0 00:00 \n",
"1 00:10 \n",
"2 00:20 \n",
"3 00:30 \n",
"4 00:40 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.head()"
]
},
{
"cell_type": "markdown",
"id": "79fbb352",
"metadata": {},
"source": [
"## Viewing exposure site trends"
]
},
{
"cell_type": "markdown",
"id": "1816e167",
"metadata": {},
"source": [
"Let's see the most popular time for an exposure site"
]
},
{
"cell_type": "markdown",
"id": "a077ec82",
"metadata": {},
"source": [
"Looks like lunch time is a bad time to go to the supermarket\n",
"\n",
"So when's the best time?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fdcbac4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"time 1970-01-01 22:30:00\n",
"exposure_count 6\n",
"exposure_count_proc 0.025424\n",
"time_in_seconds 81000.0\n",
"time_of_day 22:30\n",
"Name: 135, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.query(\"exposure_count.idxmin()\")"
]
},
{
"cell_type": "markdown",
"id": "a0e52d52",
"metadata": {},
"source": [
"Okay, close to midnight - that's during curfew so pls don't do that! Let's filter first then examine"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "97aebecd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"time 1970-01-01 08:00:00\n",
"exposure_count 13\n",
"exposure_count_proc 0.055085\n",
"time_in_seconds 28800.0\n",
"time_of_day 08:00\n",
"Name: 48, dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.query(f\"'{PLACEHOLDER_DATE}T08:00:00' <= time & time <= '{PLACEHOLDER_DATE}T19:00:00'\").\\\n",
" query(\"exposure_count.idxmin()\")"
]
},
{
"cell_type": "markdown",
"id": "5aa160ef",
"metadata": {},
"source": [
"First thing in the morning it seems! Let's visualise this"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "59d1a416",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.ticker import FuncFormatter\n",
"\n",
"# Set x tick format\n",
"def time_to_human_readable(x, position):\n",
" # Convert time in seconds to hours or minutes\n",
" hours = int(x // 3600)\n",
" minutes = int((x % 3600) // 60)\n",
" seconds = int(x % 60)\n",
" if x == 0:\n",
" return 0\n",
" s = f\"{hours:02d}:{minutes:02d}\"\n",
" return s\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7218a181",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Initialise sub plots\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"# Set title\n",
"fig.suptitle(\"Supermarket exposure sites over a day\", )\n",
"\n",
"# Plot over time\n",
"sns.lineplot(x=\"time_in_seconds\", y=\"exposure_count\", data=ten_min_ints_df, ax=ax);\n",
"\n",
"# Fill area - credit https://github.com/mwaskom/seaborn/issues/2410\n",
"ten_min_ints_df.plot(kind=\"area\", x=\"time_in_seconds\", y=\"exposure_count\", \n",
" stacked=False, color=\"tab:blue\", alpha=0.5, ax=ax);\n",
"\n",
"# Set labels\n",
"ax.set_xlabel(\"Time of Day\");\n",
"ax.set_ylabel(\"Num. Exposure Sites\");\n",
"\n",
"# Set x format\n",
"ax.xaxis.set_major_formatter(FuncFormatter(time_to_human_readable))\n",
"\n",
"# Set boudaries\n",
"ax.set_xlim(left=0, right=24*60*60) # Num seconds in a day\n",
"\n",
"# Remove legend\n",
"ax.legend().set_visible(False)\n",
"\n",
"# Add a caption\n",
"fig.text(0, 0, PLOTS_CAPTION, ha='left')\n",
"\n",
"# Package everything up\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "5dad71ee",
"metadata": {},
"source": [
"## Split by weekday / weekend"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "5955f531",
"metadata": {},
"outputs": [],
"source": [
"# Monday = 0, ... Saturday = 5, Sunday = 6\n",
"active_supermarket_cases[\"is_weekend\"] = active_supermarket_cases[\"Exposure_date_dtm\"].apply(lambda x: x.day_of_week > 4)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f19d7259",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 181\n",
"True 55\n",
"Name: is_weekend, dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_supermarket_cases[\"is_weekend\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "5eaa6c60",
"metadata": {},
"outputs": [],
"source": [
"# Initialise a list of dataframes\n",
"ten_min_ints_dfs = []\n",
"\n",
"# Partition cases by weekend or not weekend\n",
"# Create separate time point aggregate dfs for each and then merge\n",
"for is_weekend, active_supermarket_cases_by_week_type_df in active_supermarket_cases.groupby(\"is_weekend\"):\n",
" # Append to list of dataframes and create a column based on if this is the weekend or not\n",
" ten_min_ints_dfs.append(get_ten_min_ints_df_from_case_df(active_supermarket_cases_by_week_type_df).\\\n",
" assign(is_weekend=is_weekend))\n",
"\n",
"# Merge dataframes\n",
"ten_min_ints_df = pd.concat(ten_min_ints_dfs, axis=\"rows\", ignore_index=True)"
]
},
{
"cell_type": "markdown",
"id": "0edd1237",
"metadata": {},
"source": [
"Let's have a quick look at our new dataframe"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "104fb79c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 288 entries, 0 to 287\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 time 288 non-null datetime64[ns]\n",
" 1 exposure_count 288 non-null int64 \n",
" 2 exposure_count_proc 288 non-null float64 \n",
" 3 time_in_seconds 288 non-null float64 \n",
" 4 time_of_day 288 non-null object \n",
" 5 is_weekend 288 non-null bool \n",
"dtypes: bool(1), datetime64[ns](1), float64(2), int64(1), object(1)\n",
"memory usage: 11.7+ KB\n"
]
}
],
"source": [
"ten_min_ints_df.info()"
]
},
{
"cell_type": "markdown",
"id": "695f4b8e",
"metadata": {},
"source": [
"We have twice the number of rows as before, because we have split this data by weekend and non-weekend.\n",
"We will exploit this using the 'hue' keyword parameter in the plots below"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "6aba63b9",
"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>time</th>\n",
" <th>exposure_count</th>\n",
" <th>exposure_count_proc</th>\n",
" <th>time_in_seconds</th>\n",
" <th>time_of_day</th>\n",
" <th>is_weekend</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1970-01-01 00:00:00</td>\n",
" <td>7</td>\n",
" <td>0.038674</td>\n",
" <td>0.0</td>\n",
" <td>00:00</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1970-01-01 00:10:00</td>\n",
" <td>7</td>\n",
" <td>0.038674</td>\n",
" <td>600.0</td>\n",
" <td>00:10</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1970-01-01 00:20:00</td>\n",
" <td>7</td>\n",
" <td>0.038674</td>\n",
" <td>1200.0</td>\n",
" <td>00:20</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1970-01-01 00:30:00</td>\n",
" <td>7</td>\n",
" <td>0.038674</td>\n",
" <td>1800.0</td>\n",
" <td>00:30</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1970-01-01 00:40:00</td>\n",
" <td>7</td>\n",
" <td>0.038674</td>\n",
" <td>2400.0</td>\n",
" <td>00:40</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time exposure_count exposure_count_proc time_in_seconds \\\n",
"0 1970-01-01 00:00:00 7 0.038674 0.0 \n",
"1 1970-01-01 00:10:00 7 0.038674 600.0 \n",
"2 1970-01-01 00:20:00 7 0.038674 1200.0 \n",
"3 1970-01-01 00:30:00 7 0.038674 1800.0 \n",
"4 1970-01-01 00:40:00 7 0.038674 2400.0 \n",
"\n",
" time_of_day is_weekend \n",
"0 00:00 False \n",
"1 00:10 False \n",
"2 00:20 False \n",
"3 00:30 False \n",
"4 00:40 False "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "ad2c9e7e",
"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>time</th>\n",
" <th>exposure_count</th>\n",
" <th>exposure_count_proc</th>\n",
" <th>time_in_seconds</th>\n",
" <th>time_of_day</th>\n",
" <th>is_weekend</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>1970-01-01 23:10:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>83400.0</td>\n",
" <td>23:10</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>1970-01-01 23:20:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>84000.0</td>\n",
" <td>23:20</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>1970-01-01 23:30:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>84600.0</td>\n",
" <td>23:30</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>1970-01-01 23:40:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>85200.0</td>\n",
" <td>23:40</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>1970-01-01 23:50:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>85800.0</td>\n",
" <td>23:50</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time exposure_count exposure_count_proc time_in_seconds \\\n",
"283 1970-01-01 23:10:00 0 0.0 83400.0 \n",
"284 1970-01-01 23:20:00 0 0.0 84000.0 \n",
"285 1970-01-01 23:30:00 0 0.0 84600.0 \n",
"286 1970-01-01 23:40:00 0 0.0 85200.0 \n",
"287 1970-01-01 23:50:00 0 0.0 85800.0 \n",
"\n",
" time_of_day is_weekend \n",
"283 23:10 True \n",
"284 23:20 True \n",
"285 23:30 True \n",
"286 23:40 True \n",
"287 23:50 True "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "5f90c339",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Set up color schema\n",
"# Weekdays are blue,\n",
"# Weekends are orange\n",
"palette_by_weekend = {\n",
" False: \"tab:blue\",\n",
" True: \"tab:orange\"\n",
"}\n",
"\n",
"# Initialise sub plots\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"# Set title\n",
"fig.suptitle(\"Supermarket exposure sites over a day (Split by Weekend)\")\n",
"\n",
"# Plot weekday over time\n",
"sns.lineplot(x=\"time_in_seconds\", y=\"exposure_count_proc\", data=ten_min_ints_df, \n",
" hue=\"is_weekend\", hue_order=palette_by_weekend.keys(), palette=palette_by_weekend.values(), ax=ax);\n",
"\n",
"# Bit hacky, need to create separate area charts for each\n",
"for is_weekend, is_weekend_ints_df in ten_min_ints_df.groupby(\"is_weekend\"):\n",
" # Fill weekday area - credit https://github.com/mwaskom/seaborn/issues/2410\n",
" is_weekend_ints_df.plot(kind=\"area\", x=\"time_in_seconds\", y=\"exposure_count_proc\",\n",
" stacked=False, color=palette_by_weekend[is_weekend], alpha=0.5, ax=ax);\n",
"\n",
"# Set labels\n",
"ax.set_xlabel(\"Time of Day\");\n",
"ax.set_ylabel(\"Proportion of Cases in 10min interval\");\n",
"\n",
"# Set x format\n",
"ax.xaxis.set_major_formatter(FuncFormatter(time_to_human_readable))\n",
"\n",
"# Set boudaries\n",
"ax.set_xlim(left=0, right=24*60*60) # Num seconds in a day\n",
"\n",
"# Remove legend\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"ax.legend(handles, [\"Week Day\", \"Week End\"])\n",
"\n",
"# Add a caption\n",
"fig.text(0, 0, PLOTS_CAPTION, ha='left')\n",
"\n",
"# Package everything up\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "d2c32d29",
"metadata": {},
"source": [
"## Coles vs Woolworths"
]
},
{
"cell_type": "markdown",
"id": "f37b966a",
"metadata": {},
"source": [
"Let's first collect all the cases that are either coles and woolies"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "cd99cbc6",
"metadata": {},
"outputs": [],
"source": [
"# We must specify the engine kwarg for query when doing some slightly advanced string manipulation\n",
"coles_cases = active_supermarket_cases.query(\"Site_title.str.lower().str.contains('coles')\", engine='python')\n",
"woolworth_cases = active_supermarket_cases.query(\"Site_title.str.lower().str.contains('woolworths')\", engine='python')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "54854801",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coles cases: 62\n",
"Woolworth cases: 48\n"
]
}
],
"source": [
"print(f\"Coles cases: {coles_cases.shape[0]}\")\n",
"print(f\"Woolworth cases: {woolworth_cases.shape[0]}\")"
]
},
{
"cell_type": "markdown",
"id": "057186d4",
"metadata": {},
"source": [
"Let's create a column 'supermarket' and then regroup these two dataframes, like we've done for the weekend dataframes"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "9a3b71bd",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-38-853092a6b71d>:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" coles_cases[\"supermarket_name\"] = \"Coles\"\n",
"<ipython-input-38-853092a6b71d>:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" woolworth_cases[\"supermarket_name\"] = \"Woolworths\"\n"
]
}
],
"source": [
"coles_cases[\"supermarket_name\"] = \"Coles\"\n",
"woolworth_cases[\"supermarket_name\"] = \"Woolworths\""
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "2d2a067b",
"metadata": {},
"outputs": [],
"source": [
"active_supermarket_giants_cases = pd.concat([coles_cases, woolworth_cases], axis=\"rows\", ignore_index=True)"
]
},
{
"cell_type": "markdown",
"id": "333cd8c6",
"metadata": {},
"source": [
"Let's make sure we've merged this correctly"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "e8d3798c",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 110 entries, 0 to 109\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Suburb 110 non-null object \n",
" 1 Site_title 110 non-null object \n",
" 2 Site_streetaddress 110 non-null object \n",
" 3 Site_state 110 non-null object \n",
" 4 Site_postcode 110 non-null float64 \n",
" 5 Exposure_date_dtm 110 non-null datetime64[ns] \n",
" 6 Exposure_date 110 non-null object \n",
" 7 Exposure_time 110 non-null object \n",
" 8 Notes 110 non-null object \n",
" 9 Added_date_dtm 110 non-null object \n",
" 10 Added_date 110 non-null object \n",
" 11 Added_time 109 non-null object \n",
" 12 Advice_title 110 non-null object \n",
" 13 Advice_instruction 110 non-null object \n",
" 14 Exposure_time_start_24 110 non-null datetime64[ns] \n",
" 15 Exposure_time_end_24 110 non-null datetime64[ns] \n",
" 16 exposure_time_td 110 non-null timedelta64[ns]\n",
" 17 is_weekend 110 non-null bool \n",
" 18 supermarket_name 110 non-null object \n",
"dtypes: bool(1), datetime64[ns](3), float64(1), object(13), timedelta64[ns](1)\n",
"memory usage: 15.7+ KB\n"
]
}
],
"source": [
"active_supermarket_giants_cases.info()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "be1b5931",
"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>Suburb</th>\n",
" <th>Site_title</th>\n",
" <th>Site_streetaddress</th>\n",
" <th>Site_state</th>\n",
" <th>Site_postcode</th>\n",
" <th>Exposure_date_dtm</th>\n",
" <th>Exposure_date</th>\n",
" <th>Exposure_time</th>\n",
" <th>Notes</th>\n",
" <th>Added_date_dtm</th>\n",
" <th>Added_date</th>\n",
" <th>Added_time</th>\n",
" <th>Advice_title</th>\n",
" <th>Advice_instruction</th>\n",
" <th>Exposure_time_start_24</th>\n",
" <th>Exposure_time_end_24</th>\n",
" <th>exposure_time_td</th>\n",
" <th>is_weekend</th>\n",
" <th>supermarket_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brighton</td>\n",
" <td>Coles - Brighton</td>\n",
" <td>380 Bay Street</td>\n",
" <td>VIC</td>\n",
" <td>3186.0</td>\n",
" <td>2021-08-23</td>\n",
" <td>23/08/2021</td>\n",
" <td>12:15pm - 1:15pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>20:25:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 12:15:00</td>\n",
" <td>1970-01-01 13:15:00</td>\n",
" <td>0 days 01:00:00</td>\n",
" <td>False</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Footscray</td>\n",
" <td>Coles Express - Footscray</td>\n",
" <td>11 Napier Street &amp; Moreland Street</td>\n",
" <td>VIC</td>\n",
" <td>3011.0</td>\n",
" <td>2021-08-23</td>\n",
" <td>23/08/2021</td>\n",
" <td>1:10pm - 1:45pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>17:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 13:10:00</td>\n",
" <td>1970-01-01 13:45:00</td>\n",
" <td>0 days 00:35:00</td>\n",
" <td>False</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Maribyrnong</td>\n",
" <td>Coles Express - Moonee Ponds</td>\n",
" <td>783-795 Mt Alexander Road</td>\n",
" <td>VIC</td>\n",
" <td>3039.0</td>\n",
" <td>2021-08-21</td>\n",
" <td>21/08/2021</td>\n",
" <td>5:45pm - 6:20pm</td>\n",
" <td>Case attended venue\\r</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>17:50:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 17:45:00</td>\n",
" <td>1970-01-01 18:20:00</td>\n",
" <td>0 days 00:35:00</td>\n",
" <td>True</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Tarneit</td>\n",
" <td>Coles Wyndham Village</td>\n",
" <td>Wyndham Village Shopping Centre, 380 Sayers Road</td>\n",
" <td>VIC</td>\n",
" <td>3029.0</td>\n",
" <td>2021-08-23</td>\n",
" <td>23/08/2021</td>\n",
" <td>1:00pm - 1:45pm</td>\n",
" <td>Case attended venue. Some individuals will be ...</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>14:29:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 13:00:00</td>\n",
" <td>1970-01-01 13:45:00</td>\n",
" <td>0 days 00:45:00</td>\n",
" <td>False</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Hoppers Crossing</td>\n",
" <td>Coles Hoppers Crossing Shopping Centre</td>\n",
" <td>50 Old Geelong Road</td>\n",
" <td>VIC</td>\n",
" <td>3030.0</td>\n",
" <td>2021-08-21</td>\n",
" <td>21/08/2021</td>\n",
" <td>9:44am - 10:45am</td>\n",
" <td>Case attended venue</td>\n",
" <td>2021-08-25</td>\n",
" <td>25/08/2021</td>\n",
" <td>11:53:00</td>\n",
" <td>Tier 2 - Get tested urgently and isolate until...</td>\n",
" <td>Anyone who has visited this location during th...</td>\n",
" <td>1970-01-01 09:44:00</td>\n",
" <td>1970-01-01 10:45:00</td>\n",
" <td>0 days 01:01:00</td>\n",
" <td>True</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Suburb Site_title \\\n",
"0 Brighton Coles - Brighton \n",
"1 Footscray Coles Express - Footscray \n",
"2 Maribyrnong Coles Express - Moonee Ponds \n",
"3 Tarneit Coles Wyndham Village \n",
"4 Hoppers Crossing Coles Hoppers Crossing Shopping Centre \n",
"\n",
" Site_streetaddress Site_state Site_postcode \\\n",
"0 380 Bay Street VIC 3186.0 \n",
"1 11 Napier Street & Moreland Street VIC 3011.0 \n",
"2 783-795 Mt Alexander Road VIC 3039.0 \n",
"3 Wyndham Village Shopping Centre, 380 Sayers Road VIC 3029.0 \n",
"4 50 Old Geelong Road VIC 3030.0 \n",
"\n",
" Exposure_date_dtm Exposure_date Exposure_time \\\n",
"0 2021-08-23 23/08/2021 12:15pm - 1:15pm \n",
"1 2021-08-23 23/08/2021 1:10pm - 1:45pm \n",
"2 2021-08-21 21/08/2021 5:45pm - 6:20pm \n",
"3 2021-08-23 23/08/2021 1:00pm - 1:45pm \n",
"4 2021-08-21 21/08/2021 9:44am - 10:45am \n",
"\n",
" Notes Added_date_dtm \\\n",
"0 Case attended venue\\r 2021-08-25 \n",
"1 Case attended venue\\r 2021-08-25 \n",
"2 Case attended venue\\r 2021-08-25 \n",
"3 Case attended venue. Some individuals will be ... 2021-08-25 \n",
"4 Case attended venue 2021-08-25 \n",
"\n",
" Added_date Added_time Advice_title \\\n",
"0 25/08/2021 20:25:00 Tier 2 - Get tested urgently and isolate until... \n",
"1 25/08/2021 17:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"2 25/08/2021 17:50:00 Tier 2 - Get tested urgently and isolate until... \n",
"3 25/08/2021 14:29:00 Tier 2 - Get tested urgently and isolate until... \n",
"4 25/08/2021 11:53:00 Tier 2 - Get tested urgently and isolate until... \n",
"\n",
" Advice_instruction Exposure_time_start_24 \\\n",
"0 Anyone who has visited this location during th... 1970-01-01 12:15:00 \n",
"1 Anyone who has visited this location during th... 1970-01-01 13:10:00 \n",
"2 Anyone who has visited this location during th... 1970-01-01 17:45:00 \n",
"3 Anyone who has visited this location during th... 1970-01-01 13:00:00 \n",
"4 Anyone who has visited this location during th... 1970-01-01 09:44:00 \n",
"\n",
" Exposure_time_end_24 exposure_time_td is_weekend supermarket_name \n",
"0 1970-01-01 13:15:00 0 days 01:00:00 False Coles \n",
"1 1970-01-01 13:45:00 0 days 00:35:00 False Coles \n",
"2 1970-01-01 18:20:00 0 days 00:35:00 True Coles \n",
"3 1970-01-01 13:45:00 0 days 00:45:00 False Coles \n",
"4 1970-01-01 10:45:00 0 days 01:01:00 True Coles "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_supermarket_giants_cases.head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e0728f94",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"110"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_supermarket_giants_cases.shape[0]"
]
},
{
"cell_type": "markdown",
"id": "8ecb188c",
"metadata": {},
"source": [
"Looks right? That's the sum of the coles cases plus the woolies cases"
]
},
{
"cell_type": "markdown",
"id": "3865e230",
"metadata": {},
"source": [
"Let's turn our conversion of our case data into cumulative period ranges into a function"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "62d500b7",
"metadata": {},
"outputs": [],
"source": [
"# Initialise a list of dataframes\n",
"ten_min_ints_dfs = []\n",
"\n",
"# Partition cases by supermarket name (coles vs woolworths)\n",
"# Create separate time point aggregate dfs for each and then merge\n",
"for supermarket_name, active_supermarket_giants_cases_by_name in active_supermarket_giants_cases.groupby(\"supermarket_name\"):\n",
" # Append to list of dataframes and create a column based on if this is the weekend or not\n",
" ten_min_ints_dfs.append(get_ten_min_ints_df_from_case_df(active_supermarket_giants_cases_by_name).\\\n",
" assign(supermarket_name=supermarket_name))\n",
"\n",
"# Merge dataframes\n",
"ten_min_ints_df = pd.concat(ten_min_ints_dfs, axis=\"rows\", ignore_index=True)"
]
},
{
"cell_type": "markdown",
"id": "2ceed19a",
"metadata": {},
"source": [
"Let's have a look at our dataset"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "2a58f895",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 288 entries, 0 to 287\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 time 288 non-null datetime64[ns]\n",
" 1 exposure_count 288 non-null int64 \n",
" 2 exposure_count_proc 288 non-null float64 \n",
" 3 time_in_seconds 288 non-null float64 \n",
" 4 time_of_day 288 non-null object \n",
" 5 supermarket_name 288 non-null object \n",
"dtypes: datetime64[ns](1), float64(2), int64(1), object(2)\n",
"memory usage: 13.6+ KB\n"
]
}
],
"source": [
"ten_min_ints_df.info()"
]
},
{
"cell_type": "markdown",
"id": "8d7ec3b8",
"metadata": {},
"source": [
"Again, we have 288 rows, since there are 144 x 10 min intervals in a day and we have split this data over two supermarkets"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "65a09c0a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>exposure_count</th>\n",
" <th>exposure_count_proc</th>\n",
" <th>time_in_seconds</th>\n",
" <th>time_of_day</th>\n",
" <th>supermarket_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1970-01-01 00:00:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>00:00</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1970-01-01 00:10:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>600.0</td>\n",
" <td>00:10</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1970-01-01 00:20:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1200.0</td>\n",
" <td>00:20</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1970-01-01 00:30:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1800.0</td>\n",
" <td>00:30</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1970-01-01 00:40:00</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2400.0</td>\n",
" <td>00:40</td>\n",
" <td>Coles</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time exposure_count exposure_count_proc time_in_seconds \\\n",
"0 1970-01-01 00:00:00 0 0.0 0.0 \n",
"1 1970-01-01 00:10:00 0 0.0 600.0 \n",
"2 1970-01-01 00:20:00 0 0.0 1200.0 \n",
"3 1970-01-01 00:30:00 0 0.0 1800.0 \n",
"4 1970-01-01 00:40:00 0 0.0 2400.0 \n",
"\n",
" time_of_day supermarket_name \n",
"0 00:00 Coles \n",
"1 00:10 Coles \n",
"2 00:20 Coles \n",
"3 00:30 Coles \n",
"4 00:40 Coles "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "d3d3277e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>exposure_count</th>\n",
" <th>exposure_count_proc</th>\n",
" <th>time_in_seconds</th>\n",
" <th>time_of_day</th>\n",
" <th>supermarket_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>1970-01-01 23:10:00</td>\n",
" <td>6</td>\n",
" <td>0.125</td>\n",
" <td>83400.0</td>\n",
" <td>23:10</td>\n",
" <td>Woolworths</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>1970-01-01 23:20:00</td>\n",
" <td>6</td>\n",
" <td>0.125</td>\n",
" <td>84000.0</td>\n",
" <td>23:20</td>\n",
" <td>Woolworths</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>1970-01-01 23:30:00</td>\n",
" <td>6</td>\n",
" <td>0.125</td>\n",
" <td>84600.0</td>\n",
" <td>23:30</td>\n",
" <td>Woolworths</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>1970-01-01 23:40:00</td>\n",
" <td>6</td>\n",
" <td>0.125</td>\n",
" <td>85200.0</td>\n",
" <td>23:40</td>\n",
" <td>Woolworths</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>1970-01-01 23:50:00</td>\n",
" <td>6</td>\n",
" <td>0.125</td>\n",
" <td>85800.0</td>\n",
" <td>23:50</td>\n",
" <td>Woolworths</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time exposure_count exposure_count_proc time_in_seconds \\\n",
"283 1970-01-01 23:10:00 6 0.125 83400.0 \n",
"284 1970-01-01 23:20:00 6 0.125 84000.0 \n",
"285 1970-01-01 23:30:00 6 0.125 84600.0 \n",
"286 1970-01-01 23:40:00 6 0.125 85200.0 \n",
"287 1970-01-01 23:50:00 6 0.125 85800.0 \n",
"\n",
" time_of_day supermarket_name \n",
"283 23:10 Woolworths \n",
"284 23:20 Woolworths \n",
"285 23:30 Woolworths \n",
"286 23:40 Woolworths \n",
"287 23:50 Woolworths "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ten_min_ints_df.tail()"
]
},
{
"cell_type": "markdown",
"id": "974981de",
"metadata": {},
"source": [
"Now let's plot Coles vs Woolworths!"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "0e041e8b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Set up color schema\n",
"# Coles is Red\n",
"# Woolies is Green \n",
"# Obviously\n",
"palette_by_supermarket = {\n",
" \"Coles\": \"red\",\n",
" \"Woolworths\": \"limegreen\"\n",
"}\n",
"\n",
"# Initialise sub plots\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"# Set title\n",
"fig.suptitle(\"Supermarket exposure sites over a day (Coles Vs Woolworths)\")\n",
"\n",
"# Plot weekday over time\n",
"sns.lineplot(x=\"time_in_seconds\", y=\"exposure_count\", data=ten_min_ints_df, \n",
" hue=\"supermarket_name\", hue_order=palette_by_supermarket.keys(), palette=palette_by_supermarket.values(), ax=ax);\n",
"\n",
"# Bit hacky, need to create separate area charts for each\n",
"for supermarket_name, supermarket_ints_df in ten_min_ints_df.groupby(\"supermarket_name\"):\n",
" # Fill weekday area - credit https://github.com/mwaskom/seaborn/issues/2410\n",
" supermarket_ints_df.plot(kind=\"area\", x=\"time_in_seconds\", y=\"exposure_count\",\n",
" stacked=False, color=palette_by_supermarket[supermarket_name], alpha=0.5, ax=ax);\n",
"\n",
"# Set labels\n",
"ax.set_xlabel(\"Time of Day\");\n",
"ax.set_ylabel(\"Number of Exposure Sites\");\n",
"\n",
"# Set x format\n",
"ax.xaxis.set_major_formatter(FuncFormatter(time_to_human_readable))\n",
"\n",
"# Set graph boundaries\n",
"ax.set_xlim(left=0, right=24*60*60) # Num seconds in a day\n",
"\n",
"# Rename legend\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"ax.legend(handles, [\"Coles\", \"Woolies\"])\n",
"\n",
"# Add a caption\n",
"fig.text(0, 0, PLOTS_CAPTION, ha='left')\n",
"\n",
"# Package everything up\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "9bc43c6a",
"metadata": {},
"source": [
"But wait what? But there are more Coles sites and less Woolies? What is going on??\n",
"\n",
"Let's see the duration of the exposure sites by supermarket name"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "826ef8c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median exposure time at Coles: 0 days 00:59:30\n",
"Mean exposure time at Coles: 0 days 01:08:51.290322580\n",
"\n",
"Median exposure time at Woolworths: 0 days 00:59:30\n",
"Mean exposure time at Woolworths: 0 days 04:11:08.750000\n",
"\n"
]
}
],
"source": [
"for supermarket_name, supermarket_df in active_supermarket_giants_cases.groupby(\"supermarket_name\"):\n",
" print(f\"Median exposure time at {supermarket_name}: {supermarket_df['exposure_time_td'].median()}\")\n",
" print(f\"Mean exposure time at {supermarket_name}: {supermarket_df['exposure_time_td'].mean()}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"id": "392266ed",
"metadata": {},
"source": [
"Ah this makes more sense, why don't we remove the large outliers on the Woolworths data ans see what we get then"
]
},
{
"cell_type": "markdown",
"id": "2dbb2aa6",
"metadata": {},
"source": [
"## Coles vs Woolies (with exposure times < 2hr which is skewing data)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "55b4d942",
"metadata": {},
"outputs": [],
"source": [
"# Let's determine a long-exposure site as more than two hours\n",
"active_supermarket_giants_cases[\"long_exposure\"] = active_supermarket_giants_cases[\"exposure_time_td\"].apply(lambda x: True if x.total_seconds() > 7200 else False)\n",
"\n",
"# And then remove all long exposure sites (~ is the negating symbol in the query function)\n",
"active_supermarket_giants_cases_short_exposure = active_supermarket_giants_cases.query(\"~long_exposure\")"
]
},
{
"cell_type": "markdown",
"id": "85f93ef8",
"metadata": {
"scrolled": true
},
"source": [
"Let's see the breakdown of sites now"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "c02233ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"supermarket_name\n",
"Coles 56\n",
"Woolworths 38\n",
"dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_supermarket_giants_cases_short_exposure.groupby(\"supermarket_name\").size()"
]
},
{
"cell_type": "markdown",
"id": "ea094eb0",
"metadata": {},
"source": [
"And the mean and median exposures"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "a33273bc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Median exposure time at Coles: 0 days 00:55:00\n",
"Mean exposure time at Coles: 0 days 00:54:42.857142857\n",
"\n",
"Median exposure time at Woolworths: 0 days 00:51:00\n",
"Mean exposure time at Woolworths: 0 days 00:53:14.210526315\n",
"\n"
]
}
],
"source": [
"for supermarket_name, supermarket_df in active_supermarket_giants_cases_short_exposure.groupby(\"supermarket_name\"):\n",
" print(f\"Median exposure time at {supermarket_name}: {supermarket_df['exposure_time_td'].median()}\")\n",
" print(f\"Mean exposure time at {supermarket_name}: {supermarket_df['exposure_time_td'].mean()}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"id": "d26cf2a1",
"metadata": {},
"source": [
"So when is the best and worst time to go for each supermarket?\n",
"\n",
"Let's first again aggregate over the ten minute timepoints across a day split by supermarket name"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "836d8576",
"metadata": {},
"outputs": [],
"source": [
"# Initialise a list of dataframes\n",
"ten_min_ints_dfs = []\n",
"\n",
"# Partition cases by supermarket name (coles vs woolworths)\n",
"# Create separate time point aggregate dfs for each and then merge\n",
"for supermarket_name, active_supermarket_giants_cases_by_name in active_supermarket_giants_cases_short_exposure.groupby(\"supermarket_name\"):\n",
" # Append to list of dataframes and create a column based on if this is the weekend or not\n",
" ten_min_ints_dfs.append(get_ten_min_ints_df_from_case_df(active_supermarket_giants_cases_by_name).\\\n",
" assign(supermarket_name=supermarket_name))\n",
"\n",
"# Merge dataframes\n",
"ten_min_ints_df = pd.concat(ten_min_ints_dfs, axis=\"rows\", ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "fc7ba19e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best time for Coles: 00:00 with only 0 exposures\n",
"Best time within opening hours for Coles: 08:00 with only 0 exposures\n",
"Worst time is at Coles: 10:30 with 10 exposures\n",
"\n",
"Best time for Woolworths: 00:00 with only 0 exposures\n",
"Best time within opening hours for Woolworths: 08:00 with only 0 exposures\n",
"Worst time is at Woolworths: 13:50 with 7 exposures\n",
"\n"
]
}
],
"source": [
"for supermarket_name, supermarket_df in ten_min_ints_df.groupby(\"supermarket_name\"):\n",
" best_time = supermarket_df.query(\"exposure_count.idxmin()\")\n",
" best_time_within_opening_hours = supermarket_df.query(f\"'{PLACEHOLDER_DATE}T08:00:00' <= time & time <= '{PLACEHOLDER_DATE}T19:00:00'\").\\\n",
" query(\"exposure_count.idxmin()\")\n",
" worst_time = supermarket_df.query(\"exposure_count.idxmax()\")\n",
" \n",
" print(f\"Best time for {supermarket_name}: {best_time.time_of_day} with only {best_time.exposure_count} exposures\")\n",
" print(f\"Best time within opening hours for {supermarket_name}: {best_time_within_opening_hours.time_of_day} with \"\n",
" f\"only {best_time_within_opening_hours.exposure_count} exposures\")\n",
" \n",
" print(f\"Worst time is at {supermarket_name}: {worst_time.time_of_day} with {worst_time.exposure_count} exposures\")\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "4aed303d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Set up color schema\n",
"# Coles is Red\n",
"# Woolies is Green \n",
"# Obviously\n",
"palette_by_supermarket = {\n",
" \"Coles\": \"red\",\n",
" \"Woolworths\": \"limegreen\"\n",
"}\n",
"\n",
"# Initialise sub plots\n",
"fig, ax = plt.subplots(figsize=(10, 10))\n",
"\n",
"# Set title\n",
"fig.suptitle(\"Supermarket exposure sites over a day (Coles Vs Woolworths)\")\n",
"\n",
"# Plot weekday over time\n",
"sns.lineplot(x=\"time_in_seconds\", y=\"exposure_count\", data=ten_min_ints_df, \n",
" hue=\"supermarket_name\", hue_order=palette_by_supermarket.keys(), palette=palette_by_supermarket.values(), ax=ax);\n",
"\n",
"# Bit hacky, need to create separate area charts for each\n",
"for supermarket_name, supermarket_ints_df in ten_min_ints_df.groupby(\"supermarket_name\"):\n",
" # Fill weekday area - credit https://github.com/mwaskom/seaborn/issues/2410\n",
" supermarket_ints_df.plot(kind=\"area\", x=\"time_in_seconds\", y=\"exposure_count\",\n",
" stacked=False, color=palette_by_supermarket[supermarket_name], alpha=0.5, ax=ax);\n",
"\n",
"# Set labels\n",
"ax.set_xlabel(\"Time of Day\");\n",
"ax.set_ylabel(\"Number of Exposure Sites\");\n",
"\n",
"# Set x format\n",
"ax.xaxis.set_major_formatter(FuncFormatter(time_to_human_readable))\n",
"\n",
"# Set graph boundaries\n",
"ax.set_xlim(left=0, right=24*60*60) # Num seconds in a day\n",
"\n",
"# Rename legend\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"ax.legend(handles, [\"Coles\", \"Woolies\"])\n",
"\n",
"# Add a caption\n",
"fig.text(0, 0, PLOTS_CAPTION, ha='left')\n",
"\n",
"# Package everything up\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "99583645",
"metadata": {},
"source": [
"So it looks like Coles exposure sites are more likely in the morning and WoolWorths exposure sites are more likely in the afternoon. With such low numbers I wouldn't read much more into this, let's hope it stays that way!!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Raw
environment.yml
name: vic_exposure_sites
channels:
- conda-forge
- defaults
dependencies:
- numpy
- pandas
- matplotlib
- seaborn
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