How do you parse json data that is under multiple layers?

The goal of the code is take json information I am getting and parsing it into its raw location data( address,postal,etc). I am pretty new to coding and this is a one off task I got stuck with for a school project as I studying Geography and need the locations of all McDonald in Canada.So any other learning tools are welcomed. However the main issue I am having is that I want to write

for blank in blanks['']:

so I can grab the data for a csv output. However I have noticed my data is under multiply layers. For example:

{
    "features": [
        {
            "geometry": {
                "coordinates": [
                    -79.28662,
                    43.68758
                ]
            },
            "properties": {
                "name": "Vic Park/Gerrard",
                "shortDescription": "VIC PARK/G",
                "longDescription": "VIC PARK/GERRARD",
                "todayHours": "06:00 - 22:00",
                "driveTodayHours": "00:00 - 00:00",
                "id": "195500517230-en-ca",
                "filterType": [
                    "ALL_DAY_BREAKFAST",
                    "BAKERY",
                    "BREAKFAST",
                    "CYT",
                    "DRIVETHRU",
                    "INDOORDINING",
                    "MCCAFE",
                    "MOBILEOFFERS",
                    "MOBILEORDERS",
                    "PARKINGAREA",
                    "TWENTYFOURHOURS",
                    "WIFI"
                ],
                "addressLine1": "2480 GERRARD STREET EAST",
                "addressLine2": "",
                "addressLine3": "SCARBOROUGH",
                "addressLine4": "Canada",
                "subDivision": "",
                "postcode": "M1N 4C3",
                "customAddress": "SCARBOROUGH, M1N 4C3",
                "telephone": "4166903659",

The information I want is under properties to what it looks to me (not sure) but my

for store in stores['features']:

statement. Does Not allow me to grab the 'addressLine1' information or the other information individually for the csv. I am wondering if anyone has a solution to parsing through data such as this.

P.S I included my whole code just in case there is a deeper problem.

import requests
import csv
import json

url = "https://www.mcdonalds.com/googleapps/GoogleRestaurantLocAction.do?method=searchLocation&latitude=43.6936965&longitude=-79.2969938&radius=1000000&maxResults=1700&country=ca&language=en-ca&showClosed=&hours24Text=Open%2024%20hr"

payload={}
files={}
headers = {
  'authority': 'www.mcdonalds.com',
  'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"',
  'accept': '*/*',
  'x-requested-with': 'XMLHttpRequest',
  'sec-ch-ua-mobile': '?0',
  'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36',
  'sec-fetch-site': 'same-origin',
  'sec-fetch-mode': 'cors',
  'sec-fetch-dest': 'empty',
  'referer': 'https://www.mcdonalds.com/ca/en-ca/restaurant-locator.html',
  'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',
  'cookie': 'bm_sz=C04645E7F7A956C5F9D9C5A20DEAEC97~YAAQ1Cv2SEtfMBN6AQAAItxfEwwTVV2V2Tr7UWpPt1Ps7gl84FzQlmbWIm4kBBh5dxlK3w8RenwiEiKtvERE6dLmrwPwJUuy+14gU/LeEZvP+uxzyBr04oQXdcSEQuiOgdkAGasqnBrTw1mp5E5iehnRpvHBDdSqh8wRSgJV0eG4f8YwSz66BfntCBALtQNCAFK2; _abck=F05779F2345218EA4989FF467D897C5A~0~YAAQ1Cv2SExfMBN6AQAAItxfEwaIwCrBeP25JBhBb7TX+HmnLQgrj1TkosrB+oHSv9ctrxRukqEDUaHPL1KkjpqjY1XY1yyulQ0ZRhsEfhY968YVsTOqfiosAu3kykd3pJG/bQ37XHwWs5qXpIdhMXRwJwXmkYtl3ETG8kXK2iZ22Q31COaSjNVACLaa7s9tCk9ItgLvUj5x9Nldjnd8AdXR0pXicrQY1IaruJyNqwMcJv42AUHW7iH4Ex9ZOSYsgEjLMNd44mS525X/gSNUTSOzoqoWsnH4MU59vfgLTwc2hVncAv67LBViTLxbWw4eVAvz7Z5phQfCmvoIy0PD8gy5iwPDMaD3GASrK9xScDPAPUI2wquxmSJ+f2cQaxZQKhvJCeH9cz14OZfx8ksA2ss53E0l0kDvgmnw~-1~-1~-1; ak_bmsc=BA4817D8DEE20E92C1E6251C54FC124348F62BD48F5F00005F91C9608B679D5F~plUkbYfsvYr5dCayJ9dMGEJ3QDgkmkv2mLpE7pCY9vW0xrdawvmyxfSnupw/4F7C48Akdn8PKsBniqz+7F+RZb8v4AkvH3c0RuvnynqJoni+kJcDYtPOxdMvdtGdTlZGIkSQNfpcxHNQDVlzojdSBX0vyBh/8seKQv10U67M7m787olYzg9jnsUwk3/VHBrnMDogiWJT8rNV7saSXunN0pAgucZWo/XhCpTJL+tI9urt0=; MCDCountry_code=US; bm_mi=BEE06312635FD442995BC0237BAFDA7C~f/RxgMW/JJSUc/wB9ZRg9fPD/76+wq/TaoWEZR1/ttrAiVTO256xhDTsVYc/kdHIjWkxvfO4XDcBjqe4hQ4qXt8Anpfi09vna/zcC7l6OVWpWeRSoZNztl7h5VF407L3XG+9CpzjSHNcaqAPRk5d0J5gLMtL/KmR8XBkAC0Syim7ST97nxNrPfLdlkSPMGm4Oy86xvY5PH5Nu47zS/gwhanBFg69tAdrQdaZewE2eGuzoJPsZit3UsihTzhXc4LY92hfSdh3/kZRId+NE8Jp0w==; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN4a9yst3R170rBCm1egzGvCBmB1jq9aCwQm5VgIJgloPOdpiIPfD3kDxFbKhqMuS5U=; JSESSIONID=64PZkBXhhpvNjM4NganzSZ0r1npIIaM7Fo84EsxN.eap7node7; _abck=F05779F2345218EA4989FF467D897C5A~-1~YAAQ1Cv2SExyMBN6AQAA5Et0EwZueCejZbKz1VDGCq2sB43Yx4dq0SiiGeUS6gVpXRIdw3rA3OdpNGHq7tVzQ+IvPpEKwLML9736x1qB5SQxV3jai89y2B2QF6K8nKtyrDAes0qbeTyIrHu0Rh1HLs7CjNxiLi0wswbCZfSsPI6fJZiEt+Itre3lfmua/HkhIRwpVTKqlVN5eQ8XIX+s1jJbINx/jUmMTW+jB5k4A5NARGChYH7rJQGYIT/oyZYpSbS3Yweqa4FRgGMW4gYZBN39+t2xSfewADLdpihfOnoZtakw9VhcvAKaf4mEzjB7WEfNJIZSjSE8DzvbJNIF41MGuAhhrnEBwBE8uVCZsA+2qjVPSADVp2Nn8JanJXCbucnLFOLsmPz3oVtGzentht1cHog4+eYOUlmw~0~-1~-1; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN5ZCTzA250oKEeVeXaa6j4gEGJ9RRtrTXQdYXzzSx6fM9aLwif+We2vtIc1yLQgTt4=',
  'dnt': '1'
} 

response = requests.request("GET", url, headers = headers, data = payload, files = files)


stores = json.loads(response.text)

with open('Mcdonlocation.csv', mode='w') as CSVFile:
    writer = csv.writer(CSVFile, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL)

    writer.writerow([
        "addressLine1",
        "addressLine2",
        "addressLine3",
        "subDivision",
        "postcode",
        "telephone"
        ])

    for store in stores['features']:
        row = []
        Match_Address1= store['properties']["addressLine1"]
        Match_Address2= store['properties']["addressLine2"]
        Match_Address3= store['properties']["addressLine3"]
        subDivision= store['properties']["subDivision"]
        Postalcode= store['properties']["postcode"]
        telephone= store['properties']["telephone"]

        row.append(Match_Address1)
        row.append(Match_Address2)
        row.append(Match_Address3)
        row.append(subDivision)
        row.append(Postalcode)
        row.append(telephone)
        writer.writerow(row)

Solution

I think the fundamental answer to your quesions is, "look at the type". The Python json conversion table tells you what to expect for each type. Let's load your file and see what we have, according to the Python interpreter:

>>> input = dat.read()
>>> stores = json.loads(input)
>>> type(stores)
<class 'dict'>
>>> type(stores['features'])
<class 'list'>
>>> type( stores['features'][0] )
<class 'dict'>
>>> type( stores['features'][0]['properties'] )
<class 'dict'>
>>> type( stores['features'][0]['properties']['telephone'] )
<class 'str'>
>>> stores['features'][0]['properties']['telephone']
'4166903659'

Every object has a type; every type has methods. Just work your way down the list.