How do web scrape more underlying data from a websites map location?

Currently, I have successfully used python to scrape data from a competitor’s website to find out store information. The website has a map where you can enter a zip code and it will tell you all the stores in the area of a my current location. The website sends a GET request to pull store data by using this link:

https://www.homedepot.com/StoreSearchServices/v2/storesearch?address=37028&radius=50&pagesize=30

My goal is to scrape all store information not just the imaginary zip code = 12345 & pagesize=30. How should I go about getting all the store information? Would it be better to iterate through a dataset of zip codes to pull all the stores or is there a better way to do this? I’ve tried expanding past 30 page size but it looks like that is the limit on the request.

Answer

This url gives JSON with "currentPage":1 which can means it can use some kind of pagination.

I added &page=2 and it seems it works

Page 1:

https://www.homedepot.com/StoreSearchServices/v2/storesearch?address=37028&radius=250&pagesize=40&page=1

Page 2:

https://www.homedepot.com/StoreSearchServices/v2/storesearch?address=37028&radius=250&pagesize=40&page=2

Page 3:

https://www.homedepot.com/StoreSearchServices/v2/storesearch?address=37028&radius=250&pagesize=40&page=3

For test I use bigger range=250 to get JSON with "recordCount":123

I found that it works also with pagesize=40.
For bigger value it sends JSON with error message.

---

EDIT:

Minimal working code:

Page blocks request without User-Agent

import requests

headers = {
    'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:98.0) Gecko/20100101 Firefox/98.0',
}

url = 'https://www.homedepot.com/StoreSearchServices/v2/storesearch'

payload = {
    'address': 37028,
    'radius': 250,
    'pagesize': 40,
    'page': 1,
}

page = 0

while True:

    page += 1
    print('--- page:', page, '---')
    
    payload['page'] = page
    response = requests.get(url, params=payload, headers=headers)
    
    data = response.json()

    print(data['searchReport'])
                        
    if "stores" not in data:
        break
    
    for number, item in enumerate(data['stores'], 1):
        print(f'{number:2} | phone: {item["phone"]} | zip: {item["address"]["postalCode"]}')

Result:

--- page: 1 ---
{'recordCount': 123, 'currentPage': 1, 'storesPerPage': 40}
 1 | phone: [phone] | zip: 37040
 2 | phone: [phone] | zip: 42001
 3 | phone: [phone] | zip: 37221
 4 | phone: [phone] | zip: 37115
 5 | phone: [phone] | zip: 37216
 6 | phone: [phone] | zip: 37204
 7 | phone: [phone] | zip: 37075
 8 | phone: [phone] | zip: 37027
 9 | phone: [phone] | zip: 37076
10 | phone: [phone] | zip: 37064

etc. 

--- page: 2 ---
{'recordCount': 123, 'currentPage': 2, 'storesPerPage': 40}
 1 | phone: [phone] | zip: 38654
 2 | phone: [phone] | zip: 40219
 3 | phone: [phone] | zip: 47150
 4 | phone: [phone] | zip: 47129
 5 | phone: [phone] | zip: 38637
 6 | phone: [phone] | zip: 40207
 7 | phone: [phone] | zip: 38866
 8 | phone: [phone] | zip: 40220
 9 | phone: [phone] | zip: 72404
10 | phone: [phone] | zip: 35768

etc.

---

If you want to keep as DataFrame then maybe first put all items on list and later convert this list to DataFrame

# --- before loop ----

all_items = []

page = 0

# --- loop ----

while True:

    # ... code ...
    
    for number, item in enumerate(data['stores'], 1):
        print(f'{number:2} | phone: {item["phone"]} | zip: {item["address"]["postalCode"]}')
        all_items.append(item)

# --- after loop ----

import pandas as pd

df = pd.DataFrame(all_items)

print(df)

Because JSON keep address as directory {'postCode': ... , ...} so some columns may have it as directory

print(df.iloc[0])

storeId                                                             0726
name                                                     Clarksville, TN
phone                                                      [phone]
address                {'postalCode': '37040', 'county': 'Montgomery'...
coordinates                        {'lat': 36.581677, 'lng': -87.300826}
services               {'loadNGo': True, 'propane': True, 'toolRental...
storeContacts                 [{'name': 'Brenda G.', 'role': 'Manager'}]
storeHours             {'monday': {'open': '6:00', 'close': '21:00'},...
url                           /l/Clarksville-TN/TN/Clarksville/37040/726
distance                                                       32.530296
proDeskPhone                                               [phone]
flags                  {'bopisFlag': True, 'assemblyFlag': True, 'bos...
marketNbr                                                           0019
axGeoCode                                                             00
storeTimeZone                                                    CST6CDT
curbsidePickupHours    {'monday': {'open': '09:00', 'close': '18:00'}...
storeOpenDt                                                   1998-08-13
storeType                                                         retail
toolRentalPhone                                                      NaN

See: { } in address, services, storeHours,etc

It may need also to convert it to separated rows.

df['address'].apply(pd.Series)

and concat it with original df

df2 = pd.concat( [df, df['address'].apply(pd.Series)], axis=1 )

The same way you may do with other columns.