<jupyter_start><jupyter_text>Fortune 500 Banks App Reviews Collection The data is collected for 24 banks from both Apple App Store & Google Play StoreWe will start with the App Store<jupyter_code>#import required libraries
import pandas as pd
from app_store_scraper import AppStore
from pprint import pprint
bk = pd.read_csv('F500_Bank_Apps - Sheet1.csv')
bk.dropna(inplace=True)
bk['App Store ID'] = bk['App Store ID'].astype(int)

print(bk.shape)
bk.head()
import datetime

date = datetime.datetime.strptime('2019-01-01', '%Y-%m-%d')
for i in range(len(bk['App Name'])):

    x = AppStore(country=bk['Country'].iloc[i], app_name=bk['App Name'].iloc[i],
                                              app_id=bk['App Store ID'].iloc[i])

    x.review(after = date, sleep=10)

    z = pd.DataFrame(x.reviews)
    z['Store'] = 'App Store'
    z['App Name'] = bk['App Name'].iloc[i]

    if i == 0:

        df = pd.DataFrame(x.reviews)
        df['Store'] = 'App Store'
        df['App Name'] = bk['App Name'].iloc[i]

    else:

        df = df.append(z)
df
df['App Name'].value_counts()
df.to_csv('Mobile_Banking_App_Reviews2.csv')<jupyter_output><empty_output><jupyter_text>Google Play Store<jupyter_code>import json
from tqdm import tqdm
from google_play_scraper import Sort, reviews, app
from google_play_scraper import Sort, reviews_all

result = reviews_all(
    'org.westpac.bank',
    sleep_milliseconds=0, # defaults to 0
    lang='en', # defaults to 'en'
    country='us', # defaults to 'us'
    sort=Sort.MOST_RELEVANT # defaults to Sort.MOST_RELEVANT
    
)
west = pd.DataFrame(result)
west['App Name'] = 'Westpac Banking'
west
play = pd.concat([rbc, BoAM, WFRG, CITI, STDR, HSBC, BNP, Loyd, GOLD, MORG, 
                  SBI, TD, Chase, Brad, DBank, UBS, BARC, ING, Scot, cap, comm, BMO, USBANK, west])
play['Store'] = 'Google Play'
play
play.to_csv('Mobile_Banking_Play_Reviews.csv')
play.isna().sum()
play.describe()
bkls = [rbc, BoAM, WFRG, CITI, STDR, HSBC, BNP, Loyd, GOLD, MORG, SBI]
SBI['App Name'] = 'State Bank of India'
rbc<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1416867469879518, 'fraction_numerical': 0.01108433734939759, 'mean_word_length': 3.9311163895486936, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '453410', 'license': 'MIT', 'n_tokens_mistral': 811, 'n_tokens_neox': 760, 'n_words': 196, 'path': 'Scraping_Banking_App_Reviews.ipynb', 'repo_name': 'ahmedalali-do/fsdl-project', 'size': '2075'}