Questions related to finding hidden data in Python data frames.

import pandas as pd
import requests
from bs4 import BeautifulSoup
from datetime import datetime

# Code Settings
code = '005930'

# Calculating risk-free interest rates
ke_url = "https://www.kisrating.com/ratingsStatistics/statics_spread.do"
ke_res = requests.get(ke_url)
ke_df = pd.read_html(ke_res.text)
ke = float(ke_df[0].iloc[10, 8])


# Import Data Area: Import only snapshots and financial statements
snapshot_url = "http://comp.fnguide.com/SVO2/ASP/SVD_Main.asp?pGB=1&gicode=A{}&cID=&MenuYn=Y&ReportGB=&NewMenuID=101&stkGb=701".format(code)
snapshot_res = requests.get(snapshot_url)
snapshot_df = pd.read_html(snapshot_res.text)

fs_rpt_url = "http://comp.fnguide.com/SVO2/ASP/SVD_Finance.asp?pGB=1&gicode=A{}&cID=&MenuYn=Y&ReportGB=&NewMenuID=103&stkGb=701".format(code)
fs_rpt_res = requests.get(fs_rpt_url)
fs_rpt_df = pd.read_html(fs_rpt_res.text)

# Changing the NAN value to 0 in this data
for i in range(len(snapshot_df)):
    # # snapshot_df[i] = df[i].apply(pd.to_numerical, errors = 'cource') # This is very important. If you apply this, everything in letters becomes a Nan value.
    snapshot_df[i].fillna(0,inplace=True)

for i in range(len(fs_rpt_df)):
    # # fs_rpt_df[i] = df[i].apply(pd.to_numerical, errors = 'cource') # This is very important. If you apply this, everything in letters becomes a Nan value.
    fs_rpt_df[i].fillna(0,inplace=True)

# fs_rpt_df[0] : Consolidated income statement (annual)
# fs_rpt_df[1]: Consolidated income statement (quarter)
# fs_rpt_df[2]: Consolidated income statement
print(fs_rpt_df[0].iloc[3, 0])