I am trying to get specific column data out of specific sheets in one big google sheet.
For example, I have a list of sheets

Sheets = ['Sheet 1', 'Sheet 2', 'Sheet 3']

and from these sheets, I want to retrieve specific columns like

Column_headers = ['A', 'B']

What I am doing right now is getting the data from

import gspread
from gspread_dataframe import set_with_dataframe
import pandas as pd
pd.set_option("display.max_columns", None)
pd.set_option('display.max_rows', None)

sa = gspread.service_account(filename='file.json')
book = sa.open("book")
Sheets = ['Sheet 1', 'Sheet 2', 'Sheet 3']
Column_headers = ['A', 'B']

for i in Sheets:
    2022_sheet = book.worksheet(i)
    records = 2022_sheet.get_all_records()
    data_2022 = zip(*(e for e in zip(*record) if e[0] in Column_headers))
    getdata_2022 = pd.DataFrame(data_2022, columns = Column_headers)
    print(getdata_2022)

I am getting the following error

GSpreadException: the given 'expected_headers' are not uniques

that is because the headers are not unique (obv) that’s why I am retrieving specific columns, also I can’t understand the bit where I loop through the ‘Sheets’ to get the data only from specific sheets. Eventually the end result should be two columns ‘A’ and ‘B’ with all the data from the specific 3 sheets.

>Solution :

IIUC try doing the following:

import gspread
import gspread_dataframe as gd
import pandas as pd

# connect to the service account
sa = gspread.service_account(filename='file.json')

# open the Google Sheet
book = sa.open('book')

# The list of sheet names and columns that you want
Sheets = ['Sheet 1', 'Sheet 2', 'Sheet 3']
Column_headers = ['A', 'B']

# list comprehension using gd.get_as_dataframe 
# to create a list of DataFrames for each sheet
# then you concat all frames together into one DataFrame
df = pd.concat([gd.get_as_dataframe(book.worksheet(sheet))[Column_headers]
                for sheet in Sheets])

You may need to filter out NaNs if you have empty rows in your Google Sheet. Something like:

df = df[~df.isna().all(axis=1)]

update

replace df = pd.concat([gd.get_as_dataframe(book.worksheet(sheet))[Column_headers] for sheet in Sheets]) with the following

dfs = []
for sheet in Sheets:
    df = gd.get_as_dataframe(book.worksheet(sheet))
    try:
        df = df[Column_headers]
    except KeyError:
        df = df[['A']]
        df['B'] = np.nan
    # the commented out code below will add the sheet name to each frame
    # df['Sheet_Name'] = sheet
    # you can also remove all nan rows here if you need
    # df = df[~df.isna().all(axis=1)]
    dfs.append(df)
    
final_df = pd.concat(dfs)