import os.path from google.auth.transport.requests import Request from google.oauth2.credentials import Credentials from google_auth_oauthlib.flow import InstalledAppFlow from googleapiclient.discovery import build import pandas as pd # If modifying these scopes, delete the file token.json. SCOPES = ["https://www.googleapis.com/auth/spreadsheets"] # The ID and range of a sample spreadsheet. # SAMPLE_SPREADSHEET_ID = "1Z3doUDpoUmXs7p4-g7f8q3XZIBhH_Lz2w51bnpUFigw" SAMPLE_SPREADSHEET_ID = "1NJGhpMsfUySHJcMBNpLTEnt-r5Co6qwRahenIa9D55k" SERVICE = None MAX_ROW = 100 QUESTIONS = None def get_range(range_name: str) -> list[list[str]]: sheet = SERVICE.spreadsheets() result = ( sheet.values() .get( spreadsheetId=SAMPLE_SPREADSHEET_ID, range=range_name ) .execute() ) values = result.get("values", []) return values def load_google_sheets(): global SERVICE, QUESTIONS """Shows basic usage of the Sheets API. Prints values from a sample spreadsheet. """ creds = None # The file token.json stores the user's access and refresh tokens, and is # created automatically when the authorization flow completes for the first # time. if os.path.exists("token.json"): creds = Credentials.from_authorized_user_file("token.json", SCOPES) # If there are no (valid) credentials available, let the user log in. if not creds or not creds.valid: if creds and creds.expired and creds.refresh_token: creds.refresh(Request()) else: flow = InstalledAppFlow.from_client_secrets_file( "credentials.json", SCOPES ) creds = flow.run_local_server(port=0) # Save the credentials for the next run with open("token.json", "w") as token: token.write(creds.to_json()) SERVICE = build("sheets", "v4", credentials=creds) QUESTIONS = pd.DataFrame(get_range(f"Responses!B1:F1"), columns=["Q1", "Q2", "Q3", "Q4", "Q5"]) def get_team_numbers() -> list[str]: values = get_range(f"Responses!I2:I{MAX_ROW}") return [int(value[0]) for value in values] def get_questions_and_answers() -> pd.DataFrame: columns = ["Q1", "Q2", "Q3", "Q4", "Q5", "Q6", "Q7", "team_number", "email_address", "name"] answers_values = get_range(f"Responses!B2:K{MAX_ROW}") df = pd.DataFrame(answers_values, columns=columns) return df