Google Drive API v3 with Python to write data from revision file to pandas

I thought that when I saw your file ID and your revision ID of '104', the file might be Google Docs (Document, Spreadsheet, Slides, and so on). In this case, unfortunately, get_mediamethod cannot be used. Here,exportmethod is required to be used. But, unfortunately, it seems thatrevisions()has no method ofexport. So, in this case, the export link retrieved from service.revisions().get()` is used for downloading the data.

And, your goal is to put the exported values on pandas. From this, I guessed that the file of your file ID might be Google Spreadsheet. If my understanding is correct, when this is reflected in a python script, how about the following sample script?

Sample script:

service = build("drive", "v3", credentials=creds) # Please use your script.

file_id = '1E8Wbd80CbFlFSHYZQkApXMM9EQOz1lQRl4m3rfq-vdY' # This is from your showing script. From your question, I guessed that this might be Google Spreadsheet.

mimeType = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" # In this case, Google Spreadsheet is exported as XLSX format.
sheet = "Sheet1" # Please set the sheet name you want to retrieve the values.
revision_history_id = '104' # Please set the revision ID.

res = service.revisions().get(fileId=file_id, revisionId=revision_history_id, fields="*").execute()
link = res.get("exportLinks").get(mimeType)
if link:
    data = requests.get(link, headers={"Authorization": "Bearer " + creds.token})
    values = pd.read_excel(BytesIO(data.content), usecols=None, sheet_name=sheet)
    print(values)

• Here, the access token is retrieved from creds of service = build("drive", "v3", credentials=creds). Please be careful about this.

• In this case, from io import BytesIO, import pandas as pd, and import requests are also used.

• When this script is run, the Google Spreadsheet is exported with the revision ID as XLSX format. And, the values from the expected sheet are put to pandas.

Reference:

• Revisions: get

After giving it another thought, I finally got it right like this:

import pandas as pd
from google_apis import create_service

CLIENT_FILE = 'client_secret.json'
API_NAME = 'drive'
API_VERSION = 'v3'
SCOPES = ['https://www.googleapis.com/auth/drive']

# Retrieve file revision history
service = create_service(CLIENT_FILE, API_NAME, API_VERSION, SCOPES)

def get_file_revision_history(file_id):
    response = service.revisions().list(
        fileId=file_id,
        fields='*',
        pageSize=1000
    ).execute()

    revisions = response.get('revisions')
    nextPageToken = response.get('nextPageToken')

    while nextPageToken:
        response = service.revisions().list(
            fileId=file_id,
            fields='*',
            pageSize=1000,
            pageToken=nextPageToken
        ).execute()

        revisions = response.get('revisions')
        nextPageToken = response.get('nextPageToken')
    return revisions

file_id = '1E8Wbd80CbFlFSHYZQkApXMM9EQOz1lQRl4m3rfq-vdY'
revision_history = get_file_revision_history(file_id)
exportlinks = list(map(lambda x: x["exportLinks"], revision_history))
csv_urls = list(map(lambda y: y["text/csv"], exportlinks))
modified_time = list(map(lambda z: z["modifiedTime"], revision_history))

appended_data = []
for i,j in zip(csv_urls,modified_time):
    res = service._http.request(i)
    rn_string_data = list(res)[1]
    data = list(map(lambda x: x.split(','),rn_string_data.decode('utf-8').split("\r\n")))
    df = pd.DataFrame(data[1:],columns=['employee_name','hourly_rate','currency'])
    df['ModifiedTime'] = j
    appended_data.append(df)
appended_data = pd.concat(appended_data)
print(appended_data)

``