Skip to content

Latest commit

 

History

History
82 lines (53 loc) · 1.98 KB

quickstart-google-sheets-with-python.md

File metadata and controls

82 lines (53 loc) · 1.98 KB

This is a simple & minimal guide on how to use the google-sheet service with Python.

Requierements

Let's start by defining the system requirements and installing the dependencies.

System requirements:

  • Python 3.5 or greater

Dependencies:

  • google-api-python-client==1.8.3
  • (optional) jupyter==1.0.0
$ pip install google-api-python-client==1.8.3 jupyter==1.0.0

Enable the Google Speadsheet API

Create a Google Cloud project (https://console.cloud.google.com/) and enable the Google Sheets API.

Next step is to create an API Key. Using this key is the fastest way to get going.

Python Code!

Let's start with the imports, configuration variables, and singeltons:

import os
from googleapiclient import discovery

# Get the key from the environ variables
SHEETS_API_KEY = os.environ.get(
    "SHEETS_API_KEY",
    default="YOUR-KEY"  # TODO: Replace with your testing key here
) 

# Create the API Service instance
service = discovery.build(
    "sheets",
    "v4",
    developerKey=SHEETS_API_KEY
)

speadsheet_values = service.spreadsheets().values()

We should now be able to get the data from a Google Speadsheet!

Consider the following google sheet:

  • Sheet URL: here
  • Sheet Name: example
  • Sheet ID: 1uFMvhLcLxVDeFxBw8Zt5cIuVKc_h0ByqLDx2kWlNXOY

We should be able to get the data:

spreadsheet_id = "1uFMvhLcLxVDeFxBw8Zt5cIuVKc_h0ByqLDx2kWlNXOY"
spreadsheet_range = "A1:B4"


request = spreadsheet_values.get(
    spreadsheet_id=spreadsheet_id,
    range=spreadsheet_range,
)

response = request.execute()

If we add pandas to our requirements, we should be able to easily transform the results into a DataFrame.