How to Use Python to Automate Google Sheets? (2026 Edition)

8 min read

Automating tasks is a great way to save time and work more efficiently, especially for repetitive jobs. Python is a helpful tool for automating tasks in Google Sheets. This article will show you how to set up a Google service account, use Python to read and update data in Google Sheets, and analyze cricket data to find out how many runs each player scored. You’ll also learn about useful tools like gsspread and how to connect Python to Google Sheets. By the end, you’ll know how to use Python to automate Google Sheets. In this article, you will get to know all about to automate Google Sheets by using Python.

This article was published as a part of the Data Science Blogathon.

Can we Automate Data Entry Into Google Sheets?

We often spend hours daily extracting data and then copy-pasting it to spreadsheets and creating reports leading to excessive time consumption. Consequently, it would be great if we just run a script, the data is uploaded in the spreadsheet, and the report is prepared with just a click. There are multiple advantages of report automation, such as being able to save time on data collection and removing typos, and focus would be more on the analysis part. Let’s find out how we can do this.

How to Upload Python Data Into Google Sheets?

There are several ways to get Python code to output to Google Sheets.

• Using Python Google API Client is one of the easiest ways.
• We can use these two pip packages, and there are others as well
  • Gsheets 
  • Gspread

However, we are using gspread in this tutorial.

How to Create a Google Service Account?

In order to read and update the data from google spreadsheets in python, we will have to create a Service Account. It is a special type of account used to make authorized API calls to Google Cloud Services – Google Cloud Docs.

First of all, make sure that you have a google account. If you have a Google account, you can follow these steps to create a Google service account.

• Go to the developer’s console.  Now, you will see something like this. Click on the Create Project button.

👁 Create a Google Service Account

• Then provide the project name and the organization name, which is optional. Then click on the create button.

• Now that our project is created, we need to enable the APIs that we require in this project. Click on the Enable APIs and Services button to search for the APIs that Google provides.

Consequently, we will add two APIs for our project.

• Google Sheets API
• Google Drive API

• Then, in the search bar, search for these APIs and click on the enable button.

• Google Sheets API will look something like this. It will allow you to access Google Spreadsheets. You would be able to read and modify the content present in the Spreadsheets.

• Google Drive API will look something like this. It will allow you to access the resources from Google Drive.

• Once you have enabled the required APIs in your project, then it’s time to create credentials for the service account. Click on the Create Credentials button to continue.

• Now, select Google Drive API in the type of API required question. We will call the API from a non-UI-based platform, so select Other non-UI (e.g., cron job, daemon). Select the ‘Application Data’ in the next question, as we do not require any user data to run our application. And also, we are not using any cloud-based computing engine for our application. Finally, click on the ‘What credentials do I need?’ button.

• Then, share the google spreadsheets with others and provide permission like edit or view only. Similarly, we will provide access to our service account. We will give it complete access so that we can read and write the spreadsheets and download the credentials.json file of the credentials.

How to Read Data From Google Sheets Using Python?

We will read the commentary data of the India-Bangladesh cricket match. You can access the data (.csv) from here.

We have ball-by-ball data of the complete match in the spreadsheet. Now, we will do a fundamental task and calculate how many runs are scored by each batsman. We can do this by using a simple groupby in pandas. And finally, we will append the results in a separate sheet.

Provide access to the Google sheet.

Now, we need to provide access to the google sheet so that the API can access it. Open the JSON file that we downloaded from the developer’s console. Look for the client_email in the JSON file and copy it.

Then click on the Share button on the Spreadsheet and provide access to this client email.

Now, we are ready to use python to code and access the google sheet data. The following are the steps:

1. Import libraries

We will use the gspread and oauth2client services to authorize and make API calls to Google Cloud Services.

You can use the following code to install gspread and oauth2 python libraries.

!pip3 install gspread
!pip3 install --upgrade google-api-python-client oauth2client

Python Code:

#!pip3 install gspread
#!pip3 install --upgrade google-api-python-client oauth2client

#importing the required libraries
import gspread
import pandas as pd
from oauth2client.service_account import ServiceAccountCredentials

2. Define the scope of the application

Then, we will define the scope of the application and add the JSON file with the credentials to access the API.

# define the scope
scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']

# add credentials to the account
creds = ServiceAccountCredentials.from_json_keyfile_name('add_json_file_here.json', scope)

# authorize the clientsheet 
client = gspread.authorize(creds)

3. Create the sheet instance

Use the client object and open the sheet. You can either pass the title of the sheet as the argument or pass the URL of the sheet.

Access a particular sheet: We have multiple sheets in a single spreadsheet. You can use python to access particular google sheets by providing the index of that sheet in the get_worksheet function. For the first sheet, pass the index 0 and so on.

# get the instance of the Spreadsheet
sheet = client.open('commentary data')

# get the first sheet of the Spreadsheet
sheet_instance = sheet.get_worksheet(0)

Basic functionalities

The API provides some basic functionalities, such as the number of columns by using col_count and get the value in a particular cell. Here are some examples of the same.

# get the total number of columns
sheet_instance.col_count
## >> 26


# get the value at the specific cell
sheet_instance.cell(col=3,row=2)
## >> <Cell R2C3 '63881'>

4. Get all records   

Then, we will get all the data in the sheet using the get_all_records function. It will return a JSON string containing the data.

# get all the records of the data
records_data = sheet_instance.get_all_records()

# view the data
records_data

5. Convert the dictionary to the dataframe

In data science, pandas is one of the most preferred libraries for data manipulation tasks. So we will first convert the JSON string to the pandas dataframe.

In case you are not comfortable with the pandas, I would highly recommend you to enroll in this free course: Pandas for Data Analysis in Python

# convert the json to dataframe
records_df = pd.DataFrame.from_dict(records_data)

# view the top records
records_df.head()

👁 google spreadsheets with python : Provide access to google sheet

6. Grouping batsman

Then, we will create a groupby of the number of runs scored by a batsman and upload that dataframe in a separate sheet.

# number of runs by each batsman
runs = records_df.groupby(['Batsman_Name'])['Runs'].count().reset_index()
runs

Now, we will add this dataframe to google sheets.

Update Data in Google Sheets

The following are steps to update data in google sheets.

• Create a separate sheet : Firstly, we will create a separate sheet to store the results. For that, use the add_worksheet function and pass the number of rows and columns required and the sheet’s title. After that, get the instance of the second sheet by providing the index, which is 1.Once you run this command, you will see that a separate sheet has been created.

• Update values to the sheet : Then, convert the runs dataframe into the 2-D list and use the function to add values in the sheet. With this single line of code, you can update the sheet. Then, you will get a message of the number of rows and columns updated with some more details. Check the Values sheet in Github.

Conclusion

To summarize, in this article, we delved into understanding the various steps involved in the process of creating a service account. And how to read and write in Google Sheets right from your Python console using the Python Google Sheets API. We downloaded the spreadsheet data, converted it into a pandas dataframe, created a groupby table, and uploaded it to the spreadsheet again. This API can be beneficial in the automation of reports.

Hope you like the article and understand how to use Python to read Google Sheets. If you have any questions about Python in Google Sheets or how to read a Google Sheet in Python, feel free to ask!

In case you want to brush up on your spreadsheet concepts, I recommend the following article and course-

• Microsoft Excel: Formulas & Functions
• 10+ Simple Yet Powerful Excel Tricks for Data Analysis

Key Takeaways

• Google spreadsheets are essential in almost every industry and help others see the changes.
• It will help in acquiring knowledge of python programming and scripting.
• It saves a lot of time using python libraries by reducing manual work with the help of scripting.

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