How I Combine Python + Google Sheets for Lightweight Automation

A low-code workflow that saves hours and reduces reporting pain.

Not every team has a full data engineering stack. And not every automation problem needs Airflow, dbt, or even a data warehouse.

Sometimes, the most efficient solution is combining a tool everyone already knows, Google Sheets with a few lines of Python.

This edition breaks down how I use this lightweight combination to:

• Automate weekly marketing and sales reports

• Transform messy data into clean dashboards

• Streamline repetitive data pulls and exports

Whether you're a solo analyst or supporting a lean growth team, this setup saves time, improves accuracy, and builds trust in your numbers.

Why Google Sheets still matters

While it’s not “scalable” in the enterprise sense, Sheets offers:

• Easy collaboration

• Instant visualisation

• No training required for stakeholders

• Built-in API integrations (with the right setup)

For fast-moving marketing or growth teams, it’s often the most flexible workspace available.

When I use Python + Sheets instead of BI tools

1. You don’t have direct access to a warehouse or database: Python lets you connect to APIs (like Meta, GA4, HubSpot, Stripe) and send the results straight into a Google Sheet your team can view instantly.

2. You need to clean, transform, or enrich data: Pandas handles data cleaning far better than Excel formulas. Then the results can be pushed back into Sheets automatically.

3. You want to avoid manual copy-paste between tools: This is one of the biggest time drains in small teams. Python helps you eliminate it entirely.

Weekly Paid Ads Report

The problem: The performance marketing team needed a Monday morning report showing ROAS, spend, and CPA across Meta and Google Ads, broken down by campaign and channel.

Before: Someone exported CSVs, cleaned them in Excel, and pasted the results into a shared Sheet every single Monday.

Now: A Python script does the following:

• Pulls data from the Meta and Google Ads APIs

• Normalises and joins the datasets

• Calculates KPIs (e.g. ROAS, CPA)

• Writes the clean output to a Google Sheet via the Sheets API

• (Optional) Sends a Slack message with a link and short summary

All in 5 minutes. Every week. No human intervention.

Key Python Packages I Use

• : for cleaning and transforming the data

• or : to connect to Google Sheets

• : to authenticate access

• : to hit marketing or sales APIs

• : for simple time-based automation

• : to store and manage credentials securely

Sample Python Snippet: Writing to Google Sheets

You can automate this to run weekly with a cron job or using .

A Few Lessons from Doing This Often

• Always add error handling, you’ll thank yourself when APIs fail silently.

• Use naming conventions across reports and sheets (e.g. YYYY_MM_DD_Report)

• Set up a read-only dashboard for marketing teams that links to the output Sheet

• Track and log run time/errors in a separate Google Sheet

• Keep your automation scripts versioned and well-commented, others will eventually depend on them

Final Thought

You don’t need a complex data stack to build useful automations.

If you're comfortable with Python and work in a team that lives in Google Sheets, this combo can take repetitive reporting off your plate and increase trust in your data.

It's not about how advanced the tools are, it’s about whether they solve a real problem for your team.