I Replaced Excel With Python for 30 Days — Here's What I Learned

I'd been meaning to "properly learn Python for data work" for two years. I kept watching tutorials, reading docs, and going back to Excel when a deadline hit.

So in February I made a rule: no Excel for 30 days. Every data task — reports, ad hoc analysis, stakeholder exports — had to go through Python.

Here's what actually happened.

Week 1: The Pain Is Real

Everything took longer. A quick pivot table that took 90 seconds in Excel took 20 minutes in pandas because I kept Googling syntax. I nearly broke the rule three times.

The biggest shock: column name cleaning. You don't realise how much Excel silently tolerates until you're debugging a KeyError caused by a trailing space in a header.

df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')

That one line became muscle memory by day 3.

Week 2: The Click Moment

I had to merge three datasets from different source systems. In Excel, this would have been 45 minutes of VLOOKUPs, cross-checking, and praying. In pandas:

result = df1.merge(df2, on='customer_id', how='left').merge(df3, on='order_id', how='inner')

Two lines. Done in 4 seconds. I actually laughed.

This is the moment Python becomes irreversible. Once you've merged 500,000 rows without Excel crashing, you can't go back.

Week 3–4: Where Python Wins Permanently

• Reproducibility. A script I wrote in week 1 refreshed the same report in week 4 without touching it. Excel files drift — someone renames a column, a formula breaks, nobody notices.
• Data volumes. 1M+ rows. Excel's limit is 1,048,576. Python doesn't care.
• Automation. Scheduled scripts running at 6am, emailing stakeholders before they start work. Priceless.
• Version control. Git-tracked scripts beat "Report_v3_FINAL_v2_USE_THIS_ONE.xlsx" every single time.

What I Still Use Excel For

I'm being honest: quick one-off formatting tasks, sharing data with non-technical stakeholders who need to edit it, and anything where the end deliverable is an Excel file. Excel isn't dead — it's just not a data platform.

My Starter Stack for the Switch

pip install pandas openpyxl xlsxwriter matplotlib seaborn

Start with pandas for everything. Add visualisation once your data manipulation is solid. Don't learn five libraries at once — go deep on one.