TL;DR: While spreadsheets are perfect for many data tasks, Python becomes essential when you need to handle large datasets, create advanced visualizations, automate workflows, or use machine learning models. The key is knowing when to leverage each tool's strengths for your specific data analysis needs.
While Python is often considered essential for data work, spreadsheets remain the most practical tool for many analysts' daily needs – and that's perfectly fine. But knowing when to graduate beyond them is crucial for advancing your data capabilities.
If you look at any data analyst or data scientist curriculum, you'll find the same core tools: spreadsheets, SQL, Python, and various Business Intelligence (BI) solutions. Yet when I talk with data practitioners and leaders, a common question emerges: "Why switch to Python when spreadsheets handle most of my needs?"
As someone who co-founded a company built on SQL, Python, and AI, my stance might surprise you: if a spreadsheet can do the job, use it. These tools have endured since the 1970s for good reason – they're intuitive, flexible, and excellent for explaining your work to others.
But they have their limits.
When you start conducting more ad hoc analysis or exploratory data analysis or dealing with more data in the enterprise, you’ll quickly run into a few issues:
- They struggle with large datasets
- They offer limited visualization and dashboarding capabilities
- They make it difficult to build automated data pipelines
- They lack advanced statistical and machine learning capabilities
- They don't support version control, making it hard to follow engineering best practices
Below, I’ll break down why spreadsheets remain invaluable for many tasks, and when Python becomes the necessary next step in your data journey.
Why use Excel or Google Sheets?
At their core, spreadsheets are powerful because they put you in complete control of your data workspace. Like having your own custom-built dashboard, they let you instantly manipulate, visualize, and analyze data exactly how you want.
There are two main reasons that folks gravitate toward spreadsheets:
1. Spreadsheets are flexible and personalized
Let’s start with the most obvious reasons why data practitioners, regardless of skill level, love spreadsheets: They’re incredibly flexible and customizable.
In a spreadsheet, you’re working in your own environments, and you have full control over it. You want to highlight specific rows and create a quick chart? Easy. You want to add some conditional formatting to highlight a specific pattern? No problem. You even want to add a row or column to add some inputs? Go right ahead.
As you can see in the image above, if you want to know what numbers contribute most to Net Income Before Tax, you can simply click on the cell, view the dependent cells, and immediately understand what variables Net Income Before Taxes.
For these reasons, if you’re able to do your work in a spreadsheet, it’s probably a good idea.
Why use Python
While spreadsheets excel at many tasks, Python opens up a whole new world of possibilities for data work. From handling massive datasets to creating complex visualizations and automating repetitive tasks, there are five reasons why Python is a powerful tool for your data workflows.
1. Python easily tackles large amounts of data
The first and most obvious reason to use Python is illustrated when dealing with large datasets. Excel can support approximately 1M rows by 17k columns and Google Sheets can support approximately 10M cells. This may sound like a lot, and in many cases this is plenty, but chances are, you’ll quickly run up against this limit. In contrast, Python on a powerful machine can support many orders of magnitude more data. This is especially true if you leverage new technologies like polars and DuckDB.
We may see an increase in limits with spreadsheets over time, but Python (especially in tandem with SQL) will always be able to handle more.
2. Python supports advanced & customized visualizations
Spreadsheets can offer some pretty powerful visuals, but it’s only a small fraction of what you can do with Python. I’m a big believer that bar charts, line charts, and maps cover the vast majority of cases, but telling a story with data often requires breaking from the mundane and creating an engaging canvas.
For example, I love a good Sankey diagram to tell the story of how data flows from point A to point B. Or perhaps you want to create a radar plot to compare attributes from different categories.
These can be incredibly easy to build in Python with libraries like plotly, seaborn or bokeh.
To give you an example, let’s go back to our Superdope example from previous posts and say you want to compare product performance on a sunburst plot like the one below:
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