Struggling with pie charts that have too many small values? Whether you’re using Excel or Python, grouping minor slices into “Other” can make your chart easier to read and more professional. This guide compares both tools and shows how to automate clean, readable charts with Python.
Ever created a pie chart in Excel and thought, “Wow, that looks more like a rainbow exploded than a business report”? You’re not alone. You start with good intentions—maybe a nice clean chart showing your company’s sales by product category.
But suddenly, you’re staring at twelve tiny slices, each barely 1-2% of sales. The result? A chart that’s harder to read than your doctor’s handwriting.
If you’ve ever caught yourself squinting and muttering, “How do I group these small values so this thing actually makes sense?”—you’re in the right place. The good news?
Both Excel and Python can help. But they each bring their own style to the party.
The Excel Way: Tried, True… and Sometimes a Little Tricky
It’s not the end of the world, but it can get tedious if you’re producing regular reports. If you’ve ever wondered why Excel can feel so slow at times, this manual approach is part of the reason.. Excel has been the trusty sidekick of data visualization for decades. And yes, it does have a built-in trick for handling this common pie chart problem. Enter the “Pie of Pie” chart—a handy little feature that lets you bundle those pesky small slices into one neat “Other” category.
No need to go hunting in obscure menus. It’s right there: Insert tab → Pie chart dropdown → Pie of Pie. Boom.
Here’s how it works:
You start with your regular pie chart. Then you switch it to a “Pie of Pie” chart. Excel will automatically break out a secondary pie chart showing those smaller values.
You can even customize what counts as “small” by right-clicking the chart, hitting Format Data Series, and setting a percentage threshold—say, 5%. Anything below that gets grouped into “Other.”
Sounds simple, right? And it is. But there are some downsides.
Excel’s approach is a bit rigid.
You’re working inside pre-built templates, which means you can tweak colors and labels, but you’re stuck with Excel’s overall design choices. If you’re looking for full creative control, this might feel a little limiting.
And here’s another thing: if your data updates frequently, you’ll find yourself adjusting these settings over and over.
It’s not the end of the world, but it can get tedious if you’re producing regular reports.
Python: The Swiss Army Knife of Data Visualization
Now, let’s talk about Python—the tool that’s quietly become the Swiss Army knife of the data world. Python, like a Swiss Army knife, offers a versatile and powerful approach to data visualization, providing more flexibility and control than traditional tools. This versatility is also why Python significantly outperforms VLOOKUP for merging Excel files
If you haven’t dipped your toes into it yet, don’t worry. With libraries like Matplotlib and Plotly, Python gives you a level of flexibility that Excel just can’t match when it comes to collapsing tiny segments in pie charts.
No more being boxed in by preset chart types.
With Python, you get to call the shots. Want to group values under 5%? Done. Want to group by absolute value? Easy.
Want to mix and match criteria? You can do that too. It’s all programmable.
Here’s the basic idea:
In Python, you take an algorithmic approach. You set a cutoff threshold (say, 5%) and automatically group everything below that into an “Other” slice.
Then you generate your pie chart from that grouped data. Sounds fancy? It’s actually pretty simple once you see it in action—and super elegant.
But here’s where Python really shines: automation and template-driven charting.
Once you’ve written your script to group small values and generate the chart, you can run that same script on any new dataset with just a few clicks. No more fiddling with formatting.
No more trying to remember which settings you used last time. Your code becomes a repeatable template that always gives consistent results. This automation extends beyond just charts; you can even automate Excel formatting itself with Python
If you’re handling lots of reports or dashboards? That’s a game-changer. This ease of automation directly addresses some of the common challenges faced with monthly Excel reports.
Head-to-Head Comparison: Excel vs Python
When comparing Excel and Python for data visualization and reporting, each has its own strengths and trade-offs.
Excel wins in terms of ease of use. It offers a familiar point-and-click interface that most users can navigate without any technical background. Python, on the other hand, requires programming knowledge and comes with a steeper learning curve—but offers more power once mastered.
Flexibility is another major difference. Excel is limited to predefined chart types and formatting styles. With Python, you get full control over how your charts look, how your data is grouped, and how everything is exported. You’re not boxed into templates—you decide every detail.
Automation is where Python really shines. In Excel, creating charts is mostly a manual process that needs human intervention every time the data changes. Python allows full automation: once your script is written, it can handle multiple datasets without lifting a finger.
When it comes to formatting, Excel provides a good range of built-in themes and some basic customization. Python blows this wide open, giving you virtually unlimited formatting options—down to line thickness, font style, and grid transparency.
Reproducibility is another critical factor. In Excel, charts need to be recreated or updated manually whenever your data changes. With Python, your code is reusable and instantly reflects the latest data inputs—no rework required.
Finally, consider the learning curve. Excel is minimal for basic users and only moderately challenging for more advanced features. Python demands more upfront learning, but in return, it gives you scalable, automated solutions that pay off in the long run.
When Excel Makes Perfect Sense
Now, before you think I’m here to turn you into a full-blown Python convert, let’s get one thing straight: Excel still absolutely deserves a spot in your data toolbox.
If you’re whipping up a quick chart for a presentation, or working with a small, stable dataset that doesn’t change much—Excel’s “Pie of Pie” feature is perfect.
It’s fast, familiar, and you don’t need to write a single line of code. Sometimes, that’s exactly what you want.
Here’s another win for Excel: team collaboration.
Not everyone on your team is going to feel comfortable opening up a Python script.
But almost everyone can tweak an Excel chart when needed. In environments where lots of people need to jump in and make quick edits, Excel still takes the prize.
Python to the rescue
But—(you knew that was coming)—if you’re building similar charts over and over, if your data updates frequently, or if you need more advanced grouping options? That’s when Python starts to shine.
Think of it this way: Excel is like a reliable sedan. It’ll get you from point A to point B, no fuss.
Python? It’s a customizable sports car. It takes a little more skill to drive, but once you get the hang of it, it’ll take you places you didn’t even know were on the map. Do I get your attention now?
And when it comes to automating pie chart cleanup, that extra horsepower really pays off.
Where Python really flexes its muscles is with large datasets and automation.
You can consolidate minor categories exactly as you need.
Even more, you also can apply consistent formatting across dozens of charts with no extra effort. Want interactive charts? Plotly’s got you covered. Need reports that refresh themselves whenever your source data updates? Python makes that easy too.
In short: if you’re running the same reporting process again and again, or you want more power and flexibility—Python is there to help you.
Automating Pie Chart Cleanup: How Python Beats Excel
Let’s cut to the chase: when your pie chart is packed with tiny slices, Python can automate the cleanup way better than Excel can.
And it’s not just a little faster—it’s a whole different league.
Why? Three reasons: programmable thresholds, dynamic data handling, and batch processing.
In Excel, you’re stuck fiddling with the “Pie of Pie” feature every time your dataset changes.
That means manually clicking through menus, tweaking thresholds, and hoping you don’t miss something.
Meanwhile, in Python, you write your logic once, and you’re good to go—whether your dataset has 10 rows or 10,000.
Threshold Automation: Code vs. Click
Excel makes you set your grouping rules through right-clicks and dialog boxes. It works, but it’s tedious—and every time your data changes, you’re back at it again.
Now compare that to Python. You can set a threshold in your code, and boom—anything below that percentage gets grouped into “Other” automatically. Like this:
python
CopyEdit
# Set automated threshold at 5% of total
threshold = 0.05
grouped = df[df[‘value’]/df[‘value’].sum() < threshold]
df[‘category’] = np.where(df[‘value’]/df[‘value’].sum() < threshold, ‘Other’, df[‘category’])
That’s it. One variable controls everything. If your data updates tomorrow, the code still works—no extra clicks, no manual cleanup.
Excel just doesn’t have a built-in way to handle this dynamically.
Dynamic Label Management
Here’s another win for Python: smarter labels.
In Excel, it’s all or nothing—either every slice has a label, or you manually delete the ones you don’t want.
Python (thanks to Matplotlib) lets you set rules: “Don’t show labels below 2%,” for example.
It’s automated, flexible, and keeps your chart clean without lifting a finger.
The Bottom Line: It’s Not About Choosing Sides
This isn’t Excel vs. Python like some dodgeball showdown. The best data pros? They use both—each where it shines. This integrated approach can truly make Excel smarter with Python.
Excel is unbeatable for quick one-offs, fast edits, and team collaboration. But when it’s time to automate, scale, or get precise about how you want your charts to behave—Python is your secret weapon.
So if you’re an Excel user constantly wrangling pie charts full of tiny slices, this isn’t a call to abandon ship. Think of it more like a friendly nudge: “Hey, there’s a faster way if you’re ready for it.”
But knowing what’s possible with Python? That alone can help you stop using Excel in ways that limit your productivity and make smarter, faster choices.
That alone can help you make smarter, faster choices.