5 reasons why I use Python instead of Excel for visualizing data

Microsoft Excel has been a darling of businesses and individuals for data analysis. That said, Python has been gaining popularity among professionals due to its scalability, flexibility, and advanced visualization. While Python was built to automate boring stuff, the programming language has become a go-to solution for many in web development, machine learning, and, of course, data analysis.

Python’s extensive libraries and capabilities can deliver a significant upgrade to anyone looking for better data visualization. Here are the prime reasons why I have moved from relying solely on Excel to adopting Python as my preferred tool for data manipulation.

5 Customization

Quite flexible and offers a sheer amount of customization options

While Microsoft Excel does a respectable job with standard bar, line, and pie charts, Python takes the entire experience to the next level. Thanks to libraries like Matplotlib and Seaborn, you have multiple chart-type options, including scatter plots, heatmaps, histograms, box plots, violin plots, and 3D visualizations.

When it comes to customization, you can tweak every aspect of your chart, be it colors, fonts, line styles, annotations, transparency levels, line width, and more. At times, I also use the PIL (Pillow) library to resize, crop, filter, and insert images or logos to my charts.

Python’s flexibility also applies to multiple charts within a single figure. For instance, you have total control over the sizing, positioning, and alignment of subplots, unlocking complex layouts in your workflow. Excel does feel restrictive and time-consuming and simply can’t match this level of control, customization, flexibility, and expressiveness.

4 Scalability

Handles large datasets without breaking a sweat

Flawless handling of large datasets is one of the key reasons to embrace Python over Excel. The built-in core libraries, including NumPy and Pandas, can manage large datasets efficiently. In contrast, Excel’s architecture feels unoptimized, especially when you deal with a large number of rows and columns. You may feel sluggish and degraded performance in Excel, and at times, it doesn’t even open the file.

Python leverages optimized data structures and lazy evaluation (where the system loads the data into memory when it’s needed for visualization, trimming the memory footprint), and it can process millions of data points without showing any signs of slowdown. You can also use libraries like Datashade or Vaex to visualize large data points on even low-end hardware.

3 Automation

Fly through your repetitive tasks

Python’s script-based approach eliminates repetitive tasks and manual interactions entirely. You need to write code for your charts and data sources, set parameters, and tweak appearance. And that’s it. You can now save this script and run it every time you want to perform the same tasks and get the desired results in no time.

To Microsoft’s credit, the company does offer macros to automate basic tasks in Excel. However, it is prone to human errors and inconsistencies and, at times, requires a few tries to get the entire sequence right. However, Python is a step ahead, where you can change data sources, tweak chart settings, and change customization options without having to start everything from scratch. At any time, you can revert to the previous script version if needed and track changes.

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2 Advanced and interactive visualization

Understand complex data relationships in no time

Python offers interactive plots and dashboards. As for my workflow, I use the Ploty library quite frequently. I primarily use it to visualize the performance of a specific stock over time. I have set it to track historical stock price data, opening, closing, high, and low prices for each trading day and once Ploty creates an insightful data visualization, I use it to identify key trends of stock’s performance.

You also have advanced options like zooming in on specific regions for more details, data filtering, and the ability to link multiple plots to reveal complex relationships. As for Excel, you need to rely on advanced VBA knowledge or third-party add-ons to unlock such interactive visualizations.

Getting started with Python is easier than ever

Python has an active community with comprehensive documentation for popular libraries like Seaborn, Plotly, Matplotlib, and more. Thanks to countless blogs, online courses, and tutorials, beginners won’t have a hard time getting started. Whenever I hit a roadblock with a specific query in Python, its passionate online community always comes to the rescue.

Since Excel is widely used, it also has detailed documentation and tutorials, but they mostly focus on basic functions and usually don’t go in lengths for specialized visualization.

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Unlock Python’s data visualization magic

While Microsoft Excel is a solid entry-level spreadsheet tool for crunching numbers, its limitations are apparent when you work with a lot of data or perform advanced data analysis. Here is where Python comes into play, which is more robust and offers a rich ecosystem of libraries and relevant tools for data manipulation.

With the latest Excel update on Windows, Microsoft’s spreadsheet software supports Python commands to perform data analysis. Check out our separate guide to learn more about combining Python with Excel on Windows.