Key findings

• Python’s customizability and flexibility take data visualization beyond the capabilities of Excel.
• Its scalability easily handles large data sets, unlike Excel’s limited performance.
• The automation tools streamline repetitive tasks through script-based coding, saving time and reducing errors.

Microsoft Excel is popular among businesses and individuals for data analysis. However, Python has also gained popularity among professionals due to its scalability, flexibility, and advanced visualization. Although Python was originally developed to automate tedious tasks, the programming language has become the preferred solution for many in web development, machine learning, and of course, data analysis.

Python’s extensive libraries and features can be a significant improvement for anyone looking for better data visualization. Here are the main reasons why I switched from using only Excel to Python as my preferred tool for data manipulation.

5 Adjustment

Very flexible and offers a variety of customization options

While Microsoft Excel does a decent job with standard bar, line, and pie charts, Python takes the whole experience to the next level. Thanks to libraries like Matplotlib and Seaborn, you have multiple chart types at your disposal, including scatter plots, heat maps, 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. I also sometimes use the PIL (Pillow) library to resize my charts, crop them, filter them, and insert images or logos into them.

Python’s flexibility also extends to multiple charts within a single figure. For example, you have complete control over the size, positioning, and alignment of sub-charts, unlocking complex layouts in your workflow. Excel feels restrictive and time-consuming, and simply cannot match this level of control, customization, flexibility, and expressiveness.

4 Scalability

Handles large data sets with little effort

The flawless handling of large data sets is one of the main reasons to choose Python over Excel. Its built-in core libraries, including NumPy and Pandas, can handle large data sets efficiently. In contrast, Excel’s architecture seems unoptimized, especially when you are dealing with a large number of rows and columns. You may find Excel’s performance sluggish and degraded, and sometimes the file won’t even open.

Python leverages optimized data structures and lazy evaluation (where the system loads data into memory when it is needed for visualization, reducing memory requirements) and can handle millions of data points without incurring performance degradation. You can also use libraries like Datashade or Vaex to visualize large data points even on low-end hardware.

3 automation

Complete recurring tasks in no time

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

To Microsoft’s credit, the company offers macros to automate basic tasks in Excel. However, it is prone to human error and inconsistencies, and sometimes it takes multiple attempts to get the entire sequence right. However, Python is a step ahead because it lets you change data sources, tweak chart settings, and change customization options without having to start everything from scratch. You can always revert to the previous script version and track changes if needed.

2 Advanced and interactive visualization

Understand complex data relationships in no time

Python provides interactive charts and dashboards. As for my workflow, I use the Ploty library quite frequently. I mainly use it to visualize the performance of a particular stock over time. I have it set up to track historical stock price data, open, close, high and low prices for each trading day, and once Ploty has created an insightful data visualization, I use it to identify key trends in stock performance.

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

Getting started with Python is easier than ever

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

Since Excel is widely used, there is also detailed documentation and tutorials available, but these usually focus on the basic functions and do not usually go into detail about special visualizations.

Unleash the magic of Python data visualization

While Microsoft Excel is a solid entry-level spreadsheet tool for crunching numbers, its limitations become apparent when you’re working with large amounts of data or performing advanced data analysis. This is where Python comes in, which is more robust and offers a rich ecosystem of libraries and relevant data manipulation tools.

With the latest Excel update for Windows, Microsoft’s spreadsheet software supports Python commands for performing data analysis. For more information on combining Python with Excel on Windows, see our separate guide.