Data visualization is a fundamental aspect of effective data analysis. And although Excel is a versatile tool, certain visualization types remain difficult or even impossible to create with its built-in features alone. That’s where Python libraries such as Matplotlib, Seaborn, and Plotnine come in. These libraries provide advanced visualization capabilities that can be seamlessly integrated into Excel through Python in Excel.
But how do you decide which library to use? And what are some practical, time-saving techniques for creating visualizations that Excel’s native tools struggle with? This blog post will guide you through the essentials.
Download the exercise file below to follow along:
Since we’re covering a variety of visualizations, we’ll be using small, one-off datasets to keep things simple. If you need help applying these concepts to your own work—likely with larger datasets—feel free to ask any questions in the comments. AI can also likely assist in adapting these techniques to fit your needs.
For this post I assume you have some familiarity with inserting plots with Python in Excel, if that’s not the case, you can check out this post:
Matplotlib
Matplotlib is often called the “grandfather” of Python visualization libraries, and it serves as the foundation for many others. At its core, Matplotlib gives you low-level control over every aspect of a chart. You define what your chart looks like from scratch—everything from axes labels and tick marks to colors, grids, and legends is in your hands.
This level of flexibility comes at the cost of verbosity. Tasks that might take one or two lines in another library may take several in Matplotlib. However, if you’re looking for complete customization, it’s unmatched.
How It Works
Matplotlib operates through a figure-based approach: you first create a figure and an axis (or multiple axes) and then define what to draw on those axes. Think of it as creating a blank canvas and painting on it step by step.
Example 1: Creating a Gantt Chart for Project Timelines
A Gantt chart is a classic business visualization for tracking project progress. In Excel, creating one often involves tedious manual work, but with Matplotlib, it’s straightforward:
Here, you define a horizontal bar chart, setting each bar’s starting position and length to represent tasks’ timelines. The result is clean and precise—ready for inclusion in a project report or presentation.
Example 2: Custom Branding for Sales Trends
Suppose you’re creating an executive report and need visuals that align with your company’s branding. Matplotlib provides powerful customization options, allowing you to tailor colors, fonts, and annotations to match your corporate style. Here’s an example:
This flexibility is especially valuable for creating publication-ready visuals directly in Python.
Seaborn
If Matplotlib is the raw material, Seaborn is the refined product. Built on top of Matplotlib, Seaborn simplifies the process of creating aesthetically pleasing and statistically insightful visualizations. It’s designed to work seamlessly with Pandas DataFrames, which are the default data object in Python in Excel. This makes it a natural choice for business users analyzing data directly from Excel workbooks.
Seaborn automates many of the intricate details of plotting—like adding confidence intervals to line plots or managing subplots—so you can focus on interpreting the results. It excels at identifying relationships and patterns in data, making it especially useful for exploring trends, distributions, and correlations.
How It Works
Seaborn uses a dataset-oriented API, allowing you to pass an entire Pandas DataFrame as input. You simply define which columns represent your data of interest, and Seaborn handles the rest, from aesthetic defaults to the layout of the plot.
Example 1: Correlation Heatmap for Marketing Effectiveness
Heatmaps are one of Seaborn’s standout features. Let’s say you’re trying to understand how marketing spend correlates with ROI and customer engagement. A heatmap makes these relationships clear at a glance:
This visualization highlights which variables are strongly correlated, providing actionable insights into which factors drive results.
Example 2: Small Multiples for Sales Trends by Region
If you’re monitoring sales trends across regions, Seaborn’s FacetGrid is an excellent tool for creating small multiples—individual plots for each region displayed in a neatly organized grid.
This approach simplifies the process of comparing sales trends across regions by automating the creation of separate plots for each group. It streamlines your workflow, eliminating the need to generate individual charts manually, and provides an intuitive way to visualize data patterns efficiently.
Seaborn’s tight integration with Pandas DataFrames makes it a natural fit for business users working with Python in Excel. Whether you’re analyzing correlations, exploring multi-dimensional relationships, or creating small multiples, Seaborn helps you extract insights with minimal effort and maximum impact.
Plotnine
Plotnine takes a completely different approach to visualization. It’s inspired by the “grammar of graphics” philosophy, popularized by R’s ggplot2 library. Instead of thinking in terms of “chart types,” Plotnine lets you build visualizations layer by layer, combining data elements like axes, geoms (shapes), and facets to create charts.
This structure is intuitive for users who think in terms of breaking down data relationships and is ideal for layered visualizations or multi-panel plots.
How It Works
In Plotnine, you define a plot by specifying the dataset, the aesthetic mapping (e.g., what the axes represent), and the geoms (the actual chart elements). Each of these components is added incrementally, giving you fine-grained control over the final output.
Example 1: Faceted Chart for Regional Sales Trends
If you want to compare sales trends across regions, Plotnine makes it simple to create side-by-side charts:
Faceted plots like this are perfect for breaking down KPIs by categories like region, product line, or customer segment.
Example 2: KDE plot for distribution
A KDE plot (Kernel Density Estimate) visualizes the probability density of a continuous variable, smoothing the data to show its distribution without the discrete bins of a histogram. It’s useful for understanding the shape and spread of data or identifying multiple modes.
Here, plotnine builds the KDE plot using a layered approach. The ggplot function specifies the dataset and aesthetics, mapping “Sales” to the x-axis. The geom_density layer computes and plots the density curve, filling it with a semi-transparent blue color. Finally, labs adds a title and axis labels. This modular process combines data, aesthetics, and visual elements into a cohesive plot.
Conclusion
Matplotlib, Seaborn, and Plotnine each bring distinct advantages to data visualization, especially for Python users working in Excel. Matplotlib stands out for its extensive customization options, making it ideal for specialized tasks like Gantt charts or branded visuals. However, this flexibility often comes with a steeper learning curve. The table below summarizes the key pros, cons, and differences among these three libraries.
Seaborn, with its seamless integration with Pandas DataFrames, simplifies the creation of insightful statistical visualizations, making it a natural choice for uncovering patterns and relationships with minimal effort. Plotnine’s structured, grammar-of-graphics approach excels at layered and faceted visualizations like small multiples or comparisons but may take some adjustment for users accustomed to Excel’s interface.
These libraries complement Excel’s capabilities, allowing you to overcome its limitations and tell more impactful data stories. The best choice depends on your specific needs—whether it’s control, simplicity, or advanced layering.
Exploring these tools can feel overwhelming at first, but they open up a world of possibilities for enhancing your data visualizations. If you’re unsure which library to start with or how to incorporate them into your workflows, feel free to ask in the comments. I’d also love to hear about your experiences using Matplotlib, Seaborn, or Plotnine—what challenges have you faced, and how have these tools helped? Let me know below.
Leave a Reply