Do you want to create static, animated, and interactive visualizations in Python? Then, learn about the Matplotlib library. Check out all our practical Matplotlib tutorials with real examples.
When I first started building data visualizations in Python, I often found myself squinting at the screen. The default plots were great, but the axis labels were almost always too small to read once I put them into a presentation. It took me some time to realize that Matplotlib gives you total control over typography. … Read more >>
If you have ever created a plot in Matplotlib, you know it automatically adds those tiny numbers and labels to your axes. Usually, this is exactly what you want, but sometimes these labels just get in the way of a clean visual. I have found that when I’m creating a high-level dashboard or a heat … Read more >>
I still remember the first time I saw that annoying yellow squiggly line under my Matplotlib import. It was late on a Tuesday, and I was trying to wrap up a data visualization for a local housing market report in Chicago. Everything looked correct, but VS Code insisted that it couldn’t find the source for … Read more >>
In my years of developing data visualizations with Python, I have realized that the default settings are rarely perfect. Often, the default tick labels are just too small to read on a high-resolution screen or a printed report. I have spent a lot of time tweaking plots for presentations, and I found that adjusting the … Read more >>
Creating multiple circles in Matplotlib is one of those tasks that sounds simple until you actually try to align them perfectly. I have spent years building dashboards and data visualizations in Python, and I can tell you that there are at least three ways to do this. Each has its own place depending on whether … Read more >>
When I first started visualizing complex datasets, I often relied on box plots to see the distribution of my data. However, I quickly realized that box plots can hide important details, like whether a distribution has multiple peaks. That is when I discovered violin plots in Matplotlib, which combine a box plot with a kernel … Read more >>
When I first started building custom data visualizations in Python, I often found myself needing to highlight specific regions on a chart. Whether it’s marking recession periods in economic data or outlining specific clusters in a scatter plot, knowing how to draw shapes is a fundamental skill. In this tutorial, I will show you exactly … Read more >>
Plotting data with vastly different scales on the same chart can be a real headache. I’ve often found myself staring at a flat line on a linear scale, simply because one dataset overshadowed the other. In this tutorial, I will show you how to implement a secondary Y-axis and apply a logarithmic scale. This is … Read more >>
When I build dashboards for financial analysis, I often need to compare two different metrics that don’t share the same scale. For instance, you might want to visualize the S&P 500 index price alongside the daily trading volume. If you plot them on the same Y-axis, the volume (in millions) will completely flatten the index … Read more >>
In my years of developing data-driven applications in Python, I’ve found that Matplotlib’s default scaling is usually great, but it isn’t perfect. There are many times when the default view starts the axis at a value that hides the trend you are trying to highlight. I often find myself needing to force a plot to … Read more >>
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