A guide to visualizing project schedules with Python — With more than 100 years of history, this visualization continues to be very useful for project management. Henry Gantt initially created the graph for analyzing completed projects. More specifically, he designed this visualization to measure productivity and identify underperforming employees. Through the years, it became a tool for planning and…

A simple way of displaying distribution among categorical variables — There aren't many options to visualize the distribution of numerical values over categories. Often, those relationships need to be summarized before they're displayed. Boxplots and Violin plots, which include maximum and minimum values, the medians, and percentiles, can help visualize those distributions. But they can be intimidating to many viewers…

The good-looking cousin of stacked area charts — Even though it might look intimidating, stream graphs can be pretty straightforward to draw. They are almost identical to stacked area charts but without a fixed axis at the bottom. This small change can reduce the steep angles giving the chart more fluidity, usually at the cost of accuracy.

No more workarounds to switch between measures — Until recently, there were few ways to enable users to change measures or the axis of Power BI visualizations. We were either stuck copying charts and creating lots of bookmarks or had to go for a workaround, pre-calculating measures with summarized tables, creating redundancy, or awkward new dimensions in the…

Kind of the same, but also different — Seaborn is based on Matplotlib, and Pandas visualizations are Matplotlib objects, but even though they’re using the same backend, the way we plot our charts with each can be quite unique. This article will explore different bar charts to compare the usability, advantages, and disadvantages of Matplotlib, Pandas, and Seaborn…

How to simplify one of Matplotlib's most tedious tasks — Arranging subplots, texts, and annotations can quickly become a frustrating task in Matplotlib. Often, you may find yourself guessing the coordinates and making multiple minor adjustments to find the right place for your elements. It's ok to have some pattern or logic to guide you through. Things like labelling bars…

A quick guide on how to easily customize texts in Matplotlib — Texts are an unbeatable way of adding value to our data visualizations. We can draw attention to specific aspects of the chart, explain a pattern, or give extra information about the topic. This article will explore how to format Matplotlib texts with highlight-text. For the following examples, I’ll use data…

Secondary axis, interpolations, connected scatter plots, and more — Line charts are absolute rockstars in data visualization, they're familiar to most audiences, and their straightforward format makes insights, trends, and patterns easy to recognize. Even though they're easy to draw, there are grey areas that can be tricky to navigate and sometimes, we might get tempted to avoid those…

A fascinating way of visualizing multiple distributions — This visualization is composed of line charts stacked vertically with slightly overlapping lines that share the same x-axis. Those overlaps reduce the whitespace, many times at the cost of precision, and create a compact chart that’s relatively uncommon and great for catching the viewer's attention. The names Ridge plot or…