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…

SQL is crucial for data analysts because it provides a powerful and standardized way to access, manipulate, and analyze data stored in relational databases. With SQL, data analysts can write queries to extract relevant information, perform complex joins and aggregations, and gain valuable insights from large datasets. When working with…

Exploring the Synergy Between Power BI and Jupyter Notebooks — In today’s data-driven world, where information flows incessantly from every corner, extracting meaningful insights and making informed decisions has become paramount. Recognizing the significance of seamless data visualization and analysis, Power BI has taken a leap forward with its latest update. This update introduces an exciting integration between Power BI…

Simplicity, User-Focus, and Practice as the Path to Success — Data visualization is a powerful tool for understanding and communicating complex information, but creating compelling visualizations can be challenging. It’s easy to fall into the trap of using too many colors, shapes, and labels, making the chart hard to read and interpret. This article will explore how to create clear…

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. …

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…