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 >>
React Context has been a lifesaver in my development career, especially when dealing with deep component nesting. In this tutorial, I will show you exactly how to implement React Context in functional components using the modern useContext hook. What is React Context? In my eight years of building React applications, the biggest headache was always … Read more >>
As a Python developer who has spent years wrangling data, I’ve found that nothing reveals the relationship between two variables faster than a scatter plot. Whether I am analyzing housing prices in California or tracking tech stock trends on the NASDAQ, a scatter plot is my go-to tool for spotting outliers. In this tutorial, I … Read more >>
While working on a data-cleaning project for a retail company in the USA, I faced an interesting challenge. I had a Python dictionary containing customer IDs as keys and their email addresses as values.Some customers accidentally used the same email address for multiple accounts, and I needed to find all duplicate values in that dictionary. … Read more >>
Working on a data visualization project where I needed to show both positive and negative values in a single stacked bar chart using Python. At first, I thought it would be easy, but I quickly realized that handling negative values in a stacked chart requires a little extra care. In this tutorial, I’ll show you … Read more >>
I was working on a Python project where I needed to visualize a dataset that had three continuous variables, something like sales, profit, and region density across different states in the USA. While 2D scatter plots are great, I wanted to explore the data in a more interactive and visually appealing way. That’s when I … Read more >>
To create a stacked bar chart in Matplotlib, draw the first series with plt.bar(), then draw each next series with the bottom argument set to the height of everything below it. For a horizontal stacked bar chart, use plt.barh() with left instead. This guide builds stacked bar charts in Python step by step: two series, … Read more >>
As a Python developer, I have found the fill_between function to be incredibly useful for highlighting areas between curves in data visualization. Whether you’re plotting financial trends, weather data, or any other time series, knowing how to control the edges of the filled area can make your charts clearer and more professional. In this article, … Read more >>
I often get asked how to create visually appealing plots that go beyond simple lines and scatter plots. One common challenge is shading complex shapes like circles. While Matplotlib’s fill_between function is typically used for shading areas between curves, you can cleverly adapt it to shade inside a circle. In this article, I will walk … Read more >>
plt.tight_layout() automatically adjusts the spacing around and between subplots so that titles, axis labels and tick labels do not overlap or get cut off. Call it once after you have added all titles and labels and before plt.show() or savefig(). This Matplotlib tight_layout guide shows what it changes, its pad, w_pad, h_pad and rect parameters, … Read more >>