Statistical Data Visualisation: Matplotlib Subplots, Histograms, Bar graphs, Box & Violin plots

welcome to lecture 20 of our complete python, data science, and machine learning series! 🚀 now that we know how to draw and style single lines and scatter points, it is time to master statistical data distributions. in this video, we break down how to map complex data profiles using histograms, bar graphs, box plots, and violin plots. you will learn how to arrange all of these charts into a single, clean dashboard layout using matplotlib subplots so you can evaluate features like a pro data scientist. 📌 bookmark the full playlist: [insert link to your playlist here] ⏱️ timestamps: 00:00 - intro: understanding statistical data distributions 01:30 - subplots deep dive (setting up the multi-chart figure grid) 04:15 - histograms (.hist) - tracking frequency and checking for normal distribution 08:40 - bar graphs (.bar) - comparing discrete categorical counts and averages 12:10 - box plots (.boxplot) - finding outliers, quartiles, and the median 16:45 - violin plots (.violinplot) - combining box plots with kernel density estimates 21:15 - consolidating titles, shared axes, and saving the multi-plot figure what you will learn in this lecture: 1. how to build complex multi-chart subplots grids without overlapping text. 2. when to use a histogram vs. a bar chart depending on your data type. 3. how to read a box plot to instantly identify extreme outliers in your datasets. 4. why violin plots offer a deeper understanding of probability density than basic boxes. if this breakdown helped you master complex statistical charts and subplots, slam that like button, leave a comment with the plot style you prefer, and subscribe for the next video where we step into advanced pandas operations! #matplotlib #datavisualization #subplots #histogram #boxplot #violinplot #bargraph #datascience #machinelearning #learnpython #dataanalytics #mlcourse2026