Matplotlib Python SECRET to Visualize Categorical Data Distribution

How to Show the Distribution of Categorical Data in Python?
How to Use Matplotlib in Python to plot a Bar Chart?

In this Python Matplotlib Tutorial, we explore how to effectively visualize categorical and discrete data using bar charts. Whether you're a beginner or experienced data scientist, understanding how to represent categorical data distribution is crucial to bring business insights as part of improving data analysis skill. In this video, we'll cover three essential types of bar charts:
00:00:44 Clustered bar charts,
00:03:47 Stacked bar charts, and
00:07:22 100% stacked bar charts,
using Python Matplotlib library. Learn how to create grouped bar graphs to compare multiple categories, and visualize stacked bar charts to display the composition of subcategories within main categories. We'll also dive into the powerful 100% stacked bar chart for showing percentage distributions, a great way to reveal changing trends across categories.

Using real-world datasets from Kaggle, such as the Titanic dataset, we'll walk you through practical coding examples, providing clear, step-by-step instructions to implement each chart. Whether you're analyzing Titanic survival rates or looking to uncover insights in Kaggle tutorials, this video will help you sharpen your Python data analysis skills. By the end of this tutorial, you'll be able to use Python Matplotlib to create stunning visualizations, making your data storytelling more impactful. Don't miss out on mastering clustered bar charts in Python, stacked bar charts, and more!

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Keywords:
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want to learn more about continuous data visualization in scatter plot and bubble chart? check this video