Matplotlib is the foundational engine for data visualization in the Python ecosystem. While libraries like Seaborn and Plotly build upon it, true mastery of data science visualization requires a deep understanding of Matplotlib's core architecture. This structured learning path is specifically designed for beginners, offering a series of hands-on, interactive labs that move you beyond theory and into practical application. You won't just watch videos; you'll execute real code in a dedicated plotting playground, mastering the techniques needed to transform raw data into compelling visual narratives.
Matplotlib Subplots Creation
Difficulty: Beginner | Time: 25 minutes
In this lab, you will learn how to create and customize multiple subplots in a single figure using Matplotlib, a powerful plotting library in Python. You will practice creating subplots, plotting data on them, and adjusting layouts.
Practice on LabEx → | Tutorial →
Matplotlib Installation and Import
Difficulty: Beginner | Time: 25 minutes
In this lab, you will learn the fundamental steps to get started with Matplotlib, including installation, importing the library, and creating your first empty plot.
Practice on LabEx → | Tutorial →
Matplotlib Pie Charts
Difficulty: Beginner | Time: 25 minutes
In this lab, you will learn how to create and customize pie charts using Matplotlib, a popular data visualization library in Python.
Practice on LabEx → | Tutorial →
Matplotlib Scatter Plots
Difficulty: Beginner | Time: 25 minutes
In this lab, you will learn how to create and customize scatter plots using Matplotlib, a powerful plotting library in Python. You will practice generating data, plotting points, and modifying marker size and color.
Practice on LabEx → | Tutorial →
Matplotlib Histograms
Difficulty: Beginner | Time: 25 minutes
In this lab, you will learn how to create and customize histograms using Matplotlib, a powerful plotting library in Python.
Practice on LabEx → | Tutorial →
By completing these five hands-on Matplotlib labs, you transition from a novice user to a confident visualization practitioner. You will have mastered the entire workflow: setting up the environment, managing complex multi-plot layouts, and generating the three most critical plot types for EDA—scatter plots for relationships, histograms for distributions, and pie charts for composition. This practical experience, gained through interactive coding, ensures that you are not just familiar with the syntax, but truly capable of using Matplotlib to tell compelling, data-driven stories.
Top comments (0)