How do I create and customize a line plot with Matplotlib in Python?

Use plt.plot() to draw data, then customize labels, titles, colors, line styles, legends, and grid lines before calling plt.show().

What is the difference between a histogram and a bar chart in Matplotlib?

Histograms group continuous numerical values into bins to show a distribution, while bar charts compare values across separate categories.

How can I format date labels and create subplots in Matplotlib?

Use fig, ax = plt.subplots() to create multiple axes, and apply fig.autofmt_xdate() to rotate and format date labels for readability.

Free online course Matplotlib Tutorials

Duration of the online course: 3 hours and 33 minutes

New

Build job-ready Python data visuals fast with a free Matplotlib course—learn clear charts, time series, and subplots to share insights with confidence.

In this free course, learn about

Matplotlib’s primary use: creating 2D plots and visualizations in Python
Create and customize line plots (figure/axes, titles, labels, legends, styles)
Use plt.plot() to generate line plots and control colors, markers, and line styles
Build bar charts from data (including CSV); use plt.bar() and plt.barh() for horizontal
Choose pie charts for showing part-to-whole proportions and categorical composition
Create stack plots to visualize cumulative contributions of multiple series over x
Fill areas on line plots (plt.fill_between) to highlight ranges, thresholds, or deltas
Use histograms to show numerical distributions via bins (key difference vs bar charts)
Understand histogram advantages: reveal distribution shape, spread, skew, and outliers
Create scatter plots (plt.scatter) to examine relationships/correlation between variables
Plot time series data and format date ticks; use fig.autofmt_xdate() for readability
Make live, real-time plots to monitor streaming/updating data as it changes
Use subplots()/plt.subplots() to build multi-plot layouts with shared axes and control

About the free online course

Turn raw numbers into visuals people understand. This free online course helps you build practical Matplotlib skills in Python so you can present data clearly, support decisions, and communicate insights with confidence. Whether you are learning programming, moving into data analysis, or improving your reporting workflow, Matplotlib is a core tool that keeps showing up in real projects because it is flexible, widely used, and works well with common data formats.

You will start by creating and customizing your first plots, learning how to control titles, labels, legends, colors, and styling so your charts look intentional instead of default. From there, you will practice selecting the right visual for the question you are trying to answer: comparisons with bar charts, proportions with pie charts when appropriate, trends with line charts, distributions with histograms, and relationships with scatter plots. You will also work with data coming from CSV files, reinforcing the everyday task of moving from a dataset to a meaningful graphic.

As you progress, you will strengthen the kind of understanding that makes your charts more than just pictures. You will learn to interpret distributions and communicate them well, choose binning strategies for histograms, and highlight changes over time using area fills and stack plots. The course also covers plotting time series data with readable date labels, a key skill for analytics dashboards and reports.

To bring everything together, you will explore real-time plotting concepts and how live charts can help you monitor changing values during experiments or ongoing processes. Finally, you will learn to compose multiple charts in a single figure using subplots, making it easier to tell a complete story and compare views side by side. With short lessons and practice questions throughout, you will build a solid foundation to create polished Python visualizations for coursework, portfolios, and professional use.

Course content

Video class: Matplotlib Tutorial (Part 1): Creating and Customizing Our First Plots 35m
Exercise: What is the primary use of the Matplotlib library in Python?
Video class: Matplotlib Tutorial (Part 2): Bar Charts and Analyzing Data from CSVs 34m
Exercise: Which of the following is a method used in Matplotlib to create a line plot?
Exercise: When creating bar charts in Matplotlib, what method should you use to generate horizontal bar charts?
Video class: Matplotlib Tutorial (Part 3): Pie Charts 17m
Exercise: What are pie charts best used for?

Video class: Matplotlib Tutorial (Part 4): Stack Plots 14m
Video class: Matplotlib Tutorial (Part 5): Filling Area on Line Plots 15m
Video class: Matplotlib Tutorial (Part 6): Histograms 16m
Exercise: What is the primary advantage of using histograms over bar charts when visualizing numerical data distributions?
Exercise: What feature differentiates histograms from traditional bar charts when visualizing data?

Video class: Matplotlib Tutorial (Part 7): Scatter Plots 21m
Exercise: What is a scatter plot primarily used for in data visualization?
Video class: Matplotlib Tutorial (Part 8): Plotting Time Series Data 17m
Exercise: What method is used in Matplotlib to automatically format the date labels on the x-axis for better readability?
Video class: Matplotlib Tutorial (Part 9): Plotting Live Data in Real-Time 20m
Exercise: What is the primary purpose of creating real-time plots in Python using Matplotlib?
Video class: Matplotlib Tutorial (Part 10): Subplots 21m
Exercise: What is the primary advantage of using the 'subplots()' method in matplotlib?
Exercise: What is the benefit of using the 'subplots' method in Matplotlib?

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Explore free online Python courses and build in-demand programming skills at your own pace. Learn Python basics, data analysis, automation, web development, and more through flexible beginner-friendly lessons. Enroll today, study from anywhere, and earn a free certificate to showcase your Python skills and boost your career.