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The Matplotlib FREE Training Course from Python Guides is a free, five-module online course. Its published outline runs from installation and basic plot formatting through statistical and 3D charts, plotting from CSV files and databases, and embedding plots in desktop and web applications. The course page sets out what is taught. It does not show how well each lesson teaches, which Matplotlib version the examples target, or what learners can do afterward. This article covers the outline in detail so you can judge whether it matches what you want to learn.
What the course page lists
The Matplotlib FREE Training Course page on Python Guides groups its lessons into five modules. The table below reproduces the topics the page names for each module.
| Module | Topics named on the course page |
|---|---|
| 1. Overview of Matplotlib | Introduction, installation with pip and conda, getting started, legends, grids, axes, saving plots, backends, colormaps, tick formatting |
| 2. Different plot types | Multiple lines, bar charts (stacked and grouped), histograms, scatter plots, pie and donut charts, error bars, polar and quiver plots, contour plots, date plots, text and annotations, subplots, multiple figures, twin axes, logarithmic scales, shared axes |
| 3. Statistical and 3D charts | Autocorrelation, box and violin plots, heatmaps, image plots, colorbars, introductory and advanced 3D plotting |
| 4. Plotting from data sources | Pandas DataFrames, CSV files, MySQL, MariaDB, SQLite |
| 5. Embedding Matplotlib | Examples for PyQt5, Tkinter, Django, wxPython |
The sequence moves from setup, to building and formatting individual charts, to statistical and 3D work, to getting data in from outside Python, and finally to placing plots inside an application. That order suits a learner who is new to Matplotlib and wants a single path from first plot to integrated use.
Is the course free?
The course page is titled as a free course. The page does not describe a paid tier for this course. Before you start, read the page itself for any sign-up, account, or access terms, because those details are set by the publisher and can change.
#1 Best Overall
The lessons are software-based. The page does not name a required book, computer model, or other physical item. You need a computer with Python installed, and the setup steps below cover the rest.
Installing Matplotlib with pip or conda
The first module covers installation with both pip and conda. The steps below show the standard commands for each tool. Check the course lesson for any version it asks for before you install.
- Confirm Python is available. Run
python --versionin a terminal. On some Linux and macOS systems the command ispython3 --version. - Install with pip. Run
python -m pip install matplotlib. Usingpython -m pipensures pip installs into the same interpreter you will run your scripts with. - Or install with conda. In an activated conda environment, run
conda install -c conda-forge matplotlib. - Verify the installation. Run
python -c "import matplotlib; print(matplotlib.__version__)". A version number printed without an error means the package imports correctly.
Mixing pip and conda in the same environment can cause package conflicts. Pick one tool per environment.
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The course page does not state which Matplotlib version its examples were written for. If a lesson’s output looks different from what you see, compare your printed version number with the one the lesson uses, if it shows one, and check Matplotlib’s official documentation for behaviour changes.
Chart types and plot formatting
Module 2 is the largest part of the outline. It is easier to scan when grouped by purpose.
Comparing categories and values
- Bar charts, including stacked and grouped bars
- Pie and donut charts
- Multiple line plots on one set of axes
Showing distributions and relationships
- Histograms
- Scatter plots
- Error bars, which show uncertainty around values
Specialised and time-based plots
- Polar plots and quiver plots, which show direction and vector fields
- Contour plots
- Date-based plots
Layout, annotation, and axis control
- Subplots, multiple figures, and twin axes that share an x-axis but have separate y-axes
- Logarithmic scales and shared axes
- Text and annotations placed on the plot
Module 1 also covers the parts that apply to every chart: legends, grids, axes, saving plots, backends, colormaps, and tick formatting. Learn these first, because the later modules rely on them.
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Statistical and 3D charts
Module 3 moves into analysis-oriented visuals:
- Autocorrelation plots, used to check whether values in a sequence correlate with their own earlier values.
- Box and violin plots, which compare how values are spread across groups.
- Heatmaps and image plots, which display grids of values as coloured cells or images.
- Colorbars, which label what the colours in a heatmap or image represent.
- 3D plotting, presented as an introductory lesson followed by an advanced one.
If your work is mostly statistical, this module is the section of the outline that most directly matches it. If you only need standard line and bar charts, you can skip ahead.
Plotting from CSV files, Pandas, and databases
Module 4 covers getting data into Matplotlib. The general pattern is the same across sources: load the data into Python, then pass the values to Matplotlib for drawing. Matplotlib does not read files or query databases on its own.
Pandas DataFrames
A Pandas DataFrame is a table-like object in memory. Once a table is loaded, its columns can be plotted directly.
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CSV files
For CSV data, the usual first step is loading the file into a DataFrame:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("sales.csv")
df.plot(x="month", y="revenue", kind="line")
plt.show()The file name and column names above are placeholders. Replace them with your own.
MySQL, MariaDB, and SQLite
The outline names MySQL, MariaDB, and SQLite. Each requires its own setup. MySQL and MariaDB need a running database server and a Python connector. SQLite is a file-based database that works without a separate server. The course page does not name the specific driver each lesson uses, so confirm the setup in the lesson before you begin.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Embedding plots in applications
Module 5 shows how to place Matplotlib figures inside an application interface rather than a standalone script. The four named examples are:
- PyQt5, a Python binding for the Qt graphical toolkit
- Tkinter, Python’s bundled GUI toolkit
- Django, a Python web framework
- wxPython, a Python binding for the wxWidgets GUI toolkit
Each example assumes its own framework is installed. The course page does not list version requirements for these frameworks.
Who the outline suits
Good match
- You are new to Matplotlib and want one structured path from installation to application use.
- You work with CSV files or SQL databases and want to plot data stored there.
- You need statistical charts such as box plots, violin plots, heatmaps, or autocorrelation plots.
- You plan to embed plots in a PyQt5, Tkinter, Django, or wxPython application.
Check before you commit
- You need a specific Matplotlib version for a project. The page does not state one.
- You need guaranteed compatibility with a particular operating system, Python release, or database server. The page makes no such guarantees.
- You want deep coverage of a single topic, such as 3D graphics or web integration. The outline gives each topic one lesson or a short group of lessons.
What the outline does not establish
- Teaching quality and outcomes. The outline names topics. It does not show how well each lesson explains them, and it does not report learner results.
- Version coverage. The page does not say which Matplotlib release the examples were built with.
- Course length. The page does not state how long this course takes. The Python Guides homepage, checked on 7 October 2026, describes a broader free Python and machine-learning video course as “40 modules” and “70+ hours of HD video.” Those figures refer to that broader course, not to the Matplotlib course.
Because the page is an outline, the most reliable way to judge the course is to read the first lesson on installation and the first chart-building lesson, then decide whether the pace and style suit you.
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Sources: Python Guides, Matplotlib FREE Training Course; Python Guides homepage.
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