Getting started with matplotlib

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An introduction to using matplotlib in Python to create charts Part one of a six-part series of blogs
If you want to create charts in Python, the chances are that you'll do it using the matplotlib module. This blog will get you started and explain some of its foibles! Getting started with matplotlib (this blog) Subplots, figures and axes (explicit references) An introduction to formatting matplotlib charts Working with different chart types Saving charts Using style sheets to format charts consistently

An introduction to using matplotlib in Python to create charts

Part one of a six-part series of blogs

If you want to create charts in Python, the chances are that you'll do it using the matplotlib module. This blog will get you started and explain some of its foibles!

Getting started with matplotlib (this blog)
Subplots, figures and axes (explicit references)
An introduction to formatting matplotlib charts
Working with different chart types
Saving charts
Using style sheets to format charts consistently

Posted by Andy Brown on 02 June 2025

In this blog

Before you begin - what other choices are there?
Installing and importing matplotlib
Creating a basic chart

This series of blogs explains (with inimitable Wise Owl clarity) how to get started with matplotlib, the go-to module in Python for creating charts.

A simple matplotlib chart, with minimal formatting.

Sadly the word matplotlib is written entirely in lower case. The module was created to behave like the MatLab statistical programming environment (you can see some history explaining why, when and how matplotlib was written here).

Before you begin - what other choices are there?

Matplotlib isn't the only charting module that you can use in Python. Here are some alternatives (presented here in approximate descending order of popularity and use):

Alternative	Notes
Seaborn	Seaborn is based upon matplotlib, but offers a higher-level programming environment (ie it has simpler syntax)
Plotly	Plotly allows you to zoom in on charts and hover over them (it is more interactive than matplotlib)
Bokeh	Bokeh is a good choice if your main focus is on creating interactive charts for web apps
Altair	Altair has a more concise syntax than matplotlib, but is not as powerful
plotnine	Plotnine (or ggplot, a similar variant) are most suitable for users of R

However matplotlib is the de facto charting standard for Python, and if there's one thing I've learned in computing it's that it's nearly always a good idea to follow the herd!

Installing and importing matplotlib

Before you can use matplotlib, you first need to install it! Here's how to do this from the Visual Studio Code terminal window:

python -m pip install matplotlib

If you haven't already installed NumPy this will add it, since matplotlib is designed to chart data from NumPy arrays (if you don't know NumPy or Pandas you'll probably struggle learning matplotlib, since so many of the online examples assume knowledge of one or other of these two modules).

Your programs will then need to reference matplotlib (and probably NumPy too):

import numpy as np

import matplotlib.pyplot as plt

If (like me) you are wondering what's in the rest of the matplotlib library, the answer is that the pyplot module provides a "convenient wrapper that simplifies access" to the rest of the functionality in the matplotlib library (I admit I'm quoting ChatGPT for this).

Creating a basic chart

Here's a Python program to create the basic chart shown at the top of this page:

import numpy as np

import matplotlib.pyplot as plt

# get the number of days of Xmas ( 1 to 12)

days_till_xmas = np.arange(1,13)

# panic levels increase exponentially

panic_level = days_till_xmas ** 2

# chart panic level against days till Xmas

plt.plot(days_till_xmas,panic_level)

# add a title

plt.title('Panic levels over the 12 days of Christmas')

# label the axes

plt.ylabel("Panic level")

plt.xlabel("Day number")

# show the chart on screen

plt.show()

The program creates a line chart (that's what the plot method does) of my panic level against the 12 days of Christmas. Here's what the two arrays being charted look like:

The arrays giving the X axis labels and the data we're charting against each of these labels.

However this way of creating charts uses implicit references (at no point can we refer to the properties and methods of the line chart directly). It's time now to turn to using explicit references, and learn about figures, subplots and axes.

Parts of this blog
Getting started with matplotlib (this blog) Subplots, figures and axes (explicit references) An introduction to formatting matplotlib charts Working with different chart types Saving charts Using style sheets to format charts consistently

Some other pages relevant to the above blogs include:

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Before you begin - what other choices are there?

Installing and importing matplotlib

Creating a basic chart

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