Matplotlib Cheat Sheet

Matplotlib Cheatsheet

One of the most comprehensive and widely used Python plotting libraries is Matplotlib.

When we start plotting with this Python data visualization package, it will seem like there are quite a few aspects to consider. Seeing the amount of code required for some plots and not knowing where to begin or which components to use can be overwhelming. Fortunately, Matplotlib is very adaptable and contains many useful, built-in defaults that will be useful.

Here is a comprehensive Matplotlib Cheatsheet that includes most of the knowledge required for beginners and advanced learners.

Plot Anatomy and Workflow

Understanding how the core components are built will make working with Matplotlib much easier. Or, to put it another way, this is what a Matplotlib plot's anatomy looks like:

• The main window or page that everything is drawn on is called the figure.
• The region on which we plot data, along with any related ticks, labels, etc., is known as the axis. Most plotting takes place on the axes.
• Axes and Subplot are used alternately because we usually put up axes with a call to the subplot. This explains why Figures can have many Axes.
• Each axis can be customized with ticks and tick labels.
• The data space boundary is indicated by the spines, which are lines that connect the axis tick points.

Workflow

To create a plot using Matplotlib, follow these simple steps:

1. Prepare the data
2. Create the plot
3. Plotting routines
4. Customize the plot
5. Save the plot
6. Show the plot

Here is a sample code that illustrates each step:

Output:

Let’s discuss each of these steps in detail.

Preparation of Data

When we visualize data, we frequently work with data stored in arrays. As a result, we must perform operations on them, inspect the arrays, and modify them. So, when using Matplotlib, NumPy will come in handy.

1D data

2D data

Images

.npy is the file extension for numpy arrays and we can read them using numpy.load.

Some important Image functions are;

3D data

Creating the Plot

Okay, now let's make a matplotlib plot. The first step is importing the Matplot library.

Figure

The pyplot module's plt.figure() function also takes the figsize argument, which requires us to provide a tuple with the width and height of the figure in inches, for example, plt.figure(figsize=(3,4)). The same module's plt.subplots() function also accepts a figsize parameter.

Axes

Plotting is always done concerning axes. fig.add_axes()

In matplotlib, subplots can be created in three different ways:

• Using subplot() :

Here, we give three arguments to add_subplot(). The three arguments designate the number of rows (2), the number of columns (2), and the plot number (1). So it makes up one subplot.

• Using subplots(): It returns an array

• Using subplot2grid():

This function returns an axes object in a grid for a specific location and size.

Examples :

1. Subplots side-by-side on the same figure

2. Plots one on top of the other

3. The same data points plotted in 4 distinct ways, in 4 different subplots.

Plotting Routines

It's time to take a closer look at some plotting routines to start plotting the data. Check out the following functions as we begin working with various data.

1D Data

2D Data or Images

Vector Fields

Data Distributions

Customizing a Plot in Matplotlib

The fact that Matplotlib allows us to customize the plots in various ways is one reason why it stands out from other plotting libraries. In addition to changing the colors, it allows us to modify the markers, lifestyles and linewidths, text, legends, annotations, and the limits and layout of plots.

Colors, Color Bars & Color Maps

Markers

Linestyles

Text and Annotations

Mathtext

Limits, Legends & Layouts

Limits & Autoscaling

Legends

To change plot title and axes labels,

ax.set(title="A title", xlabel="x", ylabel="y") or ax.set_xlim(), ax.set_ylim() or ax.set_title().

We can also use fig.suptitle() to add a title to the plot. If we are going with the default setting, then we have to use plt.title(), plt.xlabel(), and plt.ylabel().

Ticks

Subplot Spacing

Axis Spines

Here is an example of a customized plot.

Interactive plots

Adding just one line of code, %matplotlib notebook, can make every matplotlib plot interactive in a Jupyter notebook. Only Jupyter Notebooks support this.

Adding watermarks:

We can use the text() function.

The figimage() function of matplotlib allows an image to be used as a watermark.

Show Plot

How do we save this figure? what happens if we don't like it? Well, then we can start over by clearing the image.

Save Plot

Close and Clear