Once you’ve set the rcParams in Matplotlib, you may want to reset these styles in order to ensure that the next time you run your script that default values are applied. Resetting Matplotlib Title Styles to Default Values If you’re curious about the different rcParams that are available, you can print them using the () method. Plt.ylabel('y-Axis Title', style='italic', loc='bottom') Plt.xlabel('x-Axis Label', fontweight='bold') Let’s see how we can add and style axis labels in Matplotlib: # Adding Axis Labels to a Matplotlib Plot ylabel() adds an y-axis label to your plot xlabel() adds an x-axis label to your plot We can add axis titles using the following methods:
This is part of the incredible flexibility that Matplotlib offers. Matplotlib handles the styling of axis labels in the same way that you learned above. Axis labels provide descriptive titles to your data to help your readers understand what your dad is communicating.
Matplotlib subplot title how to#
In this section, you’ll learn how to add axis labels to your Matplotlib plot. In the next section, you’ll learn how to add and style axis labels in a Matplotlib plot. While this is an official way to add a subtitle to a Matplotlib plot, it does provide the option to visually represent a subtitle. Y = Īdding a subtitle to your Matplotlib plot Let’s see how we can use these parameters to style our plot: # Adding style to our plot's title The ones above represent the key parameters that we can use to control the styling. There are many, many more attributes that you can learn about in the official documentation.
family= controls the font family of the font.fontweight= controls the the weight of the font.loc= controls the positioning of the text.fontsize= controls the size of the font and accepts an integer or a string.title() method in order to style our text: Let’s take a look at the parameters we can pass into the. Matplotlib provides you with incredible flexibility to style your plot’s title in terms of size, style, and positioning (and many more). Changing Font Sizes and Positioning in Matplotlib Titles This is what you’ll learn in the next section. We can easily control the font styling, sizing, and positioning using Matplotlib. You can find more Matplotlib tutorials here.We can see that the title is applied with Matplotlib’s default values. #add overall title and adjust it so that it doesn't overlap with subplot titles If you have an overall title, you can use the subplots_adjust() function to ensure that it doesn’t overlap with the subplot titles: import matplotlib.pyplot as plt The way to resolve this issue is by increasing the height padding between subplots using the h_pad argument: import matplotlib.pyplot as plt Unfortunately even the tight_layout() function tends to cause the subplot titles to overlap: import matplotlib.pyplot as plt In some cases you may also have titles for each of your subplots. The easiest way to resolve this overlapping issue is by using the Matplotlib tight_layout() function: import matplotlib.pyplot as plt Adjust Spacing of Subplots Using tight_layout() Notice how the subplots overlap each other a bit.
Create SubplotsĬonsider the following arrangement of 4 subplots in 2 columns and 2 rows: import matplotlib.pyplot as plt This tutorial explains how to use this function in practice. The easiest way to resolve this issue is by using the Matplotlib tight_layout() function. Unfortunately, these subplots tend to overlap each other by default. Often you may use subplots to display multiple plots alongside each other in Matplotlib.
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