Matplotlib is a data visualization library in Python that is primarily the very first data visualization library that any newbie to data science learns. One of the advantages of matplotlib is that it is used in the industry by data science professionals. But because there are many alternatives to matplotlib, sometimes graphs generated by matplotlib do not look as good as graphs generated by other data visualization libraries. So if you want to learn how to visualize better graphs with matplotlib, this article is for you. In this article, I will present you with a tutorial on how to visualize better and beautiful graphs with matplotlib in Python.
Visualize Better Graphs with Matplotlib in Python
To visualize better and beautiful visualizations, you can customize the style and looks of your graphs. Matplotlib provides style sheets to change the visual appearance of your graphs. To use this feature, you just need to use the plt.style.use function just after you import the matplotlib library. This function asks a parameter where you add the name of the style you want to use.
Below are all the matplotlib styles that you can use to make your graphs look better and beautiful:
'Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10'
You can use any of this style as shown below:
import matplotlib.pyplot as plt plt.style.use("style-name")
Let’s have a look at an example of how we can use a matplotlib style sheet using Python:
import matplotlib.pyplot as plt import numpy as np plt.style.use('fivethirtyeight') plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o') plt.show()
So, as you can see, the graph looks better as compared to the classic matplotlib graphs. This is how you can visualize better and beautiful graphs with matplotlib in Python.
So this is how you can visualize better visualizations with matplotlib in Python As there are many alternatives to matplotlib, sometimes visualizations generated by matplotlib do not look as good as generated by other data visualization libraries, so you can use the plt.style.use function to make your visualizations look better and beautiful. I hope you liked this article on how to visualize better graphs with matplotlib in Python. Feel free to ask your valuable questions in the comments section below.