Not only is this inefficient, it is not scalable to high volume plotting or to plots that you need to tweak and/or redo multiple times.įor all those friends and for all of you out there still struggling with such problems, here is your one-stop guide on how to organize your plots and subplots using only matplotlib on Python.Ī picture is worth a thousand words, so for those that want a quick preview, those that are re-reading this, or those whith TLDR syndrome, here is a sample code for what we will accomplish in this tutorial: import matplotlib.pyplot as plt from idspec import GridSpec import numpy as np time = np.linspace(0, 10, 1000) height = np.sin(time) weight = time*0.3 + 2 score = time**2 + height distribution = np.random.normal(0, 1, len(time)) fig = plt.figure(figsize=(10, 5)) gs = GridSpec(nrows=2, ncols=2) ax0 = fig.add_subplot(gs) ax0.plot(time, height) ax1 = fig.add_subplot(gs) ax1.plot(time, weight) ax2 = fig.add_subplot(gs) ax2.plot(time, score) ax3 = fig.add_axes() ax3.hist(distribution) plt.show() To do those edits, I have seen (too) many people save individual plots done with Python and later re-arrange it all together on some other image editing software.
Sometimes you might want to emphasize the importance of a plot by making it bigger, or complement a plot by adding another one as an inset on top of it. While there might be thousands of tutorials on how to change line thickness or the size of your titles, they all seem to forget that organization of subplots plays a huge role in conveying the story your data is telling. If you are reading this, it is probably because you agree with me that most matplotlib tutorials out there are missing a crucial aspect of plotting and data visualization. The Jupyter Notebook for this tutorial can be found on my Github page. Plot Organization in matplotlib - Your One-stop Guide
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