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This is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.
pyplot.subplots creates a figure and a grid of subplots with a single call, while providing reasonable control over how the individual plots are created. For more advanced use cases you can use GridSpec for a more general subplot layout or Figure.add_subplot for adding subplots at arbitrary locations within the figure.
There are three main options in matplotlib to make separate plots within a figure: subplot: access the axes array and add subplots. gridspec: control the geometric properties of the underlying figure (demo) subplots: wraps the first two in a convenient api (demo)
subplots() is the recommended method to generate simple subplot arrangements: fig , ( ax1 , ax2 ) = plt . subplots ( 2 , 1 ) fig . suptitle ( 'A tale of 2 subplots' ) ax1 . plot ( x1 , y1 , 'o-' ) ax1 . set_ylabel ( 'Damped oscillation' ) ax2 . plot ( x2 , y2 , '.-' ) ax2 . set_xlabel ( 'time (s)' ) ax2 . set_ylabel ( 'Undamped' ) plt . show ()
7 lut 2020 · Too much stuff happening in a single plot? No problem—use multiple subplots! This in-depth tutorial shows you everything you need to know to get started with Matplotlib’s subplot () function. If you want, just hit “play” and watch the explainer video. I’ll then guide you through the tutorial:
20 kwi 2020 · Examples on how to plot multiple plots on the same figure using Matplotlib and the interactive interface, pyplot. Includes common use cases and best practices.
For this purpose, plt.subplots() is the easier tool to use (note the s at the end of subplots). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.
