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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.
22 lip 2020 · # make subplots plt.subplot (grid [0, 0]) plt.subplot (grid [0, 1:]) plt.subplot (grid [1, :2]) plt.subplot (grid [1, 2]); This can be used in a wide variety of cases for plotting multiple plots in matplotlib.
plt.GridSpec: More Complicated Arrangements¶ To go beyond a regular grid to subplots that span multiple rows and columns, plt.GridSpec() is the best tool. The plt.GridSpec() object does not create a plot by itself; it is simply a convenient interface that is recognized by the plt.subplot() command. For example, a gridspec for a grid of two ...
Simple demo with multiple subplots. For more options, see Creating multiple subplots using plt.subplots . import matplotlib.pyplot as plt import numpy as np # Create some fake data. x1 = np . linspace ( 0.0 , 5.0 ) y1 = np . cos ( 2 * np . pi * x1 ) * np . exp ( - x1 ) x2 = np . linspace ( 0.0 , 2.0 ) y2 = np . cos ( 2 * np . pi * x2 )
7 wrz 2012 · Instead of counting your own number of rows and columns, I found it easier to create the subplots using plt.subplots first, then iterate through the axes object to add plots.
Multiple Subplots. Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has the concept of subplots: groups of smaller axes that can exist together within a single figure. These subplots might be insets, grids of plots, or other more complicated layouts.
