Note Click here to download the full example code Note We discourage working with multiple figures through the implicit pyplot interface because managing the current figure is cumbersome and error-prone. Instead, we recommend using the explicit approach and call methods on Figure and Axes instances. See Matplotlib Application
Interfaces [APIs] for an explanation of the trade-offs between the implicit and explicit interfaces. Create figure 1matplotlib.pyplot uses the concept of a current figure and current axes. Figures are identified via a figure number that is passed to figure. The figure with the given number is set as current
figure. Additionally, if no figure with the number exists, a new one is created.[]
Create figure 2
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Now switch back to figure 1 and make some changes
Total running time of the script: [ 0 minutes 1.074 seconds]
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Note Click here to download the full example codepyplot.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.import matplotlib.pyplot as plt
import numpy as np
# Some example data to display
x = np.linspace[0, 2 * np.pi, 400]
y = np.sin[x ** 2]
A figure with just one subplot#
subplots[] without arguments returns a Figure and a single
Axes.
This is actually the simplest and recommended way of creating a single Figure and Axes.
Text[0.5, 1.0, 'A single plot']
Stacking subplots in one direction#
The first two optional arguments of
pyplot.subplots define the number of rows and columns of the subplot grid.
When stacking in one direction only, the returned axs is a 1D numpy array containing the list of created Axes.
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If you are creating just a few Axes, it's handy to unpack them immediately to dedicated variables for each Axes. That way, we can use ax1 instead of the more verbose axs[0].
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To obtain side-by-side subplots, pass parameters 1, 2 for one row and two columns.
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Stacking subplots in two directions#
When stacking in two directions, the returned axs is a 2D NumPy array.
If you have to set parameters for each subplot it's handy to iterate over all subplots in a 2D grid using for ax in axs.flat:.
fig, axs = plt.subplots[2, 2] axs[0, 0].plot[x, y] axs[0, 0].set_title['Axis [0, 0]'] axs[0, 1].plot[x, y, 'tab:orange'] axs[0, 1].set_title['Axis [0, 1]'] axs[1, 0].plot[x, -y, 'tab:green'] axs[1, 0].set_title['Axis [1, 0]'] axs[1, 1].plot[x, -y, 'tab:red'] axs[1, 1].set_title['Axis [1, 1]'] for ax in axs.flat: ax.set[xlabel='x-label', ylabel='y-label'] # Hide x labels and tick labels for top plots and y ticks for right plots. for ax in axs.flat: ax.label_outer[]
You can use tuple-unpacking also in 2D to assign all subplots to dedicated variables:
fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots[2, 2] fig.suptitle['Sharing x per column, y per row'] ax1.plot[x, y] ax2.plot[x, y**2, 'tab:orange'] ax3.plot[x, -y, 'tab:green'] ax4.plot[x, -y**2, 'tab:red'] for ax in fig.get_axes[]: ax.label_outer[]
Sharing axes#
By default, each Axes is scaled individually. Thus, if the ranges are different the tick values of the subplots do not align.
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You can use sharex or sharey to align the horizontal or vertical axis.
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Setting sharex or sharey to True enables global sharing across the whole grid, i.e. also the y-axes of vertically stacked subplots have the same scale
when using sharey=True.
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For subplots that are sharing axes one set of tick labels is enough. Tick labels of inner Axes are automatically removed by sharex and sharey. Still there remains an unused empty space between the subplots.
To precisely control the positioning of the subplots, one can explicitly create a GridSpec with Figure.add_gridspec, and then call its
subplots method. For example, we can reduce the height between vertical subplots using add_gridspec[hspace=0].
label_outer is a handy method to remove labels and ticks from subplots that are not at the edge of the grid.
Apart from True and False, both sharex and sharey accept the values 'row' and 'col' to share the values only per row or column.
fig = plt.figure[] gs = fig.add_gridspec[2, 2, hspace=0, wspace=0] [ax1, ax2], [ax3, ax4] = gs.subplots[sharex='col', sharey='row'] fig.suptitle['Sharing x per column, y per row'] ax1.plot[x, y] ax2.plot[x, y**2, 'tab:orange'] ax3.plot[x + 1, -y, 'tab:green'] ax4.plot[x + 2, -y**2, 'tab:red'] for ax in axs.flat: ax.label_outer[]
If you want a more complex sharing structure, you can first create the grid of axes with no sharing, and then call
axes.Axes.sharex or axes.Axes.sharey to add sharing info a posteriori.
fig, axs = plt.subplots[2, 2] axs[0, 0].plot[x, y] axs[0, 0].set_title["main"] axs[1, 0].plot[x, y**2] axs[1, 0].set_title["shares x with main"] axs[1, 0].sharex[axs[0, 0]] axs[0, 1].plot[x + 1, y + 1] axs[0, 1].set_title["unrelated"] axs[1, 1].plot[x + 2, y + 2] axs[1, 1].set_title["also unrelated"] fig.tight_layout[]
Polar axes#
The parameter subplot_kw of pyplot.subplots controls the subplot properties [see also
Figure.add_subplot]. In particular, this can be used to create a grid of polar Axes.
Total running time of the script: [ 0 minutes 7.774 seconds]
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