The matplotlib.pyplot.subplots
method provides a way to plot multiple plots on a single figure. Given the number of rows
and columns
, it returns a tuple (fig
, ax
), giving a single figure fig
with an array of axes ax
.
Here is the function for matplotlib.pyplot.subplots
:
matplotlib.pyplot.subplots(nrows=1,ncols=1,sharex=False,sharey=False,squeeze=True,subplot_kw=None,gridspec_kw=None,**fig_kw)
Given below is the detail of each parameter to the matplotlib.pyplot.subplots
method:
nrows
, ncols
: Number of rows and columns of the subplot grid. Both of these are optional with a default value of 1.sharex
, sharey
: Specifies sharing of properties between axes. Possible values are none, all, row, col or a boolean with a default value of False.squeeze
: Boolean value specifying whether to squeeze out extra dimension from the returned axes array ax
. The default value is False.subplot_kw
: Dict of keywords to be passed to the add_subplot
call to add keywords to each subplot. The default value is None.gridspec_kw
: Dict of grid specifications passed to GridSpec
constructor to place grids on each subplot. The default value is None.**fig_kw
: Any additional keyword arguments to be passed to pyplot.figure call
. The default value is None.Here is an explanation of the tuple returned by the function:
fig
: The matplotlib.pyplot.figure
object to be used as a container for all the subplots.ax
: A single object of the axes.Axes
object if there is only one plot, or an array of axes.Axes
objects if there are multiple plots, as specified by the nrows
and ncols
.Here is an example on how to use the matplotlib.pyplot.subplots
method:
matplotlib.pyplot
for plotting and numpy
for generating data to plot.numpy
.ax
array to plot different subplots on the figure fig
.import matplotlib.pyplot as pltimport numpy as npfig, ax = plt.subplots(2, 2)x = np.linspace(0, 8, 1000)ax[0, 0].plot(x, np.sin(x), 'g') #row=0, col=0ax[1, 0].plot(x, np.tan(x), 'k') #row=1, col=0ax[0, 1].plot(range(100), 'b') #row=0, col=1ax[1, 1].plot(x, np.cos(x), 'r') #row=1, col=1fig.show()
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