subplots(nrows=1, ncols=1, *, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)
This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.
Number of rows/columns of the subplot grid.
Controls sharing of properties among x (sharex) or y (sharey) axes:
True or 'all': x- or y-axis will be shared among all subplots.
False or 'none': each subplot x- or y-axis will be independent.
'row': each subplot row will share an x- or y-axis.
'col': each subplot column will share an x- or y-axis.
When subplots have a shared x-axis along a column, only the x tick labels of the bottom subplot are created. Similarly, when subplots have a shared y-axis along a row, only the y tick labels of the first column subplot are created. To later turn other subplots' ticklabels on, use tick_params
.
When subplots have a shared axis that has units, calling ~matplotlib.axis.Axis.set_units
will update each axis with the new units.
If True, extra dimensions are squeezed out from the returned array of Axes
:
if only one subplot is constructed (nrows=ncols=1), the resulting single Axes object is returned as a scalar.
for Nx1 or 1xM subplots, the returned object is a 1D numpy object array of Axes objects.
for NxM, subplots with N>1 and M>1 are returned as a 2D array.
If False, no squeezing at all is done: the returned Axes object is always a 2D array containing Axes instances, even if it ends up being 1x1.
Dict with keywords passed to the add_subplot
call used to create each subplot.
Dict with keywords passed to the ~matplotlib.gridspec.GridSpec
constructor used to create the grid the subplots are placed on.
All additional keyword arguments are passed to the .pyplot.figure
call.
ax can be either a single Axes
object or an array of Axes objects if more than one subplot was created. The dimensions of the resulting array can be controlled with the squeeze keyword, see above.
Typical idioms for handling the return value are:
# using the variable ax for single a Axes fig, ax = plt.subplots() # using the variable axs for multiple Axes fig, axs = plt.subplots(2, 2) # using tuple unpacking for multiple Axes fig, (ax1, ax2) = plt.subplots(1, 2) fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
The names ax
and pluralized axs
are preferred over axes
because for the latter it's not clear if it refers to a single :None:None:`~.axes.Axes` instance or a collection of these.
Create a figure and a set of subplots.
# First create some toy data: x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2)
# Create just a figure and only one subplot fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot')
# Create two subplots and unpack the output array immediately f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot(x, y) ax1.set_title('Sharing Y axis') ax2.scatter(x, y)
# Create four polar axes and access them through the returned array fig, axs = plt.subplots(2, 2, subplot_kw=dict(projection="polar")) axs[0, 0].plot(x, y) axs[1, 1].scatter(x, y)
# Share a X axis with each column of subplots plt.subplots(2, 2, sharex='col')
# Share a Y axis with each row of subplots plt.subplots(2, 2, sharey='row')
# Share both X and Y axes with all subplots plt.subplots(2, 2, sharex='all', sharey='all')
# Note that this is the same as plt.subplots(2, 2, sharex=True, sharey=True)
See :# Create figure number 10 with a single subplot # and clears it if it already exists. fig, ax = plt.subplots(num=10, clear=True)
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