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Module « matplotlib.pyplot »

Fonction subplots - module matplotlib.pyplot

Signature de la fonction subplots

def subplots(nrows: 'int' = 1, ncols: 'int' = 1, *, sharex: "bool | Literal['none', 'all', 'row', 'col']" = False, sharey: "bool | Literal['none', 'all', 'row', 'col']" = False, squeeze: 'bool' = True, width_ratios: 'Sequence[float] | None' = None, height_ratios: 'Sequence[float] | None' = None, subplot_kw: 'dict[str, Any] | None' = None, gridspec_kw: 'dict[str, Any] | None' = None, **fig_kw) -> 'tuple[Figure, Any]' 

Description

help(matplotlib.pyplot.subplots)

Create a figure and a set of subplots.

This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.

Parameters
----------
nrows, ncols : int, default: 1
    Number of rows/columns of the subplot grid.

sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False
    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 `~matplotlib.axes.Axes.tick_params`.

    When subplots have a shared axis that has units, calling
    `.Axis.set_units` will update each axis with the new units.

    Note that it is not possible to unshare axes.

squeeze : bool, default: True
    - If True, extra dimensions are squeezed out from the returned
      array of `~matplotlib.axes.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.

width_ratios : array-like of length *ncols*, optional
    Defines the relative widths of the columns. Each column gets a
    relative width of ``width_ratios[i] / sum(width_ratios)``.
    If not given, all columns will have the same width.  Equivalent
    to ``gridspec_kw={'width_ratios': [...]}``.

height_ratios : array-like of length *nrows*, optional
    Defines the relative heights of the rows. Each row gets a
    relative height of ``height_ratios[i] / sum(height_ratios)``.
    If not given, all rows will have the same height. Convenience
    for ``gridspec_kw={'height_ratios': [...]}``.

subplot_kw : dict, optional
    Dict with keywords passed to the
    `~matplotlib.figure.Figure.add_subplot` call used to create each
    subplot.

gridspec_kw : dict, optional
    Dict with keywords passed to the `~matplotlib.gridspec.GridSpec`
    constructor used to create the grid the subplots are placed on.

**fig_kw
    All additional keyword arguments are passed to the
    `.pyplot.figure` call.

Returns
-------
fig : `.Figure`

ax : `~matplotlib.axes.Axes` or array of Axes
    *ax* can be either a single `~.axes.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
    `~.axes.Axes` instance or a collection of these.

See Also
--------
.pyplot.figure
.pyplot.subplot
.pyplot.axes
.Figure.subplots
.Figure.add_subplot

Examples
--------
::

    # 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)

    # 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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