Method hist

lib/matplotlib/axes/_axes.py:7195–7688

Compute and plot a histogram. This method uses `numpy.histogram` to bin the data in *x* and count the number of values in each bin, then draws the distribution either as a `.BarContainer` or `.Polygon`. The *bins*, *range*, *density*, and *weights* parameter

(self, x, bins=None, range=None, density=False, weights=None,
             cumulative=False, bottom=None, histtype='bar', align='mid',
             orientation='vertical', rwidth=None, log=False,
             color=None, label=None, stacked=False, **kwargs)

    @_api.make_keyword_only("3.10", "range")
    @_preprocess_data(replace_names=["x", 'weights'], label_namer="x")
    def hist(self, x, bins=None, range=None, density=False, weights=None,
             cumulative=False, bottom=None, histtype='bar', align='mid',
             orientation='vertical', rwidth=None, log=False,
             color=None, label=None, stacked=False, **kwargs):
        """
        Compute and plot a histogram.

        This method uses `numpy.histogram` to bin the data in *x* and count the
        number of values in each bin, then draws the distribution either as a
        `.BarContainer` or `.Polygon`. The *bins*, *range*, *density*, and
        *weights* parameters are forwarded to `numpy.histogram`.

        If the data has already been binned and counted, use `~.bar` or
        `~.stairs` to plot the distribution::

            counts, bins = np.histogram(x)
            plt.stairs(counts, bins)

        Alternatively, plot pre-computed bins and counts using ``hist()`` by
        treating each bin as a single point with a weight equal to its count::

            plt.hist(bins[:-1], bins, weights=counts)

        The data input *x* can be a singular array, a list of datasets of
        potentially different lengths ([*x0*, *x1*, ...]), or a 2D ndarray in
        which each column is a dataset. Note that the ndarray form is
        transposed relative to the list form. If the input is an array, then
        the return value is a tuple (*n*, *bins*, *patches*); if the input is a
        sequence of arrays, then the return value is a tuple
        ([*n0*, *n1*, ...], *bins*, [*patches0*, *patches1*, ...]).

        Masked arrays are not supported.

        Parameters
        ----------
        x : (n,) array or sequence of (n,) arrays
            Input values, this takes either a single array or a sequence of
            arrays which are not required to be of the same length.

        bins : int or sequence or str, default: :rc:`hist.bins`
            If *bins* is an integer, it defines the number of equal-width bins
            in the range.

            If *bins* is a sequence, it defines the bin edges, including the
            left edge of the first bin and the right edge of the last bin;
            in this case, bins may be unequally spaced.  All but the last
            (righthand-most) bin is half-open.  In other words, if *bins* is::

                [1, 2, 3, 4]

            then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
            the second ``[2, 3)``.  The last bin, however, is ``[3, 4]``, which
            *includes* 4.

            If *bins* is a string, it is one of the binning strategies
            supported by `numpy.histogram_bin_edges`: 'auto', 'fd', 'doane',
            'scott', 'stone', 'rice', 'sturges', or 'sqrt'.