Method grouped_bar

lib/matplotlib/axes/_axes.py:3060–3373

Make a grouped bar plot. .. versionadded:: 3.11 The API is still provisional. We may still fine-tune some aspects based on user-feedback. Grouped bar charts visualize a collection of categorical datasets. Each value in a dataset belongs to

(self, heights, *, positions=None, group_spacing=1.5, bar_spacing=0,
                    tick_labels=None, labels=None, orientation="vertical", colors=None,
                    **kwargs)

    @_docstring.interpd
    def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing=0,
                    tick_labels=None, labels=None, orientation="vertical", colors=None,
                    **kwargs):
        """
        Make a grouped bar plot.

        .. versionadded:: 3.11

            The API is still provisional. We may still fine-tune some aspects based on
            user-feedback.

        Grouped bar charts visualize a collection of categorical datasets. Each value
        in a dataset belongs to a distinct category and these categories are the same
        across all datasets. The categories typically have string names, but could
        also be dates or index keys. The values in each dataset are represented by a
        sequence of bars of the same color. The bars of all datasets are grouped
        together by their shared categories. The category names are drawn as the tick
        labels for each bar group. Each dataset has a distinct bar color, and can
        optionally get a label that is used for the legend.

        Example:

        .. code-block:: python

           grouped_bar([dataset_0, dataset_1, dataset_2],
                       tick_labels=['A', 'B'],
                       labels=['dataset 0', 'dataset 1', 'dataset 2'])

        .. plot:: _embedded_plots/grouped_bar.py

        Parameters
        ----------
        heights : list of array-like or dict of array-like or 2D array \
or pandas.DataFrame
            The heights for all x and groups. One of:

            - list of array-like: A list of datasets, each dataset must have
              the same number of elements.

              .. code-block:: none

                  #           category_A, category_B
                  dataset_0 = [value_0_A, value_0_B]
                  dataset_1 = [value_1_A, value_1_B]
                  dataset_2 = [value_2_A, value_2_B]

              Example call::

                  grouped_bar([dataset_0, dataset_1, dataset_2])

            - dict of array-like: A mapping from names to datasets. Each dataset
              (dict value) must have the same number of elements.

              Example call:

              .. code-block:: python

                data_dict = {'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2}