@_with_element
def bar_chart(self, element, data=None, width=0, height=0, use_container_width=True):
    'Display a bar chart.\n\n        This is just syntax-sugar around st.altair_chart. The main difference\n        is this command uses the data\'s own column and indices to figure out\n        the chart\'s spec. As a result this is easier to use for many "just plot\n        this" scenarios, while being less customizable.\n\n        Parameters\n        ----------\n        data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, or dict\n            Data to be plotted.\n\n        width : int\n            The chart width in pixels. If 0, selects the width automatically.\n\n        height : int\n            The chart width in pixels. If 0, selects the height automatically.\n\n        use_container_width : bool\n            If True, set the chart width to the column width. This takes\n            precedence over the width argument.\n\n        Example\n        -------\n        >>> chart_data = pd.DataFrame(\n        ...     np.random.randn(50, 3),\n        ...     columns=["a", "b", "c"])\n        ...\n        >>> st.bar_chart(chart_data)\n\n        .. output::\n           https://share.streamlit.io/0.50.0-td2L/index.html?id=5U5bjR2b3jFwnJdDfSvuRk\n           height: 220px\n\n        '
    import streamlit.elements.altair as altair
    chart = altair.generate_chart('bar', data, width, height)
    altair.marshall(element.vega_lite_chart, chart, use_container_width)
Display a bar chart.

This is just syntax-sugar around st.altair_chart. The main difference
is this command uses the data's own column and indices to figure out
the chart's spec. As a result this is easier to use for many "just plot
this" scenarios, while being less customizable.

Parameters
----------
data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, or dict
    Data to be plotted.

width : int
    The chart width in pixels. If 0, selects the width automatically.

height : int
    The chart width in pixels. If 0, selects the height automatically.

use_container_width : bool
    If True, set the chart width to the column width. This takes
    precedence over the width argument.

Example
-------
>>> chart_data = pd.DataFrame(
...     np.random.randn(50, 3),
...     columns=["a", "b", "c"])
...
>>> st.bar_chart(chart_data)

.. output::
   https://share.streamlit.io/0.50.0-td2L/index.html?id=5U5bjR2b3jFwnJdDfSvuRk
   height: 220px
@_with_element
def bar_chart(self, element, data=None, width=0, height=0, use_container_width=True):
    'Display a bar chart.\n\n        This is just syntax-sugar around st.altair_chart. The main difference\n        is this command uses the data\'s own column and indices to figure out\n        the chart\'s spec. As a result this is easier to use for many "just plot\n        this" scenarios, while being less customizable.\n\n        Parameters\n        ----------\n        data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, or dict\n            Data to be plotted.\n\n        width : int\n            The chart width in pixels. If 0, selects the width automatically.\n\n        height : int\n            The chart width in pixels. If 0, selects the height automatically.\n\n        use_container_width : bool\n            If True, set the chart width to the column width. This takes\n            precedence over the width argument.\n\n        Example\n        -------\n        >>> chart_data = pd.DataFrame(\n        ...     np.random.randn(50, 3),\n        ...     columns=["a", "b", "c"])\n        ...\n        >>> st.bar_chart(chart_data)\n\n        .. output::\n           https://share.streamlit.io/0.50.0-td2L/index.html?id=5U5bjR2b3jFwnJdDfSvuRk\n           height: 220px\n\n        '
    import streamlit.elements.altair as altair
    chart = altair.generate_chart('bar', data, width, height)
    altair.marshall(element.vega_lite_chart, chart, use_container_width)