:orphan:
:html_theme.sidebar_secondary.remove:

.. This document is auto-generated by the altair-gallery extension. Do not modify directly.

.. _gallery_sorted_error_bars_with_ci:


Sorted Error Bars showing Confidence Interval
=============================================
This example shows how to show error bars using confidence intervals, while also sorting the y-axis based on x-axis values. 


.. altair-plot::
    :remove-code:
    



    import altair as alt
    from vega_datasets import data

    source = data.barley()

    points = alt.Chart(source).mark_point(
        filled=True,
        color='black'
    ).encode(
        x=alt.X('mean(yield)', title='Barley Yield'),
        y=alt.Y(
            'variety',
             sort=alt.EncodingSortField(
                 field='yield',
                 op='mean',
                 order='descending'
             )
        )
    ).properties(
        width=400,
        height=250
    )

    error_bars = points.mark_rule().encode(
        x='ci0(yield)',
        x2='ci1(yield)',
    )

    points + error_bars


.. tab-set::

    .. tab-item:: Method syntax
        :sync: method

        .. code:: python



            import altair as alt
            from vega_datasets import data

            source = data.barley()

            points = alt.Chart(source).mark_point(
                filled=True,
                color='black'
            ).encode(
                x=alt.X('mean(yield)').title('Barley Yield'),
                y=alt.Y('variety').sort(
                    field='yield',
                    op='mean',
                    order='descending'
                )
            ).properties(
                width=400,
                height=250
            )

            error_bars = points.mark_rule().encode(
                x='ci0(yield)',
                x2='ci1(yield)',
            )

            points + error_bars


    .. tab-item:: Attribute syntax
        :sync: attribute

        .. code:: python



            import altair as alt
            from vega_datasets import data

            source = data.barley()

            points = alt.Chart(source).mark_point(
                filled=True,
                color='black'
            ).encode(
                x=alt.X('mean(yield)', title='Barley Yield'),
                y=alt.Y(
                    'variety',
                     sort=alt.EncodingSortField(
                         field='yield',
                         op='mean',
                         order='descending'
                     )
                )
            ).properties(
                width=400,
                height=250
            )

            error_bars = points.mark_rule().encode(
                x='ci0(yield)',
                x2='ci1(yield)',
            )

            points + error_bars