.. currentmodule:: altair

.. _user-guide-bar-marks:

Bar
~~~

Bar marks are useful in many visualizations, including bar charts, stacked bar charts, and timelines.

Bar Mark Properties
-------------------
.. altair-plot::
    :hide-code:
    :div_class: properties-example

    import altair as alt
    import pandas as pd

    corner_slider = alt.binding_range(min=0, max=50, step=1)
    corner_var = alt.param(bind=corner_slider, value=0, name="cornerRadius")

    source = pd.DataFrame(
        {
            "a": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
            "b": [28, 55, 43, 91, 81, 53, 19, 87, 52],
        }
    )

    alt.Chart(source).mark_bar(cornerRadius=corner_var).encode(
        x=alt.X("a:N").axis(labelAngle=0),
        y="b:Q",
    ).add_params(corner_var)



A ``bar`` mark definition can contain any :ref:`standard mark properties <mark-properties>`
and the following special properties:

.. altair-object-table:: altair.MarkDef
   :properties: width height orient align baseline binSpacing cornerRadius cornerRadiusEnd cornerRadiusTopLeft cornerRadiusTopRight cornerRadiusBottomRight cornerRadiusBottomLeft

Examples
--------

Single Bar Chart
^^^^^^^^^^^^^^^^
Mapping a quantitative field to either ``x`` or ``y`` of the ``bar`` mark produces a single bar chart.

.. altair-plot::
    import altair as alt
    from altair import datum
    from vega_datasets import data

    source = data.population.url

    alt.Chart(source).mark_bar().encode(
        alt.X("sum(people):Q").title("Population")
    ).transform_filter(
        datum.year == 2000
    )


Bar Chart
^^^^^^^^^
If we map a different discrete field to the ``y`` channel, we can produce a horizontal bar chart. Specifying ``alt.Step(20)`` will adjust the bar's height per discrete step.

.. altair-plot::
    import altair as alt
    from altair import datum
    from vega_datasets import data

    source = data.population.url

    alt.Chart(source).mark_bar().encode(
        alt.X("sum(people):Q").title("Population"),
        alt.Y("age:O"),
    ).transform_filter(
        datum.year == 2000
    ).properties(height=alt.Step(20))


Bar Chart with a Temporal Axis
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

While the ``bar`` mark typically uses the ``x`` and ``y`` channels to encode
a pair of discrete and continuous fields, it can also be used with continuous
fields on both channels. For example, given a bar chart with a temporal field
on ``x``, we can see that the x-scale is a continuous scale. By default, the size of
bars on continuous scales will be set based on the ``continuousBandSize`` config.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.seattle_weather()

    alt.Chart(source).mark_bar().encode(
        alt.X("month(date):T").title("Date"),
        alt.Y("mean(precipitation):Q"),
    )


Histograms
^^^^^^^^^^

If the data is not pre-aggregated (i.e. each record in the data field represents one item), mapping a binned quantitative field to ``x`` and aggregate ``count`` to ``y`` produces a histogram.

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.movies.url

    alt.Chart(source).mark_bar().encode(
        alt.X("IMDB_Rating:Q").bin(),
        y='count()',
    )

Stacked Bar Chart
^^^^^^^^^^^^^^^^^
Adding color to the bar chart (by using the ``color`` attribute) creates a stacked bar chart by default. Here we also customize the color’s scale range to make the color a little nicer. (See ``stack`` for more details about customizing stack.)

.. altair-plot::
    import altair as alt
    from vega_datasets import data

    source = data.barley()

    alt.Chart(source).mark_bar().encode(
        x="variety",
        y="sum(yield)",
        color="site"
    )


Grouped Bar Chart with Offset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. altair-plot::
    import altair as alt
    import pandas as pd

    source = pd.DataFrame(
        {
            "category": ["A", "A", "B", "B", "C", "C"],
            "group": ["x", "y", "z", "x", "y", "z"],
            "value": [0.1, 0.6, 0.9, 0.7, 0.2, 0.6],
        }
    )

    alt.Chart(source).mark_bar().encode(
        x=alt.X("category:N"),
        xOffset="group:N",
        y=alt.Y("value:Q"),
        color=alt.Color("group:N"),
    )