How to use the altair.value function in altair

color = alt.Color('weather:N', scale=scale)

# We create two selections:
# - a brush that is active on the top panel
# - a multi-click that is active on the bottom panel
brush = alt.selection_interval(encodings=['x'])
click = alt.selection_multi(encodings=['color'])

# Top panel is scatter plot of temperature vs time
points = alt.Chart().mark_point().encode(
    alt.X('monthdate(date):T', title='Date'),
    alt.Y('temp_max:Q',
        title='Maximum Daily Temperature (C)',
        scale=alt.Scale(domain=[-5, 40])
    ),
    color=alt.condition(brush, color, alt.value('lightgray')),
    size=alt.Size('precipitation:Q', scale=alt.Scale(range=[5, 200]))
).properties(
    width=550,
    height=300
).add_selection(
    brush
).transform_filter(
    click
)

# Bottom panel is a bar chart of weather type
bars = alt.Chart().mark_bar().encode(
    x='count()',
    y='weather:N',
    color=alt.condition(click, color, alt.value('lightgray')),
).transform_filter(

==============================
This example shows a bar chart with both positive and negative values.
"""
# category: bar charts
import altair as alt
from vega_datasets import data

source = data.us_employment()

alt.Chart(source).mark_bar().encode(
    x="month:T",
    y="nonfarm_change:Q",
    color=alt.condition(
        alt.datum.nonfarm_change > 0,
        alt.value("steelblue"),  # The positive color
        alt.value("orange")  # The negative color
    )
).properties(width=600)

color_scale = alt.Scale(domain=['M', 'F'],
                        range=['#1FC3AA', '#8624F5'])

base = alt.Chart(source).properties(
    width=250,
    height=250
).add_selection(selector)

points = base.mark_point(filled=True, size=200).encode(
    x=alt.X('mean(height):Q',
            scale=alt.Scale(domain=[0,84])),
    y=alt.Y('mean(weight):Q',
            scale=alt.Scale(domain=[0,250])),
    color=alt.condition(selector,
                        'gender:N',
                        alt.value('lightgray'),
                        scale=color_scale),
)

hists = base.mark_bar(opacity=0.5, thickness=100).encode(
    x=alt.X('age',
            bin=alt.Bin(step=5), # step keeps bin size the same
            scale=alt.Scale(domain=[0,100])),
    y=alt.Y('count()',
            stack=None,
            scale=alt.Scale(domain=[0,350])),
    color=alt.Color('gender:N',
                    scale=color_scale)
).transform_filter(
    selector
)

This example shows a histogram with a global mean overlay.
"""
# category: histograms
import altair as alt
from vega_datasets import data

source = data.movies.url

bar = alt.Chart(source).mark_bar().encode(
    alt.X('IMDB_Rating:Q', bin=True, axis=None),
    alt.Y('count()')
)

rule = alt.Chart(source).mark_rule(color='red').encode(
    x='mean(IMDB_Rating):Q',
    size=alt.value(5)
)

bar + rule

chart = source.transform_filter(
        alt.FieldOneOfPredicate(field="airport", oneOf=subset)
    )

    highlight = alt.selection(
        type="single", nearest=True, on="mouseover", fields=["airport"]
    )

    points = (
        chart.mark_point()
        .encode(
            x="day",
            y=alt.Y("count", title="# of departing flights"),
            color=alt.Color("airport", legend=alt.Legend(title=name)),
            tooltip=["day", "airport", "city", "count"],
            opacity=alt.value(0.3),
        )
        .add_selection(highlight)
    )

    lines = (
        chart.mark_line()
        .encode(
            x="day",
            y="count",
            color="airport",
            size=alt.condition(~highlight, alt.value(1), alt.value(3)),
        )
        .transform_loess("day", "count", groupby=["airport"], bandwidth=0.2)
    )

    return lines + points

x="day",
            y=alt.Y("count", title="# of departing flights"),
            color=alt.Color("airport", legend=alt.Legend(title=name)),
            tooltip=["day", "airport", "city", "count"],
            opacity=alt.value(0.3),
        )
        .add_selection(highlight)
    )

    lines = (
        chart.mark_line()
        .encode(
            x="day",
            y="count",
            color="airport",
            size=alt.condition(~highlight, alt.value(1), alt.value(3)),
        )
        .transform_loess("day", "count", groupby=["airport"], bandwidth=0.2)
    )

    return lines + points

Returns
    -------
    altair.LayerChart
        The Altair chart instance with the plotted spectrum.
    """
    if spectrum_kws is None:
        spectrum_kws = {}
    # Top spectrum.
    spec_plot = spectrum(spec_top, mirror_intensity=False, **spectrum_kws)
    # Mirrored bottom spectrum.
    spec_plot += spectrum(spec_bottom, mirror_intensity=True, **spectrum_kws)

    spec_plot += (altair.Chart(pd.DataFrame({'sep': [0]}))
                  .mark_rule(size=3).encode(
                      y='sep', color=altair.value('lightGray')))

    return spec_plot

chart = self._plot(
            data=df, width=width, height=height, title=kwds.get("title", None)
        )
        chart = chart.mark_area().encode(
            x=_x(x, df),
            y=alt.Y(
                value_name,
                type=infer_vegalite_type(df[value_name]),
                stack=(None, "zero")[stacked],
            ),
            color=alt.Color(field=var_name, type=infer_vegalite_type(df[var_name])),
        )

        if alpha is not None:
            assert 0 <= alpha <= 1
            chart = chart.encode(opacity=alt.value(alpha))

        if ax is not None:
            return ax + chart
        return chart

"""
# category: scatter plots
import altair as alt
from vega_datasets import data

cars = data.cars()

brush = alt.selection(type='interval')

tick_axis = alt.Axis(labels=False, domain=False, ticks=False)
tick_axis_notitle = alt.Axis(labels=False, domain=False, ticks=False, title='')

points = alt.Chart(cars).mark_point().encode(
    x=alt.X('Miles_per_Gallon', axis=alt.Axis(title='')),
    y=alt.Y('Horsepower', axis=alt.Axis(title='')),
    color=alt.condition(brush, 'Origin', alt.value('grey'))
).add_selection(
    brush
)

x_ticks = alt.Chart(cars).mark_tick().encode(
    alt.X('Miles_per_Gallon', axis=tick_axis),
    alt.Y('Origin', axis=tick_axis_notitle),
    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
).add_selection(
    brush
)

y_ticks = alt.Chart(cars).mark_tick().encode(
    alt.X('Origin', axis=tick_axis_notitle),
    alt.Y('Horsepower', axis=tick_axis),
    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))