Customizing Visualizations

Altair’s goal is to automatically choose useful plot settings and configurations so that the user is free to think about the data rather than the mechanics of plotting. That said, once you have a useful visualization, you will often want to adjust certain aspects of it. This section of the documentation outlines some of the ways to make these adjustments.

Global Config vs. Local Config vs. Encoding

There are often two or three different ways to specify the look of your plots depending on the situation. For example, suppose we are creating a scatter plot of the cars dataset:

                      import altair as alt
from vega_datasets import data
cars = data.cars.url

alt.Chart(cars).mark_point().encode(
    x='Acceleration:Q',
    y='Horsepower:Q'
)

                    

Suppose you wish to change the color of the points to red, and the opacity of the points to 20%. There are three possible approaches to these:

“Global Config” acts on an entire chart object
“Local Config” acts on one mark of the chart
“Encoding” channels can also be used to set some chart properties

Global Config

First, every chart type has a "config" property at the top level that acts as a sort of theme for the whole chart and all of its sub-charts. Here you can specify things like axes properties, mark properties, selection properties, and more.

Altair allows you to access these through the configure_* methods of the chart. Here we will use the configure_mark() property:

                        alt.Chart(cars).mark_point().encode(
    x='Acceleration:Q',
    y='Horsepower:Q'
).configure_mark(
    opacity=0.2,
    color='red'
)

                      

There are a couple things to be aware of when using this kind of global configuration:

By design configurations will affect every mark used within the chart
The global configuration is only permissible at the top-level; so, for example, if you tried to layer the above chart with another, it would result in an error.

For a full discussion of global configuration options, see Top-Level Chart Configuration.

Local Config

If you would like to configure the look of the mark locally, such that the setting only affects the particular chart property you reference, this can be done via a local configuration setting.

In the case of mark properties, the best approach is to set the property as an argument to the mark_* method. Here we will use mark_point():

                        alt.Chart(cars).mark_point(opacity=0.2, color='red').encode(
    x='Acceleration:Q',
    y='Horsepower:Q'
)

                      

Unlike when using the global configuration, here it is possible to use the resulting chart as a layer or facet in a compound chart.

Local config settings like this one will always override global settings.

Encoding

Finally, it is possible to set chart properties via the encoding channel (see Encodings). Rather than mapping a property to a data column, you can map a property directly to a value using the value() function:

                        alt.Chart(cars).mark_point().encode(
    x='Acceleration:Q',
    y='Horsepower:Q',
    opacity=alt.value(0.2),
    color=alt.value('red')
)

                      

Note that only a limited set of mark properties can be bound to encodings, so for some (e.g. fillOpacity, strokeOpacity, etc.) the encoding approach is not available.

Encoding settings will always override local or global configuration settings.

Which to Use?

The precedence order for the three approaches is (from lowest to highest) global config, local config, encoding. That is, if a chart property is set both globally and locally, the local setting will win-out. If a property is set both via a configuration and an encoding, the encoding will win-out.

In most usage, we recommend always using the highest-precedence means of setting properties; i.e. an encoding, or a local configuration for properties that are not tied to an encoding. Global configurations should be reserved for creating themes that are applied just before the chart is rendered.

Adjusting Axis Limits

The default axis limit used by Altair is dependent on the type of the data. To fine-tune the axis limits beyond these defaults, you can use the Scale property of the axis encodings. For example, consider the following plot:

                      import altair as alt
from vega_datasets import data

cars = data.cars.url

alt.Chart(cars).mark_point().encode(
    x='Acceleration:Q',
    y='Horsepower:Q'
)

                    

Altair inherits from Vega-Lite the convention of always including the zero-point in quantitative axes; if you would like to turn this off, you can add a Scale property to the X encoding that specifies zero=False:

                      alt.Chart(cars).mark_point().encode(
    alt.X('Acceleration:Q',
        scale=alt.Scale(zero=False)
    ),
    y='Horsepower:Q'
)

                    

To specify exact axis limits, you can use the domain property of the scale:

                      alt.Chart(cars).mark_point().encode(
    alt.X('Acceleration:Q',
        scale=alt.Scale(domain=(5, 20))
    ),
    y='Horsepower:Q'
)

                    

The problem is that the data still exists beyond the scale, and we need to tell Altair what to do with this data. One option is to “clip” the data by setting the "clip" property of the mark to True:

                      alt.Chart(cars).mark_point(clip=True).encode(
    alt.X('Acceleration:Q',
        scale=alt.Scale(domain=(5, 20))
    ),
    y='Horsepower:Q'
)

                    

Another option is to “clamp” the data; that is, to move points beyond the limit to the edge of the domain:

                      alt.Chart(cars).mark_point().encode(
    alt.X('Acceleration:Q',
        scale=alt.Scale(
            domain=(5, 20),
            clamp=True
        )
    ),
    y='Horsepower:Q'
).interactive()

                    

For interactive charts like the one above, the clamping happens dynamically, which can be useful for keeping in mind outliers as you pan and zoom on the chart.

Adjusting Axis Labels

Altair also gives you tools to easily configure the appearance of axis labels. For example consider this plot:

                      import pandas as pd
df = pd.DataFrame({'x': [0.03, 0.04, 0.05, 0.12, 0.07, 0.15],
                   'y': [10, 35, 39, 50, 24, 35]})

alt.Chart(df).mark_circle().encode(
    x='x',
    y='y'
)

                    

To fine-tune the formatting of the tick labels and to add a custom title to each axis, we can pass to the X and Y encoding a custom Axis definition. Here is an example of formatting the x labels as a percentage, and the y labels as a dollar value:

                      alt.Chart(df).mark_circle().encode(
    x=alt.X('x', axis=alt.Axis(format='%', title='percentage')),
    y=alt.Y('y', axis=alt.Axis(format='$', title='dollar amount'))
)

                    

Axis labels can also be easily removed:

                      alt.Chart(df).mark_circle().encode(
    x=alt.X('x', axis=alt.Axis(labels=False)),
    y=alt.Y('y', axis=alt.Axis(labels=False))
)

                    

Additional formatting codes are available; for a listing of these see the d3 Format Code Documentation.

Adjusting the Legend

A legend is added to the chart automatically when the color, shape or size arguments are passed to the encode() function. In this example we’ll use color.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color='species'
)

                    

In this case, the legend can be customized by introducing the Color class and taking advantage of its legend argument. The shape and size arguments have their own corresponding classes.

The legend option on all of them expects a Legend object as its input, which accepts arguments to customize many aspects of its appearance. One simple example is giving the legend a title.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color=alt.Color('species', legend=alt.Legend(title="Species by color"))
)

                    

Another thing you can do is move the legend to another position with the orient argument.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color=alt.Color('species', legend=alt.Legend(orient="left")),
)

                    

You can remove the legend entirely by submitting a null value.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color=alt.Color('species', legend=None),
)

                    

Removing the Chart Border

Basic Altair charts are drawn with both a grid and an outside border. To create a chart with no border, you will need to remove them both.

As an example, let’s start with a simple scatter plot.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color='species'
)

                    

First remove the grid using the Chart.configure_axis() method.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color='species'
).configure_axis(
    grid=False
)

                    

You’ll note that while the inside rules are gone, the outside border remains. Hide it by setting the strokeWidth or the strokeOpacity options on Chart.configure_view() to 0:

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color='species'
).configure_axis(
    grid=False
).configure_view(
    strokeWidth=0
)

                    

It is also possible to completely remove all borders and axes by combining the above option with setting axis to None during encoding.

                      import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    alt.X('petalWidth', axis=None),
    alt.Y('petalLength', axis=None),
    color='species'
).configure_axis(
    grid=False
).configure_view(
    strokeWidth=0
)

                    

Customizing Colors

As discussed in Effect of Data Type on Color Scales, Altair chooses a suitable default color scheme based on the type of the data that the color encodes. These defaults can be customized using the scale argument of the Color class.

The Scale class passed to the scale argument provides a number of options for customizing the color scale; we will discuss a few of them here.

Color Schemes

Altair includes a set of named color schemes for both categorical and sequential data, defined by the vega project; see the Vega documentation for a full gallery of available color schemes. These schemes can be passed to the scheme argument of the Scale class:

                        import altair as alt
from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color=alt.Color('species', scale=alt.Scale(scheme='dark2'))
)

                      

Color Domain and Range

To make a custom mapping of discrete values to colors, use the domain and range parameters of the Scale class for values and colors respectively.

                        import altair as alt
from vega_datasets import data

iris = data.iris()
domain = ['setosa', 'versicolor', 'virginica']
range_ = ['red', 'green', 'blue']

alt.Chart(iris).mark_point().encode(
    x='petalWidth',
    y='petalLength',
    color=alt.Color('species', scale=alt.Scale(domain=domain, range=range_))
)

                      

Raw Color Values

The scale is what maps the raw input values into an appropriate color encoding for displaying the data. If your data entries consist of raw color names or codes, you can set scale=None to use those colors directly:

                        import pandas as pd
import altair as alt

data = pd.DataFrame({
    'x': range(6),
    'color': ['red', 'steelblue', 'chartreuse', '#F4D03F', '#D35400', '#7D3C98']
})

alt.Chart(data).mark_point(
    filled=True,
    size=100
).encode(
    x='x',
    color=alt.Color('color', scale=None)
)

                      

Adjusting the width of Bar Marks

The width of the bars in a bar plot are controlled through the size property in the mark_bar():

                      import altair as alt
import pandas as pd

data = pd.DataFrame({'name': ['a', 'b'], 'value': [4, 10]})

alt.Chart(data).mark_bar(size=10).encode(
    x='name:O',
    y='value:Q'
)

                    

But since mark_bar(size=10) only controls the width of the bars, it might become possible that the width of the chart is not adjusted accordingly:

                      alt.Chart(data).mark_bar(size=30).encode(
    x='name:O',
    y='value:Q'
)

                    

The width of the chart containing the bar plot can be controlled through setting the width property of the chart, either to a pixel value for any chart, or to a step value in the case of discrete scales.

Here is an example of setting the width to a single value for the whole chart:

                      alt.Chart(data).mark_bar(size=30).encode(
    x='name:O',
    y='value:Q'
).properties(width=200)

                    

The width of the bars are set using mark_bar(size=30) and the width of the chart is set using properties(width=100)

Here is an example of setting the step width for a discrete scale:

                      alt.Chart(data).mark_bar(size=30).encode(
    x='name:N',
    y='value:Q'
).properties(width=alt.Step(100))

                    

The width of the bars are set using mark_bar(size=30) and the width that is allocated for each bar bar in the the chart is set using width=alt.Step(100)

Adjusting Chart Size

The size of charts can be adjusted using the width and height properties. For example:

                      import altair as alt
from vega_datasets import data

cars = data.cars()

alt.Chart(cars).mark_bar().encode(
    x='Origin',
    y='count()'
).properties(
    width=200,
    height=150
)

                    

Note that in the case of faceted or other compound charts, this width and height applies to the subchart rather than to the overall chart:

                      alt.Chart(cars).mark_bar().encode(
    x='Origin',
    y='count()',
    column='Cylinders:Q'
).properties(
    width=100,
    height=100
)

                    

If you want your chart size to respond to the width of the HTML page or container in which it is rendererd, you can set width or height to the string "container":

                      alt.Chart(cars).mark_bar().encode(
    x='Origin',
    y='count()',
).properties(
    width='container',
    height=200
)

                    

Note that this will only scale with the container if its parent element has a size determined outside the chart itself; For example, the container may be a <div> element that has style width: 100%; height: 300px.