The key to creating meaningful visualizations is to map properties of the data to visual properties in order to effectively communicate information. Altair abstracts this mapping through the idea of channel encodings. For example, here we will plot the cars dataset using four of the available channel encodings: x (the x-axis value), y (the y-axis value), color (the color of the marker), and shape (the shape of the point marker):

import altair as alt
from vega_datasets import data
cars = data.cars()


For data specified as a DataFrame, Altair can automatically determine the correct data type for each encoding, and creates appropriate scales and legends to represent the data.


Altair provides a number of encoding channels that can be useful in different circumstances; the following table summarizes them:

TODO: link to examples of each

Position Channels:

Channel Altair Class Description Example
x X The x-axis value  
y Y The y-axis value  
x2 X2 Second x value for ranges  
y2 Y2 Second y value for ranges  
longitude Longitude Longitude for geo charts  
latitude Latitude Latitude for geo charts  
longitude2 Longitude2 Second longitude value for ranges  
latitude2 Latitude2 Second latitude value for ranges  

Mark Property Channels:

Channel Altair Class Description Example
color Color The color of the mark  
fill Fill The fill for the mark  
opacity Opacity The opacity of the mark  
shape Shape The shape of the mark  
size Size The size of the mark  
stroke Stroke The stroke of the mark  

Text and Tooltip Channels:

Channel Altair Class Description Example
text Text Text to use for the mark  
key Key  
tooltip Tooltip The tooltip value  

Hyperlink Channel:

Channel Altair Class Description Example
href Href Hyperlink for points  

Level of Detail Channel:

Channel Altair Class Description Example
detail Detail Additional property to group by  

Order Channels:

Channel Altair Class Description Example
order Order Sets the order of the marks  

Facet Channels:

Channel Altair Class Description Example
column Column The column of a faceted plot  
row Row The row of a faceted plot  

Data Types

The details of any mapping depend on the type of the data. Altair recognizes four main data types:

Data Type Shorthand Code Description
quantitative Q a continuous real-valued quantity
ordinal O a discrete ordered quantity
nominal N a discrete unordered category
temporal T a time or date value

If types are not specified for data input as a DataFrame, Altair defaults to quantitative for any numeric data, temporal for date/time data, and nominal for string data, but be aware that these defaults are by no means always the correct choice!

The types can either be expressed in a long-form using the channel encoding classes such as X and Y, or in short-form using the Shorthand Syntax discussed below. For example, the following two methods of specifying the type will lead to identical plots:

    alt.X('Acceleration', type='quantitative'),
    alt.Y('Miles_per_Gallon', type='quantitative'),
    alt.Color('Origin', type='nominal')

The shorthand form, x="name:Q", is useful for its lack of boilerplate when doing quick data explorations. The long-form, alt.X('name', type='quantitative'), is useful when doing more fine-tuned adjustments to the encoding, such as binning, axis and scale properties, or more.

Specifying the correct type for your data is important, as it affects the way Altair represents your encoding in the resulting plot.

Effect of Data Type on Color Scales

As an example of this, here we will represent the same data three different ways, with the color encoded as a quantitative, ordinal, and nominal type, using three vertically-concatenated charts (see vconcat):

base = alt.Chart(cars).mark_point().encode(


The type specification influences the way Altair, via Vega-Lite, decides on the color scale to represent the value, and influences whether a discrete or continuous legend is used.

Effect of Data Type on Axis Scales

Similarly, for x and y axis encodings, the type used for the data will affect the scales used and the characteristics of the mark. For example, here is the difference between a quantitative and ordinal scale for an column that contains integers specifying a year:

pop = data.population.url

base = alt.Chart(pop).mark_bar().encode(
    alt.Y('mean(people):Q', axis=alt.Axis(title='total population'))


In altair, quantitative scales always start at zero unless otherwise specified, while ordinal scales are limited to the values within the data.

Overriding the behavior of including zero in the axis, we see that even then the precise appearance of the marks representing the data are affected by the data type:


Because quantitative values do not have an inherent width, the bars do not fill the entire space between the values. This view also makes clear the missing year of data that was not immediately apparent when we treated the years as categories.

This kind of behavior is sometimes surprising to new users, but it emphasizes the importance of thinking carefully about your data types when visualizing data: a visual encoding that is suitable for categorical data may not be suitable for quantitative data, and vice versa.

Binning and Aggregation

Beyond simple channel encodings, Altair’s visualizations are built on the concept of the database-style grouping and aggregation; that is, the split-apply-combine abstraction that underpins many data analysis approaches.

For example, building a histogram from a one-dimensional dataset involves splitting data based on the bin it falls in, aggregating the results within each bin using a count of the data, and then combining the results into a final figure.

In Altair, such an operation looks like this:

    alt.X('Horsepower', bin=True),
    # could also use alt.Y(aggregate='count', type='quantitative')