This website is for version 5. You can find the documentation for version 4 here.


The Lookup transform extends a primary data source by looking up values from another data source; it is similar to a one-sided join. A lookup can be added at the top level of a chart using the Chart.transform_lookup() method.

By way of example, imagine you have two sources of data that you would like to combine and plot: one is a list of names of people along with their height and weight, and the other is some information about which groups they belong to. This example data is available in vega_datasets:

from vega_datasets import data
people = data.lookup_people()
groups = data.lookup_groups()

We know how to visualize each of these datasets separately; for example:

import altair as alt

top = alt.Chart(people).mark_square(size=200).encode(
    width=400, height=200

bottom = alt.Chart(groups).mark_rect().encode(
    width=400, height=100

alt.vconcat(top, bottom)

If we would like to plot features that reference both datasets (for example, the average age within each group), we need to combine the two datasets. This can be done either as a data preprocessing step, using tools available in pandas, or as part of the visualization using a LookupTransform in Altair.

Combining Datasets with pandas.merge#

pandas provides a wide range of tools for merging and joining datasets; see Merge, Join, and Concatenate for some detailed examples. For the above data, we can merge the data and create a combined chart as follows:

import pandas as pd
merged = pd.merge(groups, people, how='left',
                  left_on='person', right_on='name')


We specify a left join, meaning that for each entry of the “person” column in the groups, we seek the “name” column in people and add the entry to the data. From this, we can easily create a bar chart representing the mean age in each group.

Combining Datasets with a Lookup Transform#

For some data sources (e.g. data available at a URL, or data that is streaming), it is desirable to have a means of joining data without having to download it for pre-processing in pandas. This is where Altair’s transform_lookup() comes in. To reproduce the above combined plot by combining datasets within the chart specification itself, we can do the following:

    from_=alt.LookupData(data=people, key='name',
                         fields=['age', 'height'])

Here lookup names the field in the groups dataset on which we will match, and the from_ argument specifies a LookupData structure where we supply the second dataset, the lookup key, and the fields we would like to extract.

Example: Lookup Transforms for Geographical Visualization#

Lookup transforms are often particularly important for geographic visualization, where it is common to combine tabular datasets with datasets that specify geographic boundaries to be visualized; for example, here is a visualization of unemployment rates per county in the US:

import altair as alt
from vega_datasets import data

counties = alt.topo_feature(data.us_10m.url, 'counties')
unemp_data = data.unemployment.url

    from_=alt.LookupData(unemp_data, 'id', ['rate'])
    projection={'type': 'albersUsa'},
    width=500, height=300

Transform Options#

The transform_lookup() method is built on the LookupTransform class, which has the following options:

Click to show table





anyOf(FieldName, array(FieldName))

The output fields on which to store the looked up data values.

For data lookups, this property may be left blank if from.fields has been specified (those field names will be used); if from.fields has not been specified, as must be a string.

For selection lookups, this property is optional: if unspecified, looked up values will be stored under a property named for the selection; and if specified, it must correspond to from.fields.



The default value to use if lookup fails.

Default value: null


anyOf(LookupData, LookupSelection)

Data source or selection for secondary data reference.



Key in primary data source.