Data Transformations

It is often necessary to transform or filter data in the process of visualizing it. In Altair you can do this one of two ways:

  1. Before the chart definition, using standard Pandas data transformations.
  2. Within the chart definition, using Vega-Lite’s data transformation tools.

In most cases, we suggest that you use the first approach, because it is more straightforward to those who are familiar with data manipulation in Python, and because the Pandas package offers much more flexibility than Vega-Lite in available data manipulations.

The second approach becomes useful when the data source is not a dataframe, but, for example, a URL pointer to a JSON or CSV file. It can also be useful in a compound chart where different views of the dataset require different transformations.

This second approach – specifying data transformations within the chart specification itself – can be accomplished using the transform_* methods of top-level objects:

Transform Method Description
Aggregate Transforms transform_aggregate() Create a new data column by aggregating an existing column.
Bin transforms transform_bin() Create a new data column by binning an existing column.
Calculate Transform transform_calculate() Create a new data column using an arithmetic calculation on an existing column.
Density Transform transform_density() Create a new data column with the kernel density estimate of the input.
Filter Transform transform_filter() Select a subset of data based on a condition.
Flatten Transform transform_flatten() Flatten array data into columns.
Fold Transform transform_fold() Convert wide-form data into long-form data (opposite of pivot).
Impute Transform transform_impute() Impute missing data.
Join Aggregate Transform transform_joinaggregate() Aggregate transform joined to original data.
LOESS Transform transform_loess() Create a new column with LOESS smoothing of data.
Lookup Transform transform_lookup() One-sided join of two datasets based on a lookup key.
Pivot Transform transform_pivot() Convert long-form data into wide-form data (opposite of fold).
Quantile Transform transform_quantile() Compute empirical quantiles of a dataset.
Regression Transform transform_regression() Fit a regression model to a dataset.
Sample Transform transform_sample() Random sub-sample of the rows in the dataset.
Stack Transform transform_stack() Compute stacked version of values.
TimeUnit Transform transform_timeunit() Discretize/group a date by a time unit (day, month, year, etc.)
Window Transform transform_window() Compute a windowed aggregation