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:

Before the chart definition, using standard Pandas data transformations.
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