altair-transform

Python evaluation of Altair/Vega-Lite transforms.

Example

The Vega-Lite specification includes the ability to apply a wide range of transformations to input data within the chart specification. As an example, here is a sliding window average of a Gaussian random walk, implemented in Altair:

import altair as alt
import numpy as np
import pandas as pd

rand = np.random.RandomState(12345)

df = pd.DataFrame({
    'x': np.arange(200),
    'y': rand.randn(200).cumsum()
})

points = alt.Chart(df).mark_point().encode(
    x='x:Q',
    y='y:Q'
)

line = alt.Chart(df).transform_window(
    ymean='mean(y)',
    sort=[alt.SortField('x')],
    frame=[5, 5]
).mark_line(color='red').encode(
    x='x:Q',
    y='ymean:Q'
)

points + line

Because the transform is encoded within the renderer, however, it is not easy from Altair to access the computed values.

This is where altair_transform comes in. It includes a (nearly) complete Python implementation of Vega-Lite's transform layer, so that you can easily extract a pandas dataframe with the computed values shown in the chart:

from altair_transform import extract_data
data = extract_data(line)
data.head()

From here, you can work with the transformed data directly in Python.