jakevdp / altair-transform

Evaluation of Vega-Lite transforms in Python

altair-transform

Python evaluation of Altair/Vega-Lite transforms.

altair-transform requires Python 3.6 or later. Install with:

$ pip install altair_transform

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, the computed values are not directly accessible from the Python layer.

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()

	x	y	ymean
0	0	-0.204708	0.457749
1	1	0.274236	0.771093
2	2	-0.245203	1.041320
3	3	-0.800933	1.336943
4	4	1.164847	1.698085

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