Quantile Transform#

The quantile transform calculates empirical quantile values for input data. If a groupby parameter is provided, quantiles are estimated separately per group. Among other uses, the quantile transform is useful for creating quantile-quantile (Q-Q) plots.

Here is an example of a quantile plot of normally-distributed data:

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

np.random.seed(42)
df = pd.DataFrame({'x': np.random.randn(200)})

alt.Chart(df).transform_quantile(
    'x', step=0.01
).mark_point().encode(
    x='prob:Q',
    y='value:Q'
)

Transform Options#

The transform_quantile() method is built on the QuantileTransform class, which has the following options:

Property	Type	Description
as	array(`FieldName`)	The output field names for the probability and quantile values. Default value: `["prob", "value"]`
groupby	array(`FieldName`)	The data fields to group by. If not specified, a single group containing all data objects will be used.
probs	array(`number`)	An array of probabilities in the range (0, 1) for which to compute quantile values. If not specified, the step parameter will be used.
quantile	`FieldName`	The data field for which to perform quantile estimation.
step	`number`	A probability step size (default 0.01) for sampling quantile values. All values from one-half the step size up to 1 (exclusive) will be sampled. This parameter is only used if the probs parameter is not provided.

Pivot Transform

Regression Transform