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.. currentmodule:: altair
.. _user-guide-loess-transform:
LOESS
~~~~~
The LOESS transform (LOcally Estimated Scatterplot Smoothing) uses a
locally-estimated regression to produce a trend line.
LOESS performs a sequence of local weighted regressions over a sliding
window of nearest-neighbor points. For standard parametric regression options,
see the :ref:`user-guide-regression-transform`.
Here is an example of using LOESS to smooth samples from a Gaussian random walk:
.. altair-plot::
import altair as alt
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.DataFrame({
'x': range(100),
'y': np.random.randn(100).cumsum()
})
chart = alt.Chart(df).mark_point().encode(
x='x',
y='y'
)
chart + chart.transform_loess('x', 'y').mark_line()
Transform Options
^^^^^^^^^^^^^^^^^
The :meth:`~Chart.transform_loess` method is built on the
:class:`~LoessTransform` class, which has the following options:
.. altair-object-table:: altair.LoessTransform
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