pandas.plotting.andrews_curves

pandas.plotting.andrews_curves(frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwargs)

Generate a matplotlib plot for visualising clusters of multivariate data.

Andrews curves have the functional form:

\[f(t) = \frac{x_1}{\sqrt{2}} + x_2 \sin(t) + x_3 \cos(t) + x_4 \sin(2t) + x_5 \cos(2t) + \cdots\]

Where \(x\) coefficients correspond to the values of each dimension and \(t\) is linearly spaced between \(-\pi\) and \(+\pi\). Each row of frame then corresponds to a single curve.

Parameters

frameDataFrame

Data to be plotted, preferably normalized to (0.0, 1.0).

class_columnlabel

Name of the column containing class names.

axaxes object, default None

Axes to use.

samplesint

Number of points to plot in each curve.

colorstr, list[str] or tuple[str], optional

Colors to use for the different classes. Colors can be strings or 3-element floating point RGB values.

colormapstr or matplotlib colormap object, default None

Colormap to select colors from. If a string, load colormap with that name from matplotlib.

**kwargs

Options to pass to matplotlib plotting method.

Returns

matplotlib.axes.Axes

Examples

>>> df = pd.read_csv(
...     'https://raw.githubusercontent.com/pandas-dev/'
...     'pandas/main/pandas/tests/io/data/csv/iris.csv'
... )
>>> pd.plotting.andrews_curves(df, 'Name')
<AxesSubplot: title={'center': 'width'}>