pandas.plotting.radviz

pandas.plotting.radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds)

Plot a multidimensional dataset in 2D.

Each Series in the DataFrame is represented as a evenly distributed slice on a circle. Each data point is rendered in the circle according to the value on each Series. Highly correlated Series in the DataFrame are placed closer on the unit circle.

RadViz allow to project a N-dimensional data set into a 2D space where the influence of each dimension can be interpreted as a balance between the influence of all dimensions.

More info available at the original article describing RadViz.

Parameters

frameDataFrame

Object holding the data.

class_columnstr

Column name containing the name of the data point category.

axmatplotlib.axes.Axes, optional

A plot instance to which to add the information.

colorlist[str] or tuple[str], optional

Assign a color to each category. Example: [‘blue’, ‘green’].

colormapstr or matplotlib.colors.Colormap, default None

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

**kwds

Options to pass to matplotlib scatter plotting method.

Returns

class:matplotlib.axes.Axes

See also

plotting.andrews_curves

Plot clustering visualization.

Examples

>>> df = pd.DataFrame(
...     {
...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
...         'Category': [
...             'virginica',
...             'virginica',
...             'setosa',
...             'virginica',
...             'virginica',
...             'versicolor',
...             'versicolor',
...             'setosa',
...             'virginica',
...             'setosa'
...         ]
...     }
... )
>>> pd.plotting.radviz(df, 'Category')

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