radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds)
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 :None:None:`original article
<https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>` describing RadViz.
Object holding the data.
Column name containing the name of the data point category.
A plot instance to which to add the information.
Assign a color to each category. Example: ['blue', 'green'].
Colormap to select colors from. If string, load colormap with that name from matplotlib.
Options to pass to matplotlib scatter plotting method.
Plot a multidimensional dataset in 2D.
plotting.andrews_curves
Plot clustering visualization.
.. plot::
('context', 'close-figs')
>>> 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')
<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them