hvplot.plotting.parallelcoordinates#

hvplot.plotting.parallel_coordinates(data, class_column, cols=None, alpha=0.5, width=600, height=300, var_name='variable', value_name='value', cmap=None, colormap=None, **kwds)[source]#

Parallel coordinates plotting.

To show a set of points in an n-dimensional space, a backdrop is drawn consisting of n parallel lines. A point in n-dimensional space is represented as a polyline with vertices on the parallel axes; the position of the vertex on the i-th axis corresponds to the i-th coordinate of the point.

Parameters:

frameDataFrame: The DataFrame to be plotted.
class_columnstr: Column name containing class names
colslist, optional: A list of column names to use
alphafloat, optional: The transparency of the lines. Default is 0.5.
cmap/colormapstr or colormap object, optional: Colormap to use for groups. Default to Colorcet’s glasbey_category10.

Returns:

objHoloViews object: The HoloViews representation of the plot.

Examples#

Basic parallel coordinates plot#

This example shows how to create a simple parallel coordinates plot from a dataframe with 4 features and a categorical column.

import hvplot
import numpy as np
import pandas as pd

np.random.seed(42)
df = pd.DataFrame({
    'feature_1': np.random.rand(10) * 20,
    'feature_2': np.random.rand(10) * 10,
    'feature_3': np.random.randint(0, 100, 10),
    'feature_4': np.random.normal(0, 1, 10),
    'class': np.random.choice(['A', 'B', 'C'], 10)
})

hvplot.plotting.parallel_coordinates(df, class_column='class')

Example with penguins#

In this example we use 4 features from the penguins dataset and analyze how they are related with their species. We can see, for instance, that Gentoo penguins dominate the flipper_length_mm feature, having the highest flipper lengths.

Note

It is important to normalize the features before plotting them. This example leverages scikit-learn and its MinMaxScaler transform.

import hvplot
from sklearn.preprocessing import MinMaxScaler

df = hvplot.sampledata.penguins("pandas")
df_scaled = df
cols = ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(df[cols])
df_scaled = pd.DataFrame(scaled_features, columns=cols)
df_scaled["species"] = df["species"]

hvplot.plotting.parallel_coordinates(df_scaled, class_column="species")

With Matplotlib#

Parallel coordinates plots can quickly become pretty large and slow to explore with the Bokeh plotting backend. This example shows how to render such a plot with Matplotlib.

import hvplot
from sklearn.preprocessing import MinMaxScaler
hvplot.extension("matplotlib")

df = hvplot.sampledata.penguins("pandas")
cols = ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
scaler = MinMaxScaler()
scaled_features = scaler.fit_transform(df[cols])
df_scaled = pd.DataFrame(scaled_features, columns=cols)
df_scaled["species"] = df["species"]

hvplot.plotting.parallel_coordinates(df_scaled, class_column="species")

hvplot.output(backend="bokeh")

Customize#

parallel_coordinates offers multiple options to customize the plot, with cols, var_name, value_name, cmap and alpha.

import hvplot
import numpy as np
import pandas as pd

np.random.seed(42)
df = pd.DataFrame({
    'feature_1': np.random.rand(10) * 20,
    'feature_2': np.random.rand(10) * 10,
    'feature_3': np.random.randint(0, 100, 10),
    'feature_4': np.random.normal(0, 1, 10),
    'class': np.random.choice(['A', 'B', 'C'], 10)
})

hvplot.plotting.parallel_coordinates(
    df, class_column='class', cols=['feature_2', 'feature_3', 'feature_4'],
    var_name='Species', value_name='Scaled', cmap='Set1', alpha=0.8,
)

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