Generating 1D scatter plots

This examle will plot the raw data points in a 1D scatter plot.

The data points can be colored according to class labels if this is available. This is done by passing the labels for each data point (using the parameter class_data) and a mapping from the labels to something more human-readable (using the parameter class_names).

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from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_iris
from psynlig import generate_1d_scatter
plt.style.use('seaborn-talk')


data_set = load_iris()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
class_data = data_set['target']
class_names = dict(enumerate(data_set['target_names']))

variables = ['sepal length (cm)', 'sepal width (cm)',
             'petal length (cm)', 'petal width (cm)']


kwargs = {
    'scatter': {
        'marker': 'o',
        's': 200,
        'alpha': 0.7,
        'edgecolor': '0.4',
    },
}

generate_1d_scatter(
    data,
    variables,
    class_names=class_names,
    class_data=class_data,
    show_legend=True,
    cmap_class='tab20b',
    **kwargs,
)

generate_1d_scatter(
    data,
    variables,
    show_legend=True,
    **kwargs,
)

plt.show()