Path: blob/master/notebooks/book1/04/iris_cov_mat.ipynb
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import numpy as np try: import pandas as pd except ModuleNotFoundError: %pip install -qq pandas import pandas as pd import seaborn as sns import matplotlib.pyplot as plt try: from sklearn.datasets import load_iris except ModuleNotFoundError: %pip install -qq scikit-learn from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target # Convert to pandas dataframe df_iris = pd.DataFrame(data=iris.data, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']) df_iris['species'] = pd.Series(iris.target_names[y], dtype='category') #df_iris = df_iris[df_iris['species'] != 'virginica'] corr = df_iris.corr() mask = np.tri(*corr.shape).T plt.figure() #sns.heatmap(corr.abs(), mask=mask, annot=True, cmap='viridis') sns.heatmap(corr, mask=mask, annot=True, cmap='viridis') plt.savefig('figures/iris_corr_mat.pdf', dpi=300, bbox_inches='tight'); plt.show() cov = df_iris.cov() plt.figure() sns.heatmap(cov, annot=True, cmap='viridis') plt.savefig('figures/iris_cov_mat.pdf', dpi=300, bbox_inches='tight'); plt.show()