Seaborn doesn't just make your charts more beautiful and easily controlled in their aspect; it also provides you with new tools for EDA that helps you discover distributions and relationships between variables.
Before proceeding, let's reload the package and have both the Iris and Boston datasets ready in pandas DataFrame format:
In: import seaborn as sns sns.set() from sklearn.datasets import load_iris iris = load_iris() X_iris, y_iris = iris.data, iris.target features_iris = [a[:-5].replace(' ','_') for a in iris.feature_names] target_labels = {j: flower \ for j, flower in enumerate(iris.target_names)} df_iris = pd.DataFrame(X_iris, columns=features_iris) df_iris['target'] = [target_labels[y] for ...
