Seaborn is a Python data-visualization library for drawing appealing, informative statistical graphics. Built on matplotlib and closely integrated with pandas, it is useful for exploring structured data during analysis. section 8.1.8
A practical correlation-analysis workflow uses seaborn to:
- Plot a pairwise Pearson-correlation heat map from a numeric-only data set. section 8.1.6 section 8.1.8
- Annotate correlation scores and use a grayscale color map to expose strong positive, negative, and inverse relationships. section 8.1.8
- Support feature selection by revealing redundant fields before modeling. section 8.1.6 section 8.1.8
Feature-correlation heat map
After converting categorical app values to numeric indicators, plot df_features_one_hot_enc.corr(method='pearson') with sns.heatmap(..., annot=True, cmap='gray'). A score of 0.75 between bytes and bytes_diff supports dropping bytes_diff; a score of −0.52 between app_windows_azure and count1 indicates an inverse relationship to evaluate. section 8.1.6 section 8.1.8
Listing 8.11 Generating pairwise correlation heat map for df_features_one_hot_enc
import matplotlib.pyplot as plt #1
import seaborn as sns #2
plt.figure(figsize=(16,9)) #3
sns.heatmap(df_features_one_hot_enc.corr(method='pearson'),\
annot=True, cmap='gray') #4
Use the listing to reproduce the annotated pairwise correlation heat map with matplotlib and seaborn. section 8.1.8
