Multivariate EDA with Pairplots and Advanced Jointplots
Multivariate exploratory data analysis (EDA) helps you uncover relationships among multiple numerical features at once, which is especially valuable in a retail context where variables like price, sales, and discount often interact in complex ways. Pairplots are a powerful visualization tool for this purpose, as they allow you to quickly scan for patterns, correlations, and unusual data points across several features simultaneously.
12345678910111213141516import seaborn as sns import pandas as pd import matplotlib.pyplot as plt # Example retail dataset data = { "price": [19.99, 24.99, 15.99, 29.99, 22.99, 18.49, 27.50, 21.00, 23.99, 16.50], "sales": [120, 80, 150, 60, 90, 130, 70, 110, 100, 140], "discount": [0.10, 0.20, 0.05, 0.15, 0.10, 0.00, 0.25, 0.05, 0.15, 0.10] } df = pd.DataFrame(data) # Create a pairplot for price, sales, and discount sns.pairplot(df[["price", "sales", "discount"]], diag_kind="kde") plt.suptitle("Pairplot of Price, Sales, and Discount", y=1.02) plt.show()
By plotting all pairwise combinations of selected features, pairplots make it easy to spot clusters of similar items, linear or nonlinear trends, and possible outliers within the retail dataset. For instance, you might observe that higher discount values are associated with increased sales, or that certain price bands correspond to unique clusters of products. Outliers—such as products with unusually high discount but low sales—stand out visually, prompting further investigation. Pairplots thus provide a comprehensive overview of how your numerical features interact, helping you prioritize which relationships to explore in greater depth.
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Multivariate exploratory data analysis (EDA) helps you uncover relationships among multiple numerical features at once, which is especially valuable in a retail context where variables like price, sales, and discount often interact in complex ways. Pairplots are a powerful visualization tool for this purpose, as they allow you to quickly scan for patterns, correlations, and unusual data points across several features simultaneously.
12345678910111213141516import seaborn as sns import pandas as pd import matplotlib.pyplot as plt # Example retail dataset data = { "price": [19.99, 24.99, 15.99, 29.99, 22.99, 18.49, 27.50, 21.00, 23.99, 16.50], "sales": [120, 80, 150, 60, 90, 130, 70, 110, 100, 140], "discount": [0.10, 0.20, 0.05, 0.15, 0.10, 0.00, 0.25, 0.05, 0.15, 0.10] } df = pd.DataFrame(data) # Create a pairplot for price, sales, and discount sns.pairplot(df[["price", "sales", "discount"]], diag_kind="kde") plt.suptitle("Pairplot of Price, Sales, and Discount", y=1.02) plt.show()
By plotting all pairwise combinations of selected features, pairplots make it easy to spot clusters of similar items, linear or nonlinear trends, and possible outliers within the retail dataset. For instance, you might observe that higher discount values are associated with increased sales, or that certain price bands correspond to unique clusters of products. Outliers—such as products with unusually high discount but low sales—stand out visually, prompting further investigation. Pairplots thus provide a comprehensive overview of how your numerical features interact, helping you prioritize which relationships to explore in greater depth.
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