Exploring and transforming shelf life data
Understanding the distribution of different variables in our data is a key aspect of any data work including experimental analysis. The food_preservation dataset captures various food preservation methods and their impact on nutrient retention and shelf life. A crucial aspect of this data involves the shelf life of preserved foods, which can vary significantly across different preservation methods and food types.
The food_preservation DataFrame, from scipy.stats import boxcox, pandas as pd, numpy as np, seaborn as sns, and matplotlib.pyplot as plt have been loaded for you.
Это упражнение является частью курса
Experimental Design in Python
Интерактивное практическое упражнение
Попробуйте выполнить это упражнение, дополнив этот пример кода.
# Visualize the original ShelfLife distribution
sns.____(____['____'])
plt.title('Original Shelf Life Distribution')
plt.show()