Summary  
This chapter shows how to compute and interpret advanced distribution metrics like skewness and kurtosis in code to assess data asymmetry and tail behavior.

General domain of usage  
Retail analytics

Advanced distribution properties like **skewness** and **kurtosis** help you uncover deeper patterns in retail data that basic summary statistics cannot reveal.

### Why These Measures Matter in Retail

- **Identify asymmetries:** detect if your sales, revenue, or customer behavior data is lopsided or has unexpected trends;
- **Spot unusual patterns:** find outliers or extreme values that could affect inventory or risk management;
- **Improve business decisions:** use insights from these properties to guide promotions, stock planning, and performance forecasting.

### Key Concepts

- **Skewness:** measures how much a distribution leans to one side, showing if most values are above or below the average;
- **Kurtosis:** describes how heavy or light the tails of a distribution are, revealing the likelihood of extreme values.

Understanding skewness and kurtosis lets you judge how typical or unusual your retail data is, so you can make smarter, data-driven decisions.

import pandas as pd
from scipy.stats import skew, kurtosis

# Sample retail dataset
data = {
    "daily_sales": [200, 220, 210, 250, 300, 800, 210, 205, 220, 215],
    "transaction_amount": [20, 22, 21, 25, 30, 80, 21, 20.5, 22, 21.5]
}
df = pd.DataFrame(data)

# Compute skewness and kurtosis for key numerical features
sales_skew = skew(df["daily_sales"])
sales_kurt = kurtosis(df["daily_sales"])
amount_skew = skew(df["transaction_amount"])
amount_kurt = kurtosis(df["transaction_amount"])

print(f"Skewness of daily_sales: {sales_skew:.2f}")
print(f"Kurtosis of daily_sales: {sales_kurt:.2f}")
print(f"Skewness of transaction_amount: {amount_skew:.2f}")
print(f"Kurtosis of transaction_amount: {amount_kurt:.2f}")

Interpreting **skewness** and **kurtosis** helps you understand the shape and risks in your retail data:

- **Positive skewness** in `daily_sales`:
  - Most days have moderate sales;
  - A few days have exceptionally high sales (often due to promotions or holidays);
  - The right tail of the distribution is longer or fatter.
- **Negative skewness**:
  - More frequent low sales days with occasional sharp drops;
  - The left tail of the distribution is longer.
- **Kurtosis**:
  - Measures the likelihood of extreme sales values;
  - High kurtosis means more outliers or "heavy tails"—increasing the risk of unexpected spikes or drops.

Understanding these properties lets you:

- Anticipate unusual demand;
- Manage stock more effectively;
- Set realistic expectations for sales performance.

import matplotlib.pyplot as plt
import seaborn as sns

# Visualize the distribution of a skewed retail feature
plt.figure(figsize=(8, 4))
sns.histplot(df["daily_sales"], kde=True, color="skyblue", bins=8)
plt.title("Distribution of Daily Sales")
plt.xlabel("Daily Sales")
plt.ylabel("Frequency")
plt.show()

Which statement best describes positive skewness in retail sales data?

Master the art of exploratory data analysis (EDA) using Python, pandas, matplotlib, and seaborn. Learn to extract insights, visualize patterns, and communicate findings effectively using a consistent retail dataset.

Establish the core principles, goals, and statistical foundations of exploratory data analysis using a retail dataset.

Analyze and visualize single features in the retail dataset to uncover patterns and anomalies.

Explore relationships between pairs of features and measure their associations in retail data.

Analyze multiple features and groups to uncover deeper patterns and outliers in retail data.

Learn to summarize, present, and communicate EDA findings effectively to stakeholders.

Distribution Properties: Skewness, Kurtosis, and Tails

Why These Measures Matter in Retail

Key Concepts