Distribution Properties: Skewness, Kurtosis, and Tails
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.
1234567891011121314151617181920import 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.
12345678910import 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()
Tout était clair ?
Merci pour vos commentaires !
Section 1. Chapitre 3
Demandez à l'IA
Demandez à l'IA
Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion
Awesome!
Completion rate improved to 5.56Distribution Properties: Skewness, Kurtosis, and Tails
Glissez pour afficher le menu
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.
1234567891011121314151617181920import 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.
12345678910import 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()
Tout était clair ?
Merci pour vos commentaires !
Section 1. Chapitre 3
