Python Skewness Explained: Right, Left & Symmetric (Numpy & Scipy)
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What does your data lean toward? In this tutorial, you’ll learn how to measure and interpret skewness in Python using NumPy and SciPy—so you can understand the shape of your distributions and improve your data analysis.
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In this video, I walk you through skewness in Python and demonstrate how it impacts the mean, median, and mode across different distributions. We explore right-skewed distributions (positively skewed), left-skewed distributions (negatively skewed), and symmetric normal distributions using real-world examples like housing prices, retirement age, and IQ scores.
I show you how to work with essential Python libraries including NumPy for data generation and calculations, SciPy for statistical analysis, Matplotlib for visualization, and Seaborn for creating professional-looking plots. We build practical examples by generating skewed data using exponential distributions and calculating key statistical measures.
The tutorial covers creating two powerful visualizations: histograms with KDE overlays to see the shape of distributions, and box plots (box and whisker plots) to identify outliers and understand data spread. I explain how to interpret these visualizations and identify skewness patterns in your data. You'll learn when the mean is greater than the median in right-skewed data, when it's less in left-skewed data, and when they align in symmetric distributions.
By the end of this video, you'll understand how to identify and analyze skewness in Python using statistical measures and data visualization techniques. Perfect for data science beginners working with statistics and distributions.
TIMESTAMPS
00:00 Introduction to Skewness
00:34 Understanding Right, Left & Symmetric Distributions
01:28 Right Skewed (Positively Skewed) Distribution
02:29 Left Skewed (Negatively Skewed) Distribution
03:55 Symmetric/Normal Distribution
04:57 Setting Up Python Environment
06:05 Generating Skewed Data with NumPy
07:00 Calculating Mean, Median & Mode
09:20 Printing Statistical Values
11:11 Creating Histogram Plots
15:17 Building Box Plots (Box & Whisker)
19:36 Analyzing Box Plot Results
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Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.
Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.
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