How to Calculate Geometric Mean in Python (Using SciPy)
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Need to calculate geometric mean for financial returns, growth rates, or skewed data? In this step-by-step tutorial, you'll learn how to compute it easily using Python and SciPy—with clear explanations, examples, and best practices.
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In this video, I walk you through how to calculate the geometric mean in Python using three different methods. We start with a manual calculation to understand the math behind it, then move to NumPy for a simplified approach, and finally use SciPy's gmean function for a one-line solution. The geometric mean is particularly useful when working with percentages, growth rates, and proportional data, which is why it differs from the arithmetic mean you learned in school.
I cover two practical examples in this tutorial. The first example uses simple whole numbers to demonstrate the core concept, while the second example tackles a more complex scenario involving percentages and negative values. This is where geometric mean really shines—when calculating average growth rates or returns where you need to account for compounding effects. I show you exactly how to convert percentages to decimals, handle negative percentages properly, and convert your results back to meaningful percentage values.
Whether you're preparing for a data science interview, working on financial analysis, or just want to expand your Python statistics toolkit, this tutorial breaks down geometric mean calculation step by step. By the end, you'll know when to use geometric mean versus arithmetic mean and how to implement it efficiently in your code.
TIMESTAMPS
00:00 Introduction to Geometric Mean
00:28 Background & Theory of Geometric Mean
01:42 Manual Calculation Example
03:02 Coding Setup & Imports
03:42 Example 1: Manual Calculation in Python
05:55 Example 2: Using NumPy
07:32 Example 3: Using SciPy
08:15 Example 4: Percentages & Negative Numbers
10:40 Converting Back to Percentages
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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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