How to Handle Multimodal Data in Python with Scipy and Numpy

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Working with data that has multiple peaks or distinct modes? In this tutorial, you’ll learn how to detect, analyze, and handle multimodal distributions using Python, with the help of NumPy and SciPy—perfect for real-world data analysis and machine learning preprocessing.


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In this Python tutorial, we dive deep into multimodal distributions and show you exactly how to identify, visualize, and understand them. We start by explaining what multimodal distributions are—distributions with two or more peaks—and why they matter when analyzing data like marathon finish times, customer purchasing patterns, or any dataset where multiple groups exist.

You'll learn the difference between bimodal, trimodal, and polymodal distributions, and discover why standard statistical measures like mean and median can be misleading with this type of data. We walk through a practical example using marathon runners, generating three distinct groups: elite runners aiming for Olympic trials, amateur runners targeting Boston qualification, and recreational marathoners.

I demonstrate multiple visualization techniques including histograms with KDE plots, empirical CDF plots, and violin plots to help you spot multimodal patterns in your data. We also use scipy's find_peaks function to programmatically identify the exact location of peaks in your distribution. By the end of this video, you'll have a complete toolkit for detecting and analyzing multimodal distributions in Python using NumPy, Matplotlib, Seaborn, and SciPy. This is essential knowledge for any data analyst or scientist working with complex, real-world datasets.

TIMESTAMPS
00:00 Introduction to Multimodal Distributions
01:42 Types of Multimodal Distributions
02:47 Issues with Multimodal Distributions
03:34 Methods to Identify Distinct Groups
04:12 Python Setup and Imports
05:05 Creating Marathon Runner Example Data
06:32 Plotting the Multimodal Distribution
08:43 Visualizing with Empirical CDF
10:40 Using Violin Plots for Analysis
12:38 Finding Peaks with SciPy
15:17 Wrap Up and Conclusion

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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.

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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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