How to Calculate Pearson Correlation Coefficient in Python (Numpy, SciPy, Pandas)
🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut...
Want to measure how strongly two variables are related? Learn how to compute the Pearson Correlation Coefficient in Python using NumPy, SciPy, and Pandas. This step-by-step guide is perfect for data analysts, scientists, and students working with real-world data!
🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da...
👨💻 Mentorships: https://ryanandmattdatascience.com/me...
📧 Email: [email protected]
🌐 Website & Blog: https://ryanandmattdatascience.com/
🖥️ Discord: / discord
📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan
📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg
🍿 WATCH NEXT
Statistics for Data Science Playlist: • Statistics for Data Science
Python Covariance: • How to Compute Covariance in Python for Da...
Python STD Variance: • How to Calculate Standard Deviation & Vari...
Python Z-Score: • Python Z-Score Explained: Code Example wit...
In this comprehensive tutorial, I break down how to calculate the Pearson correlation coefficient in Python through multiple approaches. We start by calculating this correlation metric manually to understand the underlying mathematics, then move through progressively simpler methods using NumPy, SciPy, and Pandas. This video covers everything from the basic formula to practical implementations you can use in your data science projects.
I explain what the Pearson correlation coefficient measures, including how values range from -1 (perfect negative correlation) to +1 (perfect positive correlation), with real-world examples like study hours versus exam scores. You'll see the complete manual calculation process, including finding means, calculating deviations, and working through the numerator and denominator steps.
The tutorial then demonstrates efficient shortcuts: using NumPy's corrcoef function with just one line of code, leveraging SciPy's pearsonr method (which also provides p-values), and implementing Pandas' corr method for DataFrame analysis. To round out the practical applications, I show you how to create professional scatter plots and correlation matrix heat maps using Matplotlib and Seaborn, which are essential visualization tools for understanding relationships in your data.
By the end of this video, you'll confidently calculate correlation coefficients using whichever Python method best suits your project needs, whether you're doing exploratory data analysis, statistical testing, or machine learning feature selection.
TIMESTAMPS
00:00 Introduction & Overview
01:42 Background: Pearson Correlation Coefficient
03:02 Manual Calculation Example
06:44 Correlation Matrix Explained
07:22 Python Coding: Imports & Setup
08:47 Example 1: Manual Calculation in Python
13:00 Example 2: Using NumPy
13:53 Example 3: Using SciPy
14:34 Example 4: Using Pandas
16:40 Example 5: Scatter Plot Visualization
18:20 Example 6: Correlation Matrix Heat Map
21:43 Recap & Conclusion
OTHER SOCIALS:
Ryan’s LinkedIn: / ryan-p-nolan
Matt’s LinkedIn: / matt-payne-ceo
Twitter/X: https://x.com/RyanMattDS
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.
*This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.