Compute Correlation Matrix for CSV Files Online
Compute a Pearson correlation matrix for numeric columns in CSV files directly in your browser. Instantly spot which variables move together — colour-coded heatmap, no upload required.
About this tool
Compute a Pearson correlation matrix for all numeric columns in a CSV file and download the result as a square table. Each cell shows the correlation coefficient between two columns, from −1 (perfectly inversely correlated) to 1 (perfectly correlated). Useful for feature selection before machine learning, diagnosing multicollinearity, and exploratory data analysis. Runs entirely in your browser — no data is uploaded.
Frequently Asked Questions
What does the CSV correlation matrix tool calculate?
It computes the Pearson correlation coefficient between every pair of numeric columns. The result is a symmetric matrix where each cell shows a value between −1 (perfect negative correlation) and 1 (perfect positive correlation). Values near 0 indicate no linear relationship.
How do I interpret a correlation matrix from a CSV file?
Values above 0.7 or below −0.7 typically indicate a strong relationship. Values between 0.3 and 0.7 (or their negatives) indicate a moderate relationship. Values near 0 suggest no linear correlation. Remember that correlation does not imply causation, and Pearson only captures linear relationships.
Does the CSV correlation matrix include non-numeric columns?
No. Only numeric (INTEGER, FLOAT, DOUBLE) columns are included. Text, boolean, and date columns are automatically excluded. Boolean columns encoded as 0/1 integers will be included as they are numeric.
Is my data private?
Yes — completely. Your file is never uploaded to any server. Everything runs locally in your browser using WebAssembly — processing happens entirely inside your tab. Once you close the tab, nothing is retained.
What is the maximum file size?
The free limit is 50 MB. For larger files, performance depends on your device's available memory — most modern machines handle 500 MB to 1 GB comfortably.
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