Interpretar un mapa de calor de correlaciones

With many columns, reading correlations one pair at a time is painful. A heatmap shows every pairing at once as a grid of colors. 🔥

First build a correlation matrix. In pandas, calling corr on a numeric DataFrame returns a square table of every column paired with every other.

Then turn that matrix into color. The seaborn heatmap function maps each cell to a shade, so patterns jump out instantly.

Warm colors usually mark strong positive links and cool colors mark negative ones. Pale, washed-out cells sit near zero, signaling little relationship.

The main diagonal is always 1, since every column correlates perfectly with itself. Ignore it; the real story lives in the off-diagonal cells.

A heatmap is symmetric across the diagonal because the link between A and B equals the link between B and A. You only need to read one triangle.

Set annot to True so each cell prints its exact value. Color shows the big picture while the numbers let you confirm the precise strength.

Pick a diverging colormap like coolwarm so negative and positive get opposite hues, with zero sitting neutral in the middle. It makes direction obvious.

Two features that are deeply correlated are often redundant. The heatmap flags them so you can drop one and simplify a model without losing much signal.

Scan the row or column for your target variable. The strongest cells there hint at which features may carry the most predictive value.

To cut clutter, many analysts hide the upper triangle with a mask. The result reads cleaner while keeping every meaningful pair visible.

You spot two features with a correlation near 0.97 on your heatmap. What is a common next step?

Build a matrix with corr, plot it with seaborn, and read warm and cool cells. Ignore the diagonal, watch for redundant features, and scan the target row. ✅