transform para características por grupo

Where agg returns one row per group, transform returns a value for every original row, same length as the input.

Because transform keeps the original shape, you can drop its result straight back into your DataFrame as a new column.

Each row receives its own group statistic, so every Paris row gets the Paris average, neatly aligned.

Subtract the group mean to see how far each row sits from its group average, a powerful model feature.

Divide by a group total to turn raw counts into a within-group share that compares fairly across groups.

A clean imputation trick: replace gaps using each row group mean instead of one global number. 🧩

Like agg, transform accepts a function name such as "mean" or your own callable returning a like-shaped result.

The output keeps the original index, which is why assigning it back as a column lines up perfectly.

Combine transform with rank to score each row against its group, great for top-N-per-group logic.

Choose transform when output must match input length; reach for apply only when you need full flexibility.

Your function must return a result the same length as the group, or transform raises an error instead of guessing.

What makes transform different from agg?

You can now compute group-wise features that keep the original shape, perfect for normalizing, imputing, and ranking. 🚀