text: Title: Finding indexes with `np.where(condition)` or `np.asarray(condition).nonzero()`
  Body: Throughout the repo, we use `np.where(condition)` for getting indexes, for instance in [SelectorMixin.get_support()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/feature_selection/_base.py#L73), in [SimpleImputer.transform()](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/sklearn/impute/_base.py#L670) and in several of our examples ([example](https://github.com/scikit-learn/scikit-learn/blob/fba028b07ed2b4e52dd3719dad0d990837bde28c/examples/linear_model/plot_sgd_iris.py#L58)).
  
  The numpy documentation [discourages](https://numpy.org/doc/2.1/reference/generated/numpy.where.html) the use of `np.where` with just passing a condition and recommends `np.asarray(condition).nonzero()` instead.
  
  For cleanliness of code, should we adopt this recommendation, at least in the examples? Or are there good reasons why we do that?