_safe_indexing#

sklearn.utils._safe_indexing(X, indices, *, axis=0)[source]#

Return rows, items or columns of X using indices.

Warning

This utility is documented, but private. This means that backward compatibility might be broken without any deprecation cycle.

Parameters:

Xarray-like, sparse-matrix, list, pandas.DataFrame, pandas.Series

Data from which to sample rows, items or columns. list are only supported when axis=0.

indicesbool, int, str, slice, array-like

If axis=0, boolean and integer array-like, integer slice, and scalar integer are supported.
If axis=1:
- to select a single column, indices can be of int type for all X types and str only for dataframe. The selected subset will be 1D, unless X is a sparse matrix in which case it will be 2D.
- to select multiples columns, indices can be one of the following: list, array, slice. The type used in these containers can be one of the following: int, ‘bool’ and str. However, str is only supported when X is a dataframe. The selected subset will be 2D.

axisint, default=0

The axis along which X will be subsampled. axis=0 will select rows while axis=1 will select columns.

Returns:

subset: Subset of X on axis 0 or 1.

Notes

CSR, CSC, and LIL sparse matrices are supported. COO sparse matrices are not supported.

Examples

>>> import numpy as np
>>> from sklearn.utils import _safe_indexing
>>> data = np.array([[1, 2], [3, 4], [5, 6]])
>>> _safe_indexing(data, 0, axis=0)  # select the first row
array([1, 2])
>>> _safe_indexing(data, 0, axis=1)  # select the first column
array([1, 3, 5])