scipy.sparse.csr_matrix.

nanmin#

csr_matrix.nanmin(axis=None, out=None, *, explicit=False)[source]#

Return the minimum, ignoring any Nans, along an axis.

Return the minimum, ignoring any Nans, of the array/matrix along an axis. By default this takes all elements into account, but with explicit set, only stored elements are considered.

Added in version 1.11.0.

Parameters:
axis{-2, -1, 0, 1, None} optional

Axis along which the minimum is computed. The default is to compute the minimum over all elements, returning a scalar (i.e., axis = None).

outNone, optional

This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.

explicit{False, True} optional (default: False)

When set to True, only the stored elements will be considered. If a row/column is empty, the sparse.coo_array returned has no stored element (i.e. an implicit zero) for that row/column.

Added in version 1.15.0.

Returns:
amincoo_array or scalar

Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is a sparse.coo_array of dimension a.ndim - 1.

See also

nanmax

The maximum value of a sparse array/matrix along a given axis, ignoring NaNs.

min

The minimum value of a sparse array/matrix along a given axis, propagating NaNs.

numpy.nanmin

NumPy’s implementation of ‘nanmin’.