numpy.nanmin — NumPy v2.2 Manual (original) (raw)
numpy.nanmin(a, axis=None, out=None, keepdims=, initial=, where=)[source]#
Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.
Parameters:
aarray_like
Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.
axis{int, tuple of int, None}, optional
Axis or axes along which the minimum is computed. The default is to compute the minimum of the flattened array.
outndarray, optional
Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. SeeOutput type determination for more details.
keepdimsbool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.
If the value is anything but the default, then_keepdims_ will be passed through to the min method of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.
initialscalar, optional
The maximum value of an output element. Must be present to allow computation on empty slice. See reduce for details.
New in version 1.22.0.
wherearray_like of bool, optional
Elements to compare for the minimum. See reducefor details.
New in version 1.22.0.
Returns:
nanminndarray
An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.
See also
The maximum value of an array along a given axis, ignoring any NaNs.
The minimum value of an array along a given axis, propagating any NaNs.
Element-wise minimum of two arrays, ignoring any NaNs.
Element-wise minimum of two arrays, propagating any NaNs.
Shows which elements are Not a Number (NaN).
Shows which elements are neither NaN nor infinity.
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to np.min.
Examples
import numpy as np a = np.array([[1, 2], [3, np.nan]]) np.nanmin(a) 1.0 np.nanmin(a, axis=0) array([1., 2.]) np.nanmin(a, axis=1) array([1., 3.])
When positive infinity and negative infinity are present:
np.nanmin([1, 2, np.nan, np.inf]) 1.0 np.nanmin([1, 2, np.nan, -np.inf]) -inf