numpy.nanmin

numpy.nanmin(a, axis=None, out=None, keepdims=False)

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:

a : array_like

Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.

axis : int, optional

Axis along which the minimum is computed. The default is to compute the minimum of the flattened array.

out : ndarray, 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. See doc.ufuncs for details.

New in version 1.8.0.

keepdims : bool, 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.

New in version 1.8.0.

Returns:

nanmin : ndarray

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

nanmax

The maximum value of an array along a given axis, ignoring any NaNs.

amin

The minimum value of an array along a given axis, propagating any NaNs.

fmin

Element-wise minimum of two arrays, ignoring any NaNs.

minimum

Element-wise minimum of two arrays, propagating any NaNs.

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are neither NaN nor infinity.

amax, fmax, maximum

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

>>> 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.NINF])
-inf