jax.numpy.nanmin(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]#

Return minimum of an array or minimum along an axis, ignoring any NaNs.

LAX-backend implementation of numpy.nanmin().

Original docstring below.

When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.

  • a (array_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.

  • 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.

    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.

  • initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

  • where (array_like of bool, optional) – Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

  • out (None) –


nanmin – 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.

Return type: