nanargmin(values: 'np.ndarray', *, axis: 'int | None' = None, skipna: 'bool' = True, mask: 'npt.NDArray[np.bool_] | None' = None) -> 'int | np.ndarray'
nan-mask if known
The index/indices of min value in specified axis or -1 in the NA case
>>> import pandas.core.nanops as nanopsThis example is valid syntax, but we were not able to check execution
... arr = np.array([1, 2, 3, np.nan, 4])
... nanops.nanargmin(arr) 0
>>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)This example is valid syntax, but we were not able to check execution
... arr[2:, 0] = np.nan
... arr array([[ 0., 1., 2.], [ 3., 4., 5.], [nan, 7., 8.], [nan, 10., 11.]])
>>> nanops.nanargmin(arr, axis=1) array([0, 0, 1, 1])See :
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pandas.core.nanops.nanargmin
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