xarray.DataArray.idxmin#

DataArray.idxmin(dim=None, *, skipna=None, fill_value=<NA>, keep_attrs=None)[source]#

Return the coordinate label of the minimum value along one or more dimensions.

Returns a new DataArray named after the dimension with the values of the coordinate labels along that dimension corresponding to minimum values along that dimension.

If a sequence of dimensions or ... is passed to dim, the minimum is taken over all of them and a dict of DataArray objects, one for each dimension, is returned instead, like argmin(). The result can be passed to sel().

In comparison to argmin(), this returns the coordinate label while argmin() returns the index.

Parameters:

  • dim (Hashable, sequence of Hashable or ..., optional) – Dimension(s) over which to apply idxmin. ... means all dimensions. If not given, the only dimension of a 1D array is used, and all dimensions of an array with 2 or more dimensions.

  • skipna (bool or None, default: None) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float, complex, and object dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (datetime64 or timedelta64). If False, NaN is treated as the minimum like in numpy.argmin(), so the label of the first NaN is returned.

  • fill_value (Any, default: NaN) – Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if skipna is False.

  • keep_attrs (bool or None, optional) – If True, the attributes (attrs) of the coordinate are copied to the result. If False, the result is returned without attributes.

Returns:

reduced (DataArray or dict of DataArray) – New DataArray object with idxmin applied to its data and the indicated dimension removed, or a dict of them mapping each dimension to its coordinate labels if dim is a sequence or ....

See also

Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin

Examples

>>> array = xr.DataArray(
...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
... )
>>> array.min()
<xarray.DataArray ()> Size: 8B
array(-2)
>>> array.argmin(...)
{'x': <xarray.DataArray ()> Size: 8B
array(4)}
>>> array.idxmin()
<xarray.DataArray 'x' ()> Size: 4B
array('e', dtype='<U1')

>>> array = xr.DataArray(
...     [
...         [2.0, 1.0, 2.0, 0.0, -2.0],
...         [-4.0, np.nan, 2.0, np.nan, -2.0],
...         [np.nan, np.nan, 1.0, np.nan, np.nan],
...     ],
...     dims=["y", "x"],
...     coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2},
... )
>>> array.min(dim="x")
<xarray.DataArray (y: 3)> Size: 24B
array([-2., -4.,  1.])
Coordinates:
  * y        (y) int64 24B -1 0 1
>>> array.argmin(dim="x")
<xarray.DataArray (y: 3)> Size: 24B
array([4, 0, 2])
Coordinates:
  * y        (y) int64 24B -1 0 1
>>> array.idxmin(dim="x")
<xarray.DataArray 'x' (y: 3)> Size: 24B
array([16.,  0.,  4.])
Coordinates:
  * y        (y) int64 24B -1 0 1
>>> array.idxmin(dim=["y", "x"])
{'y': <xarray.DataArray 'y' ()> Size: 8B
array(0), 'x': <xarray.DataArray 'x' ()> Size: 8B
array(0.)}
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