xarray.DataArray.sel

Contents

xarray.DataArray.sel#

DataArray.sel(indexers=None, method=None, tolerance=None, drop=False, **indexers_kwargs)[source]#

Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).

In contrast to DataArray.isel, indexers for this method should use labels instead of integers.

Under the hood, this method is powered by using pandas’s powerful Index objects. This makes label based indexing essentially just as fast as using integer indexing.

It also means this method uses pandas’s (well documented) logic for indexing. This means you can use string shortcuts for datetime indexes (e.g., ‘2000-01’ to select all values in January 2000). It also means that slices are treated as inclusive of both the start and stop values, unlike normal Python indexing.

Warning

Do not try to assign values when using any of the indexing methods isel or sel:

da = xr.DataArray([0, 1, 2, 3], dims=['x'])
# DO NOT do this
da.isel(x=[0, 1, 2])[1] = -1

Assigning values with the chained indexing using .sel or .isel fails silently.

Parameters:
  • indexers (dict, optional) – A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See Indexing and selecting data for the details. One of indexers or indexers_kwargs must be provided.

  • method ({None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional) – Method to use for inexact matches:

    • None (default): only exact matches

    • pad / ffill: propagate last valid index value forward

    • backfill / bfill: propagate next valid index value backward

    • nearest: use nearest valid index value

  • tolerance (optional) – Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

  • drop (bool, optional) – If drop=True, drop coordinates variables in indexers instead of making them scalar.

  • **indexers_kwargs ({dim: indexer, ...}, optional) – The keyword arguments form of indexers. One of indexers or indexers_kwargs must be provided.

Returns:

obj (DataArray) – A new DataArray with the same contents as this DataArray, except the data and each dimension is indexed by the appropriate indexers. If indexer DataArrays have coordinates that do not conflict with this object, then these coordinates will be attached. In general, each array’s data will be a view of the array’s data in this DataArray, unless vectorized indexing was triggered by using an array indexer, in which case the data will be a copy.

See also

Dataset.sel DataArray.isel

Indexing

Tutorial material on indexing with Xarray objects

Indexing and Selecting Data

Tutorial material on basics of indexing

Examples

>>> da = xr.DataArray(
...     np.arange(25).reshape(5, 5),
...     coords={"x": np.arange(5), "y": np.arange(5)},
...     dims=("x", "y"),
... )
>>> da
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
Coordinates:
  * x        (x) int64 40B 0 1 2 3 4
  * y        (y) int64 40B 0 1 2 3 4
>>> tgt_x = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
>>> tgt_y = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
>>> da = da.sel(x=tgt_x, y=tgt_y, method="nearest")
>>> da
<xarray.DataArray (points: 5)> Size: 40B
array([ 0,  6, 12, 18, 24])
Coordinates:
    x        (points) int64 40B 0 1 2 3 4
    y        (points) int64 40B 0 1 2 3 4
Dimensions without coordinates: points