Indexing and selecting data (original) (raw)

Xarray offers extremely flexible indexing routines that combine the best features of NumPy and pandas for data selection.

The most basic way to access elements of a DataArrayobject is to use Python’s [] syntax, such as array[i, j], wherei and j are both integers. As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to pandas.DataFrame.loc is also possible. In label-based indexing, the element position i is automatically looked-up from the coordinate values.

Dimensions of xarray objects have names, so you can also lookup the dimensions by name, instead of remembering their positional order.

Quick overview#

In total, xarray supports four different kinds of indexing, as described below and summarized in this table:

More advanced indexing is also possible for all the methods by supplying DataArray objects as indexer. See Vectorized Indexing for the details.

Positional indexing#

Indexing a DataArray directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray:

da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) da[:2]

<xarray.DataArray (time: 2, space: 3)> Size: 48B array([[0.12696983, 0.96671784, 0.26047601], [0.89723652, 0.37674972, 0.33622174]]) Coordinates:

<xarray.DataArray ()> Size: 8B array(0.12696983) Coordinates: time datetime64[ns] 8B 2000-01-01 space <U2 8B 'IA'

<xarray.DataArray (time: 4, space: 2)> Size: 64B array([[0.26047601, 0.96671784], [0.33622174, 0.37674972], [0.12310214, 0.84025508], [0.44799682, 0.37301223]]) Coordinates:

Attributes are persisted in all indexing operations.

Warning

Positional indexing deviates from the NumPy when indexing with multiple arrays like da[[0, 1], [0, 1]], as described inVectorized Indexing.

Xarray also supports label-based indexing, just like pandas. Because we use a pandas.Index under the hood, label based indexing is very fast. To do label based indexing, use the loc attribute:

da.loc["2000-01-01":"2000-01-02", "IA"]

<xarray.DataArray (time: 2)> Size: 16B array([0.12696983, 0.89723652]) Coordinates:

In this example, the selected is a subpart of the array in the range ‘2000-01-01’:’2000-01-02’ along the first coordinate timeand with ‘IA’ value from the second coordinate space.

You can perform any of the label indexing operations supported by pandas, including indexing with individual, slices and lists/arrays of labels, as well as indexing with boolean arrays. Like pandas, label based indexing in xarray is_inclusive_ of both the start and stop bounds.

Setting values with label based indexing is also supported:

da.loc["2000-01-01", ["IL", "IN"]] = -10 da

<xarray.DataArray (time: 4, space: 3)> Size: 96B array([[ 0.12696983, -10. , -10. ], [ 0.89723652, 0.37674972, 0.33622174], [ 0.45137647, 0.84025508, 0.12310214], [ 0.5430262 , 0.37301223, 0.44799682]]) Coordinates:

Indexing with dimension names#

With the dimension names, we do not have to rely on dimension order and can use them explicitly to slice data. There are two ways to do this:

  1. Use the sel() and isel()convenience methods:

    index by integer array indices

    da.isel(space=0, time=slice(None, 2))

    <xarray.DataArray (time: 2)> Size: 16B
    array([0.12696983, 0.89723652])
    Coordinates:

    • time (time) datetime64[ns] 16B 2000-01-01 2000-01-02
      space <U2 8B 'IA'

    • 0.127 0.8972
      array([0.12696983, 0.89723652])

    • Coordinates: (2)
      * time

    (time)  
    datetime64\[ns\]  
    2000-01-01 2000-01-02  
    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000'],  
          dtype='datetime64[ns]')  
    * space  
    ()  
    <U2  
    'IA'

index by dimension coordinate labels

da.sel(time=slice("2000-01-01", "2000-01-02"))

<xarray.DataArray (time: 2, space: 3)> Size: 48B
array([[ 0.12696983, -10. , -10. ],
[ 0.89723652, 0.37674972, 0.33622174]])
Coordinates:

  1. Use a dictionary as the argument for array positional or label based array indexing:

index by integer array indices

da[dict(space=0, time=slice(None, 2))]

<xarray.DataArray (time: 2)> Size: 16B
array([0.12696983, 0.89723652])
Coordinates:

index by dimension coordinate labels

da.loc[dict(time=slice("2000-01-01", "2000-01-02"))]

<xarray.DataArray (time: 2, space: 3)> Size: 48B
array([[ 0.12696983, -10. , -10. ],
[ 0.89723652, 0.37674972, 0.33622174]])
Coordinates:

The arguments to these methods can be any objects that could index the array along the dimension given by the keyword, e.g., labels for an individual value,Python slice objects or 1-dimensional arrays.

Nearest neighbor lookups#

The label based selection methods sel(),reindex() and reindex_like() all support method and tolerance keyword argument. The method parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods 'pad','backfill' or 'nearest':

da = xr.DataArray([1, 2, 3], [("x", [0, 1, 2])]) da.sel(x=[1.1, 1.9], method="nearest")

<xarray.DataArray (x: 2)> Size: 16B array([2, 3]) Coordinates:

da.sel(x=0.1, method="backfill")

<xarray.DataArray ()> Size: 8B array(2) Coordinates: x int64 8B 1

da.reindex(x=[0.5, 1, 1.5, 2, 2.5], method="pad")

<xarray.DataArray (x: 5)> Size: 40B array([1, 2, 2, 3, 3]) Coordinates:

Tolerance limits the maximum distance for valid matches with an inexact lookup:

da.reindex(x=[1.1, 1.5], method="nearest", tolerance=0.2)

<xarray.DataArray (x: 2)> Size: 16B array([ 2., nan]) Coordinates:

The method parameter is not yet supported if any of the arguments to .sel() is a slice object:

da.sel(x=slice(1, 3), method="nearest")

NotImplementedError: cannot use method argument if any indexers are slice objects

However, you don’t need to use method to do inexact slicing. Slicing already returns all values inside the range (inclusive), as long as the index labels are monotonic increasing:

da.sel(x=slice(0.9, 3.1))

<xarray.DataArray (x: 2)> Size: 16B array([2, 3]) Coordinates:

Indexing axes with monotonic decreasing labels also works, as long as theslice or .loc arguments are also decreasing:

reversed_da = da[::-1] reversed_da.loc[3.1:0.9]

<xarray.DataArray (x: 2)> Size: 16B array([3, 2]) Coordinates:

Dataset indexing#

We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:

da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) ds = da.to_dataset(name="foo") ds.isel(space=[0], time=[0])

<xarray.Dataset> Size: 24B Dimensions: (time: 1, space: 1) Coordinates:

ds.sel(time="2000-01-01")

<xarray.Dataset> Size: 56B Dimensions: (space: 3) Coordinates: time datetime64[ns] 8B 2000-01-01

Positional indexing on a dataset is not supported because the ordering of dimensions in a dataset is somewhat ambiguous (it can vary between different arrays). However, you can do normal indexing with dimension names:

ds[dict(space=[0], time=[0])]

<xarray.Dataset> Size: 24B Dimensions: (time: 1, space: 1) Coordinates:

ds.loc[dict(time="2000-01-01")]

<xarray.Dataset> Size: 56B Dimensions: (space: 3) Coordinates: time datetime64[ns] 8B 2000-01-01

Dropping labels and dimensions#

The drop_sel() method returns a new object with the listed index labels along a dimension dropped:

ds.drop_sel(space=["IN", "IL"])

<xarray.Dataset> Size: 72B Dimensions: (time: 4, space: 1) Coordinates:

drop_sel is both a Dataset and DataArray method.

Use drop_dims() to drop a full dimension from a Dataset. Any variables with these dimensions are also dropped:

<xarray.Dataset> Size: 24B Dimensions: (space: 3) Coordinates:

Masking with where#

Indexing methods on xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in xarray, use where():

da = xr.DataArray(np.arange(16).reshape(4, 4), dims=["x", "y"]) da.where(da.x + da.y < 4)

<xarray.DataArray (x: 4, y: 4)> Size: 128B array([[ 0., 1., 2., 3.], [ 4., 5., 6., nan], [ 8., 9., nan, nan], [12., nan, nan, nan]]) Dimensions without coordinates: x, y

This is particularly useful for ragged indexing of multi-dimensional data, e.g., to apply a 2D mask to an image. Note that where follows all the usual xarray broadcasting and alignment rules for binary operations (e.g.,+) between the object being indexed and the condition, as described inComputation:

<xarray.DataArray (x: 4, y: 4)> Size: 128B array([[ 0., 1., nan, nan], [ 4., 5., nan, nan], [ 8., 9., nan, nan], [12., 13., nan, nan]]) Dimensions without coordinates: x, y

By default where maintains the original size of the data. For cases where the selected data size is much smaller than the original data, use of the option drop=True clips coordinate elements that are fully masked:

da.where(da.y < 2, drop=True)

<xarray.DataArray (x: 4, y: 2)> Size: 64B array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [12., 13.]]) Dimensions without coordinates: x, y

Selecting values with isin#

To check whether elements of an xarray object contain a single object, you can compare with the equality operator == (e.g., arr == 3). To check multiple values, use isin():

da = xr.DataArray([1, 2, 3, 4, 5], dims=["x"]) da.isin([2, 4])

<xarray.DataArray (x: 5)> Size: 5B array([False, True, False, True, False]) Dimensions without coordinates: x

isin() works particularly well withwhere() to support indexing by arrays that are not already labels of an array:

lookup = xr.DataArray([-1, -2, -3, -4, -5], dims=["x"]) da.where(lookup.isin([-2, -4]), drop=True)

<xarray.DataArray (x: 2)> Size: 16B array([2., 4.]) Dimensions without coordinates: x

However, some caution is in order: when done repeatedly, this type of indexing is significantly slower than using sel().

Vectorized Indexing#

Like numpy and pandas, xarray supports indexing many array elements at once in a vectorized manner.

If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as np.ndarray, list, but notDataArray() or Variable()) indexing can be understood as orthogonally. Each indexer component selects independently along the corresponding dimension, similar to how vector indexing works in Fortran or MATLAB, or after using the numpy.ix_() helper:

da = xr.DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]}, ) da

<xarray.DataArray (x: 3, y: 4)> Size: 96B array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) Coordinates:

<xarray.DataArray (x: 3, y: 2)> Size: 48B array([[ 1, 3], [ 9, 11], [ 9, 11]]) Coordinates:

For more flexibility, you can supply DataArray() objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions:

ind_x = xr.DataArray([0, 1], dims=["x"]) ind_y = xr.DataArray([0, 1], dims=["y"]) da[ind_x, ind_y] # orthogonal indexing

<xarray.DataArray (x: 2, y: 2)> Size: 32B array([[0, 1], [4, 5]]) Coordinates:

Slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:

Because [0, 1] is used to index along dimension 'x',

it is assumed to have dimension 'x'

da[[0, 1], ind_x]

<xarray.DataArray (x: 2)> Size: 16B array([0, 5]) Coordinates:

Furthermore, you can use multi-dimensional DataArray()as indexers, where the resultant array dimension is also determined by indexers’ dimension:

ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"]) da[ind]

<xarray.DataArray (a: 2, b: 2, y: 4)> Size: 128B array([[[0, 1, 2, 3], [4, 5, 6, 7]],

   [[0, 1, 2, 3],
    [4, 5, 6, 7]]])

Coordinates: x (a, b) int64 32B 0 1 0 1

Similar to how NumPy’s advanced indexing works, vectorized indexing for xarray is based on ourbroadcasting rules. See Indexing rules for the complete specification.

Vectorized indexing also works with isel, loc, and sel:

ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"]) da.isel(y=ind) # same as da[:, ind]

<xarray.DataArray (x: 3, a: 2, b: 2)> Size: 96B array([[[0, 1], [0, 1]],

   [[4, 5],
    [4, 5]],

   [[8, 9],
    [8, 9]]])

Coordinates:

ind = xr.DataArray([["a", "b"], ["b", "a"]], dims=["a", "b"]) da.loc[:, ind] # same as da.sel(y=ind)

<xarray.DataArray (x: 3, a: 2, b: 2)> Size: 96B array([[[0, 1], [1, 0]],

   [[4, 5],
    [5, 4]],

   [[8, 9],
    [9, 8]]])

Coordinates:

These methods may also be applied to Dataset objects

ds = da.to_dataset(name="bar") ds.isel(x=xr.DataArray([0, 1, 2], dims=["points"]))

<xarray.Dataset> Size: 136B Dimensions: (points: 3, y: 4) Coordinates: x (points) int64 24B 0 1 2

Vectorized indexing may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes. To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name. In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named “points”:

ds = xr.tutorial.open_dataset("air_temperature")

Define target latitude and longitude (where weather stations might be)

target_lon = xr.DataArray([200, 201, 202, 205], dims="points") target_lat = xr.DataArray([31, 41, 42, 42], dims="points")

Retrieve data at the grid cells nearest to the target latitudes and longitudes

da = ds["air"].sel(lon=target_lon, lat=target_lat, method="nearest") da

<xarray.DataArray 'air' (time: 2920, points: 4)> Size: 93kB [11680 values with dtype=float64] Coordinates: lat (points) float32 16B 30.0 40.0 42.5 42.5 lon (points) float32 16B 200.0 200.0 202.5 205.0

Tip

If you are lazily loading your data from disk, not every form of vectorized indexing is supported (or if supported, may not be supported efficiently). You may find increased performance by loading your data into memory first, e.g., with load().

Note

If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.

Assigning values with indexing#

To select and assign values to a portion of a DataArray() you can use indexing with .loc :

ds = xr.tutorial.open_dataset("air_temperature")

add an empty 2D dataarray

ds["empty"] = xr.full_like(ds.air.mean("time"), fill_value=0)

modify one grid point using loc()

ds["empty"].loc[dict(lon=260, lat=30)] = 100

modify a 2D region using loc()

lc = ds.coords["lon"] la = ds.coords["lat"] ds["empty"].loc[ dict(lon=lc[(lc > 220) & (lc < 260)], lat=la[(la > 20) & (la < 60)]) ] = 100

or where():

modify one grid point using xr.where()

ds["empty"] = xr.where( (ds.coords["lat"] == 20) & (ds.coords["lon"] == 260), 100, ds["empty"] )

or modify a 2D region using xr.where()

mask = ( (ds.coords["lat"] > 20) & (ds.coords["lat"] < 60) & (ds.coords["lon"] > 220) & (ds.coords["lon"] < 260) ) ds["empty"] = xr.where(mask, 100, ds["empty"])

Vectorized indexing can also be used to assign values to xarray object.

da = xr.DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]}, ) da

<xarray.DataArray (x: 3, y: 4)> Size: 96B array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) Coordinates:

da[0] = -1 # assignment with broadcasting da

<xarray.DataArray (x: 3, y: 4)> Size: 96B array([[-1, -1, -1, -1], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) Coordinates:

ind_x = xr.DataArray([0, 1], dims=["x"]) ind_y = xr.DataArray([0, 1], dims=["y"]) da[ind_x, ind_y] = -2 # assign -2 to (ix, iy) = (0, 0) and (1, 1) da

<xarray.DataArray (x: 3, y: 4)> Size: 96B array([[-2, -2, -1, -1], [-2, -2, 6, 7], [ 8, 9, 10, 11]]) Coordinates:

da[ind_x, ind_y] += 100 # increment is also possible da

<xarray.DataArray (x: 3, y: 4)> Size: 96B array([[98, 98, -1, -1], [98, 98, 6, 7], [ 8, 9, 10, 11]]) Coordinates:

Like numpy.ndarray, value assignment sometimes works differently from what one may expect.

da = xr.DataArray([0, 1, 2, 3], dims=["x"]) ind = xr.DataArray([0, 0, 0], dims=["x"]) da[ind] -= 1 da

<xarray.DataArray (x: 4)> Size: 32B array([-1, 1, 2, 3]) Dimensions without coordinates: x

Where the 0th element will be subtracted 1 only once. This is because v[0] = v[0] - 1 is called three times, rather thanv[0] = v[0] - 1 - 1 - 1. See Assigning values to indexed arrays for the details.

Note

Coordinates in both the left- and right-hand-side arrays should not conflict with each other. Otherwise, IndexError will be raised.

Warning

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

DO NOT do this

da.isel(space=0) = 0

Instead, values can be assigned using dictionary-based indexing:

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

da = xr.DataArray([0, 1, 2, 3], dims=["x"])

DO NOT do this

da.isel(x=[0, 1, 2])[1] = -1 da

<xarray.DataArray (x: 4)> Size: 32B array([0, 1, 2, 3]) Dimensions without coordinates: x

You can also assign values to all variables of a Dataset at once:

ds_org = xr.tutorial.open_dataset("eraint_uvz").isel( latitude=slice(56, 59), longitude=slice(255, 258), level=0 )

set all values to 0

ds = xr.zeros_like(ds_org) ds

/home/docs/checkouts/readthedocs.org/user_builds/xray/checkouts/stable/xarray/conventions.py:204: SerializationWarning: variable 'z' has non-conforming '_FillValue' np.float64(nan) defined, dropping '_FillValue' entirely. var = coder.decode(var, name=name) /home/docs/checkouts/readthedocs.org/user_builds/xray/checkouts/stable/xarray/conventions.py:204: SerializationWarning: variable 'u' has non-conforming '_FillValue' np.float64(nan) defined, dropping '_FillValue' entirely. var = coder.decode(var, name=name) /home/docs/checkouts/readthedocs.org/user_builds/xray/checkouts/stable/xarray/conventions.py:204: SerializationWarning: variable 'v' has non-conforming '_FillValue' np.float64(nan) defined, dropping '_FillValue' entirely. var = coder.decode(var, name=name)

<xarray.Dataset> Size: 468B Dimensions: (month: 2, latitude: 3, longitude: 3) Coordinates:

by integer

ds[dict(latitude=2, longitude=2)] = 1 ds["u"]

<xarray.DataArray 'u' (month: 2, latitude: 3, longitude: 3)> Size: 144B array([[[0., 0., 0.], [0., 0., 0.], [0., 0., 1.]],

   [[0., 0., 0.],
    [0., 0., 0.],
    [0., 0., 1.]]])

Coordinates:

<xarray.DataArray 'v' (month: 2, latitude: 3, longitude: 3)> Size: 144B array([[[0., 0., 0.], [0., 0., 0.], [0., 0., 1.]],

   [[0., 0., 0.],
    [0., 0., 0.],
    [0., 0., 1.]]])

Coordinates:

by label

ds.loc[dict(latitude=47.25, longitude=[11.25, 12])] = 100 ds["u"]

<xarray.DataArray 'u' (month: 2, latitude: 3, longitude: 3)> Size: 144B array([[[ 0., 0., 0.], [100., 100., 0.], [ 0., 0., 1.]],

   [[  0.,   0.,   0.],
    [100., 100.,   0.],
    [  0.,   0.,   1.]]])

Coordinates:

dataset as new values

new_dat = ds_org.loc[dict(latitude=48, longitude=[11.25, 12])] new_dat

<xarray.Dataset> Size: 120B Dimensions: (longitude: 2, month: 2) Coordinates:

ds.loc[dict(latitude=47.25, longitude=[11.25, 12])] = new_dat ds["u"]

<xarray.DataArray 'u' (month: 2, latitude: 3, longitude: 3)> Size: 144B array([[[ 0. , 0. , 0. ], [12.74992466, 12.68701646, 0. ], [ 0. , 0. , 1. ]],

   [[ 0.        ,  0.        ,  0.        ],
    [14.87464903, 14.62458894,  0.        ],
    [ 0.        ,  0.        ,  1.        ]]])

Coordinates:

The dimensions can differ between the variables in the dataset, but all variables need to have at least the dimensions specified in the indexer dictionary. The new values must be either a scalar, a DataArray or a Dataset itself that contains all variables that also appear in the dataset to be modified.

More advanced indexing#

The use of DataArray() objects as indexers enables very flexible indexing. The following is an example of the pointwise indexing:

da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=["x", "y"]) da

<xarray.DataArray (x: 7, y: 8)> Size: 448B 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, 25, 26, 27, 28, 29, 30, 31], [32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47], [48, 49, 50, 51, 52, 53, 54, 55]]) Dimensions without coordinates: x, y

da.isel(x=xr.DataArray([0, 1, 6], dims="z"), y=xr.DataArray([0, 1, 0], dims="z"))

<xarray.DataArray (z: 3)> Size: 24B array([ 0, 9, 48]) Dimensions without coordinates: z

where three elements at (ix, iy) = ((0, 0), (1, 1), (6, 0)) are selected and mapped along a new dimension z.

If you want to add a coordinate to the new dimension z, you can supply a DataArray with a coordinate,

da.isel( x=xr.DataArray([0, 1, 6], dims="z", coords={"z": ["a", "b", "c"]}), y=xr.DataArray([0, 1, 0], dims="z"), )

<xarray.DataArray (z: 3)> Size: 24B array([ 0, 9, 48]) Coordinates:

Analogously, label-based pointwise-indexing is also possible by the .selmethod:

da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) times = xr.DataArray( pd.to_datetime(["2000-01-03", "2000-01-02", "2000-01-01"]), dims="new_time" ) da.sel(space=xr.DataArray(["IA", "IL", "IN"], dims=["new_time"]), time=times)

<xarray.DataArray (new_time: 3)> Size: 24B array([0.9195404 , 0.34044494, 0.590426 ]) Coordinates: time (new_time) datetime64[ns] 24B 2000-01-03 2000-01-02 2000-01-01 space (new_time) <U2 24B 'IA' 'IL' 'IN'

Align and reindex#

Xarray’s reindex, reindex_like and align impose a DataArray orDataset onto a new set of coordinates corresponding to dimensions. The original values are subset to the index labels still found in the new labels, and values corresponding to new labels not found in the original object are in-filled with NaN.

Xarray operations that combine multiple objects generally automatically align their arguments to share the same indexes. However, manual alignment can be useful for greater control and for increased performance.

To reindex a particular dimension, use reindex():

da.reindex(space=["IA", "CA"])

<xarray.DataArray (time: 4, space: 2)> Size: 64B array([[0.57401177, nan], [0.24534982, nan], [0.9195404 , nan], [0.75356885, nan]]) Coordinates:

The reindex_like() method is a useful shortcut. To demonstrate, we will make a subset DataArray with new values:

foo = da.rename("foo") baz = (10 * da[:2, :2]).rename("baz") baz

<xarray.DataArray 'baz' (time: 2, space: 2)> Size: 32B array([[5.74011775, 0.61269962], [2.45349819, 3.40444937]]) Coordinates:

Reindexing foo with baz selects out the first two values along each dimension:

<xarray.DataArray 'foo' (time: 2, space: 2)> Size: 32B array([[0.57401177, 0.06126996], [0.24534982, 0.34044494]]) Coordinates:

The opposite operation asks us to reindex to a larger shape, so we fill in the missing values with NaN:

<xarray.DataArray 'baz' (time: 4, space: 3)> Size: 96B array([[5.74011775, 0.61269962, nan], [2.45349819, 3.40444937, nan], [ nan, nan, nan], [ nan, nan, nan]]) Coordinates:

The align() function lets us perform more flexible database-like'inner', 'outer', 'left' and 'right' joins:

xr.align(foo, baz, join="inner")

(<xarray.DataArray 'foo' (time: 2, space: 2)> Size: 32B array([[0.57401177, 0.06126996], [0.24534982, 0.34044494]]) Coordinates:

xr.align(foo, baz, join="outer")

(<xarray.DataArray 'foo' (time: 4, space: 3)> Size: 96B array([[0.57401177, 0.06126996, 0.590426 ], [0.24534982, 0.34044494, 0.98472874], [0.9195404 , 0.03777169, 0.86154929], [0.75356885, 0.40517876, 0.34352588]]) Coordinates:

Both reindex_like and align work interchangeably betweenDataArray and Dataset objects, and with any number of matching dimension names:

<xarray.Dataset> Size: 468B Dimensions: (month: 2, latitude: 3, longitude: 3) Coordinates:

<xarray.Dataset> Size: 468B Dimensions: (month: 2, latitude: 3, longitude: 3) Coordinates:

other = xr.DataArray(["a", "b", "c"], dims="other")

this is a no-op, because there are no shared dimension names

ds.reindex_like(other)

<xarray.Dataset> Size: 468B Dimensions: (month: 2, latitude: 3, longitude: 3) Coordinates:

Missing coordinate labels#

Coordinate labels for each dimension are optional (as of xarray v0.9). Label based indexing with .sel and .loc uses standard positional, integer-based indexing as a fallback for dimensions without a coordinate label:

da = xr.DataArray([1, 2, 3], dims="x") da.sel(x=[0, -1])

<xarray.DataArray (x: 2)> Size: 16B array([1, 3]) Dimensions without coordinates: x

Alignment between xarray objects where one or both do not have coordinate labels succeeds only if all dimensions of the same name have the same length. Otherwise, it raises an informative error:

AlignmentError: cannot reindex or align along dimension 'x' because of conflicting dimension sizes: {2, 3}

Underlying Indexes#

Xarray uses the pandas.Index internally to perform indexing operations. If you need to access the underlying indexes, they are available through the indexes attribute.

da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) da

<xarray.DataArray (time: 4, space: 3)> Size: 96B array([[0.17091717, 0.39465901, 0.64166617], [0.27459243, 0.46235433, 0.87137165], [0.40113122, 0.61058827, 0.11796713], [0.70218436, 0.41403366, 0.34234521]]) Coordinates:

Indexes: time DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', name='time', freq='D') space Index(['IA', 'IL', 'IN'], dtype='object', name='space')

DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', name='time', freq='D')

Use get_index() to get an index for a dimension, falling back to a default pandas.RangeIndex if it has no coordinate labels:

da = xr.DataArray([1, 2, 3], dims="x") da

<xarray.DataArray (x: 3)> Size: 24B array([1, 2, 3]) Dimensions without coordinates: x

RangeIndex(start=0, stop=3, step=1, name='x')

Copies vs. Views#

Whether array indexing returns a view or a copy of the underlying data depends on the nature of the labels.

For positional (integer) indexing, xarray follows the same rules as NumPy:

The rules for label based indexing are more complex:

Whether data is a copy or a view is more predictable in xarray than in pandas, so unlike pandas, xarray does not produce SettingWithCopy warnings. However, you should still avoid assignment with chained indexing.

Note that other operations (such as values()) may also return views rather than copies.

Multi-level indexing#

Just like pandas, advanced indexing on multi-level indexes is possible withloc and sel. You can slice a multi-index by providing multiple indexers, i.e., a tuple of slices, labels, list of labels, or any selector allowed by pandas:

midx = pd.MultiIndex.from_product([list("abc"), [0, 1]], names=("one", "two")) mda = xr.DataArray(np.random.rand(6, 3), [("x", midx), ("y", range(3))]) mda

<xarray.DataArray (x: 6, y: 3)> Size: 144B array([[0.59592532, 0.19986426, 0.09973676], [0.73459622, 0.01654451, 0.4813845 ], [0.09593887, 0.49730633, 0.83879627], [0.89733326, 0.73259152, 0.75872436], [0.56065718, 0.47147793, 0.13876812], [0.09446113, 0.94225634, 0.13409924]]) Coordinates:

mda.sel(x=(list("ab"), [0]))

<xarray.DataArray (x: 2, y: 3)> Size: 48B array([[0.59592532, 0.19986426, 0.09973676], [0.09593887, 0.49730633, 0.83879627]]) Coordinates:

You can also select multiple elements by providing a list of labels or tuples or a slice of tuples:

mda.sel(x=[("a", 0), ("b", 1)])

<xarray.DataArray (x: 2, y: 3)> Size: 48B array([[0.59592532, 0.19986426, 0.09973676], [0.89733326, 0.73259152, 0.75872436]]) Coordinates:

Additionally, xarray supports dictionaries:

mda.sel(x={"one": "a", "two": 0})

<xarray.DataArray (y: 3)> Size: 24B array([0.59592532, 0.19986426, 0.09973676]) Coordinates: x object 8B ('a', np.int64(0)) one <U1 4B 'a' two int64 8B 0

For convenience, sel also accepts multi-index levels directly as keyword arguments:

<xarray.DataArray (y: 3)> Size: 24B array([0.59592532, 0.19986426, 0.09973676]) Coordinates: x object 8B ('a', np.int64(0)) one <U1 4B 'a' two int64 8B 0

Note that using sel it is not possible to mix a dimension indexer with level indexers for that dimension (e.g., mda.sel(x={'one': 'a'}, two=0) will raise a ValueError).

Like pandas, xarray handles partial selection on multi-index (level drop). As shown below, it also renames the dimension / coordinate when the multi-index is reduced to a single index.

mda.loc[{"one": "a"}, ...]

<xarray.DataArray (two: 2, y: 3)> Size: 48B array([[0.59592532, 0.19986426, 0.09973676], [0.73459622, 0.01654451, 0.4813845 ]]) Coordinates:

Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using loc in some ambiguous cases. For example, formda.loc[{'one': 'a', 'two': 0}] and mda.loc['a', 0] xarray always interprets (‘one’, ‘two’) and (‘a’, 0) as the names and labels of the 1st and 2nd dimension, respectively. You must specify all dimensions or use the ellipsis in the loc specifier, e.g. in the example above, mda.loc[{'one': 'a', 'two': 0}, :] or mda.loc[('a', 0), ...].

Indexing rules#

Here we describe the full rules xarray uses for vectorized indexing. Note that this is for the purposes of explanation: for the sake of efficiency and to support various backends, the actual implementation is different.

  1. (Only for label based indexing.) Look up positional indexes along each dimension from the corresponding pandas.Index.
  2. A full slice object : is inserted for each dimension without an indexer.
  3. slice objects are converted into arrays, given bynp.arange(*slice.indices(...)).
  4. Assume dimension names for array indexers without dimensions, such asnp.ndarray and list, from the dimensions to be indexed along. For example, v.isel(x=[0, 1]) is understood asv.isel(x=xr.DataArray([0, 1], dims=['x'])).
  5. For each variable in a Dataset or DataArray (the array and its coordinates):
    1. Broadcast all relevant indexers based on their dimension names (see Broadcasting by dimension name for full details).
    2. Index the underling array by the broadcast indexers, using NumPy’s advanced indexing rules.
  6. If any indexer DataArray has coordinates and no coordinate with the same name exists, attach them to the indexed object.

Note

Only 1-dimensional boolean arrays can be used as indexers.