Indexing and selecting data

Similarly to pandas objects, xarray objects support both integer and label based lookups along each dimension. However, xarray objects also have named dimensions, so you can optionally use dimension names instead of relying on the positional ordering of dimensions.

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

Dimension lookup Index lookup DataArray syntax Dataset syntax
Positional By integer arr[:, 0] not available
Positional By label arr.loc[:, 'IA'] not available
By name By integer arr.isel(space=0) or
arr[dict(space=0)]
ds.isel(space=0) or
ds[dict(space=0)]
By name By label arr.sel(space='IA') or
arr.loc[dict(space='IA')]
ds.sel(space='IA') or
ds.loc[dict(space='IA')]

Positional indexing

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

In [1]: arr = xr.DataArray(np.random.rand(4, 3),
   ...:                    [('time', pd.date_range('2000-01-01', periods=4)),
   ...:                     ('space', ['IA', 'IL', 'IN'])])
   ...: 

In [2]: arr[:2]
Out[2]: 
<xarray.DataArray (time: 2, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 0.897237,  0.37675 ,  0.336222]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02
  * space    (space) <U2 'IA' 'IL' 'IN'

In [3]: arr[0, 0]
Out[3]: 
<xarray.DataArray ()>
array(0.12696983303810094)
Coordinates:
    time     datetime64[ns] 2000-01-01
    space    <U2 'IA'

In [4]: arr[:, [2, 1]]
Out[4]: 
<xarray.DataArray (time: 4, space: 2)>
array([[ 0.260476,  0.966718],
       [ 0.336222,  0.37675 ],
       [ 0.123102,  0.840255],
       [ 0.447997,  0.373012]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IN' 'IL'

Attributes are persisted in all indexing operations.

Warning

Positional indexing deviates from the NumPy when indexing with multiple arrays like arr[[0, 1], [0, 1]], as described in Orthogonal (outer) vs. vectorized indexing. See Pointwise indexing for how to achieve this functionality in xarray.

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:

In [5]: arr.loc['2000-01-01':'2000-01-02', 'IA']
Out[5]: 
<xarray.DataArray (time: 2)>
array([ 0.12697 ,  0.897237])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02
    space    <U2 'IA'

You can perform any of the label indexing operations supported by pandas, including indexing with individual, slices and 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:

In [6]: arr.loc['2000-01-01', ['IL', 'IN']] = -10

In [7]: arr
Out[7]: 
<xarray.DataArray (time: 4, space: 3)>
array([[  0.12697 , -10.      , -10.      ],
       [  0.897237,   0.37675 ,   0.336222],
       [  0.451376,   0.840255,   0.123102],
       [  0.543026,   0.373012,   0.447997]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'

Indexing with labeled dimensions

With labeled dimensions, 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 a dictionary as the argument for array positional or label based array indexing:

    # index by integer array indices
    In [8]: arr[dict(space=0, time=slice(None, 2))]
    Out[8]: 
    <xarray.DataArray (time: 2)>
    array([ 0.12697 ,  0.897237])
    Coordinates:
      * time     (time) datetime64[ns] 2000-01-01 2000-01-02
        space    <U2 'IA'
    
    # index by dimension coordinate labels
    In [9]: arr.loc[dict(time=slice('2000-01-01', '2000-01-02'))]
    Out[9]: 
    <xarray.DataArray (time: 2, space: 3)>
    array([[  0.12697 , -10.      , -10.      ],
           [  0.897237,   0.37675 ,   0.336222]])
    Coordinates:
      * time     (time) datetime64[ns] 2000-01-01 2000-01-02
      * space    (space) <U2 'IA' 'IL' 'IN'
    
  2. Use the sel() and isel() convenience methods:

    # index by integer array indices
    In [10]: arr.isel(space=0, time=slice(None, 2))
    Out[10]: 
    <xarray.DataArray (time: 2)>
    array([ 0.12697 ,  0.897237])
    Coordinates:
      * time     (time) datetime64[ns] 2000-01-01 2000-01-02
        space    <U2 'IA'
    
    # index by dimension coordinate labels
    In [11]: arr.sel(time=slice('2000-01-01', '2000-01-02'))
    Out[11]: 
    <xarray.DataArray (time: 2, space: 3)>
    array([[  0.12697 , -10.      , -10.      ],
           [  0.897237,   0.37675 ,   0.336222]])
    Coordinates:
      * time     (time) datetime64[ns] 2000-01-01 2000-01-02
      * space    (space) <U2 'IA' 'IL' 'IN'
    

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.

Note

We would love to be able to do indexing with labeled dimension names inside brackets, but unfortunately, Python does yet not support indexing with keyword arguments like arr[space=0]

Warning

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

# DO NOT do this
arr.isel(space=0) = 0

Depending on whether the underlying numpy indexing returns a copy or a view, the method will fail, and when it fails, it will fail silently. Instead, you should use normal index assignment:

# this is safe
arr[dict(space=0)] = 0

Pointwise indexing

xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects. While isel() performs orthogonal indexing, the isel_points() method provides similar numpy indexing behavior as if you were using multiple lists to index an array (e.g. arr[[0, 1], [0, 1]] ):

# index by integer array indices
In [12]: da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=['x', 'y'])

In [13]: da
Out[13]: 
<xarray.DataArray (x: 7, y: 8)>
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

In [14]: da.isel_points(x=[0, 1, 6], y=[0, 1, 0])
Out[14]: 
<xarray.DataArray (points: 3)>
array([ 0,  9, 48])
Dimensions without coordinates: points

There is also sel_points(), which analogously allows you to do point-wise indexing by label:

In [15]: times = pd.to_datetime(['2000-01-03', '2000-01-02', '2000-01-01'])

In [16]: arr.sel_points(space=['IA', 'IL', 'IN'], time=times)
Out[16]: 
<xarray.DataArray (points: 3)>
array([  0.451376,   0.37675 , -10.      ])
Coordinates:
    time     (points) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    space    (points) <U2 'IA' 'IL' 'IN'
Dimensions without coordinates: points

The equivalent pandas method to sel_points is lookup().

Dataset indexing

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

In [17]: ds = arr.to_dataset(name='foo')

In [18]: ds.isel(space=[0], time=[0])
Out[18]: 
<xarray.Dataset>
Dimensions:  (space: 1, time: 1)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01
  * space    (space) <U2 'IA'
Data variables:
    foo      (time, space) float64 0.127

In [19]: ds.sel(time='2000-01-01')
Out[19]: 
<xarray.Dataset>
Dimensions:  (space: 3)
Coordinates:
    time     datetime64[ns] 2000-01-01
  * space    (space) <U2 'IA' 'IL' 'IN'
Data variables:
    foo      (space) float64 0.127 -10.0 -10.0

In [20]: ds2 = da.to_dataset(name='bar')

In [21]: ds2.isel_points(x=[0, 1, 6], y=[0, 1, 0], dim='points')
Out[21]: 
<xarray.Dataset>
Dimensions:  (points: 3)
Dimensions without coordinates: points
Data variables:
    bar      (points) int64 0 9 48

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 labeled dimensions:

In [22]: ds[dict(space=[0], time=[0])]
Out[22]: 
<xarray.Dataset>
Dimensions:  (space: 1, time: 1)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01
  * space    (space) <U2 'IA'
Data variables:
    foo      (time, space) float64 0.127

In [23]: ds.loc[dict(time='2000-01-01')]
Out[23]: 
<xarray.Dataset>
Dimensions:  (space: 3)
Coordinates:
    time     datetime64[ns] 2000-01-01
  * space    (space) <U2 'IA' 'IL' 'IN'
Data variables:
    foo      (space) float64 0.127 -10.0 -10.0

Using indexing to assign values to a subset of dataset (e.g., ds[dict(space=0)] = 1) is not yet supported.

Dropping labels

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

In [24]: ds.drop(['IN', 'IL'], dim='space')
Out[24]: 
<xarray.Dataset>
Dimensions:  (space: 1, time: 4)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA'
Data variables:
    foo      (time, space) float64 0.127 0.8972 0.4514 0.543

drop is both a Dataset and DataArray method.

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':

In [25]: data = xr.DataArray([1, 2, 3], [('x', [0, 1, 2])])

In [26]: data.sel(x=[1.1, 1.9], method='nearest')
Out[26]: 
<xarray.DataArray (x: 2)>
array([2, 3])
Coordinates:
  * x        (x) int64 1 2

In [27]: data.sel(x=0.1, method='backfill')
Out[27]: 
<xarray.DataArray ()>
array(2)
Coordinates:
    x        int64 1

In [28]: data.reindex(x=[0.5, 1, 1.5, 2, 2.5], method='pad')
Out[28]: 
<xarray.DataArray (x: 5)>
array([1, 2, 2, 3, 3])
Coordinates:
  * x        (x) float64 0.5 1.0 1.5 2.0 2.5

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

In [29]: data.reindex(x=[1.1, 1.5], method='nearest', tolerance=0.2)
Out[29]: 
<xarray.DataArray (x: 2)>
array([  2.,  nan])
Coordinates:
  * x        (x) float64 1.1 1.5

Using method='nearest' or a scalar argument with .sel() requires pandas version 0.16 or newer. Using tolerance requries pandas version 0.17 or newer.

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

In [30]: data.sel(x=slice(1, 3), method='nearest')
NotImplementedError

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:

In [31]: data.sel(x=slice(0.9, 3.1))
Out[31]: 
<xarray.DataArray (x: 2)>
array([2, 3])
Coordinates:
  * x        (x) int64 1 2

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

In [32]: reversed_data = data[::-1]

In [33]: reversed_data.loc[3.1:0.9]
Out[33]: 
<xarray.DataArray (x: 2)>
array([3, 2])
Coordinates:
  * x        (x) int64 2 1

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():

In [34]: arr2 = xr.DataArray(np.arange(16).reshape(4, 4), dims=['x', 'y'])

In [35]: arr2.where(arr2.x + arr2.y < 4)
Out[35]: 
<xarray.DataArray (x: 4, y: 4)>
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 in Computation:

In [36]: arr2.where(arr2.y < 2)
Out[36]: 
<xarray.DataArray (x: 4, y: 4)>
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:

In [37]: arr2.where(arr2.y < 2, drop=True)
Out[37]: 
<xarray.DataArray (x: 4, y: 2)>
array([[  0.,   1.],
       [  4.,   5.],
       [  8.,   9.],
       [ 12.,  13.]])
Dimensions without coordinates: x, y

Multi-level indexing

Just like pandas, advanced indexing on multi-level indexes is possible with loc 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:

In [38]: midx = pd.MultiIndex.from_product([list('abc'), [0, 1]],
   ....:                                   names=('one', 'two'))
   ....: 

In [39]: mda = xr.DataArray(np.random.rand(6, 3),
   ....:                    [('x', midx), ('y', range(3))])
   ....: 

In [40]: mda
Out[40]: 
<xarray.DataArray (x: 6, y: 3)>
array([[ 0.129441,  0.859879,  0.820388],
       [ 0.352054,  0.228887,  0.776784],
       [ 0.594784,  0.137554,  0.8529  ],
       [ 0.235507,  0.146227,  0.589869],
       [ 0.574012,  0.06127 ,  0.590426],
       [ 0.24535 ,  0.340445,  0.984729]])
Coordinates:
  * x        (x) MultiIndex
  - one      (x) object 'a' 'a' 'b' 'b' 'c' 'c'
  - two      (x) int64 0 1 0 1 0 1
  * y        (y) int64 0 1 2

In [41]: mda.sel(x=(list('ab'), [0]))
Out[41]: 
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.129441,  0.859879,  0.820388],
       [ 0.594784,  0.137554,  0.8529  ]])
Coordinates:
  * x        (x) MultiIndex
  - one      (x) object 'a' 'b'
  - two      (x) int64 0 0
  * y        (y) int64 0 1 2

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

In [42]: mda.sel(x=[('a', 0), ('b', 1)])
Out[42]: 
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.129441,  0.859879,  0.820388],
       [ 0.235507,  0.146227,  0.589869]])
Coordinates:
  * x        (x) MultiIndex
  - one      (x) object 'a' 'b'
  - two      (x) int64 0 1
  * y        (y) int64 0 1 2

Additionally, xarray supports dictionaries:

In [43]: mda.sel(x={'one': 'a', 'two': 0})
Out[43]: 
<xarray.DataArray (y: 3)>
array([ 0.129441,  0.859879,  0.820388])
Coordinates:
    x        object ('a', 0)
  * y        (y) int64 0 1 2

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

In [44]: mda.sel(one='a', two=0)
Out[44]: 
<xarray.DataArray (y: 3)>
array([ 0.129441,  0.859879,  0.820388])
Coordinates:
    x        object ('a', 0)
  * y        (y) int64 0 1 2

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.

In [45]: mda.loc[{'one': 'a'}, ...]
Out[45]: 
<xarray.DataArray (two: 2, y: 3)>
array([[ 0.129441,  0.859879,  0.820388],
       [ 0.352054,  0.228887,  0.776784]])
Coordinates:
  * two      (two) int64 0 1
  * y        (y) int64 0 1 2

Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using loc in some ambiguous cases. For example, for mda.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), ...].

Multi-dimensional indexing

xarray does not yet support efficient routines for generalized multi-dimensional indexing or regridding. However, we are definitely interested in adding support for this in the future (see GH475 for the ongoing discussion).

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:

  • Positional indexing with only integers and slices returns a view.
  • Positional indexing with arrays or lists returns a copy.

The rules for label based indexing are more complex:

  • Label-based indexing with only slices returns a view.
  • Label-based indexing with arrays returns a copy.
  • Label-based indexing with scalars returns a view or a copy, depending upon if the corresponding positional indexer can be represented as an integer or a slice object. The exact rules are determined by pandas.

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.

Orthogonal (outer) vs. vectorized indexing

Indexing with xarray objects has one important difference from indexing numpy arrays: you can only use one-dimensional arrays to index xarray objects, and each indexer is applied “orthogonally” along independent axes, instead of using numpy’s broadcasting rules to vectorize indexers. This means you can do indexing like this, which would require slightly more awkward syntax with numpy arrays:

In [46]: arr[arr['time.day'] > 1, arr['space'] != 'IL']
Out[46]: 
<xarray.DataArray (time: 3, space: 2)>
array([[ 0.897237,  0.336222],
       [ 0.451376,  0.123102],
       [ 0.543026,  0.447997]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IN'

This is a much simpler model than numpy’s advanced indexing. If you would like to do advanced-style array indexing in xarray, you have several options:

In [47]: arr.values[arr.values > 0.5]
Out[47]: array([ 0.897,  0.84 ,  0.543])

Align and reindex

xarray’s reindex, reindex_like and align impose a DataArray or Dataset 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():

In [48]: arr.reindex(space=['IA', 'CA'])
Out[48]: 
<xarray.DataArray (time: 4, space: 2)>
array([[ 0.12697 ,       nan],
       [ 0.897237,       nan],
       [ 0.451376,       nan],
       [ 0.543026,       nan]])
Coordinates:
  * space    (space) <U2 'IA' 'CA'
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04

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

In [49]: foo = arr.rename('foo')

In [50]: baz = (10 * arr[:2, :2]).rename('baz')

In [51]: baz
Out[51]: 
<xarray.DataArray 'baz' (time: 2, space: 2)>
array([[   1.269698, -100.      ],
       [   8.972365,    3.767497]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02
  * space    (space) <U2 'IA' 'IL'

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

In [52]: foo.reindex_like(baz)
Out[52]: 
<xarray.DataArray 'foo' (time: 2, space: 2)>
array([[  0.12697 , -10.      ],
       [  0.897237,   0.37675 ]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02
  * space    (space) object 'IA' 'IL'

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

In [53]: baz.reindex_like(foo)
Out[53]: 
<xarray.DataArray 'baz' (time: 4, space: 3)>
array([[   1.269698, -100.      ,         nan],
       [   8.972365,    3.767497,         nan],
       [        nan,         nan,         nan],
       [        nan,         nan,         nan]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) object 'IA' 'IL' 'IN'

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

In [54]: xr.align(foo, baz, join='inner')
Out[54]: 
(<xarray.DataArray 'foo' (time: 2, space: 2)>
 array([[  0.12697 , -10.      ],
        [  0.897237,   0.37675 ]])
 Coordinates:
   * time     (time) datetime64[ns] 2000-01-01 2000-01-02
   * space    (space) object 'IA' 'IL',
 <xarray.DataArray 'baz' (time: 2, space: 2)>
 array([[   1.269698, -100.      ],
        [   8.972365,    3.767497]])
 Coordinates:
   * time     (time) datetime64[ns] 2000-01-01 2000-01-02
   * space    (space) object 'IA' 'IL')

In [55]: xr.align(foo, baz, join='outer')
Out[55]: 
(<xarray.DataArray 'foo' (time: 4, space: 3)>
 array([[  0.12697 , -10.      , -10.      ],
        [  0.897237,   0.37675 ,   0.336222],
        [  0.451376,   0.840255,   0.123102],
        [  0.543026,   0.373012,   0.447997]])
 Coordinates:
   * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
   * space    (space) object 'IA' 'IL' 'IN',
 <xarray.DataArray 'baz' (time: 4, space: 3)>
 array([[   1.269698, -100.      ,         nan],
        [   8.972365,    3.767497,         nan],
        [        nan,         nan,         nan],
        [        nan,         nan,         nan]])
 Coordinates:
   * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
   * space    (space) object 'IA' 'IL' 'IN')

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

In [56]: ds
Out[56]: 
<xarray.Dataset>
Dimensions:  (space: 3, time: 4)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'
Data variables:
    foo      (time, space) float64 0.127 -10.0 -10.0 0.8972 0.3767 0.3362 ...

In [57]: ds.reindex_like(baz)
Out[57]: 
<xarray.Dataset>
Dimensions:  (space: 2, time: 2)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02
  * space    (space) object 'IA' 'IL'
Data variables:
    foo      (time, space) float64 0.127 -10.0 0.8972 0.3767

In [58]: other = xr.DataArray(['a', 'b', 'c'], dims='other')

# this is a no-op, because there are no shared dimension names
In [59]: ds.reindex_like(other)
Out[59]: 
<xarray.Dataset>
Dimensions:  (space: 3, time: 4)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'
Data variables:
    foo      (time, space) float64 0.127 -10.0 -10.0 0.8972 0.3767 0.3362 ...

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:

In [60]: array = xr.DataArray([1, 2, 3], dims='x')

In [61]: array.sel(x=[0, -1])
Out[61]: 
<xarray.DataArray (x: 2)>
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:

In [62]: xr.align(array, array[:2])
ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different 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.

In [63]: arr
Out[63]: 
<xarray.DataArray (time: 4, space: 3)>
array([[  0.12697 , -10.      , -10.      ],
       [  0.897237,   0.37675 ,   0.336222],
       [  0.451376,   0.840255,   0.123102],
       [  0.543026,   0.373012,   0.447997]])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'

In [64]: arr.indexes
Out[64]: 
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')

In [65]: arr.indexes['time']
Out[65]: 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:

In [66]: array
Out[66]: 
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Dimensions without coordinates: x

In [67]: array.get_index('x')
Out[67]: RangeIndex(start=0, stop=3, step=1, name='x')