# Combining data¶

• For combining datasets or data arrays along a dimension, see concatenate.
• For combining datasets with different variables, see merge.

## Concatenate¶

To combine arrays along existing or new dimension into a larger array, you can use concat(). concat takes an iterable of DataArray or Dataset objects, as well as a dimension name, and concatenates along that dimension:

In [1]: arr = xr.DataArray(np.random.randn(2, 3),
...:                    [('x', ['a', 'b']), ('y', [10, 20, 30])])
...:

In [2]: arr[:, :1]
Out[2]:
<xarray.DataArray (x: 2, y: 1)>
array([[ 0.4691123 ],
[-1.13563237]])
Coordinates:
* x        (x) |S1 'a' 'b'
* y        (y) int64 10

# this resembles how you would use np.concatenate
In [3]: xr.concat([arr[:, :1], arr[:, 1:]], dim='y')
Out[3]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.4691123 , -0.28286334, -1.5090585 ],
[-1.13563237,  1.21211203, -0.17321465]])
Coordinates:
* x        (x) |S1 'a' 'b'
* y        (y) int64 10 20 30


In addition to combining along an existing dimension, concat can create a new dimension by stacking lower dimensional arrays together:

In [4]: arr[0]
Out[4]:
<xarray.DataArray (y: 3)>
array([ 0.4691123 , -0.28286334, -1.5090585 ])
Coordinates:
x        |S1 'a'
* y        (y) int64 10 20 30

# to combine these 1d arrays into a 2d array in numpy, you would use np.array
In [5]: xr.concat([arr[0], arr[1]], 'x')
Out[5]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.4691123 , -0.28286334, -1.5090585 ],
[-1.13563237,  1.21211203, -0.17321465]])
Coordinates:
* y        (y) int64 10 20 30
* x        (x) |S1 'a' 'b'


If the second argument to concat is a new dimension name, the arrays will be concatenated along that new dimension, which is always inserted as the first dimension:

In [6]: xr.concat([arr[0], arr[1]], 'new_dim')
Out[6]:
<xarray.DataArray (new_dim: 2, y: 3)>
array([[ 0.4691123 , -0.28286334, -1.5090585 ],
[-1.13563237,  1.21211203, -0.17321465]])
Coordinates:
* y        (y) int64 10 20 30
x        (new_dim) |S1 'a' 'b'
* new_dim  (new_dim) int64 0 1


The second argument to concat can also be an Index or DataArray object as well as a string, in which case it is used to label the values along the new dimension:

In [7]: xr.concat([arr[0], arr[1]], pd.Index([-90, -100], name='new_dim'))
Out[7]:
<xarray.DataArray (new_dim: 2, y: 3)>
array([[ 0.4691123 , -0.28286334, -1.5090585 ],
[-1.13563237,  1.21211203, -0.17321465]])
Coordinates:
* y        (y) int64 10 20 30
x        (new_dim) |S1 'a' 'b'
* new_dim  (new_dim) int64 -90 -100


Of course, concat also works on Dataset objects:

In [8]: ds = arr.to_dataset(name='foo')

In [9]: xr.concat([ds.sel(x='a'), ds.sel(x='b')], 'x')
Out[9]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 3)
Coordinates:
* y        (y) int64 10 20 30
* x        (x) |S1 'a' 'b'
Data variables:
foo      (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732


concat() has a number of options which provide deeper control over which variables are concatenated and how it handles conflicting variables between datasets. With the default parameters, xarray will load some coordinate variables into memory to compare them between datasets. This may be prohibitively expensive if you are manipulating your dataset lazily using Out of core computation with dask.

## Merge¶

To combine variables and coordinates between multiple Datasets, you can use the merge() and update() methods. Merge checks for conflicting variables before merging and by default it returns a new Dataset:

In [10]: ds.merge({'hello': ('space', np.arange(3) + 10)})
Out[10]:
<xarray.Dataset>
Dimensions:  (space: 3, x: 2, y: 3)
Coordinates:
* x        (x) |S1 'a' 'b'
* y        (y) int64 10 20 30
* space    (space) int64 0 1 2
Data variables:
foo      (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732
hello    (space) int64 10 11 12


If you merge another dataset (or a dictionary including data array objects), by default the resulting dataset will be aligned on the union of all index coordinates:

In [11]: other = xr.Dataset({'bar': ('x', [1, 2, 3, 4]), 'x': list('abcd')})

In [12]: ds.merge(other)
Out[12]:
<xarray.Dataset>
Dimensions:  (x: 4, y: 3)
Coordinates:
* x        (x) object 'a' 'b' 'c' 'd'
* y        (y) int64 10 20 30
Data variables:
foo      (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732 nan ...
bar      (x) int64 1 2 3 4


This ensures that the merge is non-destructive.

The same non-destructive merging between DataArray index coordinates is used in the Dataset constructor:

In [13]: xr.Dataset({'a': arr[:-1], 'b': arr[1:]})
Out[13]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 3)
Coordinates:
* x        (x) object 'a' 'b'
* y        (y) int64 10 20 30
Data variables:
a        (x, y) float64 0.4691 -0.2829 -1.509 nan nan nan
b        (x, y) float64 nan nan nan -1.136 1.212 -0.1732


## Update¶

In contrast to merge, update modifies a dataset in-place without checking for conflicts, and will overwrite any existing variables with new values:

In [14]: ds.update({'space': ('space', [10.2, 9.4, 3.9])})
Out[14]:
<xarray.Dataset>
Dimensions:  (space: 3, x: 2, y: 3)
Coordinates:
* x        (x) |S1 'a' 'b'
* y        (y) int64 10 20 30
* space    (space) float64 10.2 9.4 3.9
Data variables:
foo      (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732


However, dimensions are still required to be consistent between different Dataset variables, so you cannot change the size of a dimension unless you replace all dataset variables that use it.

update also performs automatic alignment if necessary. Unlike merge, it maintains the alignment of the original array instead of merging indexes:

In [15]: ds.update(other)
Out[15]:
<xarray.Dataset>
Dimensions:  (space: 3, x: 2, y: 3)
Coordinates:
* x        (x) object 'a' 'b'
* y        (y) int64 10 20 30
* space    (space) float64 10.2 9.4 3.9
Data variables:
foo      (x, y) float64 0.4691 -0.2829 -1.509 -1.136 1.212 -0.1732
bar      (x) int64 1 2


The exact same alignment logic when setting a variable with __setitem__ syntax:

In [16]: ds['baz'] = xr.DataArray([9, 9, 9, 9, 9], coords=[('x', list('abcde'))])

In [17]: ds.baz
Out[17]:
<xarray.DataArray 'baz' (x: 2)>
array([9, 9])
Coordinates:
* x        (x) object 'a' 'b'


## Equals and identical¶

xarray objects can be compared by using the equals(), identical() and broadcast_equals() methods. These methods are used by the optional compat argument on concat and merge.

equals checks dimension names, indexes and array values:

In [18]: arr.equals(arr.copy())
Out[18]: True


identical also checks attributes, and the name of each object:

In [19]: arr.identical(arr.rename('bar'))
Out[19]: False


broadcast_equals does a more relaxed form of equality check that allows variables to have different dimensions, as long as values are constant along those new dimensions:

In [20]: left = xr.Dataset(coords={'x': 0})

In [21]: right = xr.Dataset({'x': [0, 0, 0]})

Out[22]: True


Like pandas objects, two xarray objects are still equal or identical if they have missing values marked by NaN in the same locations.

In contrast, the == operation performs element-wise comparison (like numpy):

In [23]: arr == arr.copy()
Out[23]:
<xarray.DataArray (x: 2, y: 3)>
array([[ True,  True,  True],
[ True,  True,  True]], dtype=bool)
Coordinates:
* x        (x) |S1 'a' 'b'
* y        (y) int64 10 20 30


Note that NaN does not compare equal to NaN in element-wise comparison; you may need to deal with missing values explicitly.