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# Reshaping and reorganizing data#

Reshaping and reorganizing data refers to the process of changing the structure or organization of data by modifying dimensions, array shapes, order of values, or indexes. Xarray provides several methods to accomplish these tasks.

These methods are particularly useful for reshaping xarray objects for use in machine learning packages, such as scikit-learn, that usually require two-dimensional numpy arrays as inputs. Reshaping can also be required before passing data to external visualization tools, for example geospatial data might expect input organized into a particular format corresponding to stacks of satellite images.

## Reordering dimensions#

To reorder dimensions on a `DataArray` or across all variables on a `Dataset`, use `transpose()`. An ellipsis (â€¦) can be used to represent all other dimensions:

```In [1]: ds = xr.Dataset({"foo": (("x", "y", "z"), [[[42]]]), "bar": (("y", "z"), [[24]])})

In [2]: ds.transpose("y", "z", "x")
Out[2]:
<xarray.Dataset>
Dimensions:  (x: 1, y: 1, z: 1)
Dimensions without coordinates: x, y, z
Data variables:
foo      (y, z, x) int64 42
bar      (y, z) int64 24

In [3]: ds.transpose(..., "x")  # equivalent
Out[3]:
<xarray.Dataset>
Dimensions:  (x: 1, y: 1, z: 1)
Dimensions without coordinates: x, y, z
Data variables:
foo      (y, z, x) int64 42
bar      (y, z) int64 24

In [4]: ds.transpose()  # reverses all dimensions
Out[4]:
<xarray.Dataset>
Dimensions:  (x: 1, y: 1, z: 1)
Dimensions without coordinates: x, y, z
Data variables:
foo      (z, y, x) int64 42
bar      (z, y) int64 24
```

## Expand and squeeze dimensions#

To expand a `DataArray` or all variables on a `Dataset` along a new dimension, use `expand_dims()`

```In [5]: expanded = ds.expand_dims("w")

In [6]: expanded
Out[6]:
<xarray.Dataset>
Dimensions:  (w: 1, x: 1, y: 1, z: 1)
Dimensions without coordinates: w, x, y, z
Data variables:
foo      (w, x, y, z) int64 42
bar      (w, y, z) int64 24
```

This method attaches a new dimension with size 1 to all data variables.

To remove such a size-1 dimension from the `DataArray` or `Dataset`, use `squeeze()`

```In [7]: expanded.squeeze("w")
Out[7]:
<xarray.Dataset>
Dimensions:  (x: 1, y: 1, z: 1)
Dimensions without coordinates: x, y, z
Data variables:
foo      (x, y, z) int64 42
bar      (y, z) int64 24
```

## Converting between datasets and arrays#

To convert from a Dataset to a DataArray, use `to_dataarray()`:

```In [8]: arr = ds.to_dataarray()

In [9]: arr
Out[9]:
<xarray.DataArray (variable: 2, x: 1, y: 1, z: 1)>
array([[[[42]]],

[[[24]]]])
Coordinates:
* variable  (variable) object 'foo' 'bar'
Dimensions without coordinates: x, y, z
```

This method broadcasts all data variables in the dataset against each other, then concatenates them along a new dimension into a new array while preserving coordinates.

To convert back from a DataArray to a Dataset, use `to_dataset()`:

```In [10]: arr.to_dataset(dim="variable")
Out[10]:
<xarray.Dataset>
Dimensions:  (x: 1, y: 1, z: 1)
Dimensions without coordinates: x, y, z
Data variables:
foo      (x, y, z) int64 42
bar      (x, y, z) int64 24
```

The broadcasting behavior of `to_dataarray` means that the resulting array includes the union of data variable dimensions:

```In [11]: ds2 = xr.Dataset({"a": 0, "b": ("x", [3, 4, 5])})

# the input dataset has 4 elements
In [12]: ds2
Out[12]:
<xarray.Dataset>
Dimensions:  (x: 3)
Dimensions without coordinates: x
Data variables:
a        int64 0
b        (x) int64 3 4 5

# the resulting array has 6 elements
In [13]: ds2.to_dataarray()
Out[13]:
<xarray.DataArray (variable: 2, x: 3)>
array([[0, 0, 0],
[3, 4, 5]])
Coordinates:
* variable  (variable) object 'a' 'b'
Dimensions without coordinates: x
```

Otherwise, the result could not be represented as an orthogonal array.

If you use `to_dataset` without supplying the `dim` argument, the DataArray will be converted into a Dataset of one variable:

```In [14]: arr.to_dataset(name="combined")
Out[14]:
<xarray.Dataset>
Dimensions:   (variable: 2, x: 1, y: 1, z: 1)
Coordinates:
* variable  (variable) object 'foo' 'bar'
Dimensions without coordinates: x, y, z
Data variables:
combined  (variable, x, y, z) int64 42 24
```

## Stack and unstack#

As part of xarrayâ€™s nascent support for `pandas.MultiIndex`, we have implemented `stack()` and `unstack()` method, for combining or splitting dimensions:

```In [15]: array = xr.DataArray(
....:     np.random.randn(2, 3), coords=[("x", ["a", "b"]), ("y", [0, 1, 2])]
....: )
....:

In [16]: stacked = array.stack(z=("x", "y"))

In [17]: stacked
Out[17]:
<xarray.DataArray (z: 6)>
array([ 0.469, -0.283, -1.509, -1.136,  1.212, -0.173])
Coordinates:
* z        (z) object MultiIndex
* x        (z) <U1 'a' 'a' 'a' 'b' 'b' 'b'
* y        (z) int64 0 1 2 0 1 2

In [18]: stacked.unstack("z")
Out[18]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.469, -0.283, -1.509],
[-1.136,  1.212, -0.173]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 0 1 2
```

As elsewhere in xarray, an ellipsis (â€¦) can be used to represent all unlisted dimensions:

```In [19]: stacked = array.stack(z=[..., "x"])

In [20]: stacked
Out[20]:
<xarray.DataArray (z: 6)>
array([ 0.469, -1.136, -0.283,  1.212, -1.509, -0.173])
Coordinates:
* z        (z) object MultiIndex
* y        (z) int64 0 0 1 1 2 2
* x        (z) <U1 'a' 'b' 'a' 'b' 'a' 'b'
```

These methods are modeled on the `pandas.DataFrame` methods of the same name, although in xarray they always create new dimensions rather than adding to the existing index or columns.

Like `DataFrame.unstack`, xarrayâ€™s `unstack` always succeeds, even if the multi-index being unstacked does not contain all possible levels. Missing levels are filled in with `NaN` in the resulting object:

```In [21]: stacked2 = stacked[::2]

In [22]: stacked2
Out[22]:
<xarray.DataArray (z: 3)>
array([ 0.469, -0.283, -1.509])
Coordinates:
* z        (z) object MultiIndex
* y        (z) int64 0 1 2
* x        (z) <U1 'a' 'a' 'a'

In [23]: stacked2.unstack("z")
Out[23]:
<xarray.DataArray (y: 3, x: 1)>
array([[ 0.469],
[-0.283],
[-1.509]])
Coordinates:
* y        (y) int64 0 1 2
* x        (x) <U1 'a'
```

However, xarrayâ€™s `stack` has an important difference from pandas: unlike pandas, it does not automatically drop missing values. Compare:

```In [24]: array = xr.DataArray([[np.nan, 1], [2, 3]], dims=["x", "y"])

In [25]: array.stack(z=("x", "y"))
Out[25]:
<xarray.DataArray (z: 4)>
array([nan,  1.,  2.,  3.])
Coordinates:
* z        (z) object MultiIndex
* x        (z) int64 0 0 1 1
* y        (z) int64 0 1 0 1

In [26]: array.to_pandas().stack()
Out[26]:
x  y
0  1    1.0
1  0    2.0
1    3.0
dtype: float64
```

We departed from pandasâ€™s behavior here because predictable shapes for new array dimensions is necessary for Parallel computing with Dask.

### Stacking different variables together#

These stacking and unstacking operations are particularly useful for reshaping xarray objects for use in machine learning packages, such as scikit-learn, that usually require two-dimensional numpy arrays as inputs. For datasets with only one variable, we only need `stack` and `unstack`, but combining multiple variables in a `xarray.Dataset` is more complicated. If the variables in the dataset have matching numbers of dimensions, we can call `to_dataarray()` and then stack along the the new coordinate. But `to_dataarray()` will broadcast the dataarrays together, which will effectively tile the lower dimensional variable along the missing dimensions. The method `xarray.Dataset.to_stacked_array()` allows combining variables of differing dimensions without this wasteful copying while `xarray.DataArray.to_unstacked_dataset()` reverses this operation. Just as with `xarray.Dataset.stack()` the stacked coordinate is represented by a `pandas.MultiIndex` object. These methods are used like this:

```In [27]: data = xr.Dataset(
....:     data_vars={"a": (("x", "y"), [[0, 1, 2], [3, 4, 5]]), "b": ("x", [6, 7])},
....:     coords={"y": ["u", "v", "w"]},
....: )
....:

In [28]: data
Out[28]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 3)
Coordinates:
* y        (y) <U1 'u' 'v' 'w'
Dimensions without coordinates: x
Data variables:
a        (x, y) int64 0 1 2 3 4 5
b        (x) int64 6 7

In [29]: stacked = data.to_stacked_array("z", sample_dims=["x"])

In [30]: stacked
Out[30]:
<xarray.DataArray 'a' (x: 2, z: 4)>
array([[0, 1, 2, 6],
[3, 4, 5, 7]])
Coordinates:
* z         (z) object MultiIndex
* variable  (z) object 'a' 'a' 'a' 'b'
* y         (z) object 'u' 'v' 'w' nan
Dimensions without coordinates: x

In [31]: unstacked = stacked.to_unstacked_dataset("z")

In [32]: unstacked
Out[32]:
<xarray.Dataset>
Dimensions:  (y: 3, x: 2)
Coordinates:
* y        (y) object 'u' 'v' 'w'
Dimensions without coordinates: x
Data variables:
a        (x, y) int64 0 1 2 3 4 5
b        (x) int64 6 7
```

In this example, `stacked` is a two dimensional array that we can easily pass to a scikit-learn or another generic numerical method.

Note

Unlike with `stack`, in `to_stacked_array`, the user specifies the dimensions they do not want stacked. For a machine learning task, these unstacked dimensions can be interpreted as the dimensions over which samples are drawn, whereas the stacked coordinates are the features. Naturally, all variables should possess these sampling dimensions.

## Set and reset index#

Complementary to stack / unstack, xarrayâ€™s `.set_index`, `.reset_index` and `.reorder_levels` allow easy manipulation of `DataArray` or `Dataset` multi-indexes without modifying the data and its dimensions.

You can create a multi-index from several 1-dimensional variables and/or coordinates using `set_index()`:

```In [33]: da = xr.DataArray(
....:     np.random.rand(4),
....:     coords={
....:         "band": ("x", ["a", "a", "b", "b"]),
....:         "wavenumber": ("x", np.linspace(200, 400, 4)),
....:     },
....:     dims="x",
....: )
....:

In [34]: da
Out[34]:
<xarray.DataArray (x: 4)>
array([0.123, 0.543, 0.373, 0.448])
Coordinates:
band        (x) <U1 'a' 'a' 'b' 'b'
wavenumber  (x) float64 200.0 266.7 333.3 400.0
Dimensions without coordinates: x

In [35]: mda = da.set_index(x=["band", "wavenumber"])

In [36]: mda
Out[36]:
<xarray.DataArray (x: 4)>
array([0.123, 0.543, 0.373, 0.448])
Coordinates:
* x           (x) object MultiIndex
* band        (x) <U1 'a' 'a' 'b' 'b'
* wavenumber  (x) float64 200.0 266.7 333.3 400.0
```

These coordinates can now be used for indexing, e.g.,

```In [37]: mda.sel(band="a")
Out[37]:
<xarray.DataArray (wavenumber: 2)>
array([0.123, 0.543])
Coordinates:
* wavenumber  (wavenumber) float64 200.0 266.7
band        <U1 'a'
```

Conversely, you can use `reset_index()` to extract multi-index levels as coordinates (this is mainly useful for serialization):

```In [38]: mda.reset_index("x")
Out[38]:
<xarray.DataArray (x: 4)>
array([0.123, 0.543, 0.373, 0.448])
Coordinates:
band        (x) <U1 'a' 'a' 'b' 'b'
wavenumber  (x) float64 200.0 266.7 333.3 400.0
Dimensions without coordinates: x
```

`reorder_levels()` allows changing the order of multi-index levels:

```In [39]: mda.reorder_levels(x=["wavenumber", "band"])
Out[39]:
<xarray.DataArray (x: 4)>
array([0.123, 0.543, 0.373, 0.448])
Coordinates:
* x           (x) object MultiIndex
* wavenumber  (x) float64 200.0 266.7 333.3 400.0
* band        (x) <U1 'a' 'a' 'b' 'b'
```

As of xarray v0.9 coordinate labels for each dimension are optional. You can also use `.set_index` / `.reset_index` to add / remove labels for one or several dimensions:

```In [40]: array = xr.DataArray([1, 2, 3], dims="x")

In [41]: array
Out[41]:
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Dimensions without coordinates: x

In [42]: array["c"] = ("x", ["a", "b", "c"])

In [43]: array.set_index(x="c")
Out[43]:
<xarray.DataArray (x: 3)>
array([1, 2, 3])
Coordinates:
* x        (x) <U1 'a' 'b' 'c'

In [44]: array = array.set_index(x="c")

In [45]: array = array.reset_index("x", drop=True)
```

## Shift and roll#

To adjust coordinate labels, you can use the `shift()` and `roll()` methods:

```In [46]: array = xr.DataArray([1, 2, 3, 4], dims="x")

In [47]: array.shift(x=2)
Out[47]:
<xarray.DataArray (x: 4)>
array([nan, nan,  1.,  2.])
Dimensions without coordinates: x

In [48]: array.roll(x=2, roll_coords=True)
Out[48]:
<xarray.DataArray (x: 4)>
array([3, 4, 1, 2])
Dimensions without coordinates: x
```

## Sort#

One may sort a DataArray/Dataset via `sortby()` and `sortby()`. The input can be an individual or list of 1D `DataArray` objects:

```In [49]: ds = xr.Dataset(
....:     {
....:         "A": (("x", "y"), [[1, 2], [3, 4]]),
....:         "B": (("x", "y"), [[5, 6], [7, 8]]),
....:     },
....:     coords={"x": ["b", "a"], "y": [1, 0]},
....: )
....:

In [50]: dax = xr.DataArray([100, 99], [("x", [0, 1])])

In [51]: day = xr.DataArray([90, 80], [("y", [0, 1])])

In [52]: ds.sortby([day, dax])
Out[52]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 2)
Coordinates:
* x        (x) <U1 'b' 'a'
* y        (y) int64 1 0
Data variables:
A        (x, y) int64 1 2 3 4
B        (x, y) int64 5 6 7 8
```

As a shortcut, you can refer to existing coordinates by name:

```In [53]: ds.sortby("x")
Out[53]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 2)
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 1 0
Data variables:
A        (x, y) int64 3 4 1 2
B        (x, y) int64 7 8 5 6

In [54]: ds.sortby(["y", "x"])
Out[54]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 2)
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 0 1
Data variables:
A        (x, y) int64 4 3 2 1
B        (x, y) int64 8 7 6 5

In [55]: ds.sortby(["y", "x"], ascending=False)
Out[55]:
<xarray.Dataset>
Dimensions:  (x: 2, y: 2)
Coordinates:
* x        (x) <U1 'b' 'a'
* y        (y) int64 1 0
Data variables:
A        (x, y) int64 1 2 3 4
B        (x, y) int64 5 6 7 8
```

## Reshaping via coarsen#

Whilst `coarsen` is normally used for reducing your dataâ€™s resolution by applying a reduction function (see the page on computation), it can also be used to reorganise your data without applying a computation via `construct()`.

Taking our example tutorial air temperature dataset over the Northern US

```In [56]: air = xr.tutorial.open_dataset("air_temperature")["air"]

In [57]: air.isel(time=0).plot(x="lon", y="lat")
```

we can split this up into sub-regions of size `(9, 18)` points using `construct()`:

```In [58]: regions = air.coarsen(lat=9, lon=18, boundary="pad").construct(
....:     lon=("x_coarse", "x_fine"), lat=("y_coarse", "y_fine")
....: )
....:

In [59]: regions
Out[59]:
<xarray.DataArray 'air' (time: 2920, y_coarse: 3, y_fine: 9, x_coarse: 3,
x_fine: 18)>
array([[[[[241.2 , 242.5 , 243.5 , ..., 238.7 , 239.6 , 241.  ],
[242.89, 244.8 , 246.5 , ..., 248.6 , 249.  , 249.5 ],
[249.6 , 249.1 , 247.8 , ..., 235.5 , 238.6 ,    nan]],

[[243.8 , 244.5 , 244.7 , ..., 237.1 , 237.2 , 238.  ],
[239.3 , 240.7 , 242.  , ..., 244.3 , 243.89, 244.  ],
[244.6 , 245.6 , 246.8 , ..., 235.3 , 239.3 ,    nan]],

[[250.  , 249.8 , 248.89, ..., 241.  , 240.1 , 239.7 ],
[239.8 , 240.1 , 240.39, ..., 249.1 , 246.8 , 243.7 ],
[240.6 , 239.1 , 240.2 , ..., 236.39, 241.7 ,    nan]],

...,

[[273.7 , 273.6 , 273.79, ..., 275.5 , 276.  , 273.7 ],
[269.  , 262.7 , 256.2 , ..., 252.89, 252.5 , 254.3 ],
[258.1 , 262.29, 265.1 , ..., 274.2 , 275.1 ,    nan]],

[[274.79, 275.2 , 275.6 , ..., 272.79, 274.9 , 275.5 ],
[273.79, 269.  , 261.9 , ..., 253.6 , 252.7 , 253.  ],
...
[289.89, 290.59, 291.19, ..., 295.69, 295.69, 295.49],
[296.19, 297.19, 297.09, ..., 292.49, 292.09,    nan]],

[[291.49, 291.39, 292.39, ..., 291.19, 290.99, 291.39],
[291.89, 292.99, 294.59, ..., 297.29, 297.69, 298.19],
[298.59, 298.29, 297.89, ..., 293.09, 293.19,    nan]],

...,

[[297.69, 298.09, 298.09, ..., 297.79, 298.39, 298.89],
[298.99, 298.89, 299.19, ..., 299.89, 300.19, 300.29],
[300.09, 300.39, 300.69, ..., 296.19, 295.69,    nan]],

[[   nan,    nan,    nan, ...,    nan,    nan,    nan],
[   nan,    nan,    nan, ...,    nan,    nan,    nan],
[   nan,    nan,    nan, ...,    nan,    nan,    nan]],

[[   nan,    nan,    nan, ...,    nan,    nan,    nan],
[   nan,    nan,    nan, ...,    nan,    nan,    nan],
[   nan,    nan,    nan, ...,    nan,    nan,    nan]]]]], dtype=float32)
Coordinates:
lat      (y_coarse, y_fine) float32 75.0 72.5 70.0 67.5 ... 15.0 nan nan
lon      (x_coarse, x_fine) float32 200.0 202.5 205.0 ... 327.5 330.0 nan
* time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Dimensions without coordinates: y_coarse, y_fine, x_coarse, x_fine
Attributes:
long_name:     4xDaily Air temperature at sigma level 995
units:         degK
precision:     2
GRIB_id:       11
GRIB_name:     TMP
var_desc:      Air temperature
dataset:       NMC Reanalysis
level_desc:    Surface
statistic:     Individual Obs
parent_stat:   Other
actual_range:  [185.16 322.1 ]
```

9 new regions have been created, each of size 9 by 18 points. The `boundary="pad"` kwarg ensured that all regions are the same size even though the data does not evenly divide into these sizes.

By plotting these 9 regions together via faceting we can see how they relate to the original data.

```In [60]: regions.isel(time=0).plot(
....:     x="x_fine", y="y_fine", col="x_coarse", row="y_coarse", yincrease=False
....: )
....:
Out[60]: <xarray.plot.facetgrid.FacetGrid at 0x7fa365da07f0>
```

We are now free to easily apply any custom computation to each coarsened region of our new dataarray. This would involve specifying that applied functions should act over the `"x_fine"` and `"y_fine"` dimensions, but broadcast over the `"x_coarse"` and `"y_coarse"` dimensions.