Data Structures

DataArray

xarray.DataArray is xarray’s implementation of a labeled, multi-dimensional array. It has several key properties:

  • values: a numpy.ndarray holding the array’s values
  • dims: dimension names for each axis (e.g., ('x', 'y', 'z'))
  • coords: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings)
  • attrs: an OrderedDict to hold arbitrary metadata (attributes)

xarray uses dims and coords to enable its core metadata aware operations. Dimensions provide names that xarray uses instead of the axis argument found in many numpy functions. Coordinates enable fast label based indexing and alignment, building on the functionality of the index found on a pandas DataFrame or Series.

DataArray objects also can have a name and can hold arbitrary metadata in the form of their attrs property (an ordered dictionary). Names and attributes are strictly for users and user-written code: xarray makes no attempt to interpret them, and propagates them only in unambiguous cases (see FAQ, What is your approach to metadata?).

Creating a DataArray

The DataArray constructor takes:

  • data: a multi-dimensional array of values (e.g., a numpy ndarray, Series, DataFrame or Panel)
  • coords: a list or dictionary of coordinates
  • dims: a list of dimension names. If omitted, dimension names are taken from coords if possible.
  • attrs: a dictionary of attributes to add to the instance
  • name: a string that names the instance
In [1]: data = np.random.rand(4, 3)

In [2]: locs = ['IA', 'IL', 'IN']

In [3]: times = pd.date_range('2000-01-01', periods=4)

In [4]: foo = xr.DataArray(data, coords=[times, locs], dims=['time', 'space'])

In [5]: foo
Out[5]: 
<xarray.DataArray (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 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'

Only data is required; all of other arguments will be filled in with default values:

In [6]: xr.DataArray(data)
Out[6]: 
<xarray.DataArray (dim_0: 4, dim_1: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 0.897237,  0.37675 ,  0.336222],
       [ 0.451376,  0.840255,  0.123102],
       [ 0.543026,  0.373012,  0.447997]])
Dimensions without coordinates: dim_0, dim_1

As you can see, dimension names are always present in the xarray data model: if you do not provide them, defaults of the form dim_N will be created. However, coordinates are always optional, and dimensions do not have automatic coordinate labels.

Note

This is different from pandas, where axes always have tick labels, which default to the integers [0, ..., n-1].

Prior to xarray v0.9, xarray copied this behavior: default coordinates for each dimension would be created if coordinates were not supplied explicitly. This is no longer the case.

Coordinates can be specified in the following ways:

  • A list of values with length equal to the number of dimensions, providing coordinate labels for each dimension. Each value must be of one of the following forms:
    • A DataArray or Variable
    • A tuple of the form (dims, data[, attrs]), which is converted into arguments for Variable
    • A pandas object or scalar value, which is converted into a DataArray
    • A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it’s name
  • A dictionary of {coord_name: coord} where values are of the same form as the list. Supplying coordinates as a dictionary allows other coordinates than those corresponding to dimensions (more on these later). If you supply coords as a dictionary, you must explicitly provide dims.

As a list of tuples:

In [7]: xr.DataArray(data, coords=[('time', times), ('space', locs)])
Out[7]: 
<xarray.DataArray (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 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'

As a dictionary:

In [8]: xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42,
   ...:                            'ranking': ('space', [1, 2, 3])},
   ...:              dims=['time', 'space'])
   ...: 
Out[8]: 
<xarray.DataArray (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 0.897237,  0.37675 ,  0.336222],
       [ 0.451376,  0.840255,  0.123102],
       [ 0.543026,  0.373012,  0.447997]])
Coordinates:
    const    int64 42
  * space    (space) <U2 'IA' 'IL' 'IN'
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
    ranking  (space) int64 1 2 3

As a dictionary with coords across multiple dimensions:

In [9]: xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42,
   ...:                            'ranking': (('time', 'space'), np.arange(12).reshape(4,3))},
   ...:              dims=['time', 'space'])
   ...: 
Out[9]: 
<xarray.DataArray (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 0.897237,  0.37675 ,  0.336222],
       [ 0.451376,  0.840255,  0.123102],
       [ 0.543026,  0.373012,  0.447997]])
Coordinates:
    const    int64 42
  * space    (space) <U2 'IA' 'IL' 'IN'
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
    ranking  (time, space) int64 0 1 2 3 4 5 6 7 8 9 10 11

If you create a DataArray by supplying a pandas Series, DataFrame or Panel, any non-specified arguments in the DataArray constructor will be filled in from the pandas object:

In [10]: df = pd.DataFrame({'x': [0, 1], 'y': [2, 3]}, index=['a', 'b'])

In [11]: df.index.name = 'abc'

In [12]: df.columns.name = 'xyz'

In [13]: df
Out[13]: 
xyz  x  y
abc      
a    0  2
b    1  3

In [14]: xr.DataArray(df)
Out[14]: 
<xarray.DataArray (abc: 2, xyz: 2)>
array([[0, 2],
       [1, 3]])
Coordinates:
  * abc      (abc) object 'a' 'b'
  * xyz      (xyz) object 'x' 'y'

DataArray properties

Let’s take a look at the important properties on our array:

In [15]: foo.values
Out[15]: 
array([[ 0.127,  0.967,  0.26 ],
       [ 0.897,  0.377,  0.336],
       [ 0.451,  0.84 ,  0.123],
       [ 0.543,  0.373,  0.448]])

In [16]: foo.dims
Out[16]: ('time', 'space')

In [17]: foo.coords
Out[17]: 
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'

In [18]: foo.attrs
Out[18]: OrderedDict()

In [19]: print(foo.name)
None

You can modify values inplace:

In [20]: foo.values = 1.0 * foo.values

Note

The array values in a DataArray have a single (homogeneous) data type. To work with heterogeneous or structured data types in xarray, use coordinates, or put separate DataArray objects in a single Dataset (see below).

Now fill in some of that missing metadata:

In [21]: foo.name = 'foo'

In [22]: foo.attrs['units'] = 'meters'

In [23]: foo
Out[23]: 
<xarray.DataArray 'foo' (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 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'
Attributes:
    units: meters

The rename() method is another option, returning a new data array:

In [24]: foo.rename('bar')
Out[24]: 
<xarray.DataArray 'bar' (time: 4, space: 3)>
array([[ 0.12697 ,  0.966718,  0.260476],
       [ 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'
Attributes:
    units: meters

DataArray Coordinates

The coords property is dict like. Individual coordinates can be accessed from the coordinates by name, or even by indexing the data array itself:

In [25]: foo.coords['time']
Out[25]: 
<xarray.DataArray 'time' (time: 4)>
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
       '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04

In [26]: foo['time']
Out[26]: 
<xarray.DataArray 'time' (time: 4)>
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
       '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04

These are also DataArray objects, which contain tick-labels for each dimension.

Coordinates can also be set or removed by using the dictionary like syntax:

In [27]: foo['ranking'] = ('space', [1, 2, 3])

In [28]: foo.coords
Out[28]: 
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'
    ranking  (space) int64 1 2 3

In [29]: del foo['ranking']

In [30]: foo.coords
Out[30]: 
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04
  * space    (space) <U2 'IA' 'IL' 'IN'

Dataset

xarray.Dataset is xarray’s multi-dimensional equivalent of a DataFrame. It is a dict-like container of labeled arrays (DataArray objects) with aligned dimensions. It is designed as an in-memory representation of the data model from the netCDF file format.

In addition to the dict-like interface of the dataset itself, which can be used to access any variable in a dataset, datasets have four key properties:

  • dims: a dictionary mapping from dimension names to the fixed length of each dimension (e.g., {'x': 6, 'y': 6, 'time': 8})
  • data_vars: a dict-like container of DataArrays corresponding to variables
  • coords: another dict-like container of DataArrays intended to label points used in data_vars (e.g., arrays of numbers, datetime objects or strings)
  • attrs: an OrderedDict to hold arbitrary metadata

The distinction between whether a variables falls in data or coordinates (borrowed from CF conventions) is mostly semantic, and you can probably get away with ignoring it if you like: dictionary like access on a dataset will supply variables found in either category. However, xarray does make use of the distinction for indexing and computations. Coordinates indicate constant/fixed/independent quantities, unlike the varying/measured/dependent quantities that belong in data.

Here is an example of how we might structure a dataset for a weather forecast:

_images/dataset-diagram.png

In this example, it would be natural to call temperature and precipitation “data variables” and all the other arrays “coordinate variables” because they label the points along the dimensions. (see [1] for more background on this example).

Creating a Dataset

To make an Dataset from scratch, supply dictionaries for any variables (data_vars), coordinates (coords) and attributes (attrs).

  • data_vars should be a dictionary with each key as the name of the variable and each value as one of:
    • A DataArray or Variable
    • A tuple of the form (dims, data[, attrs]), which is converted into arguments for Variable
    • A pandas object, which is converted into a DataArray
    • A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it’s name
  • coords should be a dictionary of the same form as data_vars.
  • attrs should be a dictionary.

Let’s create some fake data for the example we show above:

In [31]: temp = 15 + 8 * np.random.randn(2, 2, 3)

In [32]: precip = 10 * np.random.rand(2, 2, 3)

In [33]: lon = [[-99.83, -99.32], [-99.79, -99.23]]

In [34]: lat = [[42.25, 42.21], [42.63, 42.59]]

# for real use cases, its good practice to supply array attributes such as
# units, but we won't bother here for the sake of brevity
In [35]: ds = xr.Dataset({'temperature': (['x', 'y', 'time'],  temp),
   ....:                  'precipitation': (['x', 'y', 'time'], precip)},
   ....:                 coords={'lon': (['x', 'y'], lon),
   ....:                         'lat': (['x', 'y'], lat),
   ....:                         'time': pd.date_range('2014-09-06', periods=3),
   ....:                         'reference_time': pd.Timestamp('2014-09-05')})
   ....: 

In [36]: ds
Out[36]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y
Data variables:
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...

Here we pass xarray.DataArray objects or a pandas object as values in the dictionary:

In [37]: xr.Dataset({'bar': foo})
Out[37]: 
<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:
    bar      (time, space) float64 0.127 0.9667 0.2605 0.8972 0.3767 0.3362 ...
In [38]: xr.Dataset({'bar': foo.to_pandas()})
Out[38]: 
<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) object 'IA' 'IL' 'IN'
Data variables:
    bar      (time, space) float64 0.127 0.9667 0.2605 0.8972 0.3767 0.3362 ...

Where a pandas object is supplied as a value, the names of its indexes are used as dimension names, and its data is aligned to any existing dimensions.

You can also create an dataset from:

Dataset contents

Dataset implements the Python dictionary interface, with values given by xarray.DataArray objects:

In [39]: 'temperature' in ds
Out[39]: True

In [40]: ds.keys()
Out[40]: 
KeysView(<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y
Data variables:
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...)

In [41]: ds['temperature']
Out[41]: 
<xarray.DataArray 'temperature' (x: 2, y: 2, time: 3)>
array([[[ 11.040566,  23.57443 ,  20.772441],
        [  9.345831,   6.6834  ,  17.174879]],

       [[ 11.600221,  19.536163,  17.209856],
        [  6.300794,   9.610482,  15.909187]]])
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y

The valid keys include each listed coordinate and data variable.

Data and coordinate variables are also contained separately in the data_vars and coords dictionary-like attributes:

In [42]: ds.data_vars
Out[42]: 
Data variables:
    precipitation  (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 0.3777 ...
    temperature    (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...

In [43]: ds.coords
Out[43]: 
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59

Finally, like data arrays, datasets also store arbitrary metadata in the form of attributes:

In [44]: ds.attrs
Out[44]: OrderedDict()

In [45]: ds.attrs['title'] = 'example attribute'

In [46]: ds
Out[46]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y
Data variables:
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
Attributes:
    title: example attribute

xarray does not enforce any restrictions on attributes, but serialization to some file formats may fail if you use objects that are not strings, numbers or numpy.ndarray objects.

As a useful shortcut, you can use attribute style access for reading (but not setting) variables and attributes:

In [47]: ds.temperature
Out[47]: 
<xarray.DataArray 'temperature' (x: 2, y: 2, time: 3)>
array([[[ 11.040566,  23.57443 ,  20.772441],
        [  9.345831,   6.6834  ,  17.174879]],

       [[ 11.600221,  19.536163,  17.209856],
        [  6.300794,   9.610482,  15.909187]]])
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y

This is particularly useful in an exploratory context, because you can tab-complete these variable names with tools like IPython.

Dictionary like methods

We can update a dataset in-place using Python’s standard dictionary syntax. For example, to create this example dataset from scratch, we could have written:

In [48]: ds = xr.Dataset()

In [49]: ds['temperature'] = (('x', 'y', 'time'), temp)

In [50]: ds['precipitation'] = (('x', 'y', 'time'), precip)

In [51]: ds.coords['lat'] = (('x', 'y'), lat)

In [52]: ds.coords['lon'] = (('x', 'y'), lon)

In [53]: ds.coords['time'] = pd.date_range('2014-09-06', periods=3)

In [54]: ds.coords['reference_time'] = pd.Timestamp('2014-09-05')

To change the variables in a Dataset, you can use all the standard dictionary methods, including values, items, __delitem__, get and update(). Note that assigning a DataArray or pandas object to a Dataset variable using __setitem__ or update will automatically align the array(s) to the original dataset’s indexes.

You can copy a Dataset by calling the copy() method. By default, the copy is shallow, so only the container will be copied: the arrays in the Dataset will still be stored in the same underlying numpy.ndarray objects. You can copy all data by calling ds.copy(deep=True).

Transforming datasets

In addition to dictionary-like methods (described above), xarray has additional methods (like pandas) for transforming datasets into new objects.

For removing variables, you can select and drop an explicit list of variables by indexing with a list of names or using the drop() methods to return a new Dataset. These operations keep around coordinates:

In [55]: list(ds[['temperature']])
Out[55]: ['temperature', 'time', 'lon', 'reference_time', 'lat']

In [56]: list(ds[['x']])
Out[56]: ['x', 'reference_time']

In [57]: list(ds.drop('temperature'))
Out[57]: ['precipitation', 'lat', 'lon', 'time', 'reference_time']

If a dimension name is given as an argument to drop, it also drops all variables that use that dimension:

In [58]: list(ds.drop('time'))
Out[58]: ['temperature', 'precipitation', 'lat', 'lon', 'reference_time']

As an alternate to dictionary-like modifications, you can use assign() and assign_coords(). These methods return a new dataset with additional (or replaced) or values:

In [59]: ds.assign(temperature2 = 2 * ds.temperature)
Out[59]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    temperature2    (x, y, time) float64 22.08 47.15 41.54 18.69 13.37 34.35 ...

There is also the pipe() method that allows you to use a method call with an external function (e.g., ds.pipe(func)) instead of simply calling it (e.g., func(ds)). This allows you to write pipelines for transforming you data (using “method chaining”) instead of writing hard to follow nested function calls:

# these lines are equivalent, but with pipe we can make the logic flow
# entirely from left to right
In [60]: plt.plot((2 * ds.temperature.sel(x=0)).mean('y'))
Out[60]: [<matplotlib.lines.Line2D at 0x7f11b32fb7b8>]

In [61]: (ds.temperature
   ....:  .sel(x=0)
   ....:  .pipe(lambda x: 2 * x)
   ....:  .mean('y')
   ....:  .pipe(plt.plot))
   ....: 
Out[61]: [<matplotlib.lines.Line2D at 0x7f11b32fbfd0>]

Both pipe and assign replicate the pandas methods of the same names (DataFrame.pipe and DataFrame.assign).

With xarray, there is no performance penalty for creating new datasets, even if variables are lazily loaded from a file on disk. Creating new objects instead of mutating existing objects often results in easier to understand code, so we encourage using this approach.

Renaming variables

Another useful option is the rename() method to rename dataset variables:

In [62]: ds.rename({'temperature': 'temp', 'precipitation': 'precip'})
Out[62]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
Dimensions without coordinates: x, y
Data variables:
    temp            (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
    precip          (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...

The related swap_dims() method allows you do to swap dimension and non-dimension variables:

In [63]: ds.coords['day'] = ('time', [6, 7, 8])

In [64]: ds.swap_dims({'time': 'day'})
Out[64]: 
<xarray.Dataset>
Dimensions:         (day: 3, x: 2, y: 2)
Coordinates:
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    time            (day) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
  * day             (day) int64 6 7 8
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, day) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
    precipitation   (x, y, day) float64 5.904 2.453 3.404 9.847 9.195 0.3777 ...

Coordinates

Coordinates are ancillary variables stored for DataArray and Dataset objects in the coords attribute:

In [65]: ds.coords
Out[65]: 
Coordinates:
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
    day             (time) int64 6 7 8

Unlike attributes, xarray does interpret and persist coordinates in operations that transform xarray objects.

One dimensional coordinates with a name equal to their sole dimension (marked by * when printing a dataset or data array) take on a special meaning in xarray. They are used for label based indexing and alignment, like the index found on a pandas DataFrame or Series. Indeed, these “dimension” coordinates use a pandas.Index internally to store their values.

Other than for indexing, xarray does not make any direct use of the values associated with coordinates. Coordinates with names not matching a dimension are not used for alignment or indexing, nor are they required to match when doing arithmetic (see Coordinates).

Modifying coordinates

To entirely add or remove coordinate arrays, you can use dictionary like syntax, as shown above.

To convert back and forth between data and coordinates, you can use the set_coords() and reset_coords() methods:

In [66]: ds.reset_coords()
Out[66]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
Dimensions without coordinates: x, y
Data variables:
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    day             (time) int64 6 7 8

In [67]: ds.set_coords(['temperature', 'precipitation'])
Out[67]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
    temperature     (x, y, time) float64 11.04 23.57 20.77 9.346 6.683 17.17 ...
    precipitation   (x, y, time) float64 5.904 2.453 3.404 9.847 9.195 ...
    lat             (x, y) float64 42.25 42.21 42.63 42.59
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
    day             (time) int64 6 7 8
Dimensions without coordinates: x, y
Data variables:
    *empty*

In [68]: ds['temperature'].reset_coords(drop=True)
Out[68]: 
<xarray.DataArray 'temperature' (x: 2, y: 2, time: 3)>
array([[[ 11.040566,  23.57443 ,  20.772441],
        [  9.345831,   6.6834  ,  17.174879]],

       [[ 11.600221,  19.536163,  17.209856],
        [  6.300794,   9.610482,  15.909187]]])
Coordinates:
  * time     (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
Dimensions without coordinates: x, y

Notice that these operations skip coordinates with names given by dimensions, as used for indexing. This mostly because we are not entirely sure how to design the interface around the fact that xarray cannot store a coordinate and variable with the name but different values in the same dictionary. But we do recognize that supporting something like this would be useful.

Coordinates methods

Coordinates objects also have a few useful methods, mostly for converting them into dataset objects:

In [69]: ds.coords.to_dataset()
Out[69]: 
<xarray.Dataset>
Dimensions:         (time: 3, x: 2, y: 2)
Coordinates:
    day             (time) int64 6 7 8
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
    reference_time  datetime64[ns] 2014-09-05
    lat             (x, y) float64 42.25 42.21 42.63 42.59
Dimensions without coordinates: x, y
Data variables:
    *empty*

The merge method is particularly interesting, because it implements the same logic used for merging coordinates in arithmetic operations (see Computation):

In [70]: alt = xr.Dataset(coords={'z': [10], 'lat': 0, 'lon': 0})

In [71]: ds.coords.merge(alt.coords)
Out[71]: 
<xarray.Dataset>
Dimensions:         (time: 3, z: 1)
Coordinates:
  * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
    reference_time  datetime64[ns] 2014-09-05
    day             (time) int64 6 7 8
  * z               (z) int64 10
Data variables:
    *empty*

The coords.merge method may be useful if you want to implement your own binary operations that act on xarray objects. In the future, we hope to write more helper functions so that you can easily make your functions act like xarray’s built-in arithmetic.

Indexes

To convert a coordinate (or any DataArray) into an actual pandas.Index, use the to_index() method:

In [72]: ds['time'].to_index()
Out[72]: DatetimeIndex(['2014-09-06', '2014-09-07', '2014-09-08'], dtype='datetime64[ns]', name='time', freq='D')

A useful shortcut is the indexes property (on both DataArray and Dataset), which lazily constructs a dictionary whose keys are given by each dimension and whose the values are Index objects:

In [73]: ds.indexes
Out[73]: time: DatetimeIndex(['2014-09-06', '2014-09-07', '2014-09-08'], dtype='datetime64[ns]', name='time', freq='D')

MultiIndex coordinates

Xarray supports labeling coordinate values with a pandas.MultiIndex:

In [74]: midx = pd.MultiIndex.from_arrays([['R', 'R', 'V', 'V'], [.1, .2, .7, .9]],
   ....:                                  names=('band', 'wn'))
   ....: 

In [75]: mda = xr.DataArray(np.random.rand(4), coords={'spec': midx}, dims='spec')

In [76]: mda
Out[76]: 
<xarray.DataArray (spec: 4)>
array([ 0.641666,  0.274592,  0.462354,  0.871372])
Coordinates:
  * spec     (spec) MultiIndex
  - band     (spec) object 'R' 'R' 'V' 'V'
  - wn       (spec) float64 0.1 0.2 0.7 0.9

For convenience multi-index levels are directly accessible as “virtual” or “derived” coordinates (marked by - when printing a dataset or data array):

In [77]: mda['band']
Out[77]: 
<xarray.DataArray 'band' (spec: 4)>
array(['R', 'R', 'V', 'V'], dtype=object)
Coordinates:
  * spec     (spec) MultiIndex
  - band     (spec) object 'R' 'R' 'V' 'V'
  - wn       (spec) float64 0.1 0.2 0.7 0.9

In [78]: mda.wn
Out[78]: 
<xarray.DataArray 'wn' (spec: 4)>
array([ 0.1,  0.2,  0.7,  0.9])
Coordinates:
  * spec     (spec) MultiIndex
  - band     (spec) object 'R' 'R' 'V' 'V'
  - wn       (spec) float64 0.1 0.2 0.7 0.9

Indexing with multi-index levels is also possible using the sel method (see Multi-level indexing).

Unlike other coordinates, “virtual” level coordinates are not stored in the coords attribute of DataArray and Dataset objects (although they are shown when printing the coords attribute). Consequently, most of the coordinates related methods don’t apply for them. It also can’t be used to replace one particular level.

Because in a DataArray or Dataset object each multi-index level is accessible as a “virtual” coordinate, its name must not conflict with the names of the other levels, coordinates and data variables of the same object. Even though Xarray set default names for multi-indexes with unnamed levels, it is recommended that you explicitly set the names of the levels.

[1]Latitude and longitude are 2D arrays because the dataset uses projected coordinates. reference_time refers to the reference time at which the forecast was made, rather than time which is the valid time for which the forecast applies.