Computation¶

The labels associated with DataArray and Dataset objects enables some powerful shortcuts for computation, notably including aggregation and broadcasting by dimension names.

Basic array math¶

Arithmetic operations with a single DataArray automatically vectorize (like numpy) over all array values:

In [1]: arr = xr.DataArray(
...:     np.random.RandomState(0).randn(2, 3), [("x", ["a", "b"]), ("y", [10, 20, 30])]
...: )
...:

In [2]: arr - 3
Out[2]:
<xarray.DataArray (x: 2, y: 3)>
array([[-1.23594765, -2.59984279, -2.02126202],
[-0.7591068 , -1.13244201, -3.97727788]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30

In [3]: abs(arr)
Out[3]:
<xarray.DataArray (x: 2, y: 3)>
array([[1.76405235, 0.40015721, 0.97873798],
[2.2408932 , 1.86755799, 0.97727788]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


You can also use any of numpy’s or scipy’s many ufunc functions directly on a DataArray:

In [4]: np.sin(arr)
Out[4]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.9813841 ,  0.38956314,  0.82979374],
[ 0.78376151,  0.95628847, -0.82897801]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


Use where() to conditionally switch between values:

In [5]: xr.where(arr > 0, "positive", "negative")
Out[5]:
<xarray.DataArray (x: 2, y: 3)>
array([['positive', 'positive', 'positive'],
['positive', 'positive', 'negative']], dtype='<U8')
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


Use @ to perform matrix multiplication:

In [6]: arr @ arr
Out[6]:
<xarray.DataArray ()>
array(13.69438174)


Data arrays also implement many numpy.ndarray methods:

In [7]: arr.round(2)
Out[7]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 1.76,  0.4 ,  0.98],
[ 2.24,  1.87, -0.98]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30

In [8]: arr.T
Out[8]:
<xarray.DataArray (y: 3, x: 2)>
array([[ 1.76405235,  2.2408932 ],
[ 0.40015721,  1.86755799],
[ 0.97873798, -0.97727788]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


Missing values¶

xarray objects borrow the isnull(), notnull(), count(), dropna(), fillna(), ffill(), and bfill() methods for working with missing data from pandas:

In [9]: x = xr.DataArray([0, 1, np.nan, np.nan, 2], dims=["x"])

In [10]: x.isnull()
Out[10]:
<xarray.DataArray (x: 5)>
array([False, False,  True,  True, False])
Dimensions without coordinates: x

In [11]: x.notnull()
Out[11]:
<xarray.DataArray (x: 5)>
array([ True,  True, False, False,  True])
Dimensions without coordinates: x

In [12]: x.count()
Out[12]:
<xarray.DataArray ()>
array(3)

In [13]: x.dropna(dim="x")
Out[13]:
<xarray.DataArray (x: 3)>
array([0., 1., 2.])
Dimensions without coordinates: x

In [14]: x.fillna(-1)
Out[14]:
<xarray.DataArray (x: 5)>
array([ 0.,  1., -1., -1.,  2.])
Dimensions without coordinates: x

In [15]: x.ffill("x")
Out[15]:
<xarray.DataArray (x: 5)>
array([0., 1., 1., 1., 2.])
Dimensions without coordinates: x

In [16]: x.bfill("x")
Out[16]:
<xarray.DataArray (x: 5)>
array([0., 1., 2., 2., 2.])
Dimensions without coordinates: x


Like pandas, xarray uses the float value np.nan (not-a-number) to represent missing values.

xarray objects also have an interpolate_na() method for filling missing values via 1D interpolation.

In [17]: x = xr.DataArray(
....:     [0, 1, np.nan, np.nan, 2],
....:     dims=["x"],
....:     coords={"xx": xr.Variable("x", [0, 1, 1.1, 1.9, 3])},
....: )
....:

In [18]: x.interpolate_na(dim="x", method="linear", use_coordinate="xx")
Out[18]:
<xarray.DataArray (x: 5)>
array([0.  , 1.  , 1.05, 1.45, 2.  ])
Coordinates:
xx       (x) float64 0.0 1.0 1.1 1.9 3.0
Dimensions without coordinates: x


Note that xarray slightly diverges from the pandas interpolate syntax by providing the use_coordinate keyword which facilitates a clear specification of which values to use as the index in the interpolation. xarray also provides the max_gap keyword argument to limit the interpolation to data gaps of length max_gap or smaller. See interpolate_na() for more.

Aggregation¶

Aggregation methods have been updated to take a dim argument instead of axis. This allows for very intuitive syntax for aggregation methods that are applied along particular dimension(s):

In [19]: arr.sum(dim="x")
Out[19]:
<xarray.DataArray (y: 3)>
array([4.00494555e+00, 2.26771520e+00, 1.46010423e-03])
Coordinates:
* y        (y) int64 10 20 30

In [20]: arr.std(["x", "y"])
Out[20]:
<xarray.DataArray ()>
array(1.09038344)

In [21]: arr.min()
Out[21]:
<xarray.DataArray ()>
array(-0.97727788)


If you need to figure out the axis number for a dimension yourself (say, for wrapping code designed to work with numpy arrays), you can use the get_axis_num() method:

In [22]: arr.get_axis_num("y")
Out[22]: 1


These operations automatically skip missing values, like in pandas:

In [23]: xr.DataArray([1, 2, np.nan, 3]).mean()
Out[23]:
<xarray.DataArray ()>
array(2.)


If desired, you can disable this behavior by invoking the aggregation method with skipna=False.

Rolling window operations¶

DataArray objects include a rolling() method. This method supports rolling window aggregation:

In [24]: arr = xr.DataArray(np.arange(0, 7.5, 0.5).reshape(3, 5), dims=("x", "y"))

In [25]: arr
Out[25]:
<xarray.DataArray (x: 3, y: 5)>
array([[0. , 0.5, 1. , 1.5, 2. ],
[2.5, 3. , 3.5, 4. , 4.5],
[5. , 5.5, 6. , 6.5, 7. ]])
Dimensions without coordinates: x, y


rolling() is applied along one dimension using the name of the dimension as a key (e.g. y) and the window size as the value (e.g. 3). We get back a Rolling object:

In [26]: arr.rolling(y=3)
Out[26]: DataArrayRolling [window->3,center->False,dim->y,keep_attrs->False]


Aggregation and summary methods can be applied directly to the Rolling object:

In [27]: r = arr.rolling(y=3)

In [28]: r.reduce(np.std)
Out[28]:
<xarray.DataArray (x: 3, y: 5)>
array([[       nan,        nan, 0.40824829, 0.40824829, 0.40824829],
[       nan,        nan, 0.40824829, 0.40824829, 0.40824829],
[       nan,        nan, 0.40824829, 0.40824829, 0.40824829]])
Dimensions without coordinates: x, y

In [29]: r.mean()
Out[29]:
<xarray.DataArray (x: 3, y: 5)>
array([[nan, nan, 0.5, 1. , 1.5],
[nan, nan, 3. , 3.5, 4. ],
[nan, nan, 5.5, 6. , 6.5]])
Dimensions without coordinates: x, y


Aggregation results are assigned the coordinate at the end of each window by default, but can be centered by passing center=True when constructing the Rolling object:

In [30]: r = arr.rolling(y=3, center=True)

In [31]: r.mean()
Out[31]:
<xarray.DataArray (x: 3, y: 5)>
array([[nan, 0.5, 1. , 1.5, nan],
[nan, 3. , 3.5, 4. , nan],
[nan, 5.5, 6. , 6.5, nan]])
Dimensions without coordinates: x, y


As can be seen above, aggregations of windows which overlap the border of the array produce nans. Setting min_periods in the call to rolling changes the minimum number of observations within the window required to have a value when aggregating:

In [32]: r = arr.rolling(y=3, min_periods=2)

In [33]: r.mean()
Out[33]:
<xarray.DataArray (x: 3, y: 5)>
array([[ nan, 0.25, 0.5 , 1.  , 1.5 ],
[ nan, 2.75, 3.  , 3.5 , 4.  ],
[ nan, 5.25, 5.5 , 6.  , 6.5 ]])
Dimensions without coordinates: x, y

In [34]: r = arr.rolling(y=3, center=True, min_periods=2)

In [35]: r.mean()
Out[35]:
<xarray.DataArray (x: 3, y: 5)>
array([[0.25, 0.5 , 1.  , 1.5 , 1.75],
[2.75, 3.  , 3.5 , 4.  , 4.25],
[5.25, 5.5 , 6.  , 6.5 , 6.75]])
Dimensions without coordinates: x, y


Tip

Note that rolling window aggregations are faster and use less memory when bottleneck is installed. This only applies to numpy-backed xarray objects.

We can also manually iterate through Rolling objects:

for label, arr_window in r:
# arr_window is a view of x
...


While rolling provides a simple moving average, DataArray also supports an exponential moving average with rolling_exp(). This is similiar to pandas’ ewm method. numbagg is required.

arr.rolling_exp(y=3).mean()


The rolling_exp method takes a window_type kwarg, which can be 'alpha', 'com' (for center-of-mass), 'span', and 'halflife'. The default is span.

Finally, the rolling object has a construct method which returns a view of the original DataArray with the windowed dimension in the last position. You can use this for more advanced rolling operations such as strided rolling, windowed rolling, convolution, short-time FFT etc.

# rolling with 2-point stride
In [36]: rolling_da = r.construct("window_dim", stride=2)

In [37]: rolling_da
Out[37]:
<xarray.DataArray (x: 3, y: 3, window_dim: 3)>
array([[[nan, 0. , 0.5],
[0.5, 1. , 1.5],
[1.5, 2. , nan]],

[[nan, 2.5, 3. ],
[3. , 3.5, 4. ],
[4. , 4.5, nan]],

[[nan, 5. , 5.5],
[5.5, 6. , 6.5],
[6.5, 7. , nan]]])
Dimensions without coordinates: x, y, window_dim

In [38]: rolling_da.mean("window_dim", skipna=False)
Out[38]:
<xarray.DataArray (x: 3, y: 3)>
array([[nan, 1. , nan],
[nan, 3.5, nan],
[nan, 6. , nan]])
Dimensions without coordinates: x, y


Because the DataArray given by r.construct('window_dim') is a view of the original array, it is memory efficient. You can also use construct to compute a weighted rolling sum:

In [39]: weight = xr.DataArray([0.25, 0.5, 0.25], dims=["window"])

In [40]: arr.rolling(y=3).construct("window").dot(weight)
Out[40]:
<xarray.DataArray (x: 3, y: 5)>
array([[nan, nan, 0.5, 1. , 1.5],
[nan, nan, 3. , 3.5, 4. ],
[nan, nan, 5.5, 6. , 6.5]])
Dimensions without coordinates: x, y


Note

numpy’s Nan-aggregation functions such as nansum copy the original array. In xarray, we internally use these functions in our aggregation methods (such as .sum()) if skipna argument is not specified or set to True. This means rolling_da.mean('window_dim') is memory inefficient. To avoid this, use skipna=False as the above example.

Weighted array reductions¶

DataArray and Dataset objects include DataArray.weighted() and Dataset.weighted() array reduction methods. They currently support weighted sum and weighted mean.

In [41]: coords = dict(month=("month", [1, 2, 3]))

In [42]: prec = xr.DataArray([1.1, 1.0, 0.9], dims=("month",), coords=coords)

In [43]: weights = xr.DataArray([31, 28, 31], dims=("month",), coords=coords)


Create a weighted object:

In [44]: weighted_prec = prec.weighted(weights)

In [45]: weighted_prec
Out[45]: DataArrayWeighted with weights along dimensions: month


Calculate the weighted sum:

In [46]: weighted_prec.sum()
Out[46]:
<xarray.DataArray ()>
array(90.)


Calculate the weighted mean:

In [47]: weighted_prec.mean(dim="month")
Out[47]:
<xarray.DataArray ()>
array(1.)


The weighted sum corresponds to:

In [48]: weighted_sum = (prec * weights).sum()

In [49]: weighted_sum
Out[49]:
<xarray.DataArray ()>
array(90.)


and the weighted mean to:

In [50]: weighted_mean = weighted_sum / weights.sum()

In [51]: weighted_mean
Out[51]:
<xarray.DataArray ()>
array(1.)


However, the functions also take missing values in the data into account:

In [52]: data = xr.DataArray([np.NaN, 2, 4])

In [53]: weights = xr.DataArray([8, 1, 1])

In [54]: data.weighted(weights).mean()
Out[54]:
<xarray.DataArray ()>
array(3.)


Using (data * weights).sum() / weights.sum() would (incorrectly) result in 0.6.

If the weights add up to to 0, sum returns 0:

In [55]: data = xr.DataArray([1.0, 1.0])

In [56]: weights = xr.DataArray([-1.0, 1.0])

In [57]: data.weighted(weights).sum()
Out[57]:
<xarray.DataArray ()>
array(0.)


and mean returns NaN:

In [58]: data.weighted(weights).mean()
Out[58]:
<xarray.DataArray ()>
array(nan)


Note

weights must be a DataArray and cannot contain missing values. Missing values can be replaced manually by weights.fillna(0).

Coarsen large arrays¶

DataArray and Dataset objects include a coarsen() and coarsen() methods. This supports the block aggregation along multiple dimensions,

In [59]: x = np.linspace(0, 10, 300)

In [60]: t = pd.date_range("15/12/1999", periods=364)

In [61]: da = xr.DataArray(
....:     np.sin(x) * np.cos(np.linspace(0, 1, 364)[:, np.newaxis]),
....:     dims=["time", "x"],
....:     coords={"time": t, "x": x},
....: )
....:

In [62]: da
Out[62]:
<xarray.DataArray (time: 364, x: 300)>
array([[ 0.        ,  0.03343858,  0.06683976, ..., -0.48672119,
-0.51565952, -0.54402111],
[ 0.        ,  0.03343845,  0.06683951, ..., -0.48671934,
-0.51565756, -0.54401905],
[ 0.        ,  0.03343807,  0.06683875, ..., -0.4867138 ,
-0.51565169, -0.54401285],
...,
[ 0.        ,  0.0182217 ,  0.03642301, ..., -0.26522911,
-0.28099849, -0.29645358],
[ 0.        ,  0.01814439,  0.03626848, ..., -0.26410385,
-0.27980632, -0.29519584],
[ 0.        ,  0.01806694,  0.03611368, ..., -0.26297658,
-0.27861203, -0.29393586]])
Coordinates:
* time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
* x        (x) float64 0.0 0.03344 0.06689 0.1003 ... 9.9 9.933 9.967 10.0


In order to take a block mean for every 7 days along time dimension and every 2 points along x dimension,

In [63]: da.coarsen(time=7, x=2).mean()
Out[63]:
<xarray.DataArray (time: 52, x: 150)>
array([[ 0.01671847,  0.08349886,  0.14990579, ..., -0.41198807,
-0.47195655, -0.52981418],
[ 0.01671269,  0.08347003,  0.14985403, ..., -0.41184582,
-0.47179359, -0.52963124],
[ 0.01670071,  0.08341016,  0.14974655, ..., -0.41155042,
-0.47145519, -0.52925136],
...,
[ 0.00968205,  0.04835611,  0.0868139 , ..., -0.23859177,
-0.2733209 , -0.30682759],
[ 0.00941742,  0.04703446,  0.08444113, ..., -0.23207067,
-0.26585059, -0.29844148],
[ 0.00914929,  0.04569531,  0.08203696, ..., -0.22546326,
-0.25828142, -0.2899444 ]])
Coordinates:
* time     (time) datetime64[ns] 1999-12-18 1999-12-25 ... 2000-12-09
* x        (x) float64 0.01672 0.08361 0.1505 0.2174 ... 9.849 9.916 9.983


coarsen() raises an ValueError if the data length is not a multiple of the corresponding window size. You can choose boundary='trim' or boundary='pad' options for trimming the excess entries or padding nan to insufficient entries,

In [64]: da.coarsen(time=30, x=2, boundary="trim").mean()
Out[64]:
<xarray.DataArray (time: 12, x: 150)>
array([[ 0.01670121,  0.08341265,  0.14975103, ..., -0.41156272,
-0.47146929, -0.52926718],
[ 0.0165891 ,  0.08285275,  0.14874584, ..., -0.40880017,
-0.46830462, -0.52571455],
[ 0.01636376,  0.08172729,  0.14672529, ..., -0.40324704,
-0.46194319, -0.51857326],
...,
[ 0.01183847,  0.05912615,  0.10614938, ..., -0.29173175,
-0.33419587, -0.37516528],
[ 0.01082401,  0.05405954,  0.09705329, ..., -0.26673283,
-0.30555813, -0.34301681],
[ 0.00973567,  0.04862391,  0.08729468, ..., -0.23991312,
-0.27483458, -0.30852683]])
Coordinates:
* time     (time) datetime64[ns] 1999-12-29T12:00:00 ... 2000-11-23T12:00:00
* x        (x) float64 0.01672 0.08361 0.1505 0.2174 ... 9.849 9.916 9.983


If you want to apply a specific function to coordinate, you can pass the function or method name to coord_func option,

In [65]: da.coarsen(time=7, x=2, coord_func={"time": "min"}).mean()
Out[65]:
<xarray.DataArray (time: 52, x: 150)>
array([[ 0.01671847,  0.08349886,  0.14990579, ..., -0.41198807,
-0.47195655, -0.52981418],
[ 0.01671269,  0.08347003,  0.14985403, ..., -0.41184582,
-0.47179359, -0.52963124],
[ 0.01670071,  0.08341016,  0.14974655, ..., -0.41155042,
-0.47145519, -0.52925136],
...,
[ 0.00968205,  0.04835611,  0.0868139 , ..., -0.23859177,
-0.2733209 , -0.30682759],
[ 0.00941742,  0.04703446,  0.08444113, ..., -0.23207067,
-0.26585059, -0.29844148],
[ 0.00914929,  0.04569531,  0.08203696, ..., -0.22546326,
-0.25828142, -0.2899444 ]])
Coordinates:
* time     (time) datetime64[ns] 1999-12-15 1999-12-22 ... 2000-12-06
* x        (x) float64 0.01672 0.08361 0.1505 0.2174 ... 9.849 9.916 9.983


Computation using Coordinates¶

Xarray objects have some handy methods for the computation with their coordinates. differentiate() computes derivatives by central finite differences using their coordinates,

In [66]: a = xr.DataArray([0, 1, 2, 3], dims=["x"], coords=[[0.1, 0.11, 0.2, 0.3]])

In [67]: a
Out[67]:
<xarray.DataArray (x: 4)>
array([0, 1, 2, 3])
Coordinates:
* x        (x) float64 0.1 0.11 0.2 0.3

In [68]: a.differentiate("x")
Out[68]:
<xarray.DataArray (x: 4)>
array([100.        ,  91.11111111,  10.58479532,  10.        ])
Coordinates:
* x        (x) float64 0.1 0.11 0.2 0.3


This method can be used also for multidimensional arrays,

In [69]: a = xr.DataArray(
....:     np.arange(8).reshape(4, 2), dims=["x", "y"], coords={"x": [0.1, 0.11, 0.2, 0.3]}
....: )
....:

In [70]: a.differentiate("x")
Out[70]:
<xarray.DataArray (x: 4, y: 2)>
array([[200.        , 200.        ],
[182.22222222, 182.22222222],
[ 21.16959064,  21.16959064],
[ 20.        ,  20.        ]])
Coordinates:
* x        (x) float64 0.1 0.11 0.2 0.3
Dimensions without coordinates: y


integrate() computes integration based on trapezoidal rule using their coordinates,

In [71]: a.integrate("x")
Out[71]:
<xarray.DataArray (y: 2)>
array([0.78, 0.98])
Dimensions without coordinates: y


Note

These methods are limited to simple cartesian geometry. Differentiation and integration along multidimensional coordinate are not supported.

Fitting polynomials¶

Xarray objects provide an interface for performing linear or polynomial regressions using the least-squares method. polyfit() computes the best fitting coefficients along a given dimension and for a given order,

In [72]: x = xr.DataArray(np.arange(10), dims=["x"], name="x")

In [73]: a = xr.DataArray(3 + 4 * x, dims=["x"], coords={"x": x})

In [74]: out = a.polyfit(dim="x", deg=1, full=True)

In [75]: out
Out[75]:
<xarray.Dataset>
Dimensions:               (degree: 2)
Coordinates:
* degree                (degree) int64 1 0
Data variables:
x_matrix_rank         int64 2
x_singular_values     (degree) float64 1.358 0.3963
polyfit_coefficients  (degree) float64 4.0 3.0
polyfit_residuals     float64 4.522e-28


The method outputs a dataset containing the coefficients (and more if full=True). The inverse operation is done with polyval(),

In [76]: xr.polyval(coord=x, coeffs=out.polyfit_coefficients)
Out[76]:
<xarray.DataArray (x: 10)>
array([ 3.,  7., 11., 15., 19., 23., 27., 31., 35., 39.])
Coordinates:
* x        (x) int64 0 1 2 3 4 5 6 7 8 9


Note

These methods replicate the behaviour of numpy.polyfit() and numpy.polyval().

DataArray objects are automatically align themselves (“broadcasting” in the numpy parlance) by dimension name instead of axis order. With xarray, you do not need to transpose arrays or insert dimensions of length 1 to get array operations to work, as commonly done in numpy with numpy.reshape() or numpy.newaxis.

This is best illustrated by a few examples. Consider two one-dimensional arrays with different sizes aligned along different dimensions:

In [77]: a = xr.DataArray([1, 2], [("x", ["a", "b"])])

In [78]: a
Out[78]:
<xarray.DataArray (x: 2)>
array([1, 2])
Coordinates:
* x        (x) <U1 'a' 'b'

In [79]: b = xr.DataArray([-1, -2, -3], [("y", [10, 20, 30])])

In [80]: b
Out[80]:
<xarray.DataArray (y: 3)>
array([-1, -2, -3])
Coordinates:
* y        (y) int64 10 20 30


With xarray, we can apply binary mathematical operations to these arrays, and their dimensions are expanded automatically:

In [81]: a * b
Out[81]:
<xarray.DataArray (x: 2, y: 3)>
array([[-1, -2, -3],
[-2, -4, -6]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


Moreover, dimensions are always reordered to the order in which they first appeared:

In [82]: c = xr.DataArray(np.arange(6).reshape(3, 2), [b["y"], a["x"]])

In [83]: c
Out[83]:
<xarray.DataArray (y: 3, x: 2)>
array([[0, 1],
[2, 3],
[4, 5]])
Coordinates:
* y        (y) int64 10 20 30
* x        (x) <U1 'a' 'b'

In [84]: a + c
Out[84]:
<xarray.DataArray (x: 2, y: 3)>
array([[1, 3, 5],
[3, 5, 7]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30


This means, for example, that you always subtract an array from its transpose:

In [85]: c - c.T
Out[85]:
<xarray.DataArray (y: 3, x: 2)>
array([[0, 0],
[0, 0],
[0, 0]])
Coordinates:
* y        (y) int64 10 20 30
* x        (x) <U1 'a' 'b'


You can explicitly broadcast xarray data structures by using the broadcast() function:

In [86]: a2, b2 = xr.broadcast(a, b)

In [87]: a2
Out[87]:
<xarray.DataArray (x: 2, y: 3)>
array([[1, 1, 1],
[2, 2, 2]])
Coordinates:
* x        (x) <U1 'a' 'b'
* y        (y) int64 10 20 30

In [88]: b2
Out[88]:
<xarray.DataArray (x: 2, y: 3)>
array([[-1, -2, -3],
[-1, -2, -3]])
Coordinates:
* y        (y) int64 10 20 30
* x        (x) <U1 'a' 'b'


Automatic alignment¶

xarray enforces alignment between index Coordinates (that is, coordinates with the same name as a dimension, marked by *) on objects used in binary operations.

Similarly to pandas, this alignment is automatic for arithmetic on binary operations. The default result of a binary operation is by the intersection (not the union) of coordinate labels:

In [89]: arr = xr.DataArray(np.arange(3), [("x", range(3))])

In [90]: arr + arr[:-1]
Out[90]:
<xarray.DataArray (x: 2)>
array([0, 2])
Coordinates:
* x        (x) int64 0 1


If coordinate values for a dimension are missing on either argument, all matching dimensions must have the same size:

In [91]: arr + xr.DataArray([1, 2], dims="x")
ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different dimension size(s) {2} than the size of the aligned dimension labels: 3


However, one can explicitly change this default automatic alignment type (“inner”) via set_options() in context manager:

In [92]: with xr.set_options(arithmetic_join="outer"):
....:     arr + arr[:1]
....:

In [93]: arr + arr[:1]
Out[93]:
<xarray.DataArray (x: 1)>
array([0])
Coordinates:
* x        (x) int64 0


Before loops or performance critical code, it’s a good idea to align arrays explicitly (e.g., by putting them in the same Dataset or using align()) to avoid the overhead of repeated alignment with each operation. See Align and reindex for more details.

Note

There is no automatic alignment between arguments when performing in-place arithmetic operations such as +=. You will need to use manual alignment. This ensures in-place arithmetic never needs to modify data types.

Coordinates¶

Although index coordinates are aligned, other coordinates are not, and if their values conflict, they will be dropped. This is necessary, for example, because indexing turns 1D coordinates into scalar coordinates:

In [94]: arr[0]
Out[94]:
<xarray.DataArray ()>
array(0)
Coordinates:
x        int64 0

In [95]: arr[1]
Out[95]:
<xarray.DataArray ()>
array(1)
Coordinates:
x        int64 1

# notice that the scalar coordinate 'x' is silently dropped
In [96]: arr[1] - arr[0]
Out[96]:
<xarray.DataArray ()>
array(1)


Still, xarray will persist other coordinates in arithmetic, as long as there are no conflicting values:

# only one argument has the 'x' coordinate
In [97]: arr[0] + 1
Out[97]:
<xarray.DataArray ()>
array(1)
Coordinates:
x        int64 0

# both arguments have the same 'x' coordinate
In [98]: arr[0] - arr[0]
Out[98]:
<xarray.DataArray ()>
array(0)
Coordinates:
x        int64 0


Math with datasets¶

Datasets support arithmetic operations by automatically looping over all data variables:

In [99]: ds = xr.Dataset(
....:     {
....:         "x_and_y": (("x", "y"), np.random.randn(3, 5)),
....:         "x_only": ("x", np.random.randn(3)),
....:     },
....:     coords=arr.coords,
....: )
....:

In [100]: ds > 0
Out[100]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) bool True False False False True ... True True False False
x_only   (x) bool True False True


Datasets support most of the same methods found on data arrays:

In [101]: ds.mean(dim="x")
Out[101]:
<xarray.Dataset>
Dimensions:  (y: 5)
Dimensions without coordinates: y
Data variables:
x_and_y  (y) float64 -0.06634 0.3027 -0.6106 -0.9014 -0.644
x_only   float64 0.138

In [102]: abs(ds)
Out[102]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) float64 0.4691 0.2829 1.509 1.136 ... 0.7216 0.7068 1.04
x_only   (x) float64 0.2719 0.425 0.567


Datasets also support NumPy ufuncs (requires NumPy v1.13 or newer), or alternatively you can use map() to map a function to each variable in a dataset:

In [103]: np.sin(ds)
Out[103]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) float64 0.4521 -0.2791 -0.9981 ... 0.6606 -0.6494 -0.8622
x_only   (x) float64 0.2685 -0.4123 0.5371

In [104]: ds.map(np.sin)
Out[104]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) float64 0.4521 -0.2791 -0.9981 ... 0.6606 -0.6494 -0.8622
x_only   (x) float64 0.2685 -0.4123 0.5371


Datasets also use looping over variables for broadcasting in binary arithmetic. You can do arithmetic between any DataArray and a dataset:

In [105]: ds + arr
Out[105]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) float64 0.4691 -0.2829 -1.509 -1.136 ... 2.722 1.293 0.9604
x_only   (x) float64 0.2719 0.575 2.567


Arithmetic between two datasets matches data variables of the same name:

In [106]: ds2 = xr.Dataset({"x_and_y": 0, "x_only": 100})

In [107]: ds - ds2
Out[107]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 5)
Coordinates:
* x        (x) int64 0 1 2
Dimensions without coordinates: y
Data variables:
x_and_y  (x, y) float64 0.4691 -0.2829 -1.509 ... 0.7216 -0.7068 -1.04
x_only   (x) float64 -99.73 -100.4 -99.43


Similarly to index based alignment, the result has the intersection of all matching data variables.

Wrapping custom computation¶

It doesn’t always make sense to do computation directly with xarray objects:

• In the inner loop of performance limited code, using xarray can add considerable overhead compared to using NumPy or native Python types. This is particularly true when working with scalars or small arrays (less than ~1e6 elements). Keeping track of labels and ensuring their consistency adds overhead, and xarray’s core itself is not especially fast, because it’s written in Python rather than a compiled language like C. Also, xarray’s high level label-based APIs removes low-level control over how operations are implemented.

• Even if speed doesn’t matter, it can be important to wrap existing code, or to support alternative interfaces that don’t use xarray objects.

For these reasons, it is often well-advised to write low-level routines that work with NumPy arrays, and to wrap these routines to work with xarray objects. However, adding support for labels on both Dataset and DataArray can be a bit of a chore.

To make this easier, xarray supplies the apply_ufunc() helper function, designed for wrapping functions that support broadcasting and vectorization on unlabeled arrays in the style of a NumPy universal function (“ufunc” for short). apply_ufunc takes care of everything needed for an idiomatic xarray wrapper, including alignment, broadcasting, looping over Dataset variables (if needed), and merging of coordinates. In fact, many internal xarray functions/methods are written using apply_ufunc.

Simple functions that act independently on each value should work without any additional arguments:

In [108]: squared_error = lambda x, y: (x - y) ** 2

In [109]: arr1 = xr.DataArray([0, 1, 2, 3], dims="x")

In [110]: xr.apply_ufunc(squared_error, arr1, 1)
Out[110]:
<xarray.DataArray (x: 4)>
array([1, 0, 1, 4])
Dimensions without coordinates: x


For using more complex operations that consider some array values collectively, it’s important to understand the idea of “core dimensions” from NumPy’s generalized ufuncs. Core dimensions are defined as dimensions that should not be broadcast over. Usually, they correspond to the fundamental dimensions over which an operation is defined, e.g., the summed axis in np.sum. A good clue that core dimensions are needed is the presence of an axis argument on the corresponding NumPy function.

With apply_ufunc, core dimensions are recognized by name, and then moved to the last dimension of any input arguments before applying the given function. This means that for functions that accept an axis argument, you usually need to set axis=-1. As an example, here is how we would wrap numpy.linalg.norm() to calculate the vector norm:

def vector_norm(x, dim, ord=None):
return xr.apply_ufunc(
np.linalg.norm, x, input_core_dims=[[dim]], kwargs={"ord": ord, "axis": -1}
)

In [111]: vector_norm(arr1, dim="x")
Out[111]:
<xarray.DataArray ()>
array(3.74165739)


Because apply_ufunc follows a standard convention for ufuncs, it plays nicely with tools for building vectorized functions, like numpy.broadcast_arrays() and numpy.vectorize. For high performance needs, consider using Numba’s vectorize and guvectorize.

In addition to wrapping functions, apply_ufunc can automatically parallelize many functions when using dask by setting dask='parallelized'. See Automatic parallelization with apply_ufunc and map_blocks for details.

apply_ufunc() also supports some advanced options for controlling alignment of variables and the form of the result. See the docstring for full details and more examples.