# xarray.DataArray.differentiate¶

DataArray.differentiate(coord, edge_order=1, datetime_unit=None)

Differentiate the array with the second order accurate central differences.

Note

This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional.

Parameters
• coord (hashable) – The coordinate to be used to compute the gradient.

• edge_order (1 or 2. Default 1) – N-th order accurate differences at the boundaries.

• datetime_unit (None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',) – ‘us’, ‘ns’, ‘ps’, ‘fs’, ‘as’} Unit to compute gradient. Only valid for datetime coordinate.

Returns

differentiated

Return type

DataArray

numpy.gradient()

corresponding numpy function

Examples

>>> da = xr.DataArray(
...     np.arange(12).reshape(4, 3),
...     dims=["x", "y"],
...     coords={"x": [0, 0.1, 1.1, 1.2]},
... )
>>> da
<xarray.DataArray (x: 4, y: 3)>
array([[ 0,  1,  2],
[ 3,  4,  5],
[ 6,  7,  8],
[ 9, 10, 11]])
Coordinates:
* x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y
>>>
>>> da.differentiate("x")
<xarray.DataArray (x: 4, y: 3)>
array([[30.      , 30.      , 30.      ],
[27.545455, 27.545455, 27.545455],
[27.545455, 27.545455, 27.545455],
[30.      , 30.      , 30.      ]])
Coordinates:
* x        (x) float64 0.0 0.1 1.1 1.2
Dimensions without coordinates: y