xarray.Dataset.equals

Dataset.equals(other)

Two Datasets are equal if they have matching variables and coordinates, all of which are equal.

Datasets can still be equal (like pandas objects) if they have NaN values in the same locations.

This method is necessary because v1 == v2 for Dataset does element-wise comparisons (like numpy.ndarrays).

Examples

# 2D array with shape (1, 3)

>>> data = np.array([[1, 2, 3]])
>>> dataset1 = xr.Dataset(
...     {"variable_name": (("space", "time"), data)},
...     coords={"space": [0], "time": [0, 1, 2]},
... )
>>> dataset1
<xarray.Dataset>
Dimensions:        (space: 1, time: 3)
Coordinates:
  * space          (space) int64 0
  * time           (time) int64 0 1 2
Data variables:
    variable_name  (space, time) int64 1 2 3

# 2D array with shape (3, 1)

>>> data = np.array([[1], [2], [3]])
>>> dataset2 = xr.Dataset(
...     {"variable_name": (("time", "space"), data)},
...     coords={"time": [0, 1, 2], "space": [0]},
... )
>>> dataset2
<xarray.Dataset>
Dimensions:        (time: 3, space: 1)
Coordinates:
  * time           (time) int64 0 1 2
  * space          (space) int64 0
Data variables:
    variable_name  (time, space) int64 1 2 3
>>> dataset1.equals(dataset2)
False

>>> dataset1.broadcast_equals(dataset2)
True

.equals returns True if two Datasets have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two Datasets against each other have the same values, dimensions, and coordinates.

Similar for missing values too:

>>> ds1 = xr.Dataset(
...     {
...         "temperature": (["x", "y"], [[1, np.nan], [3, 4]]),
...     },
...     coords={"x": [0, 1], "y": [0, 1]},
... )

>>> ds2 = xr.Dataset(
...     {
...         "temperature": (["x", "y"], [[1, np.nan], [3, 4]]),
...     },
...     coords={"x": [0, 1], "y": [0, 1]},
... )
>>> ds1.equals(ds2)
True

See also

Dataset.broadcast_equals, Dataset.identical