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DOC: Added examples to docstrings of DataArray methods (#7123) (#7123)
* rebase this PR * fix missing spaces Co-authored-by: Michael Niklas <mick.niklas@gmail.com>
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‎doc/whats-new.rst‎

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@@ -81,6 +81,8 @@ Documentation
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By `Jimmy Westling <https://github.com/illviljan>`_.
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- Add documentation of specific BackendEntrypoints (:pull:`7200`).
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By `Michael Niklas <https://github.com/headtr1ck>`_.
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- Add examples to docstring for :py:meth:`DataArray.drop_vars`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interp_like`. (:issue:`6793`, :pull:`7123`)
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By `Daniel Goman <https://github.com/DanielGoman>`_.
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Internal Changes
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~~~~~~~~~~~~~~~~

‎xarray/core/dataarray.py‎

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@@ -1834,6 +1834,109 @@ def reindex_like(
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Another dataset array, with this array's data but coordinates from
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the other object.
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Examples
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--------
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>>> data = np.arange(12).reshape(4, 3)
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>>> da1 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
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... )
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>>> da1
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* x (x) int64 10 20 30 40
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* y (y) int64 70 80 90
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>>> da2 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [40, 30, 20, 10], "y": [90, 80, 70]},
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... )
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>>> da2
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* x (x) int64 40 30 20 10
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* y (y) int64 90 80 70
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Reindexing with both DataArrays having the same coordinates set, but in different order:
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>>> da1.reindex_like(da2)
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<xarray.DataArray (x: 4, y: 3)>
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array([[11, 10, 9],
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[ 8, 7, 6],
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[ 5, 4, 3],
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[ 2, 1, 0]])
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Coordinates:
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* x (x) int64 40 30 20 10
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* y (y) int64 90 80 70
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Reindexing with the other array having coordinates which the source array doesn't have:
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>>> data = np.arange(12).reshape(4, 3)
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>>> da1 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
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... )
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>>> da2 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [20, 10, 29, 39], "y": [70, 80, 90]},
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... )
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>>> da1.reindex_like(da2)
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 3., 4., 5.],
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[ 0., 1., 2.],
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[nan, nan, nan],
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[nan, nan, nan]])
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Coordinates:
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* x (x) int64 20 10 29 39
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* y (y) int64 70 80 90
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Filling missing values with the previous valid index with respect to the coordinates' value:
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>>> da1.reindex_like(da2, method="ffill")
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<xarray.DataArray (x: 4, y: 3)>
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array([[3, 4, 5],
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[0, 1, 2],
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[3, 4, 5],
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[6, 7, 8]])
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Coordinates:
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* x (x) int64 20 10 29 39
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* y (y) int64 70 80 90
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Filling missing values while tolerating specified error for inexact matches:
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>>> da1.reindex_like(da2, method="ffill", tolerance=5)
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 3., 4., 5.],
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[ 0., 1., 2.],
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[nan, nan, nan],
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[nan, nan, nan]])
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Coordinates:
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* x (x) int64 20 10 29 39
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* y (y) int64 70 80 90
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Filling missing values with manually specified values:
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>>> da1.reindex_like(da2, fill_value=19)
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 3, 4, 5],
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[ 0, 1, 2],
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[19, 19, 19],
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[19, 19, 19]])
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Coordinates:
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* x (x) int64 20 10 29 39
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* y (y) int64 70 80 90
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See Also
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--------
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DataArray.reindex
@@ -2130,6 +2233,62 @@ def interp_like(
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Another dataarray by interpolating this dataarray's data along the
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coordinates of the other object.
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Examples
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--------
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>>> data = np.arange(12).reshape(4, 3)
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>>> da1 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
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... )
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>>> da1
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* x (x) int64 10 20 30 40
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* y (y) int64 70 80 90
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>>> da2 = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [10, 20, 29, 39], "y": [70, 80, 90]},
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... )
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>>> da2
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* x (x) int64 10 20 29 39
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* y (y) int64 70 80 90
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Interpolate the values in the coordinates of the other DataArray with respect to the source's values:
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>>> da2.interp_like(da1)
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<xarray.DataArray (x: 4, y: 3)>
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array([[0. , 1. , 2. ],
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[3. , 4. , 5. ],
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[6.3, 7.3, 8.3],
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[nan, nan, nan]])
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Coordinates:
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* x (x) int64 10 20 30 40
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* y (y) int64 70 80 90
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Could also extrapolate missing values:
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>>> da2.interp_like(da1, kwargs={"fill_value": "extrapolate"})
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0. , 1. , 2. ],
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[ 3. , 4. , 5. ],
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[ 6.3, 7.3, 8.3],
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[ 9.3, 10.3, 11.3]])
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Coordinates:
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* x (x) int64 10 20 30 40
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* y (y) int64 70 80 90
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Notes
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-----
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scipy is required.
@@ -2791,6 +2950,46 @@ def drop_vars(
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-------
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dropped : Dataset
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New Dataset copied from `self` with variables removed.
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Examples
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-------
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>>> data = np.arange(12).reshape(4, 3)
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>>> da = xr.DataArray(
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... data=data,
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... dims=["x", "y"],
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... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]},
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... )
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>>> da
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* x (x) int64 10 20 30 40
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* y (y) int64 70 80 90
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Removing a single variable:
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>>> da.drop_vars("x")
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Coordinates:
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* y (y) int64 70 80 90
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Dimensions without coordinates: x
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Removing a list of variables:
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>>> da.drop_vars(["x", "y"])
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<xarray.DataArray (x: 4, y: 3)>
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array([[ 0, 1, 2],
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[ 3, 4, 5],
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[ 6, 7, 8],
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[ 9, 10, 11]])
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Dimensions without coordinates: x, y
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"""
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ds = self._to_temp_dataset().drop_vars(names, errors=errors)
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return self._from_temp_dataset(ds)

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