Add head, tail and thin methods

doc/api.rst

@@ -117,6 +117,9 @@ Indexing
    Dataset.loc
    Dataset.isel
    Dataset.sel
+   Dataset.head
+   Dataset.tail
+   Dataset.thin
    Dataset.squeeze
    Dataset.interp
    Dataset.interp_like
@@ -279,6 +282,9 @@ Indexing
    DataArray.loc
    DataArray.isel
    DataArray.sel
+   Dataset.head
+   Dataset.tail
+   Dataset.thin
    DataArray.squeeze
    DataArray.interp
    DataArray.interp_like

doc/whats-new.rst

@@ -87,6 +87,9 @@ New functions/methods
   Currently only :py:meth:`Dataset.plot.scatter` is implemented.
   By `Yohai Bar Sinai <https://github.com/yohai>`_ and `Deepak Cherian <https://github.com/dcherian>`_
 
+- Added `head`, `tail` and `thin` methods to `Dataset` and `DataArray`. (:issue:`319`)
+  By `Gerardo Rivera <https://github.com/dangomelon>`_.
+
 Enhancements
 ~~~~~~~~~~~~
 
@@ -102,7 +105,7 @@ Enhancements
 
 - Added the ability to initialize an empty or full DataArray
   with a single value. (:issue:`277`)
-  By `Gerardo Rivera <http://github.com/dangomelon>`_.
+  By `Gerardo Rivera <https://github.com/dangomelon>`_.
 
 - :py:func:`~xarray.Dataset.to_netcdf()` now supports the ``invalid_netcdf`` kwarg when used
   with ``engine="h5netcdf"``. It is passed to :py:func:`h5netcdf.File`.

xarray/core/dataarray.py

@@ -1040,6 +1040,55 @@ def sel(
         )
         return self._from_temp_dataset(ds)
 
+    def head(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "DataArray":
+        """Return a new DataArray whose data is given by the the first `n`
+        values along the specified dimension(s).
+
+        See Also
+        --------
+        Dataset.head
+        DataArray.tail
+        DataArray.thin
+        """
+
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "head")
+        ds = self._to_temp_dataset().head(indexers=indexers)
+        return self._from_temp_dataset(ds)
+
+    def tail(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "DataArray":
+        """Return a new DataArray whose data is given by the the last `n`
+        values along the specified dimension(s).
+
+        See Also
+        --------
+        Dataset.tail
+        DataArray.head
+        DataArray.thin
+        """
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "tail")
+        ds = self._to_temp_dataset().tail(indexers=indexers)
+        return self._from_temp_dataset(ds)
+
+    def thin(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "DataArray":
+        """Return a new DataArray whose data is given by each `n` value
+        along the specified dimension(s).
+
+        See Also
+        --------
+        Dataset.thin
+        DataArray.head
+        DataArray.tail
+        """
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "thin")
+        ds = self._to_temp_dataset().thin(indexers=indexers)
+        return self._from_temp_dataset(ds)
+
     def broadcast_like(
         self, other: Union["DataArray", Dataset], exclude: Iterable[Hashable] = None
     ) -> "DataArray":

xarray/core/dataset.py

@@ -2008,6 +2008,90 @@ def sel(
         result = self.isel(indexers=pos_indexers, drop=drop)
         return result._overwrite_indexes(new_indexes)
 
+    def head(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "Dataset":
+        """Returns a new dataset with the first `n` values of each array
+        for the specified dimension(s).
+
+        Parameters
+        ----------
+        indexers : dict, optional
+            A dict with keys matching dimensions and integer values `n`.
+            One of indexers or indexers_kwargs must be provided.
+        **indexers_kwarg : {dim: n, ...}, optional
+            The keyword arguments form of ``indexers``.
+            One of indexers or indexers_kwargs must be provided.
+
+
+        See Also
+        --------
+        Dataset.tail
+        Dataset.thin
+        DataArray.head
+        """
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "head")
+        indexers = {k: slice(val) for k, val in indexers.items()}
+        return self.isel(indexers)
+
+    def tail(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "Dataset":
+        """Returns a new dataset with the last `n` values of each array
+        for the specified dimension(s).
+
+        Parameters
+        ----------
+        indexers : dict, optional
+            A dict with keys matching dimensions and integer values `n`.
+            One of indexers or indexers_kwargs must be provided.
+        **indexers_kwarg : {dim: n, ...}, optional
+            The keyword arguments form of ``indexers``.
+            One of indexers or indexers_kwargs must be provided.
+
+
+        See Also
+        --------
+        Dataset.head
+        Dataset.thin
+        DataArray.tail
+        """
+
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "tail")
+        indexers = {
+            k: slice(-val, None) if val != 0 else slice(val)
+            for k, val in indexers.items()
+        }
+        return self.isel(indexers)
+
+    def thin(
+        self, indexers: Mapping[Hashable, Any] = None, **indexers_kwargs: Any
+    ) -> "Dataset":
+        """Returns a new dataset with each array indexed along every `n`th
+        value for the specified dimension(s)
+
+        Parameters
+        ----------
+        indexers : dict, optional
+            A dict with keys matching dimensions and integer values `n`.
+            One of indexers or indexers_kwargs must be provided.
+        **indexers_kwarg : {dim: n, ...}, optional
+            The keyword arguments form of ``indexers``.
+            One of indexers or indexers_kwargs must be provided.
+
+
+        See Also
+        --------
+        Dataset.head
+        Dataset.tail
+        DataArray.thin
+        """
+        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "thin")
+        if 0 in indexers.values():
+            raise ValueError("step cannot be zero")
+        indexers = {k: slice(None, None, val) for k, val in indexers.items()}
+        return self.isel(indexers)
+
     def broadcast_like(
         self, other: Union["Dataset", "DataArray"], exclude: Iterable[Hashable] = None
     ) -> "Dataset":

xarray/tests/test_dataset.py

@@ -1411,6 +1411,38 @@ def test_isel_drop(self):
         selected = data.isel(x=0, drop=False)
         assert_identical(expected, selected)
 
+    def test_head(self):
+        data = create_test_data()
+
+        expected = data.isel(time=slice(5), dim2=slice(6))
+        actual = data.head(time=5, dim2=6)
+        assert_equal(expected, actual)
+
+        expected = data.isel(time=slice(0))
+        actual = data.head(time=0)
+        assert_equal(expected, actual)
+
+    def test_tail(self):
+        data = create_test_data()
+
+        expected = data.isel(time=slice(-5, None), dim2=slice(-6, None))
+        actual = data.tail(time=5, dim2=6)
+        assert_equal(expected, actual)
+
+        expected = data.isel(dim1=slice(0))
+        actual = data.tail(dim1=0)
+        assert_equal(expected, actual)
+
+    def test_thin(self):
+        data = create_test_data()
+
+        expected = data.isel(time=slice(None, None, 5), dim2=slice(None, None, 6))
+        actual = data.thin(time=5, dim2=6)
+        assert_equal(expected, actual)
+
+        with raises_regex(ValueError, "cannot be zero"):
+            data.thin(time=0)
+
     @pytest.mark.filterwarnings("ignore::DeprecationWarning")
     def test_sel_fancy(self):
         data = create_test_data()