feat: add df.take and series.take

bigframes/dataframe.py

@@ -2174,6 +2174,18 @@ def add_suffix(self, suffix: str, axis: int | str | None = None) -> DataFrame:
         axis = 1 if axis is None else axis
         return DataFrame(self._get_block().add_suffix(suffix, axis))
 
+    def take(
+        self, indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs
+    ) -> DataFrame:
+        if not utils.is_list_like(indices):
+            raise ValueError("indices should be a list-like object.")
+        if axis == 0 or axis == "index":
+            return self.iloc[indices]
+        elif axis == 1 or axis == "columns":
+            return self.iloc[:, indices]
+        else:
+            raise ValueError(f"No axis named {axis} for object type DataFrame")
+
     def filter(
         self,
         items: typing.Optional[typing.Iterable] = None,

bigframes/series.py

@@ -1631,6 +1631,13 @@ def add_prefix(self, prefix: str, axis: int | str | None = None) -> Series:
     def add_suffix(self, suffix: str, axis: int | str | None = None) -> Series:
         return Series(self._get_block().add_suffix(suffix))
 
+    def take(
+        self, indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs
+    ) -> Series:
+        if not utils.is_list_like(indices):
+            raise ValueError("indices should be a list-like object.")
+        return typing.cast(Series, self.iloc[indices])
+
     def filter(
         self,
         items: typing.Optional[typing.Iterable] = None,

tests/system/small/test_dataframe.py

@@ -807,6 +807,24 @@ def test_get_df_column_name_duplicate(scalars_dfs):
     pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)
 
 
+@pytest.mark.parametrize(
+    ("indices", "axis"),
+    [
+        ([1, 3, 5], 0),
+        ([2, 4, 6], 1),
+        ([1, -3, -5, -6], "index"),
+        ([-2, -4, -6], "columns"),
+    ],
+)
+def test_take_df(scalars_dfs, indices, axis):
+    scalars_df, scalars_pandas_df = scalars_dfs
+
+    bf_result = scalars_df.take(indices, axis=axis).to_pandas()
+    pd_result = scalars_pandas_df.take(indices, axis=axis)
+
+    assert_pandas_df_equal(bf_result, pd_result)
+
+
 def test_filter_df(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
 

tests/system/small/test_series.py

@@ -1543,6 +1543,23 @@ def test_indexing_using_selected_series(scalars_dfs):
     )
 
 
+@pytest.mark.parametrize(
+    ("indices"),
+    [
+        ([1, 3, 5]),
+        ([5, -3, -5, -6]),
+        ([-2, -4, -6]),
+    ],
+)
+def test_take(scalars_dfs, indices):
+    scalars_df, scalars_pandas_df = scalars_dfs
+
+    bf_result = scalars_df.take(indices).to_pandas()
+    pd_result = scalars_pandas_df.take(indices)
+
+    assert_pandas_df_equal(bf_result, pd_result)
+
+
 def test_nested_filter(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     string_col = scalars_df["string_col"]

third_party/bigframes_vendored/pandas/core/generic.py

@@ -910,6 +910,29 @@ def notna(self) -> NDFrame:
 
     notnull = notna
 
+    def take(self, indices, axis=0, **kwargs) -> NDFrame:
+        """Return the elements in the given positional indices along an axis.
+
+        This means that we are not indexing according to actual values in the index
+        attribute of the object. We are indexing according to the actual position of
+        the element in the object.
+
+        Args:
+            indices (list-like):
+                An array of ints indicating which positions to take.
+            axis ({0 or 'index', 1 or 'columns', None}, default 0):
+                The axis on which to select elements. 0 means that we are selecting rows,
+                1 means that we are selecting columns. For Series this parameter is
+                unused and defaults to 0.
+            **kwargs:
+                For compatibility with numpy.take(). Has no effect on the output.
+
+        Returns:
+            bigframes.pandas.DataFrame or bigframes.pandas.Series:
+                Same type as input object.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def filter(
         self,
         items=None,