Adds list.take, python binding for cudf::lists::segmented_gather

python/cudf/cudf/_lib/copying.pyx

@@ -3,7 +3,7 @@
 import pandas as pd
 
 from libcpp cimport bool
-from libcpp.memory cimport make_unique, unique_ptr
+from libcpp.memory cimport make_unique, unique_ptr, shared_ptr, make_shared
 from libcpp.vector cimport vector
 from libcpp.utility cimport move
 from libc.stdint cimport int32_t, int64_t
@@ -24,6 +24,10 @@ from cudf._lib.cpp.scalar.scalar cimport scalar
 from cudf._lib.cpp.table.table cimport table
 from cudf._lib.cpp.table.table_view cimport table_view
 from cudf._lib.cpp.types cimport size_type
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.lists.gather cimport (
+    segmented_gather as cpp_segmented_gather
+)
 cimport cudf._lib.cpp.copying as cpp_copying
 
 # workaround for https://github.com/cython/cython/issues/3885
@@ -704,3 +708,22 @@ def sample(Table input, size_type n,
             else input._index_names
         )
     )
+
+
+def segmented_gather(Column source_column, Column gather_map):
+    cdef shared_ptr[lists_column_view] source_LCV = (
+        make_shared[lists_column_view](source_column.view())
+    )
+    cdef shared_ptr[lists_column_view] gather_map_LCV = (
+        make_shared[lists_column_view](gather_map.view())
+    )
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_segmented_gather(
+                source_LCV.get()[0], gather_map_LCV.get()[0])
+        )
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result

python/cudf/cudf/_lib/cpp/lists/gather.pxd

@@ -0,0 +1,13 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+
+
+cdef extern from "cudf/lists/gather.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] segmented_gather(
+        const lists_column_view source_column,
+        const lists_column_view gather_map_list
+    ) except +

python/cudf/cudf/core/column/lists.py

@@ -2,14 +2,16 @@
 
 import pickle
 
+import numpy as np
 import pyarrow as pa
 
 import cudf
+from cudf._lib.copying import segmented_gather
 from cudf._lib.lists import count_elements
 from cudf.core.buffer import Buffer
-from cudf.core.column import ColumnBase, column
+from cudf.core.column import ColumnBase, as_column, column
 from cudf.core.column.methods import ColumnMethodsMixin
-from cudf.utils.dtypes import is_list_dtype
+from cudf.utils.dtypes import is_list_dtype, is_numerical_dtype
 
 
 class ListColumn(ColumnBase):
@@ -228,3 +230,53 @@ def len(self):
         dtype: int32
         """
         return self._return_or_inplace(count_elements(self._column))
+
+    def take(self, lists_indices):
+        """
+        Collect list elements based on given indices.
+
+        Parameters
+        ----------
+        lists_indices: List type arrays
+        Specifies what to collect from each row
+
+        Returns
+        -------
+        ListColumn
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
+        >>> s
+        0    [1, 2, 3]
+        1         None
+        2       [4, 5]
+        dtype: list
+        >>> s.list.take([[0, 1], [], []])
+        0    [1, 2]
+        1      None
+        2        []
+        dtype: list
+        """
+
+        lists_indices_col = as_column(lists_indices)
+        if not isinstance(lists_indices_col, ListColumn):
+            raise ValueError("lists_indices should be list type array.")
+        if not lists_indices_col.size == self._column.size:
+            raise ValueError(
+                "lists_indices and list column is of different " "size."
+            )
+        if lists_indices_col.null_count > 0:
+            raise ValueError("lists_indices contains null elements.")
+        if not is_numerical_dtype(
+            lists_indices_col.children[1].dtype
+        ) or not np.issubdtype(
+            lists_indices_col.children[1].dtype, np.integer
+        ):
+            raise TypeError(
+                "lists_indices should be column of values of index types."
+            )
+
+        return self._return_or_inplace(
+            segmented_gather(self._column, lists_indices_col)
+        )

python/cudf/cudf/tests/test_list.py

@@ -112,3 +112,50 @@ def test_len(data):
     got = gsr.list.len()
 
     assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    ("data", "idx"),
+    [
+        ([[1, 2, 3], [3, 4, 5], [4, 5, 6]], [[0, 1], [2], [1, 2]]),
+        ([[1, 2, 3], [3, 4, 5], [4, 5, 6]], [[1, 2, 0], [1, 0, 2], [0, 1, 2]]),
+        ([[1, 2, 3], []], [[0, 1], []]),
+        ([[1, 2, 3], [None]], [[0, 1], []]),
+        ([[1, None, 3], None], [[0, 1], []]),
+    ],
+)
+def test_take(data, idx):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.Series(zip(ps, idx)).map(
+        lambda x: [x[0][i] for i in x[1]] if x[0] is not None else None
+    )
+    got = gs.list.take(idx)
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    ("invalid", "exception"),
+    [
+        ([[0]], pytest.raises(ValueError, match="different size")),
+        ([1, 2, 3, 4], pytest.raises(ValueError, match="should be list type")),
+        (
+            [["a", "b"], ["c"]],
+            pytest.raises(
+                TypeError, match="should be column of values of index types"
+            ),
+        ),
+        (
+            [[[1], [0]], [[0]]],
+            pytest.raises(
+                TypeError, match="should be column of values of index types"
+            ),
+        ),
+        ([[0, 1], None], pytest.raises(ValueError, match="contains null")),
+    ],
+)
+def test_take_invalid(invalid, exception):
+    gs = cudf.Series([[0, 1], [2, 3]])
+    with exception:
+        gs.list.take(invalid)