Enable join on decimal columns (#7764)

This enables joins on decimal columns with the same precision and scale.

Closes #7497 
Depends on #7788

Authors:
  - https://github.com/ChrisJar

Approvers:
  - Keith Kraus (https://github.com/kkraus14)
  - Ashwin Srinath (https://github.com/shwina)
  - https://github.com/brandon-b-miller
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #7764

python/cudf/cudf/_lib/replace.pyx

@@ -1,4 +1,4 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
 
 from libcpp.memory cimport unique_ptr
 from libcpp.utility cimport move

python/cudf/cudf/core/column/decimal.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2021, NVIDIA CORPORATION.
 
 from decimal import Decimal
-from typing import cast
+from typing import cast, Any
 
 import cupy as cp
 import numpy as np
@@ -170,6 +170,18 @@ def sum_of_squares(
             "sum_of_squares", skipna=skipna, dtype=dtype, min_count=min_count
         )
 
+    def fillna(
+        self, value: Any = None, method: str = None, dtype: Dtype = None
+    ):
+        """Fill null values with ``value``.
+
+        Returns a copy with null filled.
+        """
+        result = libcudf.replace.replace_nulls(
+            input_col=self, replacement=value, method=method, dtype=dtype
+        )
+        return self._copy_type_metadata(result)
+
 
 def _binop_scale(l_dtype, r_dtype, op):
     # This should at some point be hooked up to libcudf's

python/cudf/cudf/core/join/_join_helpers.py

@@ -97,6 +97,14 @@ def _match_join_keys(
     if pd.api.types.is_dtype_equal(ltype, rtype):
         return lcol, rcol
 
+    if isinstance(ltype, cudf.Decimal64Dtype) or isinstance(
+        rtype, cudf.Decimal64Dtype
+    ):
+        raise TypeError(
+            "Decimal columns can only be merged with decimal columns "
+            "of the same precision and scale"
+        )
+
     if (np.issubdtype(ltype, np.number)) and (np.issubdtype(rtype, np.number)):
         common_type = (
             max(ltype, rtype)

python/cudf/cudf/tests/test_joining.py

@@ -6,7 +6,7 @@
 
 import cudf
 from cudf.core._compat import PANDAS_GE_120
-from cudf.core.dtypes import CategoricalDtype
+from cudf.core.dtypes import CategoricalDtype, Decimal64Dtype
 from cudf.tests.utils import (
     INTEGER_TYPES,
     NUMERIC_TYPES,
@@ -89,7 +89,7 @@ def assert_join_results_equal(expect, got, how, **kwargs):
         ):  # can't sort_values() on a df without columns
             return assert_eq(expect, got, **kwargs)
 
-        return assert_eq(
+        assert_eq(
             expect.sort_values(expect.columns.to_list()).reset_index(
                 drop=True
             ),
@@ -1152,6 +1152,137 @@ def test_typecast_on_join_overflow_unsafe(dtypes):
         merged = lhs.merge(rhs, on="a", how="left")  # noqa: F841
 
 
+@pytest.mark.parametrize(
+    "dtype",
+    [Decimal64Dtype(5, 2), Decimal64Dtype(7, 5), Decimal64Dtype(12, 7)],
+)
+def test_decimal_typecast_inner(dtype):
+    other_data = ["a", "b", "c", "d", "e"]
+
+    join_data_l = cudf.Series(["1.6", "9.5", "7.2", "8.7", "2.3"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(["1.6", "9.5", "7.2", "4.5", "2.3"]).astype(
+        dtype
+    )
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = ["1.6", "9.5", "7.2", "2.3"]
+    exp_other_data = ["a", "b", "c", "e"]
+
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expected, got, how="inner")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [Decimal64Dtype(7, 3), Decimal64Dtype(9, 5), Decimal64Dtype(14, 10)],
+)
+def test_decimal_typecast_left(dtype):
+    other_data = ["a", "b", "c", "d"]
+
+    join_data_l = cudf.Series(["95.05", "384.26", "74.22", "1456.94"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(
+        ["95.05", "62.4056", "74.22", "1456.9472"]
+    ).astype(dtype)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = ["95.05", "74.22", "384.26", "1456.94"]
+    exp_other_data_x = ["a", "c", "b", "d"]
+    exp_other_data_y = ["a", "c", None, None]
+
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data_x,
+            "B_y": exp_other_data_y,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="left")
+
+    assert_join_results_equal(expected, got, how="left")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [Decimal64Dtype(7, 3), Decimal64Dtype(10, 5), Decimal64Dtype(18, 9)],
+)
+def test_decimal_typecast_outer(dtype):
+    other_data = ["a", "b", "c"]
+    join_data_l = cudf.Series(["741.248", "1029.528", "3627.292"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(["9284.103", "1029.528", "948.637"]).astype(
+        dtype
+    )
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+    exp_join_data = ["9284.103", "948.637", "1029.528", "741.248", "3627.292"]
+    exp_other_data_x = [None, None, "b", "a", "c"]
+    exp_other_data_y = ["a", "c", "b", None, None]
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data_x,
+            "B_y": exp_other_data_y,
+        }
+    )
+    got = gdf_l.merge(gdf_r, on="join_col", how="outer")
+
+    assert_join_results_equal(expected, got, how="outer")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype_l", [Decimal64Dtype(7, 3), Decimal64Dtype(9, 5)],
+)
+@pytest.mark.parametrize(
+    "dtype_r", [Decimal64Dtype(8, 3), Decimal64Dtype(11, 6)],
+)
+def test_mixed_decimal_typecast(dtype_l, dtype_r):
+    other_data = ["a", "b", "c", "d"]
+
+    join_data_l = cudf.Series(["95.05", "34.6", "74.22", "14.94"]).astype(
+        dtype_r
+    )
+    join_data_r = cudf.Series(["95.05", "62.4056", "74.22", "1.42"]).astype(
+        dtype_l
+    )
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    with pytest.raises(
+        TypeError,
+        match="Decimal columns can only be merged with decimal columns "
+        "of the same precision and scale",
+    ):
+        gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+
 @pytest.mark.parametrize(
     "dtype_l",
     ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],