Fugue count matching rows

datacompy/__init__.py

@@ -19,6 +19,7 @@
 from datacompy.fugue import (
     all_columns_match,
     all_rows_overlap,
+    count_matching_rows,
     intersect_columns,
     is_match,
     report,

datacompy/fugue.py

@@ -291,6 +291,101 @@ def all_rows_overlap(
     return all(overlap)
 
 
+def count_matching_rows(
+    df1: AnyDataFrame,
+    df2: AnyDataFrame,
+    join_columns: Union[str, List[str]],
+    abs_tol: float = 0,
+    rel_tol: float = 0,
+    df1_name: str = "df1",
+    df2_name: str = "df2",
+    ignore_spaces: bool = False,
+    ignore_case: bool = False,
+    cast_column_names_lower: bool = True,
+    parallelism: Optional[int] = None,
+    strict_schema: bool = False,
+) -> int:
+    """Count the number of rows match (on overlapping fields)
+
+    Parameters
+    ----------
+    df1 : ``AnyDataFrame``
+        First dataframe to check
+    df2 : ``AnyDataFrame``
+        Second dataframe to check
+    join_columns : list or str, optional
+        Column(s) to join dataframes on.  If a string is passed in, that one
+        column will be used.
+    abs_tol : float, optional
+        Absolute tolerance between two values.
+    rel_tol : float, optional
+        Relative tolerance between two values.
+    df1_name : str, optional
+        A string name for the first dataframe.  This allows the reporting to
+        print out an actual name instead of "df1", and allows human users to
+        more easily track the dataframes.
+    df2_name : str, optional
+        A string name for the second dataframe
+    ignore_spaces : bool, optional
+        Flag to strip whitespace (including newlines) from string columns (including any join
+        columns)
+    ignore_case : bool, optional
+        Flag to ignore the case of string columns
+    cast_column_names_lower: bool, optional
+        Boolean indicator that controls of column names will be cast into lower case
+    parallelism: int, optional
+        An integer representing the amount of parallelism. Entering a value for this
+        will force to use of Fugue over just vanilla Pandas
+    strict_schema: bool, optional
+        The schema must match exactly if set to ``True``. This includes the names and types. Allows for a fast fail.
+
+    Returns
+    -------
+    int
+        Number of matching rows
+    """
+    if (
+        isinstance(df1, pd.DataFrame)
+        and isinstance(df2, pd.DataFrame)
+        and parallelism is None  # user did not specify parallelism
+        and fa.get_current_parallelism() == 1  # currently on a local execution engine
+    ):
+        comp = Compare(
+            df1=df1,
+            df2=df2,
+            join_columns=join_columns,
+            abs_tol=abs_tol,
+            rel_tol=rel_tol,
+            df1_name=df1_name,
+            df2_name=df2_name,
+            ignore_spaces=ignore_spaces,
+            ignore_case=ignore_case,
+            cast_column_names_lower=cast_column_names_lower,
+        )
+        return comp.count_matching_rows()
+
+    try:
+        count_matching_rows = _distributed_compare(
+            df1=df1,
+            df2=df2,
+            join_columns=join_columns,
+            return_obj_func=lambda comp: comp.count_matching_rows(),
+            abs_tol=abs_tol,
+            rel_tol=rel_tol,
+            df1_name=df1_name,
+            df2_name=df2_name,
+            ignore_spaces=ignore_spaces,
+            ignore_case=ignore_case,
+            cast_column_names_lower=cast_column_names_lower,
+            parallelism=parallelism,
+            strict_schema=strict_schema,
+        )
+    except _StrictSchemaError:
+        return False
+
+    return sum(count_matching_rows)
+
+
 def report(
     df1: AnyDataFrame,
     df2: AnyDataFrame,
@@ -460,7 +555,6 @@ def _any(col: str) -> int:
     any_mismatch = len(match_sample) > 0
 
     # Column Matching
-    cnt_intersect = shape0("intersect_rows_shape")
     rpt += render(
         "column_comparison.txt",
         len([col for col in column_stats if col["unequal_cnt"] > 0]),

tests/test_fugue/conftest.py

@@ -1,6 +1,6 @@
-import pytest
 import numpy as np
 import pandas as pd
+import pytest
 
 
 @pytest.fixture
@@ -24,7 +24,8 @@ def ref_df():
             c=np.random.choice(["aaa", "b_c", "csd"], 100),
         )
     )
-    return [df1, df1_copy, df2, df3, df4]
+    df5 = df1.sample(frac=0.1)
+    return [df1, df1_copy, df2, df3, df4, df5]
 
 
 @pytest.fixture
@@ -87,3 +88,16 @@ def large_diff_df2():
     np.random.seed(0)
     data = np.random.randint(6, 11, size=10000)
     return pd.DataFrame({"x": data, "y": np.array([9] * 10000)}).convert_dtypes()
+
+
+@pytest.fixture
+def count_matching_rows_df():
+    np.random.seed(0)
+    df1 = pd.DataFrame(
+        dict(
+            a=np.arange(0, 100),
+            b=np.arange(0, 100),
+        )
+    )
+    df2 = df1.sample(frac=0.1)
+    return [df1, df2]

tests/test_fugue/test_duckdb.py

@@ -20,6 +20,7 @@
 from datacompy import (
     all_columns_match,
     all_rows_overlap,
+    count_matching_rows,
     intersect_columns,
     is_match,
     unq_columns,
@@ -138,3 +139,40 @@ def test_all_rows_overlap_duckdb(
             duckdb.sql("SELECT 'a' AS a, 'b' AS b"),
             join_columns="a",
         )
+
+
+def test_count_matching_rows_duckdb(count_matching_rows_df):
+    with duckdb.connect():
+        df1 = duckdb.from_df(count_matching_rows_df[0])
+        df1_copy = duckdb.from_df(count_matching_rows_df[0])
+        df2 = duckdb.from_df(count_matching_rows_df[1])
+
+        assert (
+            count_matching_rows(
+                df1,
+                df1_copy,
+                join_columns="a",
+            )
+            == 100
+        )
+        assert count_matching_rows(df1, df2, join_columns="a") == 10
+        # Fugue
+
+        assert (
+            count_matching_rows(
+                df1,
+                df1_copy,
+                join_columns="a",
+                parallelism=2,
+            )
+            == 100
+        )
+        assert (
+            count_matching_rows(
+                df1,
+                df2,
+                join_columns="a",
+                parallelism=2,
+            )
+            == 10
+        )

tests/test_fugue/test_fugue_pandas.py

@@ -24,6 +24,7 @@
     Compare,
     all_columns_match,
     all_rows_overlap,
+    count_matching_rows,
     intersect_columns,
     is_match,
     report,
@@ -144,7 +145,6 @@ def test_report_pandas(
 
 def test_unique_columns_native(ref_df):
     df1 = ref_df[0]
-    df1_copy = ref_df[1]
     df2 = ref_df[2]
     df3 = ref_df[3]
 
@@ -192,3 +192,41 @@ def test_all_rows_overlap_native(
     # Fugue
     assert all_rows_overlap(ref_df[0], shuffle_df, join_columns="a", parallelism=2)
     assert not all_rows_overlap(ref_df[0], ref_df[4], join_columns="a", parallelism=2)
+
+
+def test_count_matching_rows_native(count_matching_rows_df):
+    # defaults to Compare class
+    assert (
+        count_matching_rows(
+            count_matching_rows_df[0],
+            count_matching_rows_df[0].copy(),
+            join_columns="a",
+        )
+        == 100
+    )
+    assert (
+        count_matching_rows(
+            count_matching_rows_df[0], count_matching_rows_df[1], join_columns="a"
+        )
+        == 10
+    )
+    # Fugue
+
+    assert (
+        count_matching_rows(
+            count_matching_rows_df[0],
+            count_matching_rows_df[0].copy(),
+            join_columns="a",
+            parallelism=2,
+        )
+        == 100
+    )
+    assert (
+        count_matching_rows(
+            count_matching_rows_df[0],
+            count_matching_rows_df[1],
+            join_columns="a",
+            parallelism=2,
+        )
+        == 10
+    )

tests/test_fugue/test_fugue_polars.py

@@ -20,6 +20,7 @@
 from datacompy import (
     all_columns_match,
     all_rows_overlap,
+    count_matching_rows,
     intersect_columns,
     is_match,
     unq_columns,
@@ -122,3 +123,37 @@ def test_all_rows_overlap_polars(
     assert all_rows_overlap(rdf, rdf_copy, join_columns="a")
     assert all_rows_overlap(rdf, sdf, join_columns="a")
     assert not all_rows_overlap(rdf, rdf4, join_columns="a")
+
+
+def test_count_matching_rows_polars(count_matching_rows_df):
+    df1 = pl.from_pandas(count_matching_rows_df[0])
+    df2 = pl.from_pandas(count_matching_rows_df[1])
+    assert (
+        count_matching_rows(
+            df1,
+            df1.clone(),
+            join_columns="a",
+        )
+        == 100
+    )
+    assert count_matching_rows(df1, df2, join_columns="a") == 10
+    # Fugue
+
+    assert (
+        count_matching_rows(
+            df1,
+            df1.clone(),
+            join_columns="a",
+            parallelism=2,
+        )
+        == 100
+    )
+    assert (
+        count_matching_rows(
+            df1,
+            df2,
+            join_columns="a",
+            parallelism=2,
+        )
+        == 10
+    )

tests/test_fugue/test_fugue_spark.py

@@ -22,6 +22,7 @@
     Compare,
     all_columns_match,
     all_rows_overlap,
+    count_matching_rows,
     intersect_columns,
     is_match,
     report,
@@ -200,3 +201,44 @@ def test_all_rows_overlap_spark(
         spark_session.sql("SELECT 'a' AS a, 'b' AS b"),
         join_columns="a",
     )
+
+
+def test_count_matching_rows_spark(spark_session, count_matching_rows_df):
+    count_matching_rows_df[0].iteritems = count_matching_rows_df[
+        0
+    ].items  # pandas 2 compatibility
+    count_matching_rows_df[1].iteritems = count_matching_rows_df[
+        1
+    ].items  # pandas 2 compatibility
+    df1 = spark_session.createDataFrame(count_matching_rows_df[0])
+    df1_copy = spark_session.createDataFrame(count_matching_rows_df[0])
+    df2 = spark_session.createDataFrame(count_matching_rows_df[1])
+    assert (
+        count_matching_rows(
+            df1,
+            df1_copy,
+            join_columns="a",
+        )
+        == 100
+    )
+    assert count_matching_rows(df1, df2, join_columns="a") == 10
+    # Fugue
+
+    assert (
+        count_matching_rows(
+            df1,
+            df1_copy,
+            join_columns="a",
+            parallelism=2,
+        )
+        == 100
+    )
+    assert (
+        count_matching_rows(
+            df1,
+            df2,
+            join_columns="a",
+            parallelism=2,
+        )
+        == 10
+    )