test: add coalesce function tests for handling default values

Author: kosiew

python/tests/test_dataframe.py

@@ -1207,7 +1207,7 @@ def test_fill_null(df):
     assert result["d"] == [0, 0, 0]
 
     # Test filling nulls with string value
-    df_with_nulls = df.with_column("d", literal(None).cast(pa.int64()))    
+    df_with_nulls = df.with_column("d", literal(None).cast(pa.string()))   
     df_filled = df_with_nulls.fill_null("missing")
     result = df_filled.to_pydict()
     assert result["e"] == ["missing", "missing", "missing"]

python/tests/test_functions.py

@@ -1173,3 +1173,46 @@ def test_between_default(df):
 
     actual = df.collect()[0].to_pydict()
     assert actual == expected
+
+def test_coalesce(df):
+    # Create a DataFrame with null values
+    ctx = SessionContext()
+    batch = pa.RecordBatch.from_arrays(
+        [
+            pa.array(["Hello", None, "!"]),  # string column with null
+            pa.array([4, None, 6]),          # integer column with null
+            pa.array(["hello ", None, " !"]), # string column with null
+            pa.array([datetime(2022, 12, 31), None, datetime(2020, 7, 2)]),  # datetime with null
+            pa.array([False, None, True]),    # boolean column with null
+        ],
+        names=["a", "b", "c", "d", "e"],
+    )
+    df_with_nulls = ctx.create_dataframe([[batch]])
+
+    # Test coalesce with different data types
+    result_df = df_with_nulls.select(
+        f.coalesce(column("a"), literal("default")).alias("a_coalesced"),
+        f.coalesce(column("b"), literal(0)).alias("b_coalesced"),
+        f.coalesce(column("c"), literal("default")).alias("c_coalesced"),
+        f.coalesce(column("d"), literal(datetime(2000, 1, 1))).alias("d_coalesced"),
+        f.coalesce(column("e"), literal(False)).alias("e_coalesced"),
+    )
+    
+    result = result_df.collect()[0]
+
+    # Verify results
+    assert result.column(0) == pa.array(["Hello", "default", "!"], type=pa.string_view())
+    assert result.column(1) == pa.array([4, 0, 6], type=pa.int64())
+    assert result.column(2) == pa.array(["hello ", "default", " !"], type=pa.string_view())
+    assert result.column(3) == pa.array(
+        [datetime(2022, 12, 31), datetime(2000, 1, 1), datetime(2020, 7, 2)],
+        type=pa.timestamp("us"),
+    )
+    assert result.column(4) == pa.array([False, False, True], type=pa.bool_())
+
+    # Test multiple arguments
+    result_df = df_with_nulls.select(
+        f.coalesce(column("a"), literal(None), literal("fallback")).alias("multi_coalesce")
+    )
+    result = result_df.collect()[0]
+    assert result.column(0) == pa.array(["Hello", "fallback", "!"], type=pa.string_view())