Add unit tests for output_names in map_rows and _is_directory in io.py to improve code coverage

tests/nested_pandas/nestedframe/test_io.py

@@ -10,16 +10,18 @@
 import pyarrow.fs
 import pyarrow.parquet as pq
 import pytest
+from pandas.testing import assert_frame_equal
+from upath import UPath
+
 from nested_pandas import NestedFrame, read_parquet
 from nested_pandas.datasets import generate_data
 from nested_pandas.nestedframe.io import (
     FSSPEC_BLOCK_SIZE,
     _get_storage_options_and_path,
+    _is_directory,
     _transform_read_parquet_data_arg,
     from_pyarrow,
 )
-from pandas.testing import assert_frame_equal
-from upath import UPath
 
 
 def test_read_parquet():
@@ -497,3 +499,31 @@ def test__get_storage_options_and_path():
     # Test with invalid type
     with pytest.raises(TypeError):
         _get_storage_options_and_path(123)
+
+
+def test__is_directory():
+    """Test _is_directory function with various input types."""
+    # Test with Path object pointing to a directory
+    dir_path = Path("tests/test_data")
+    assert _is_directory(dir_path) is True
+
+    # Test with Path object pointing to a file
+    file_path = Path("tests/test_data/nested.parquet")
+    assert _is_directory(file_path) is False
+
+    # Test with string pointing to a directory
+    str_dir_path = "tests/test_data"
+    assert _is_directory(str_dir_path) is True
+
+    # Test with string pointing to a file
+    str_file_path = "tests/test_data/nested.parquet"
+    assert _is_directory(str_file_path) is False
+
+    # Test with non-existent path (should return False)
+    non_existent = Path("tests/non_existent_directory")
+    assert _is_directory(non_existent) is False
+
+    # Test with other types (should return False)
+    assert _is_directory(123) is False
+    assert _is_directory(None) is False
+    assert _is_directory([]) is False

tests/nested_pandas/nestedframe/test_nestedframe.py

@@ -1177,6 +1177,29 @@ def make_id(row, prefix_str):
             get_max, columns=["packed.c", "packed.d"], output_names=["only_one_name"], row_container="args"
         )
 
+    # Test output_names as a string (single output)
+    def get_single_max(row):
+        return row["packed.c"].max()
+
+    result = nf.map_rows(get_single_max, columns=["packed.c"], output_names="max_c")
+    assert len(result) == len(nf)
+    assert list(result.columns) == ["max_c"]
+    for i in range(len(result)):
+        assert result["max_c"].values[i] == expected_max_c[i]
+
+    # Test output_names as a list (multiple outputs)
+    def get_max_pair(row):
+        return pd.Series([row["packed.c"].max(), row["packed.d"].max()], index=["max_col1", "max_col2"])
+
+    result = nf.map_rows(
+        get_max_pair, columns=["packed.c", "packed.d"], output_names=["custom_max1", "custom_max2"]
+    )
+    assert len(result) == len(nf)
+    assert list(result.columns) == ["custom_max1", "custom_max2"]
+    for i in range(len(result)):
+        assert result["custom_max1"].values[i] == expected_max_c[i]
+        assert result["custom_max2"].values[i] == expected_max_d[i]
+
     # Verify that append_columns=True works as expected.
     # Ensure that even with non-unique indexes, the final result retains
     # the original index (nested-pandas#301)