Skip to content

BUG: from_dataframe with empty objects #53161

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
May 15, 2023
Merged

BUG: from_dataframe with empty objects #53161

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
d926117
BUG: from_dataframe with empty objects
mroeschke May 9, 2023
c47f306
Merge remote-tracking branch 'upstream/main' into bug/interchange/empty
mroeschke May 10, 2023
0449ada
Use protocol_df_chunk_to_pandas
mroeschke May 10, 2023
a7f4f3e
Merge remote-tracking branch 'upstream/main' into bug/interchange/empty
mroeschke May 11, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -447,6 +447,7 @@ Metadata
Other
^^^^^
- Bug in :class:`FloatingArray.__contains__` with ``NaN`` item incorrectly returning ``False`` when ``NaN`` values are present (:issue:`52840`)
- Bug in :func:`api.interchange.from_dataframe` when converting an empty DataFrame object (:issue:`53155`)
- Bug in :func:`assert_almost_equal` now throwing assertion error for two unequal sets (:issue:`51727`)
- Bug in :func:`assert_frame_equal` checks category dtypes even when asked not to check index type (:issue:`52126`)
- Bug in :meth:`DataFrame.reindex` with a ``fill_value`` that should be inferred with a :class:`ExtensionDtype` incorrectly inferring ``object`` dtype (:issue:`52586`)
Expand Down
4 changes: 3 additions & 1 deletion pandas/core/interchange/from_dataframe.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,9 @@ def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True):
raise RuntimeError(
"To join chunks a copy is required which is forbidden by allow_copy=False"
)
if len(pandas_dfs) == 1:
if not pandas_dfs:
pandas_df = pd.DataFrame({col: [] for col in df.column_names()})
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

do we care about the dtype(s)?

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Good call. Using protocol_df_chunk_to_pandas here will preserve the dtypes

elif len(pandas_dfs) == 1:
pandas_df = pandas_dfs[0]
else:
pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
Expand Down
10 changes: 10 additions & 0 deletions pandas/tests/interchange/test_impl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -272,3 +272,13 @@ def test_categorical_to_numpy_dlpack():
result = np.from_dlpack(col.get_buffers()["data"][0])
expected = np.array([0, 1, 0], dtype="int8")
tm.assert_numpy_array_equal(result, expected)


@pytest.mark.parametrize("data", [{}, {"a": []}])
def test_empty_pyarrow(data):
# GH 53155
pa_interchange = pytest.importorskip("pyarrow.interchange", "11.0.0")
expected = pd.DataFrame(data)
arrow_df = pa_interchange.from_dataframe(expected)
result = from_dataframe(arrow_df)
tm.assert_frame_equal(result, expected)