Skip to content

PERF: DataFrame.transpose for pyarrow-backed #54224

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 6 commits into from
Jul 24, 2023
Merged

PERF: DataFrame.transpose for pyarrow-backed #54224

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
79dbfd8
PERF: DataFrame.transpose for pyarrow-backed
lukemanley Jul 22, 2023
821a927
whatsnew
lukemanley Jul 22, 2023
7353cab
mypy
lukemanley Jul 22, 2023
dfd38ce
Merge remote-tracking branch 'upstream/main' into perf-arrow-transpose
lukemanley Jul 23, 2023
a24cc1b
move condition into _iter_column_arrays
lukemanley Jul 23, 2023
5a2418f
shorten name
lukemanley Jul 24, 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 @@ -403,6 +403,7 @@ Performance improvements
- Performance improvement in :meth:`DataFrame.isin` for extension dtypes (:issue:`53514`)
- Performance improvement in :meth:`DataFrame.loc` when selecting rows and columns (:issue:`53014`)
- Performance improvement in :meth:`DataFrame.transpose` when transposing a DataFrame with a single masked dtype, e.g. :class:`Int64` (:issue:`52836`)
- Performance improvement in :meth:`DataFrame.transpose` when transposing a DataFrame with a single pyarrow dtype (:issue:`54224`)
- Performance improvement in :meth:`Series.add` for pyarrow string and binary dtypes (:issue:`53150`)
- Performance improvement in :meth:`Series.corr` and :meth:`Series.cov` for extension dtypes (:issue:`52502`)
- Performance improvement in :meth:`Series.ffill`, :meth:`Series.bfill`, :meth:`DataFrame.ffill`, :meth:`DataFrame.bfill` with pyarrow dtypes (:issue:`53950`)
Expand Down
15 changes: 15 additions & 0 deletions pandas/core/arrays/arrow/array.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2564,3 +2564,18 @@ def _dt_tz_convert(self, tz):
current_unit = self.dtype.pyarrow_dtype.unit
result = self._pa_array.cast(pa.timestamp(current_unit, tz))
return type(self)(result)


def transpose_homogeneous_arrow_extension_arrays(
arrays: Sequence[ArrowExtensionArray],
) -> list[ArrowExtensionArray]:
"""Transpose arrow extension arrays in a list, but faster.

Input should be a list of arrays of equal length and all have the same
dtype. The caller is responsible for ensuring validity of input data.
"""
arr = pa.chunked_array([chunk for arr in arrays for chunk in arr._pa_array.chunks])
nrows, ncols = len(arrays[0]), len(arrays)
indices = np.arange(nrows * ncols).reshape(ncols, nrows).T.flatten()
arr = arr.take(indices)
return [ArrowExtensionArray(arr.slice(i * ncols, ncols)) for i in range(nrows)]
2 changes: 1 addition & 1 deletion pandas/core/arrays/masked.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1464,7 +1464,7 @@ def _groupby_op(
return self._maybe_mask_result(res_values, result_mask)


def transpose_homogenous_masked_arrays(
def transpose_homogeneous_masked_arrays(
masked_arrays: Sequence[BaseMaskedArray],
) -> list[BaseMaskedArray]:
"""Transpose masked arrays in a list, but faster.
Expand Down
21 changes: 19 additions & 2 deletions pandas/core/frame.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3648,17 +3648,34 @@ def transpose(self, *args, copy: bool = False) -> DataFrame:
and dtypes
and isinstance(dtypes[0], ExtensionDtype)
):
new_values: list
if isinstance(dtypes[0], BaseMaskedDtype):
# We have masked arrays with the same dtype. We can transpose faster.
from pandas.core.arrays.masked import transpose_homogenous_masked_arrays
from pandas.core.arrays.masked import (
transpose_homogeneous_masked_arrays,
)

if isinstance(self._mgr, ArrayManager):
masked_arrays = self._mgr.arrays
else:
masked_arrays = list(self._iter_column_arrays())
new_values = transpose_homogenous_masked_arrays(
new_values = transpose_homogeneous_masked_arrays(
cast(list[BaseMaskedArray], masked_arrays)
)
elif isinstance(dtypes[0], ArrowDtype):
# We have arrow EAs with the same dtype. We can transpose faster.
from pandas.core.arrays.arrow.array import (
ArrowExtensionArray,
transpose_homogeneous_arrow_extension_arrays,
)

if isinstance(self._mgr, ArrayManager):
arrays = self._mgr.arrays
else:
arrays = list(self._iter_column_arrays())
Copy link
Member

Choose a reason for hiding this comment

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

cant do this unconditionally?

Copy link
Member Author

Choose a reason for hiding this comment

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

Do you mean use _iter_column_arrays in both cases? The masked array implementation just above this uses the condition as well, presumably for performance:

import pandas as pd

pd.options.mode.data_manager = "array"

df = pd.DataFrame(columns=range(100000), dtype="int64[pyarrow]")

%timeit list(df._iter_column_arrays())
# 57.9 ms ± 2.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit df._mgr.arrays
# 110 ns ± 8.79 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)

Copy link
Member

Choose a reason for hiding this comment

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

Fair enough, thanks for taking a look

Copy link
Member Author

Choose a reason for hiding this comment

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

on second thought, I moved the condition into DataFrame._iter_column_arrays to avoid repeating it elsewhere

new_values = transpose_homogeneous_arrow_extension_arrays(
cast(list[ArrowExtensionArray], arrays)
)
else:
# We have other EAs with the same dtype. We preserve dtype in transpose.
dtyp = dtypes[0]
Expand Down