BUG: "ValueError: ndarray is not C-contiguous" when aggregating concatenated dataframe

[zephyrai-niq]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import numpy as np
import pandas as pd

for dtype in [np.int8, pd.Int8Dtype()]:
    df1 = pd.DataFrame(
        data={
            "col1": pd.Series([1, 2, 3], index=["A", "B", "C"], dtype=dtype),
        },
        index=pd.Index(["A", "B", "C"], dtype=pd.StringDtype()),
    )
    df2 = pd.DataFrame(
        data={
            "col2": pd.Series([4, 5, 6], index=["D", "E", "F"], dtype=dtype),
        },
        index=pd.Index(["D", "E", "F"], dtype=pd.StringDtype()),
    )
    df = pd.concat([df1, df2])
    group = df.T.groupby(df.columns)
    max_df = group.max()
    print(max_df)
    print()

Issue Description

I'm running into a ValueError: ndarray is not C-contiguous exception when attempting to aggregate a concatenated dataframe. Strangely though, the exception only seems to occur if the column dtypes are pd.Int8Dtype(), but not if they are np.int8.

The example illustrates this issue. The first iteration of the loop runs properly, printing the following output:

        A    B    C    D    E    F
col1  1.0  2.0  3.0  NaN  NaN  NaN
col2  NaN  NaN  NaN  4.0  5.0  6.0

However, the second iteration raises an unhandled exception on the group.max() call. Here is my (sanitized) callstack:

Traceback (most recent call last):
  File "/Users/redacted/git/myproject/foo.py", line 19, in <module>
    max_df = group.max()
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 3330, in max
    return self._agg_general(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1906, in _agg_general
    result = self._cython_agg_general(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1998, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1473, in grouped_reduce
    applied = blk.apply(func)
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py", line 393, in apply
    result = func(self.values, **kwargs)
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1973, in array_func
    result = self._grouper._cython_operation(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 830, in _cython_operation
    return cy_op.cython_operation(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 540, in cython_operation
    return values._groupby_op(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/arrays/masked.py", line 1587, in _groupby_op
    res_values = op._cython_op_ndim_compat(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 329, in _cython_op_ndim_compat
    res = self._call_cython_op(
  File "/Users/redacted/Library/Caches/pypoetry/virtualenvs/myproject-f89bfaSs-py3.9/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 418, in _call_cython_op
    func(
  File "groupby.pyx", line 1835, in pandas._libs.groupby.group_max
  File "<stringsource>", line 663, in View.MemoryView.memoryview_cwrapper
  File "<stringsource>", line 353, in View.MemoryView.memoryview.__cinit__
ValueError: ndarray is not C-contiguous

I discovered this issue when upgrading my code from pandas v1.5.3 to v2.2.0. With some trial and error though, I was able to determine that this issue was introduced with the v2.1.0 release. (i.e. this issue does not occur with v2.0.3)

Expected Behavior

I would expect both iterations of the for-loop in the example code to behave identically. However, only the first iteration succeeds; the second iteration raises an unhandled exception on the group.max() call.

Installed Versions

INSTALLED VERSIONS

commit : fd3f571
python : 3.9.17.final.0
python-bits : 64
OS : Darwin
OS-release : 23.3.0
Version : Darwin Kernel Version 23.3.0: Wed Dec 20 21:28:58 PST 2023; root:xnu-10002.81.5~7/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.0.3
pip : 23.0.1
Cython : None
pytest : 7.4.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.9
jinja2 : 3.1.3
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 12.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2023.12.2
scipy : 1.12.0
sqlalchemy : 2.0.25
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. Here is a simpler reproducer showing this isn't caused by concat, StringDtype columns, nor the presence of NA values:

dtype = "Int64"
df = pd.DataFrame(
    {
        "col1": pd.Series([1, 2, 3], dtype=dtype),
        "col2": pd.Series([1, 2, 3], dtype=dtype),
    },
    index=pd.Index(["A", "B", "C"]),
)
print(df._mgr.blocks[0].values._data.flags['F_CONTIGUOUS'])
# True
print(df.T._mgr.blocks[0].values._data.flags['F_CONTIGUOUS'])
# False

Doing the same with non-EA dtypes, the transpose is F-contiguous (note: the groupby code takes a transpose, so F- and C-continuous flags get swapped).

[zephyrai-niq]

@rhshadrach Slight correction to your example. The first print() statement actually outputs True, not False:

print(df._mgr.blocks[0].values._data.flags["F_CONTIGUOUS"])
# True

[rhshadrach]

Thanks! Fixed.