BUG: ArrowInvalid Error when Converting Concatenated Modin DataFrame to PyArrow Table

[DimitarSirakov]

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Reproducible Example

import modin.pandas as mpd
import pyarrow as pa

# Creating Modin DataFrames
mpd_df_1 = mpd.DataFrame({'a': ['1', '2', '3'], 'b': ['4', '5', '6']})
mpd_df_2 = mpd.DataFrame({'a': ['7', '8', '9'], 'b': ['10', '11', '12']})
mpd_df_3 = mpd.DataFrame({'a': ['13', '14', '15'], 'b': ['16', '17', '18']})

# Concatenating DataFrames
test_df = mpd.concat([mpd_df_1, mpd_df_2, mpd_df_3])

# Converting to PyArrow Table (Error Occurs Here)
table_from_modin = pa.Table.from_pandas(df=test_df)

Issue Description

I encountered an ArrowInvalid error when attempting to convert a concatenated Modin DataFrame into a PyArrow table. This issue arises specifically when concatenating multiple Modin DataFrames and then converting the resulting DataFrame to a PyArrow table. The same process works correctly with Pandas DataFrames. The same process works correctly with non concatenated modin DataFrame.

Expected Behavior

The concatenated Modin DataFrame should be converted to a PyArrow table without any errors, similar to how a concatenated Pandas DataFrame or not concat modin Dataframe behaves

Error Logs

ArrowInvalid: ('Could not convert 0     1\n0     7\n0    13\nName: a, dtype: object with type Series: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column a with type object')

Installed Versions

INSTALLED VERSIONS

commit : b99cf06
python : 3.10.5.final.0
python-bits : 64
OS : Darwin
OS-release : 23.0.0
Version : Darwin Kernel Version 23.0.0: Fri Sep 15 14:42:57 PDT 2023; root:xnu-10002.1.13~1/RELEASE_ARM64_T8112
machine : arm64
processor : arm
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

Modin dependencies

modin : 0.25.1
ray : 2.8.1
dask : None
distributed : None
hdk : None

pandas dependencies

pandas : 2.1.3
numpy : 1.26.2
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : None
pytest : 7.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.9
jinja2 : 3.1.2
IPython : 8.18.1
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : 2023.12.0
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.23
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[anmyachev]

Hi @DimitarSirakov, thanks for the contribution!

I can reproduce the problem. Take a look.

[DimitarSirakov]

I found out that ignore_index=True fixes the problem but I can't figure out why and why this is not the case in pandas.

[anmyachev]

@DimitarSirakov iteration over the index occurs when converting; if the index is not unique, then it may return not a scalar, but a Modin Series, with which pyarrow cannot work.

An interchange protocol was developed for interaction between libraries. An implementation of this protocol can be used through https://arrow.apache.org/docs/13.0/python/generated/pyarrow.interchange.from_dataframe.html#pyarrow.interchange.from_dataframe. However, as far as I know, it appears in pyarrow starting from version 13.0.0. Do you have the opportunity to try this option?

[anmyachev]

@DimitarSirakov if this option is not suitable for you, then we may consider implementing def __arrow_array__(self, type=None): for Series object, which will fix the problems with your current reproducer, but will most likely be slower.

[DimitarSirakov]

The implementation is awesome. However, the DataFrame exchange protocol doesn't fully support certain types, such as datetime64[ms]. Fortunately, I've found some workable solutions, so it's manageable.

Given my goal of maximizing efficiency with the resources at hand, I'll have to pass on this one :). I thought Modin would be an almost perfect drop-in replacement for Pandas, and perhaps that led me to expect a similar outcome here.

Thanks for the support – it's been a real time-saver.