BUG: usecols in pandas.read_csv has incorrect behavior when using pyarrow engine · Issue #54459 · pandas-dev/pandas (original) (raw)

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Reproducible Example

import pandas

df = pandas.read_csv(file, header=None, sep="\t", usecols=[0, 1, 2], dtype="string[pyarrow]", dtype_backend="pyarrow", engine="pyarrow")

Issue Description

my file is like this(no header, tab delimiter):
chr1 11874 12227 chr1 11874 12227 DDX11L1:NR_046018.2:+:3:exon1
chr1 12228 12612 chr1 12228 12612 DDX11L1:NR_046018.2:+:3:intron1
chr1 12613 12721 chr1 12613 12721 DDX11L1:NR_046018.2:+:3:exon2
chr1 12722 13220 chr1 12722 13220 DDX11L1:NR_046018.2:+:3:intron2
chr1 13221 14829 chr1 14362 14829 WASH7P:NR_024540.1:-:11:exon11

when I use pyarrow engine:
df = pandas.read_csv(file, header=None, sep="\t", usecols=[0, 1, 2], dtype="string[pyarrow]", dtype_backend="pyarrow", engine="pyarrow")

I get:
TypeError: expected bytes, int found

when I use C engine:
df = pandas.read_csv(file, header=None, sep="\t", usecols=[0, 1, 2])

I get a correct df.

I know we can use df.loc/df.iloc/df.drop or anything like these to get the same output. However, In some case, the col num in input file may be very big, so use "usecols" instead of "read all then drop some cols" is very important.

Expected Behavior

When using pyarrow engine, pandas.read_csv's behavior should consistent with C engine.

Installed Versions

INSTALLED VERSIONS

commit : 0f43794
python : 3.10.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936

pandas : 2.0.3
numpy : 1.23.0
pytz : 2022.1
dateutil : 2.8.2
setuptools : 57.5.0
pip : 22.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.9
lxml.etree : None
html5lib : None
pymysql : 1.0.3
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 12.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None