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Reproducible Example

with open(file_, "r") as f:
    df = pd.read_xml(
        f,
        parser="etree",
        iterparse={root: list(elements)}
    )

Issue Description

the method read_xml with iterparse as parms is used to read large xml file, but it's restricted to read only files on local disk. this design is not justified since that the module iterparse from xml.etree.Elements does allow this features (reading from a file like object) 

if (
    not isinstance(self.path_or_buffer, str) # -> condition that raise the Error
    or is_url(self.path_or_buffer)
    or is_fsspec_url(self.path_or_buffer)
    or self.path_or_buffer.startswith(("<?xml", "<"))
    or infer_compression(self.path_or_buffer, "infer") is not None
):
    raise ParserError(
        "iterparse is designed for large XML files that are fully extracted on "
        "local disk and not as compressed files or online sources."
    )

maybe something like this is better:

if isinstance(self.path_or_buffer,str) or hasattr(self.path_or_buffer, "read"):
 #do something
else:
  raise ParserError(...)

Expected Behavior

this must return a dataframe reither than raise Exception

Installed Versions

INSTALLED VERSIONS ------------------ commit : 87cfe4e38bafe7300a6003a1d18bd80f3f77c763 python : 3.10.4.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-136-generic Version : #153-Ubuntu SMP Thu Nov 24 15:56:58 UTC 2022 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.5.0 numpy : 1.23.4 pytz : 2022.5 dateutil : 2.8.2 setuptools : 58.1.0 pip : 22.0.4 Cython : None pytest : 7.1.3 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None

Comment From: phofl

Hi, thanks for your report. Please provide a reproducible example that is copy-paste able

Comment From: ParfaitG

This is a straightforward fix and not a use case originally considered for very large files requiring iterparse. For lxml parser, the read mode must be rb as its iterparse method expects only binary input.

Comment From: bama-chi

@phofl dunno what can I provide more than the example above, you only have to replace the file_ variable with your xml file let me know if I can help with something

@ParfaitG thank you for ur PR.

Comment From: phofl

That's exactly what copy-and-pasteable means. For example a simple read_csv reproducer:

data = """a,b
1,2
"""
pd.read_csv(StringIO(data))

Same goes for xml for future bug reports

Comment From: bama-chi

okey thanks