Pandas version checks

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Reproducible Example

import pandas as pd
df = pd.read_csv(filename, sep="\t\t|\t", header=5, engine='python') # file is about 250 MB on disk
## memory used: 3.5GB
df.memory_usage() ## 14294152bytes x 24 columns == 327MB
from copy import deepcopy
df2 = deepcopy(df)
## memory 3.9GB
del df
## memory 3.5GB
del df2
## memory 0.1GB

Issue Description

Somehow there is a memory usage of 3.5GB when loading this 250MB file. I understand that pandas keeps metadata but somehow this does not seem reasonable.

When doing pd.read_csv(filename, sep="\t", header=5) (only \t and not engine='python') the memory usage is 416.764KB) which I think is reasonable.

Expected Behavior

Memory usage is the same when using both backends. Deepcopy of df and deleting df should remove data from memory that is not relevant to the values in the table.

Installed Versions

D:\miniforge3\lib\site-packages_distutils_hack_init_.py:32: UserWarning: Setuptools is replacing distutils. Support for replacing an already imported distutils is deprecated. In the future, this condition will fail. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml
warnings.warn(

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.10.14.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_Germany.1252

pandas : 2.2.2
numpy : 2.0.1
pytz : 2024.1
dateutil : 2.9.0
setuptools : 72.1.0
pip : 24.2
Cython : None
pytest : 8.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.9.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : 0.23.0
tzdata : 2024.1
qtpy : None
pyqt5 : None