PERF: Importing pandas_parser lib takes 50MB of memory

[LucaCerina]

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Reproducible Example

Hello,
I was doing some memory profiling of an application that employs, among other libraries, Pandas. I have noticed it was using more than 50MB of memory just from imports, so I dug up and found that this line
import pandas._libs.pandas_parser
is the culprit.

Looking at the imported lib files they seem pretty small, so I wonder, what would be causing this memory blowup?
I have added some files for reproducibility. Machine and Python details below.

test_pandas_import_mem.py

mem_logs.txt

Installed Versions

INSTALLED VERSIONS

commit : c888af6
python : 3.12.11
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.26100
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United Kingdom.1252

pandas : 2.3.1
numpy : 2.0.2
pytz : 2025.2
dateutil : 2.9.0.post0
pip : None
Cython : 3.1.2
sphinx : None
IPython : 9.3.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : 1.5.0
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.5.1
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.3
numba : 0.61.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 20.0.0
pyreadstat : None
pytest : 8.4.1
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.16.0
sqlalchemy : 2.0.41
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.2
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

Prior Performance

No response

[LucaCerina]

Tested also on a Linux machine, got the same results.

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
   197   23.051 MiB   23.051 MiB           1   @profile
   198                                         def import_8():
   199   74.832 MiB   51.781 MiB           1       import pandas._libs.pandas_parser  # isort: skip # type: ignore[reportUnusedImport]
   200   74.832 MiB    0.000 MiB           1       import pandas._libs.pandas_datetime  # noqa: F401 # isort: skip # type: ignore[reportUnusedImport]
   201   74.832 MiB    0.000 MiB           1       from pandas._libs.interval import Interval
   202   74.832 MiB    0.000 MiB           1       from pandas._libs.tslibs import (
   203                                                 NaT,
   204                                                 NaTType,
   205                                                 OutOfBoundsDatetime,
   206                                                 Period,
   207                                                 Timedelta,
   208                                                 Timestamp,
   209                                                 iNaT,
   210                                             )

Installed Versions

INSTALLED VERSIONS

commit : c888af6
python : 3.13.5
python-bits : 64
OS : Linux
OS-release : 6.15.9-zen1-1.1-zen
Version : #1 ZEN SMP PREEMPT_DYNAMIC Fri, 08 Aug 2025 01:59:09 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8

pandas : 2.3.1
numpy : 2.3.2
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.2
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[jbrockmendel]

Aside from "make pandas smaller", what is the ask here? parsers.pyx imports from all over pandas. Even if we could get stuff out of it, those imports would still occur elsewhere

[LucaCerina]

I am just trying to understand why if blows up so much at import time.
I checked the parser C and pyx files and at a first glance I didn't see any giant malloc.

Is it a known problem I can help with?

There are obvious benefits in a smaller Pandas, and other Cython/C/etc heavy libraries (Numpy is 7MB in comparison) don't seem to have the same problem.

[jbrockmendel]

If you can find a way to trim the import size, that would be very welcome. xref #52654

[rhshadrach]

@LucaCerina

I checked the parser C and pyx files and at a first glance I didn't see any giant malloc.

Are you considering the size of modules / compiled code that are imported from these files, and so on?

Please feel free to post further here, but until there is something actionable, closing.