Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this issue exists on the latest version of pandas.
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I have confirmed this issue exists on the main branch of pandas.
Reproducible Example
I made a file
import pandas as pd
dates = pd.date_range('1900', '2000').tz_localize('+01:00').strftime('%Y-%d-%m %H:%M:%S%z').tolist()
dates.append('2020-01-01 00:00:00+02:00')
pd.to_datetime(dates, format='%Y-%d-%m %H:%M:%S%z')I then ran
python -m cProfile -o out.prof perf.py
snakeviz out.prof --server
I can then see that, of the 0.329 seconds spent in to_datetime, a whole 0.248 are spent in _return_parsed_timezone_results. That's a whole 75%!
This function is a pair of Python for-loops
pandas/pandas/core/tools/datetimes.py
Lines 316 to 328 in 0cebd75
Would this be a good candidate for Cython to speed it up?
cc @lukemanley
Installed Versions
INSTALLED VERSIONS
commit : e93ee07
python : 3.8.15.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 2.0.0.dev0+863.ge93ee07729
numpy : 1.23.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.5.1
pip : 22.3.1
Cython : 0.29.32
pytest : 7.2.0
hypothesis : 6.59.0
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 8.7.0
pandas_datareader: 0.10.0
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : 2022.11.0
fsspec : 2021.11.0
gcsfs : 2021.11.0
matplotlib : 3.6.2
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.0.10
pandas_gbq : 0.17.9
pyarrow : 9.0.0
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2021.11.0
scipy : 1.9.3
snappy :
sqlalchemy : 1.4.44
tables : 3.7.0
tabulate : 0.9.0
xarray : 2022.11.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None
None
Prior Performance
No response