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Keywords {🔍}

returnparsedtimezoneresults, issue, marcogorelli, pandas, performance, sign, perfcythonize, datetime, projects, tzresults, speed, navigation, pull, requests, actions, security, closed, version, confirmed, exists, python, outprof, spent, nparray, lukemanley, added, memory, execution, data, dtype, mentioned, github, type, milestone, footer, skip, content, menu, product, solutions, resources, open, source, enterprise, pricing, search, jump, pandasdev, public, notifications,

Topics {✒️}

loops pandas/pandas/core/tools/datetimes personal information perf comment metadata assignees format='%y-%d-% latest version type projects projects milestone issue exists prof perf '%y-%d-% pandas cythonize _return_parsed_timezone_results main branch py snakeviz prof --server good candidate milestone relationships issue tz_results = np 329 seconds spent github speed utc=false perf pd _return_parsed_timezone_results dates tz_results spent python utc skip jump sign checked reported confirmed reproducible made date_range tz_localize strftime %z' tolist append to_datetime cprofile function pair 0cebd75

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Questions {❓}

• Already have an account?
• DEPR deprecate mixed timezone offsets with utc=False?
• Would this be a good candidate for Cython to speed it up?

Schema {🗺️}

DiscussionForumPosting:
      context:https://schema.org
      headline:PERF:cythonize _return_parsed_timezone_results?
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this issue exists on the main branch of pandas. ### Reproducible Example I made a file ```python 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 https://github.com/pandas-dev/pandas/blob/0cebd7508e252aa4aef7be7590537dc3e20a0282/pandas/core/tools/datetimes.py#L316-L328 Would this be a good candidate for Cython to speed it up? cc @lukemanley ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : e93ee07729afe0bc7661655755df6adad657c23b 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 </details> ### Prior Performance _No response_
      author:
         url:https://github.com/MarcoGorelli
         type:Person
         name:MarcoGorelli
      datePublished:2022-12-07T17:34:54.000Z
      interactionStatistic:
         type:InteractionCounter
         interactionType:https://schema.org/CommentAction
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      url:https://github.com/50107/pandas/issues/50107
      context:https://schema.org
      headline:PERF:cythonize _return_parsed_timezone_results?
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this issue exists on the main branch of pandas. ### Reproducible Example I made a file ```python 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 https://github.com/pandas-dev/pandas/blob/0cebd7508e252aa4aef7be7590537dc3e20a0282/pandas/core/tools/datetimes.py#L316-L328 Would this be a good candidate for Cython to speed it up? cc @lukemanley ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : e93ee07729afe0bc7661655755df6adad657c23b 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 </details> ### Prior Performance _No response_
      author:
         url:https://github.com/MarcoGorelli
         type:Person
         name:MarcoGorelli
      datePublished:2022-12-07T17:34:54.000Z
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      url:https://github.com/50107/pandas/issues/50107
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