Mailman 3 June 2022 - Pandas-dev

PDEPs: pandas enhancement proposals
by Marc Garcia Nov. 30, 2022

Nov. 30, 2022

We're in the process of implementing PDEPs, equivalent to Python's PEPs and NumPy's NEPs, but for pandas. This should help build the roadmap, make discussions more efficient, obtain more structured feedback from the community, and add visibility to agreed future plans for pandas. The initial implementation (workflow) is a bit simpler than PEP or NEP, but we'll iterate in the future as convenient. You can see the PR for PDEP-1 with the purpose, scope and guidelines here: https://github.com/pandas-dev/pandas/pull/47444 Feedback is very welcome.

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Series.apply "regression" and raw assert
by Pål Grønås Drange June 30, 2022

June 30, 2022

Consider the following MWE ``` import pandas as pd print(pd.__version__) df = pd.DataFrame({"x": [1]}) class F: def __iter__(self): yield 0 def __call__(self, x): return x f = F() df["x"].apply(lambda e: f(e)) # ok df["x"].apply(f.__call__) # ok df["x"].apply(f) # AssertionError: f not callable ``` In Pandas < 1.3, the three calls all return the same value, namely a Series containing only [1]. Starting from Pandas 1.3, the last of the three calls raises an AssertionError. This is a backwards incompatible change that happened in 1.3. However, 1. I have realized that I should use Series.map rather than apply. This fixes my issue. 2. The documentation for Series.apply says that it expects a "Python function" or "Numpy ufunc". Apparently, it doesn't support "Python callables". The only output from Pandas is "AssertionError", and in the code (pandas/core/apply.py:1046 [1106 in trunk]), it simply says "assert callable(f)": ``` # string, list-like, and dict-like are entirely handled in super assert callable(f) ``` This is weird, since _my f_ clearly is callable. Would it be possible to at least add a string to the assert to make it easier for users to understand what's going on? Here is the output: ``` (e39) [~]$ python applytest.py 1.2.5 (e39) [~]$ pip install -q pandas==1.3 (e39) [~]$ python applytest.py 1.3.0 Traceback (most recent call last): File "/home/p/applytest.py", line 14, in <module> df["x"].apply(f) # AssertionError: f not callable File "/home/p/e39/lib/python3.9/site-packages/pandas/core/series.py", line 4356, in apply return SeriesApply(self, func, convert_dtype, args, kwargs).apply() [...] File "/home/p/e39/lib/python3.9/site-packages/pandas/core/apply.py", line 1046, in agg assert callable(f) AssertionError ``` Best regards, Pål GD

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ANN: pandas v1.4.3
by Simon Hawkins June 27, 2022

June 27, 2022

Hi all, I'm pleased to announce the release of pandas v1.4.3. This is a patch release in the 1.4.x series and includes some regression fixes and bug fixes. We recommend that all users upgrade to this version. See the release notes <https://pandas.pydata.org/pandas-docs/version/1.4/whatsnew/v1.4.3.html> for a list of all the changes. The release can be installed from PyPI python -m pip install --upgrade pandas==1.4.3 Or from conda-forge conda install -c conda-forge pandas==1.4.3 Please report any issues with the release on the pandas issue tracker <https://github.com/pandas-dev/pandas/issues>. Thanks to all the contributors who made this release possible.

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feedback on typing-stubs philosophy welcome
by Irv Lustig June 26, 2022

June 26, 2022

All: I wrote up a page on the philosophy of the pandas-stubs that I just published. https://github.com/pandas-dev/pandas-stubs/blob/main/docs/philosophy.md Your criticism and comments are welcome. Feel free to do a PR to address changes that you feel would be good to make. It's not clear to me whether this material should be part of the standard pandas docs, or if we should just have a small blurb about pandas-stubs in the regular docs. Opinions are welcome and encouraged! -Dr-Irv

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First release of pandas-stubs from the pandas team
by Irv Lustig June 25, 2022

June 25, 2022

Hi all, I'm pleased to announce the release of pandas-stubs 1.4.2.220622, which is the first release of pandas stubs supported by the pandas core team. This release is the result of a strategic effort led by the core pandas team to integrate the Microsoft type stub repository https://github.com/microsoft/python-type-stubs together with the VirtusLabs pandas_stubs repository https://github.com/VirtusLab/pandas-stubs . The release can be installed from PyPI python -m pip install pandas-stubs It should soon be available on conda-forge, and when that occurs, can be installed by: conda install -c conda-forge pandas-stubs=1.4.2.220622 Please report any issues with the release on the pandas-stubs issue tracker <https://github.com/pandas-dev/pandas-stubs/issues <https://github.com/pandas-dev/pandas/issues>>.

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API: numeric_only in Series ops behavior
by Richard Shadrach June 24, 2022

June 24, 2022

https://github.com/pandas-dev/pandas/issues/47500 Details are in the issue above. Almost all Series ops will raise when passed `numeric_only=True`, regardless of their dtype. However SeriesGroupBy ops will only raise when passed `numeric_only=True` if they have a non-numeric dtype. I'm looking for feedback on which of these (or alternative) behaviors is preferable. Best, Richard

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Reading file with time dependent format
by MATIAS ANDRES LERDON June 16, 2022

June 16, 2022

Dear users, I"m working with LAMMPS, an application to simulate particles and the outputs comes in this format ----------------------- HEADERS timestep1(It"s a number) N°atoms ... Bin Value 1 24 2 54 3 63 ... 99 32 timestep2 N°atoms ... Bin Value 1 64 2 75 3 65 ... 99 65 timestep3 .... and goes on ---------------------------- So I would like to know if there is a way to create an array with 3 dimensions Array = [Atom,Value,Timestep] with just a few lines reading the output file. Thanks !

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Pandas + Carbon
by Mohamed Hossain June 9, 2022

June 9, 2022

Hi Pandas Team, I’m with Carbon <https://t.sidekickopen07.com/s3t/c/5/f18dQhb0S7lM8dMp8VVLWxkw2-B9wyN6zkb8_2…>. Our mission is to help developers generate revenue on their projects by displaying relevant ads on their websites. Our ads are custom built and seamlessly integrated into your website, so we do not disrupt the user experience. Some of our partners are Bootstrap <https://t.sidekickopen07.com/s3t/c/5/f18dQhb0S7lM8dMp8VVLWxkw2-B9wyN6zkb8_2…> , JS Fiddle <https://t.sidekickopen07.com/s3t/c/5/f18dQhb0S7lM8dMp8VVLWxkw2-B9wyN6zkb8_2…>, and FullstackPython <https://t.sidekickopen07.com/s3t/c/5/f18dQhb0S7lM8dMp8VVLWxkw2-B9wyN6zkb8_2…>. Pandas would be a great fit for our ad network. Do you mind if I share more details about potential revenue with you? Thanks, Mohamed

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