Mailman 3 March 2023 - Pandas-dev

pandas new infrastructure (OVH donation)
by Marc Garcia Sept. 21, 2024

Sept. 21, 2024

Hi all, pandas has received a donation from OVHcloud <https://www.ovhcloud.com/> to support the project infrastructure, with OVHcloud public cloud credits (an initial amount of 10,000 EUR for a period of one year). OVH is open to sponsor longer term and also other projects of the ecosystem (or NumFOCUS as a whole), but we started with this to have feedback at a smaller scale first. The credits will be used initially for: - Hosting of the pandas website - Running the pandas benchmarks - Speeding up the project CI I detail next what I have in mind to set up for each. If anyone is interested in getting involved, or has ideas, comments... please let me know. I'll publish updates here as there is progress on this. Website: I'm planning to experiment on splitting the website in two (it'll be transparent for users). The website and the stable docs which receive most of the traffic can probably be stored in Cloudflare pages. We're already using Cloudflare as a CDN, so instead of using it as a cache, we can publish the documents there. The rest of the docs (old versions and the dev version) can be hosted in bucket storage of the OVHcloud. Response times may be a bit slower, but our website is bigger than the Cloudflare quota, and having old docs rarely accessed in a CDN seems unnecessary anyway. - Benchmarks: OVHcloud instances have guaranteed hardware, and we'll be checking if this is enough for the results of the benchmarks to be consistent over runs, or if there is too much variability and we need dedicated hardware. If consistency is good enough that would be great, since our benchmarks mostly use one core, and using dedicated hardware is likely to be a decent waste of resources, since most servers will likely have 16 cores or more. We'll discuss with OVH if dedicated hardware is needed, as at the moment their public cloud doesn't offer it (there is an alpha for providing dedicated instances, but we need to check with them). - Faster CI: Our GitHub runners are small, and most builds take around one hour or more to finish. We should be able to use bigger OVH instances for our existing CI pretty easily, via their OpenStack API and CIrun.

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February 2023 bi-monthly community meeting (Wednesday February 8, UTC 18:00)
by Joris Van den Bossche Sept. 11, 2024

Sept. 11, 2024

Hi all, A late reminder that the next bi-monthly (twice a month) dev call is today in a bit more than 1 hour (Wednesday, February 8) at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time. The pandas Community Meeting is a regular sync meeting for the project's maintainers which is open to the community. All are welcome to attend! Video Call: https://us06web.zoom.us/j/84484803210?pwd=TjUxNmcyNHcvcG9SNGJvbE53Y21GZz09 Meeting notes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-… Joris

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Is it time for a bi-weekly pandas call?
by Marco Gorelli May 13, 2024

May 13, 2024

Currently, there's a monthly pandas call, which has been in place for several years. Seeing as there's now more people working on pandas as part of their jobs, might it be time to increase the frequency? E.g. to meet every 2 weeks, instead of once a month?

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combinations of all rows and cols from a dataframe
by marc nicole March 29, 2023

March 29, 2023

Hello everyone, Given a dataframe like this: 2 6 8 5 I want to yield the following list of lists: [ [[2],[6,5]], [[2],[6]], [[2],[5]], [[8],[6,5]], [[8],[6]], [[8],[5]], [[6],[2,8]], [[6],[8]], [[6],[2]], [[5],[2,8]], [[5],[2]], [[5],[8]], [[6,5],[2,8]] ] I have written the following (which doesn't yield the expected results) import pandas as pd > from itertools import combinations > import numpy as np > resList=[] > resListTmp=[] > resListTmp2=[] > dataframe = > pd.read_excel("C:\\Users\\user\\Desktop\\testData.xlsx",index_col=False,header=None) for i in range(0, len(dataframe)+1): > for j in range(0, len(dataframe.columns)): > for k in range (0,len(dataframe)+1): > for xVals in list(combinations(dataframe.iloc[k:i,j], i)): > if list(xVals) not in resListTmp: > resListTmp.append(list(xVals)) > resListTmp2.append(resListTmp) > resList.append(resListTmp2) > print(resList) > What is wrong with my code?

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(no subject)
by marc nicole March 27, 2023

March 27, 2023

SUBSCRIBE

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PDEP6: open for discussion / request for comments
by Marco Gorelli March 23, 2023

March 23, 2023

Hello everybody, I've opened an enhancement proposal to ban upcasting it setitem-like operations. You can find the text here <https://github.com/pandas-dev/pandas/pull/50424>. Anyone is welcome to comment - assuming no major discussion points are raised, the core team will hold a vote on it in a couple weeks' time.

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Error got multiple values for argument ‘Schema’
by William Kuenne March 22, 2023

March 22, 2023

Hi Mike and Pandas team, I hope you are well. I am trying to run a script that uses SQLAlchemy and there is a very simple test query of a single table from a single schema and I get an error at df = pd.read_sql(query, engine) I have confirmed the query definitely queries fine and there is only one schema name but I am still getting this error: TypeError: __init__() got multiple values for argument ‘Schema’ I am using python 3.11 SQLAlchemy 1.4.46 Pandas 1.5.3 Do you guys have any advice for me as to how to work around this error? Thank you for your time and consideration I really appreciate it. Best, Bill Kuenne

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ANN: pandas 2.0.0 RC1
by Marc Garcia March 17, 2023

March 17, 2023

We are happy to announce the *second release candidate* of pandas 2.0.0. It can be installed from our conda-forge and PyPI packages via mamba, conda and pip, for example: mamba install -c conda-forge/label/pandas_rc pandas==2.0.0rc1 python -m pip install --upgrade --pre pandas==2.0.0rc1 Users who have pandas code in production and maintainers of libraries with pandas as a dependency are *strongly* recommended to run their test suites with the release candidate, and report any problem to our issue tracker <https://github.com/pandas-dev/pandas/issues/new/choose> before the official 2.0.0 release. You can find the documentation of pandas 2.0.0 here <https://pandas.pydata.org/pandas-docs/version/2.0/index.html>, and the list of changes in 2.0.0, in the release notes page <https://pandas.pydata.org/pandas-docs/version/2.0/whatsnew/v2.0.0.html>. We expect to release the final version of pandas 2.0.0 in around two weeks, but the final date will depend on the issues reported to the release candidate.

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