Mailman 3 August 2018 - Pandas-dev

Datetime (with timezone?) as extension array?
by Pietro Battiston Sept. 26, 2018

Sept. 26, 2018

Hi all, I assumed that Datetime (with timezone, or maybe in general?) was also planned to follow the extension array interface, which is related to issue https://github.com/pandas-dev/pandas/issues/19041 , to the annoying fact that datetimeindexwithtz._values returns the index itself, and also to the fact that https://pandas.pydata.org/pandas-docs/stable/extending.html currently states "Pandas itself uses the extension system for some types that aren’t built into NumPy (categorical, period, interval, datetime with timezone).", which is false. ... but I didn't find an issue for this? Did I miss it? Should I create it? Or was there a decision to leave datetimeindextz as it is, maybe for better compatibility with numpy? Pietro

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pandas types
by Marc Garcia Aug. 29, 2018

Aug. 29, 2018

I was thinking that it could be a good idea to start using pandas types before pandas 1.0 (I think this change was assumed to happen sooner or later). Meaning that instead of something like `df.astype(numpy.uint8)` or `df.astype('category')` users would have to use `df.astype(pandas.uint8)` or `df.astype(pandas.category)`. I see 3 advantages on doing it before 1.0: - The API would be clearer and more consistent for users (and creating new extension types will be more controlled). - IMO users will be excited about migrating to pandas 1.0, and as the change will be quite trivial for them, I think the adoption of the new syntax will be faster, than if left until later. - I think it should allow us to make some internal changes transparently (e.g. replacing numpy). I think as a first version, the change could be almost as simple as implementing the pandas types as classes extending a base class, with an attribute that maps the current type. And then in every function/method that receives a dtype, check if the type is a pandas type, do the lookup if it is, and show a deprecation warning if it's not. Does this make sense? Am I missing something?

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columns names issues using pd.read_csv
by John Paul Aug. 6, 2018

Aug. 6, 2018

Hi list, I am pretty new to python. I am trying to import the SMSSpam Collection Data using pandas read_csv module. I The import went went. But as the file does not have header I tried to include columns names(variables names : "status" and "message" and ended up with empty file. Here is my code: << import numpy as np import pandas as pd file_loc="C:\\Users\\User\Documents\\JP\\SMSCollection.txt" df=pd.read_csv(file_loc,sep='\t') >> The above code works well I got the I got the 5571 rows x 2 columns]. But when I add columns using the following line of code df.columns=["status","message"] I ended up with an empty df Any help on this ? Thanks John

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ANN: Pandas 0.23.4 Released
by Tom Augspurger Aug. 5, 2018

Aug. 5, 2018

Hi all, I'm happy to announce pandas that pandas 0.23.4 has been released. This is a minor bug-fix release in the 0.23.x series and includes some regression fixes, bug fixes, and performance improvements. We recommend that all users upgrade to this version. See the full whatsnew <https://pandas.pydata.org/pandas-docs/version/0.23.4/whatsnew.html> for a list of all the changes. The release can be installed with conda from the default channel and conda-forge:: conda install pandas Or via PyPI: python -m pip install --upgrade pandas A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Jeff Reback * Tom Augspurger * chris-b1 * h-vetinari

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