Mailman 3 October 2021 - Pandas-dev

Re: [Pandas-dev] A case for a simplified (non-consolidating) BlockManager with 1D blocks
by Brock Mendel Dec. 17, 2021

Dec. 17, 2021

> We actually *have* prototypes: the prototype of the split-policy discussed AFAICT that is a 5 year old branch. Is there a version of this based off of master that you can show asv results for? > Also, if performance is in the end the decisive criterion, I repeat my earlier remark in this thread: we need to be clearer about what we want / expect. In principle, this is pretty much exactly what the asvs are supposed to represent. ---- You have demonstrated that you are willing to repeat yourself more than I am, to the point that I find pandas interactions more frustrating than fulfilling. I'm going to step away for a little while. On Thu, Jun 11, 2020 at 1:29 PM Daniel Scott <scottdaniel539(a)yahoo.com> wrote: > > > Sent from Yahoo Mail on Android > <https://go.onelink.me/107872968?pid=InProduct&c=Global_Internal_YGrowth_And…> > > On Thu, Jun 11, 2020 at 3:10 PM, Joris Van den Bossche > <jorisvandenbossche(a)gmail.com> wrote: > _______________________________________________ > Pandas-dev mailing list > Pandas-dev(a)python.org > https://mail.python.org/mailman/listinfo/pandas-dev > >

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API: Make silent casting behavior consistent by deprecating silent _object_-dtype casting
by Brock Mendel Nov. 10, 2021

Nov. 10, 2021

TLDR ---- We have inconsistent silent-casting vs raising logic for numpy vs EA dtypes (and inconsistencies within EA dtypes). By deprecating silently casting to *object* dtype, we can *mostly* make the behaviors match. Background ---------- A number of Series/DataFrame methods will silently cast when dealing with mismatched values. With a numpy dtype, each of the following silently cast to float64: ser = pd.Series([1, 2, 3], dtype="i8") ser.shift(1, fill_value=1.5) ser.mask([True, False, False], 1.5) ser.where([False, True, True], 1.5) ser.replace(1, 1.5) ser[0] = 1.5 ser.fillna(1.5) # <- this one doesn't cast as it is a no-op If we were to pass "foo" or a pd.Period, these would coerce to object instead of float. By contrast, similar mixed-type operations with an ExtensionDtype Series _mostly_ raise: ser2 = pd.Series(pd.period_range("2016-01-01", periods=3, freq="D")) ser2.shift(1, fill_value=1.5) # <- ValueError ser2.mask([True, False, False], 1.5) # <- ValueError ser2.where([False, True, True], 1.5) # <- ValueError ser2.fillna(1.5) # <- TypeError ser2.replace(ser2[0], 1.5) # <- coerces to object ser2[0] = 1.5 # <- coerces to object ser3 = pd.Series([pd.NA, 2, 3], dtype="Int64") ser3.shift(1, fill_value=1.5) # <- TypeError ser3.mask([True, False, False], 1.5) # <- TypeError ser3.where([False, True, True], 1.5) # <- TypeError ser3.fillna(1.5) # <- TypeError ser3.replace(ser3[0], 1.5) # <- TypeError ser3[0] = 1.5 # <- TypeError timedelta64, datetime64, and datetime64tz mostly behave like the numpy dtypes, with a few exceptions: - shift raises on mismatch - fillna raises on mismatch for timedelta64, casts for the others Categorical mostly behaves like other ExtensionDtypes, except for replace which has special logic. Goals ----- - Have matching behavior across dtypes. - Share code. Options ------- 1) Change EA (and dt64/td64) behavior to match non-EA behavior 2) Change non-EA behavior to match EA behavior (or stricter xref https://github.com/pandas-dev/pandas/issues/39584) 3) Deprecate (and eventually raise on) silent casting to _object_ dtype, allowing silent casting otherwise. Here I am advocating for option 3). The advantages as I see them: A) For numpy dtypes, we retain the most useful cases (int->float) B) Deprecates cases most likely to be unintentional (e.g. typo "2016-01-01" -> "2p16-01-01" causing a datetime64 Series to silently cast) C) For td64/dt64/dt64tz/period, the *only* silent casting is to object, so this completely gets rid of special-casing among that code D) For IntegerArray, FloatingArray, IntervalArray leaves open the option of allowing e.g. Integer->Floating casting (xref https://github.com/pandas-dev/pandas/issues/25288#issuecomment-941762174) E) Does not preclude later deciding on the stricter options in 2)

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How to apply a self defined function in Pandas
by Shaozhong SHI Oct. 31, 2021

Oct. 31, 2021

I defined a function and apply it to a column in Pandas. But it does not return correct values. I am trying to test which url in a column full of url to see which one can be connected to or not def connect(url): try: urllib.request.urlopen(url) return True except: return False df['URL'] = df.apply(lambda x: connect(df['URL']), axis=1) I ran without any error, but did not return any true. I just could not find any error with it. Can anyone try and find out why Regards, David

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(Help)Contribution requests and methods
by 구민석 Oct. 31, 2021

Oct. 31, 2021

Hello, I am studying at a university in Korea. I am a student who dreams of becoming a programmer. I'm using the Pandas library well. I wonder if I can contribute to the project by translating the pandas project documents into Korean. So, I want to help many Korean students understand Pandas. And if i can contribute in this way, I’d appreciate it if you could let me know which documents would be helpful to translate. Please reply this mail Thank you for reading this long post amidst your busy schedule.

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(Help)Contribution requests and methods
by 구민석 Oct. 29, 2021

Oct. 29, 2021

Hello, I am studying at a university in Korea. I am a student who dreams of becoming a programmer. I wonder if I can contribute to the project by translating the pandas project documents into Korean. So, I want to help many Korean students understand Pandas. And if i can contribute in this way, I’d appreciate it if you could let me know which documents would be helpful to translate. Please reply this mail Thank you for reading this long post amidst your busy schedule.

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(no subject)
by Aryan Agarwal Oct. 24, 2021

Oct. 24, 2021

I am not able to install pandas package in my pycharm. It shows failed building wheels

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Allow me to make page(Korean Wikipedia)
by 김종훈 Oct. 23, 2021

Oct. 23, 2021

Hi, I'm a student who dreams of becoming a programmer in Korea. I'm using the Pandas library well. I found the page "https://en.wikipedia.org/wiki/Pandas_(software)". But there is no page in Korean Wikipedia. So, I hope to make Pandas Wikipedia page for myself. Would you allow me to do that? Thanks for reading my mail. Reply please :)

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Python 3.10 Wheel for Windows
by darren.frimponglebrun＠nomura.com Oct. 23, 2021

Oct. 23, 2021

Hi Development Team We are looking to deploy Python 3.10 soon. Is there a scheduled date for the pypi availability for Wheels for win32 and win64? Thanks, Darren Fingal Technology Nomura This e-mail (including any attachments) is private and confidential, may contain proprietary or privileged information and is intended for the named recipient(s) only. Unintended recipients are strictly prohibited from taking action on the basis of information in this e-mail and must contact the sender immediately, delete this e-mail (and all attachments) and destroy any hard copies. Nomura will not accept responsibility or liability for the accuracy or completeness of, or the presence of any virus or disabling code in, this e-mail. If verification is sought please request a hard copy. Any reference to the terms of executed transactions should be treated as preliminary only and subject to formal written confirmation by Nomura. Nomura reserves the right to retain, monitor and intercept e-mail communications through its networks (subject to and in accordance with applicable laws). No confidentiality or privilege is waived or lost by Nomura by any mistransmission of this e-mail. Any reference to "Nomura" is a reference to any entity in the Nomura Holdings, Inc. group. Please read our Electronic Communications Legal Notice which forms part of this e-mail: http://www.Nomura.com/email_disclaimer.htm

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ANN: pandas v1.3.4
by Simon Hawkins Oct. 18, 2021

Oct. 18, 2021

Hi all, I'm pleased to announce the release of pandas v1.3.4. This is a patch release in the 1.3.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/1.3/whatsnew/v1.3.4.html> for a list of all the changes. The release can be installed from PyPI python -m pip install --upgrade pandas==1.3.4 Or from conda-forge conda install -c conda-forge pandas==1.3.4 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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October 2021 monthly community meeting (Wednesday October 13, UTC 18:00)
by Joris Van den Bossche Oct. 12, 2021

Oct. 12, 2021

Hi all, A reminder that the next monthly dev call is tomorrow (Wednesday, October 13th) at 18:00 UTC (1 pm Central). Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time. All are welcome to attend! Video Call: https://us06web.zoom.us/j/84484803210?pwd=TjUxNmcyNHcvcG9SNGJvbE53Y21GZz09 Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-… Joris

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