Mailman 3 February 2019 - NumPy-Discussion

Adding count parameter to unpackbits (PR-10855)
by Sebastian Berg Feb. 21, 2019

Feb. 21, 2019

Hi all, I was about to merge it, but was noot sure it was really discussed before (and it would be a long time ago). The pull requests: https://github.com/numpy/numpy/pull/10855 Proposes to add a count parameter to unpackbits: ``count`` allows subsetting the number of bits that will be unpacked up-front, rather than reshaping and subsetting later, making the ``packbits`` operation invertible, and the unpacking less wasteful. Counts larger than the number of available bits add zero padding. Negative counts trim bits off the end instead of counting from the beginning. None counts implement the existing behavior of unpacking everything. This seems like a pretty small API addition, but I thought I would ping the list again before merging in case anyone objects to it or feels it should be modified. In other words, the count parameter is much the same as indexing the result: np.unpackbits(x)[:count] although it will zero pad if count is out of bounds. Which is necessary if it is to invert a `np.packbits` when less than 8 bits are packed. If no one speaks up, I plan to merge it soon. Best, Sebastian

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Re: [Numpy-discussion] [SciPy-User] Why slicing Pandas column and then subtract gives NaN?
by Avi Gross Feb. 18, 2019

Feb. 18, 2019

Changing horses mid-stream is not generally a good idea. But python is easy to extend so there may be other choices out there. The reality is that what matters more than what choices a language makes is consistency across the language. If you can learn the choices and even quirks and work with them, you can get things done. I have used languages where vectors or lists are zero-based and some where they are one-based. Both work, albeit you may need to adjust an algorithm to add or subtract one from the index you use or leave the first entry empty. If you use a pandas data structure without specifying your own index, you get stuck with the default behavior. If you wanted a 1 or 0 based index, you could add that carefully and so on. In theory, you could create an alternate numpy or alternate pandas just like many of the alternate modules you can find which often have very different designs for graphics and there probably already are some out there. Integrating them with other tools may not be as simple. If you want to use the machine learning tools available, some functions demand they be given the data as a pandas DataFrame or a numpy array. Giving them something else like a python list, even if it is just an object extended from the above, may well break things. This makes it very hard to port some things to or from other languages with different design constraints as an algorithm that looks straightforward in one because it goes with the overall grain of the language may need major surgery to fit into the other and perhaps might best be rewritten using some other algorithm that fits better. A different discussion elsewhere about errors was instructive. Many programmers used to write complex code that checks for all kinds of errors or conditions. Some python programmers take an approach of expecting errors to be something you catch so why bother checking for things like an array index being non-existent. Well, if an error you expect in pandas is not caught, consider checking before using software that does not enforce it. And some "errors" are not errors. As mentioned, in R it is NOT a design error that using an NA value in a calculation silently propagates the NA. Throughout the language you must either check if there is an NA using the is.na() function or by telling routines to drop/ignore any NA as in sum(something, na.rm=TRUE). There are many design decisions you make this way. So, if your language does something you don't want when it uses an implicit index, fine. Give it an explicit index. If it does not properly check the range you provide for validity, do it yourself. And if the language is so unsuitable for your needs, you can often switch. In particular, there are now even ways you can start your code in one language like python or R and then shunt some data structures across a divide where the other language can do things another way and if needed, shunt it back and repeat. That may not work well for your application and, as noted, switching rides in midstream has its dangers. From: NumPy-Discussion <numpy-discussion-bounces+avigross=verizon.net(a)python.org> On Behalf Of Robert Kern Sent: Sunday, February 17, 2019 3:15 PM To: Discussion of Numerical Python <numpy-discussion(a)python.org> Subject: Re: [Numpy-discussion] [SciPy-User] Why slicing Pandas column and then subtract gives NaN? On Sat, Feb 16, 2019 at 8:42 PM C W <tmrsg11(a)gmail.com <mailto:tmrsg11@gmail.com> > wrote: Dan, No one here is trying to convince you to use Python. If you don't like it, don't use it. The problem is not me, it's the language. No need to take it out on me personally. I've used other languages, Python is lacking in this area. I'm being very frank here, just think about it. No one was taking it out on you personally. We're just stating that we're not interested in having a discussion about which semantics are best, much less convincing you that Python's choice is the right one. They have been that way for a long time, and the time for making those decisions is long past. We could not change them now if we wanted to. Empirically, I've been on these lists for about 20 years now, and I have not seen this pop up as a frequent issue causing bugs in real code, so I would submit that if there is a lack (or benefit) compared to other languages, it is small That said, Python is not alone here. Perl and Ruby have Python's semantics. R introduces NaNs but does not raise an error. Matlab and Julia do raise errors. -- Robert Kern

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Re: [Numpy-discussion] [SciPy-User] Why slicing Pandas column and then subtract gives NaN?
by Mike C Feb. 17, 2019

Feb. 17, 2019

Thanks a lot, Thomas. I don’t have index when I read in the data. I just want to slice two series to the same length, and subtract. That’s it! I also don’t what numpy methods wrapped within methods. They work, but hard do understand. How would you do it? In Matlab or R, it’s very simple, one line. ________________________________ From: SciPy-User <scipy-user-bounces+tmrsg11=gmail.com(a)python.org> on behalf of Thomas Kluyver <takowl(a)gmail.com> Sent: Thursday, February 14, 2019 4:54 PM To: SciPy Users List Cc: Discussion of Numerical Python Subject: Re: [SciPy-User] [Numpy-discussion] Why slicing Pandas column and then subtract gives NaN? Maybe it's useful to look a bit more at what pandas is doing and why. The 'index' on a series or dataframe labels each row - e.g. if your series is measuring total sales for each day, its index would be the dates. When you combine (e.g. subtract) two series, pandas automatically lines up the indices. So it will join up the numbers for February 14th, even if they're not in the same position in the data. In your example, you haven't specified an index, so pandas generates an integer index which doesn't really mean anything, and aligning on it doesn't do what you want. What are you trying to do? If Numpy does exactly what you want, then the answer might be to use Numpy. > Isn't Numpy built on top of Pandas? It's the other way round: pandas is built on Numpy. Pandas indices are an extra layer of functionality on top of what Numpy does. On Thu, 14 Feb 2019 at 20:22, C W <tmrsg11(a)gmail.com<mailto:tmrsg11@gmail.com>> wrote: Hi Paul, Thanks for your response! I did not find a Pandas list for users, only for developers. I'd love to be on there. result = a.subtract(b.shift()).dropna() This seems verbose, several layers of parenthesis follow by a dot method. I'm new to Python, I thought Python code would be pity and short. Is this what everyone will write? Thank you! On Wed, Feb 13, 2019 at 6:50 PM Paul Hobson <pmhobson(a)gmail.com<mailto:pmhobson@gmail.com>> wrote: This is more a question for the pandas list, but since i'm here i'll take a crack. * numpy aligns arrays by position. * pandas aligns by label. So what you did in pandas is roughly equivalent to the following: a = pandas.Series([85, 86, 87, 86], name='a').iloc[1:4].to_frame() b = pandas.Series([15, 72, 2, 3], name='b').iloc[0:3].to_frame() result = a.join(b,how='outer').assign(diff=lambda df: df['a'] - df['b']) print(result) a b diff 0 NaN 15.0 NaN 1 86.0 72.0 14.0 2 87.0 2.0 85.0 3 86.0 NaN NaN So what I think you want would be the following: a = pandas.Series([85, 86, 87, 86], name='a') b = pandas.Series([15, 72, 2, 3], name='b') result = a.subtract(b.shift()).dropna() print(result) 1 71.0 2 15.0 3 84.0 dtype: float64 On Wed, Feb 13, 2019 at 2:51 PM C W <tmrsg11(a)gmail.com<mailto:tmrsg11@gmail.com>> wrote: Dear list, I have the following to Pandas Series: a, b. I want to slice and then subtract. Like this: a[1:4] - b[0:3]. Why does it give me NaN? But it works in Numpy. Example 1: did not work >>>a = pd.Series([85, 86, 87, 86]) >>>b = pd.Series([15, 72, 2, 3]) >>> a[1:4]-b[0:3] 0 NaN 1 14.0 2 85.0 3 NaN >>> type(a[1:4]) <class 'pandas.core.series.Series'> Example 2: worked If I use values() method, it's converted to a Numpy object. And it works! >>> a.values[1:4]-b.values[0:3] array([71, 15, 84]) >>> type(a.values[1:4]) <class 'numpy.ndarray'> What's the reason that Pandas in example 1 did not work? Isn't Numpy built on top of Pandas? So, why is everything ok in Numpy, but not in Pandas? Thanks in advance! _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)python.org<mailto:NumPy-Discussion@python.org> https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ SciPy-User mailing list SciPy-User(a)python.org<mailto:SciPy-User@python.org> https://mail.python.org/mailman/listinfo/scipy-user _______________________________________________ SciPy-User mailing list SciPy-User(a)python.org<mailto:SciPy-User@python.org> https://mail.python.org/mailman/listinfo/scipy-user

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Reminder: weekly status meeting Tues Feb 19 12:00 PST
by Matti Picus Feb. 17, 2019

Feb. 17, 2019

This week the status meeting will be on Tues Feb 19 at 12:00 PST, not at the usual time The draft agenda is at https://hackmd.io/TtqnUDvPQgaGeej7zR8seA?both Everyone is invited to join. Matti, Tyler and Stefan

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Why slicing Pandas column and then subtract gives NaN?
by C W Feb. 14, 2019

Feb. 14, 2019

Dear list, I have the following to Pandas Series: a, b. I want to slice and then subtract. Like this: a[1:4] - b[0:3]. Why does it give me NaN? But it works in Numpy. Example 1: did not work >>>a = pd.Series([85, 86, 87, 86]) >>>b = pd.Series([15, 72, 2, 3]) >>> a[1:4]-b[0:3] 0 NaN 1 14.0 2 85.0 3 NaN >>> type(a[1:4]) <class 'pandas.core.series.Series'> Example 2: worked If I use values() method, it's converted to a Numpy object. And it works! >>> a.values[1:4]-b.values[0:3] array([71, 15, 84]) >>> type(a.values[1:4]) <class 'numpy.ndarray'> What's the reason that Pandas in example 1 did not work? Isn't Numpy built on top of Pandas? So, why is everything ok in Numpy, but not in Pandas? Thanks in advance!

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'nansqrt' function?
by Mauro Cavalcanti Feb. 14, 2019

Feb. 14, 2019

Dear ALL, In the process of porting an existing (but abandoned) package to the latest version of Numpy, I stumbled upon a call to a 'numpy.nansqrt' function, which seems not to exist. Here is the specific code: def normTrans(y): denom = np.nansqrt(np.nansum(y**2)) return y/denom As far as I could find, there is no such 'nansqrt' function in the current version of Numpy, so I suspect that the above code has not been properly tested. Am I right, or that function had existed in some past version of Numpy? Thanks in advance for any comments or suggestions. Best regards,

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Porting code for Numpy 1.13+ to get rid of "boolean index did not match indexed array along dimension 1" error
by Mauro Cavalcanti Feb. 13, 2019

Feb. 13, 2019

Dear ALL, I am trying to port an eigenalysis function that runs smoothly on Numpy 1.12 but fail miserably on Numpy 1.13 or higher with the dreadful error "boolean index did not match indexed array along dimension 1". Here is a fragment of the code, where the error occurrs: evals, evecs = np.linalg.eig(Syy) idx = evals.argsort()[::-1] evals = np.real(evals[idx]) U = np.real(evecs[:, idx]) evals = evals[evals > tolerance] U = U[:, evals > tolerance] # Here is where the error occurs So, I ask: is there a way out of this? Thanks in advance for any assistance you can provide.

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Reminder: weekly status meeting Feb. 13 at 12:00 Pacific Time
by Tyler Reddy Feb. 12, 2019

Feb. 12, 2019

Draft agenda: https://hackmd.io/f_e6dnssTkuIC0Pa0TEV6w?view There is a section for community suggested topics, feel free to join the conversation and add in topics that need attention. BIDS team

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ANN: SciPy 1.2.1
by Tyler Reddy Feb. 9, 2019

Feb. 9, 2019

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.2.1, which is a bug fix release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.2.1 One of a few ways to install this release with pip: pip install scipy==1.2.1 ========================== SciPy 1.2.1 Release Notes ========================== SciPy 1.2.1 is a bug-fix release with no new features compared to 1.2.0. Most importantly, it solves the issue that 1.2.0 cannot be installed from source on Python 2.7 because of non-ASCII character issues. It is also notable that SciPy 1.2.1 wheels were built with OpenBLAS 0.3.5.dev, which may alleviate some linear algebra issues observed in SciPy 1.2.0. Authors ======= * Eric Larson * Mark Mikofski * Evgeni Burovski * Ralf Gommers * Eric Moore * Tyler Reddy Issues closed for 1.2.1 ------------------------ * `#9606 <https://github.com/scipy/scipy/issues/9606>`__: SyntaxError: Non-ASCII character '\xe2' in file scipy/stats/_continuous_distns.py on line 3346, but no encoding declared * `#9608 <https://github.com/scipy/scipy/issues/9608>`__: Version 1.2.0 introduces `too many indices for array` error in... * `#9709 <https://github.com/scipy/scipy/issues/9709>`__: scipy.stats.gaussian_kde normalizes the weights keyword argument... * `#9733 <https://github.com/scipy/scipy/issues/9733>`__: scipy.linalg.qr_update gives NaN result * `#9724 <https://github.com/scipy/scipy/issues/9724>`__: CI: Is scipy.scipy Windows Python36-32bit-full working? Pull requests for 1.2.1 ------------------------ * `#9612 <https://github.com/scipy/scipy/pull/9612>`__: BUG: don't use array newton unless size is greater than 1 * `#9615 <https://github.com/scipy/scipy/pull/9615>`__: ENH: Add test for encoding * `#9720 <https://github.com/scipy/scipy/pull/9720>`__: BUG: stats: weighted KDE does not modify the weights array * `#9739 <https://github.com/scipy/scipy/pull/9739>`__: BUG: qr_updates fails if u is exactly in span Q * `#9725 <https://github.com/scipy/scipy/pull/9725>`__: TST: pin mingw for Azure Win CI * `#9736 <https://github.com/scipy/scipy/pull/9736>`__: TST: adjust to vmImage dispatch in Azure * `#9681 <https://github.com/scipy/scipy/pull/9681>`__: BUG: Fix failing stats tests (partial backport) * `#9662 <https://github.com/scipy/scipy/pull/9662>`__: TST: interpolate: avoid pytest deprecations Checksums ========= MD5 ~~~ 982810997da9daab2e512a6c27918889 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New sorting routines.
by Charles R Harris Feb. 8, 2019

Feb. 8, 2019

Hi All, I've put up gh-12945 <https://github.com/numpy/numpy/pull/12945> that preserves forward compatibility of NumPy after the addition of timsort by reusing the mergesort slot in PyArray_ArrFuncs for stable sorts in general, not just mergesort. The same method can be used when we add radixsort for integer types. See the discussion at the PR link for more details on the nature of the problem and considerations for the dtype proposals. I'm waiting on agreement that this is the way to go before proceeding. Chuck

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