Mailman 3 March 2016 - Pandas-dev

Re: [Pandas-dev] Thoughts on adopting a 1-PR-1-commit policy?
by Wes McKinney June 8, 2016

June 8, 2016

hey Jan, I'm adding the mailing list back. Several comments inline On Sun, Jan 17, 2016 at 1:09 AM, Jan Schulz <jasc(a)gmx.net> wrote: > Hi, > > Just a different opinion: I like having commits do one logical thing > and not squash multiple "logical complete"things together (this means > that commit is a logical step, not the PR and that the commits should > be clean and not contain "fixup", "typo" style commits). During the > categorical work, I found that a few times I regretted that I couldn't > go back to look up the specific change in a commit and look up what > and why that commit was done because it was all mixed up with the rest > of the squashed commits in that PR. > I'm not suggesting that you should have to squash your commits inside the PR. This only concerns how *patches are applied to pandas's master branch". Ideally, no squashing occurs inside the developer branch (so the "story", so to speak, about the patch is preserved), but what the Apache patch tool does is - Turns a multi-commit PR into a single-commit patch - Puts the individual commit hashes in the commit message; so you can always visit the original commits - Puts the description from the PR into the commit message - Cherry-picks instead of merging, so you can observe evolution of pandas/master in a clear and linear way > My feeling was always that squashes are performed because the rebases > are so hard because of multiple PRs fixing stuff in the same files at > the same time. If this refactorings come through (both better > separation of backend-frontend specific code and the suggested split > up of frame.py, etc), I think this is not so much a problem anymore. > > I'm not sure where the commit history with merges is a problem: during > balme (in github, never used git itself), I don't see any merges?! > Here is something that would be very hard: create release notes given the commit history. > So my suggestion would go in a different direction: better commits in > the PRs with proper commit messages (not only the headline but also > explanations in the message. > > Jan > -- > Jan Schulz > mail: jasc(a)gmx.net > web: http://www.katzien.de > > > On 17 January 2016 at 07:29, Wes McKinney <wesmckinn(a)gmail.com> wrote: >> The script performs the squashing automatically, so users can squash >> manually if they wish or let us do it. >> >> On Sat, Jan 16, 2016 at 8:11 PM, Jeff Reback <jeffreback(a)gmail.com> wrote: >>> seems find to use the cherry picking script >>> >>> though I don't think should relax users from squashing >>> >>>> On Jan 16, 2016, at 6:46 PM, Wes McKinney <wesmckinn(a)gmail.com> wrote: >>>> >>>> Copying the mailing list. Indeed makes rebasing unnecessary if there >>>> are no cherry-pick conflicts. >>>> >>>> On Sat, Jan 16, 2016 at 3:34 PM, Tom Augspurger >>>> <tom.augspurger88(a)gmail.com> wrote: >>>>> Rebasing can be tough for new contributors, so for that alone I'd say let's try it. >>>>> >>>>> -Tom >>>>> >>>>>> On Jan 16, 2016, at 5:20 PM, Wes McKinney <wesmckinn(a)gmail.com> wrote: >>>>>> >>>>>> I've grown very fond of the PR cherry-picking style used in many >>>>>> Apache projects. >>>>>> >>>>>> Here's an example of a very large commit to Apache Spark that was >>>>>> performed in this fashion: >>>>>> >>>>>> https://github.com/apache/spark/commit/2fe0a1aaeebbf7f60bd4130847d738c29f1e… >>>>>> >>>>>> If you compare pandas's commit history with a project like this, >>>>>> you'll see it is much easier to follow because there is one commit for >>>>>> each patch to the project, rather than a merge commit plus 1 or more >>>>>> merged commits (depending on whether the person merging the PR did an >>>>>> interactive rebase). >>>>>> >>>>>> The script to do this is not too complex, and is even less complex for >>>>>> pandas because we do not use JIRA: >>>>>> >>>>>> https://github.com/apache/spark/blob/master/dev/merge_spark_pr.py >>>>>> >>>>>> I've been using a pared down version of the script in Ibis: >>>>>> >>>>>> https://github.com/cloudera/ibis/blob/master/dev/merge-pr.py >>>>>> >>>>>> Here is an example of what a merge commit with multiple subcommits >>>>>> looks like using this tool: >>>>>> >>>>>> https://github.com/cloudera/ibis/commit/eafabe060dcaaea0a6076342eaa374929b9… >>>>>> >>>>>> It's pretty easy to use: run the script and enter the PR # you are >>>>>> merging. It automatically squashes and closes the merged PR. >>>>>> >>>>>> Let me know if this is something that would interest the team. I know >>>>>> there are varying opinions on the GitHub Green Button =) >>>>>> >>>>>> - Wes >>>>>> _______________________________________________ >>>>>> Pandas-dev mailing list >>>>>> Pandas-dev(a)python.org >>>>>> https://mail.python.org/mailman/listinfo/pandas-dev >>>> _______________________________________________ >>>> Pandas-dev mailing list >>>> Pandas-dev(a)python.org >>>> https://mail.python.org/mailman/listinfo/pandas-dev >> _______________________________________________ >> Pandas-dev mailing list >> Pandas-dev(a)python.org >> https://mail.python.org/mailman/listinfo/pandas-dev

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pandas microperformance, do we care?
by Wes McKinney March 29, 2016

March 29, 2016

I've noticed there's been a slow degrading in pandas microperformance as time has gone by. I looked into this when I found that df.icol(i) has been deprecated in favor of df.iloc[:, i] df = pd.DataFrame(np.random.randn(10, 5)) So here we go: pandas v0.12 %timeit df.icol(2) 100000 loops, best of 3: 13.5 µs per loop pandas v0.18 %timeit df.icol(2) 10000 loops, best of 3: 25.4 µs per loop In [6]: timeit df.iloc[:, 2] 10000 loops, best of 3: 60.8 µs per loop Once upon a time, I spent a lot of time shaving microseconds off some of these data accessor methods. For example, pandas v0.12 again: In [17]: s = df[2] In [18]: timeit s.get_value(5) 1000000 loops, best of 3: 609 ns per loop In [21]: timeit s[5] 1000000 loops, best of 3: 860 ns per loop And pandas v0.18 In [15]: timeit s.get_value(5) 100000 loops, best of 3: 7.17 µs per loop In [16]: timeit s[5] 100000 loops, best of 3: 9.31 µs per loop I understand that the performance was made worse in order to add various layers of indirection in order to make new features available (and fix bugs). I'm hoping as part of looking at revamping pandas's internals (and closing the gap to the "metal") that we are able to tighten up some of these "inner loop" methods, preferably back to pandas 0.12-level performance. It's true that writing a lot of Python for-loops isn't optimal for lots of reasons, but we should avoid overly penalizing users when this does happen. Thanks, Wes

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Re: [Pandas-dev] Rewriting some of internals of pandas in C/C++? / Roadmap
by Jeff Reback March 24, 2016

March 24, 2016

Here are some of my thoughts about pandas Roadmap / status and some responses to Wes's thoughts. In the last few (and upcoming) major releases we have been made the following changes: - dtype enhancements (Categorical, Timedelta, Datetime w/tz) & making these first class objects - code refactoring to remove subclassing of ndarrays for Series & Index - carving out / deprecating non-core parts of pandas - datareader - SparsePanel, WidePanel & other aliases (TImeSeries) - rpy, rplot, irow et al. - google-analytics - API changes to make things more consistent - pd.rolling/expanding * -> .rolling/expanding (this is in master now) - .resample becoming a full defered like groupby. - multi-index slicing along any level (obviates need for .xs) and allows assignment - .loc/.iloc - for the most part obviates use of .ix - .pipe & .assign - plotting accessors - fixing of the sorting API - many performance enhancements both micro & macro (e.g. release GIL) Some on-deck enhancements are (meaning these are basically ready to go in): - IntervalIndex (and eventually make PeriodIndex just a sub-class of this) - RangeIndex so lots of changes, though nothing really earth shaking, just more convenience, reducing magicness somewhat and providing flexibility. Of course we are getting increasing issues, mostly bug reports (and lots of dupes), some edge case enhancements which can add to the existing API's and of course, requests to expand the (already) large code to other usecases. Balancing this are a good many pull-requests from many different users, some even deep into the internals. Here are some things that I have talked about and could be considered for the roadmap. Disclaimer: I do work for Continuum but these views are of course my own; furthermore obviously I am a bit more familiar with some of the 'sponsored' open-source libraries, but always open to new things. - integration / automatic deferral to numba for JIT (this would be thru .apply) - automatic deferal to dask from groubpy where appropriate / maybe a .to_parallel (to simply return a dask.DataFrame object) - incorporation of quantities / units (as part of the dtype) - use of DyND to allow missing values for int dtypes - make Period a first class dtype. - provide some copy-on-write semantics to alleviate the chained-indexing issues which occasionaly come up with the mis-use of the indexing API - allow a 'policy' to automatically provide column blocks for dict-like input (e.g. each column would be a block), this would allow a pass-thru API where you could put in numpy arrays where you have views and have them preserved rather than copied automatically. Note that this would also allow what I call 'split' where a passed in multi-dim numpy array could be split up to individual blocks (which actually gives a nice perf boost after the splitting costs). In working towards some of these goals. I have come to the opinion that it would make sense to have a neutral API protocol layer that would allow us to swap out different engines as needed, for particular dtypes, or *maybe* out-of-core type computations. E.g. imagine that we replaced the in-memory block structure with a bclolz / memap type; in theory this should be 'easy' and just work. I could also see us adopting *some* of the SFrame code to allow easier interop with this API layer. In practice, I think a nice API layer would need to be created to make this clean / nice. So this comes around to Wes's point about creating a c++ library for the internals (and possibly even some of the indexing routines). In an ideal world, or course this would be desirable. Getting there is a bit non-trivial I think, and IMHO might not be worth the effort. I don't really see big performance bottlenecks. We *already* defer much of the computation to libraries like numexpr & bottleneck (where appropriate). Adding numba / dask to the list would be helpful. I think that almost all performance issues are the result of: a) gross misuse of the pandas API. How much code have you seen that does df.apply(lambda x: x.sum()) b) routines which operate column-by-column rather block-by-block and are in python space (e.g. we have an issue right now about .quantile) So I am glossing over a big goal of having a c++ library that represents the pandas internals. This would by definition have a c-API that so you *could* use pandas like semantics in c/c++ and just have it work (and then pandas would be a thin wrapper around this library). I am not averse to this, but I think would be quite a big effort, and not a huge perf boost IMHO. Further there are a number of API issues w.r.t. indexing which need to be clarified / worked out (e.g. should we simply deprecate []) that are much easier to test / figure out in python space. I also thing that we have quite a large number of contributors. Moving to c++ might make the internals a bit more impenetrable that the current internals. (though this would allow c++ people to contribute, so that might balance out). We have a limited core of devs whom right now are familar with things. If someone happened to have a starting base for a c++ library, then I might change opinions here. my 4c. Jeff On Thu, Dec 24, 2015 at 7:18 PM, Wes McKinney <wesmckinn(a)gmail.com> wrote: > Deep thoughts during the holidays. > > I might be out of line here, but the interpreter-heaviness of the > inside of pandas objects is likely to be a long-term liability and > source of performance problems and technical debt. > > Has anyone put any thought into planning and beginning to execute on a > rewrite that moves as much as possible of the internals into native / > compiled code? I'm talking about: > > - pandas/core/internals > - indexing and assignment > - much of pandas/core/common > - categorical and custom dtypes > - all indexing mechanisms > > I'm concerned we've already exposed too much internals to users, so > this might lead to a lot of API breakage, but it might be for the > Greater Good. As a first step, beginning a partial migration of > internals into some C++ classes that encapsulate the insides of > DataFrame objects and implement indexing and block-level manipulations > would be a good place to start. I think you could do this wouldn't too > much disruption. > > As part of this internal retooling we might give consideration to > alternative data structures for representing data internal to pandas > objects. Now in 2015/2016, continuing to be hamstrung by NumPy's > limitations feels somewhat anachronistic. User code is riddled with > workarounds for data type fidelity issues and the like. Like, really, > why not add a bitndarray (similar to ilanschnell/bitarray) for storing > nullness for problematic types and hide this from the user? =) > > Since we are now a NumFOCUS-sponsored project, I feel like we might > consider establishing some formal governance over pandas and > publishing meetings notes and roadmap documents describing plans for > the project and meetings notes from committers. There's no real > "committer culture" for NumFOCUS projects like there is with the > Apache Software Foundation, but we might try leading by example! > > Also, I believe pandas as a project has reached a level of importance > where we ought to consider planning and execution on larger scale > undertakings such as this for safeguarding the future. > > As for myself, well, I have my hands full in Big Data-land. I wish I > could be helping more with pandas, but there a quite a few fundamental > issues (like data interoperability nested data handling and file > format support — e.g. Parquet, see > > http://wesmckinney.com/blog/the-problem-with-the-data-science-language-wars/ > ) > preventing Python from being more useful in industry analytics > applications. > > Aside: one of the bigger mistakes I made with pandas's API design was > making it acceptable to call class constructors — like > pandas.DataFrame — directly (versus factory functions). Sorry about > that! If we could convince everyone to start writing pandas.data_frame > or dataframe instead of using the class reference it would help a lot > with code cleanup. It's hard to plan for these things — NumPy > interoperability seemed a lot more important in 2008 than it does now, > so I forgive myself. > > cheers and best wishes for 2016, > Wes > _______________________________________________ > Pandas-dev mailing list > Pandas-dev(a)python.org > https://mail.python.org/mailman/listinfo/pandas-dev >

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On bug-fix releases and maintenance branches
by Joris Van den Bossche March 16, 2016

March 16, 2016

Hi all, I wanted to stir some discussion on pandas its policy on bug-fx releases and upgrading pains. First some context: *Context part 1*: Currently we do not use maintenance branches for bugfix releases, and we actually also do not *really *do bugfix releases. We just develop further on master, and try to not merge breaking changes the first weeks/months, so we can do a minor kind of bug-fix release (but usually also with a lot of new features). But we don't, for example, backport fixes of regressions if they are fixed after master is pointing to the next major release. *Context part 2*: pandas is not yet that stable, in the sense that there are still quite some breaking changes in each release. I am not arguing for not doing these breaking changes, as some of these changes are really needed to clean up the API (although there are also arguments for that, but I think that is another discussion). This has the consequence that updating your pandas version is not always that pleasant. Sidenote: I have not that much experience with using pandas in a larger company or in larger codebases that need to be upgraded, rather with just my own code for my PhD. So it is difficult for me to judge on how much this is a problem or if bug-fx releases would help. Questions: - What are other people's experiences with upgrading pandas? And would more bug-fix releases actually ease the upgrading? - Do we want to do more bug-fix releases? - Having a maintenance branch and backporting fixes is extra work. Would we be able to handle this? Would it be worth the effort? (It has been mentioned before, but I think the main point raised was lack of manpower to maintain separate branches) To put it another way. In our whatsnew notice there is "*We recommend that all users upgrade to this version*", but I am actually not sure we should recommend that. I personally do not always recommend that no matter what *without careful consideration*. Regards, Joris

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Fwd: GSoC2016 - NumFOCUS
by Jeff Reback March 14, 2016

March 14, 2016

If anyone else is interested in being a pandas mentor for GSOC, pls e-mail me ASAP. (and Raniere). Jeff ---------- Forwarded message ---------- From: Raniere Silva <raniere(a)rgaiacs.com> Date: Mon, Mar 14, 2016 at 4:08 PM Subject: GSoC2016 - NumFOCUS To: Bryan Van de Ven <bryanv(a)continuum.io>, Devasena Inupakutika < devasena.prasad(a)gmail.com>, Ethan White <ethan(a)weecology.org>, Greg Wilson < gvwilson.third.bit(a)gmail.com>, henry senyondo <henrykironde(a)gmail.com>, Jeff Reback <jeffreback(a)gmail.com>, joehuchette <joehuchette(a)gmail.com>, "lev.konst" <lev(a)radimrehurek.com>, Madeleine <madeleine.udell(a)gmail.com>, Mark Wiebe <mwwiebe(a)gmail.com>, Michael Droettboom <mdboom(a)gmail.com>, Miles Lubin <miles.lubin(a)gmail.com>, piotrb <piotr(a)banaszkiewicz.org>, QuLogic <quantum.analyst(a)gmail.com>, Radim <me(a)radimrehurek.com>, sckott < myrmecocystus(a)gmail.com>, Stephan Hoyer <shoyer(a)gmail.com>, tacaswell < tcaswell(a)gmail.com> Hi all, thanks to volunteer to be a mentor for GSoC 2016 under NumFOCUS umbrella. Could you look at the addressed to list of this email and verify that all your colegues that will be mentors are on it? If they aren't please ask him to send me a email or email me on their behalf. Today the students will start send their application. This year they will submit their final proposal as PDF (for better or worse) and we will learn during the process. We suggest that the students that want comments on their proposal send it as a pull request against https://github.com/numfocus/gsoc/ because it makes easy for some of us to keep track of the proposals that want feedback and show us that the student knows how to use Git and GitHub. If you want that your students use Google Docs or another tool to get feedback on their proposals you are free to request it to them. If you need anything, let me know. Cheers, Raniere

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ANN: pandas v0.18.0 Final released
by Jeff Reback March 12, 2016

March 12, 2016

Hi, This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. This was a release of 3.5 months with 381 commits by 100 authors encompassing 465 issues and 290 pull-requests. *What is it:* *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. *Highlights*: - pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273> - Moving and expanding window functions are now methods on Series and DataFrame similar to .groupby like objects, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - API breaking .resample changes to make it more .groupby like, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - The .to_xarray() function has been added for compatibility with the xarray package <http://xarray.pydata.org/en/stable/> see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - The read_sas() function has been enhanced to read sas7bdat files, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - Addition of the .str.extractall() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…>, and API changes to the the .str.extract() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…>, and the .str.cat() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - pd.test() top-level nose test runner is available GH4327 <https://github.com/pydata/pandas/issues/4327> See the Whatsnew <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html> for much more information and the full Documentation <http://pandas.pydata.org/pandas-docs/version/0.18.0/> link. *How to get it:* Source tarballs, windows wheels, and macosx wheels are available on PyPI <https://pypi.python.org/pypi/pandas> Installation via conda is: - conda install pandas windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.10 macosx wheels are courtesy of Matthew Brett. *Issues:* Please report any issues on our issue tracker <https://github.com/pydata/pandas/issues>: Jeff *Thanks to all of the contributors* - ARF - Alex Alekseyev - Andrew McPherson - Andrew Rosenfeld - Anthonios Partheniou - Anton I. Sipos - Ben - Ben North - Bran Yang - Chris - Chris Carroux - Christopher C. Aycock - Christopher Scanlin - Cody - Da Wang - Daniel Grady - Dorozhko Anton - Dr-Irv - Erik M. Bray - Evan Wright - Francis T. O'Donovan - Frank Cleary - Gianluca Rossi - Graham Jeffries - Guillaume Horel - Henry Hammond - Isaac Schwabacher - Jean-Mathieu Deschenes - Jeff Reback - Joe Jevnik - John Freeman - John Fremlin - Jonas Hoersch - Joris Van den Bossche - Joris Vankerschaver - Justin Lecher - Justin Lin - Ka Wo Chen - Keming Zhang - Kerby Shedden - Kyle - Marco Farrugia - MasonGallo - MattRijk - Matthew Lurie - Maximilian Roos - Mayank Asthana - Mortada Mehyar - Moussa Taifi - Navreet Gill - Nicolas Bonnotte - Paul Reiners - Philip Gura - Pietro Battiston - RahulHP - Randy Carnevale - Rinoc Johnson - Rishipuri - Sangmin Park - Scott E Lasley - Sereger13 - Shannon Wang - Skipper Seabold - Thierry Moisan - Thomas A Caswell - Toby Dylan Hocking - Tom Augspurger - Travis - Trent Hauck - Tux1 - Varun - Wes McKinney - Will Thompson - Yoav Ram - Yoong Kang Lim - Yoshiki Vázquez Baeza - Young Joong Kim - Younggun Kim - Yuval Langer - alex argunov - behzad nouri - boombard - ian-pantano - chromy - daniel - dgram0 - gfyoung - hack-c - hcontrast - jfoo - kaustuv deolal - llllllllll - ranarag - rockg - scls19fr - seales - sinhrks - srib - surveymedia.ca - tworec

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ANN: pandas v0.18.0rc2 - RELEASE CANDIDATE
by Jeff Reback March 9, 2016

March 9, 2016

Hi, I'm pleased to announce the availability of the second release candidate of Pandas 0.18.0. Please try this RC and report any issues here: Pandas Issues <https://github.com/pydata/pandas/issues>. Compared to RC1, we have added updated read_sas and fixed float indexing. We will be releasing officially very shortly. THIS IS NOT A PRODUCTION RELEASE This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Highlights include: - pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273> - Moving and expanding window functions are now methods on Series and DataFrame similar to .groupby like objects, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - API breaking .resample changes to make it more .groupby like, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - The .to_xarray() function has been added for compatibility with the xarray package <http://xarray.pydata.org/en/stable/> see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - The read_sas() function has been enhanced to read sas7bdat files, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - Addition of the .str.extractall() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, and API changes to the the .str.extract() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, and the .str.cat() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - pd.test() top-level nose test runner is available GH4327 <https://github.com/pydata/pandas/issues/4327> See the Whatsnew <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html> for much more information. Best way to get this is to install via conda <http://pandas-docs.github.io/pandas-docs-travis/install.html#installing-pan…> from our development channel. Builds for osx-64,linux-64,win-64 for Python 2.7 and Python 3.5 are all available. conda install pandas=v0.18.0rc2 -c pandas Thanks to all who made this release happen. It is a very large release! Jeff

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GSOC 2016
by Jeff Reback March 8, 2016

March 8, 2016

Ok, so we are setup with NUMFocus for 2016 to participate in GSOC 2016. If you'd like to be a mentor, pls e-mail me & raniere(a)rgaiacs.com (from NUMFocus) and he will send you an invite. myself and Stephan are already signed up. thanks Jeff

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