[Pandas-dev] Rewriting some of internals of pandas in C/C++? / Roadmap

[Wes McKinney]

Thanks Jeff. Can you create and share a shared Drive folder containing
this where I can put other auxiliary / follow up documents?

On Fri, Jan 1, 2016 at 5:23 PM, Jeff Reback <jeffreback at gmail.com> wrote:
> I changed the doc so that the core dev people can edit. I *think* that
> everyone should be able to view/comment though.
>
> On Fri, Jan 1, 2016 at 8:13 PM, Wes McKinney <wesmckinn at gmail.com> wrote:
>>
>> Jeff -- can you require log-in for editing on this document?
>>
>> https://docs.google.com/document/d/151ct8jcZWwh7XStptjbLsda6h2b3C0IuiH_hfZnUA58/edit#
>>
>> There are a number of anonymous edits.
>>
>> On Wed, Dec 30, 2015 at 6:04 PM, Wes McKinney <wesmckinn at gmail.com> wrote:
>> > I cobbled together an ugly start of a c++->cython->pandas toolchain here
>> >
>> > https://github.com/wesm/pandas/tree/libpandas-native-core
>> >
>> > I used a mix of Kudu, Impala, and dynd-python cmake sources, so it's a
>> > bit messy at the moment but it should be sufficient to run some real
>> > experiments with a little more work. I reckon it's like a 6 month
>> > project to tear out the insides of Series and DataFrame and replace it
>> > with a new "native core", but we should be able to get enough info to
>> > see whether it's a viable plan within a month or so.
>> >
>> > The end goal is to create "private" extension types in Cython that can
>> > be the new base classes for Series and NDFrame; these will hold a
>> > reference to a C++ object that contains wrappered NumPy arrays and
>> > other metadata (like pandas-only dtypes).
>> >
>> > It might be too hard to try to replace a single usage of block manager
>> > as a first experiment, so I'll try to create a minimal "SeriesLite"
>> > that supports 3 dtypes
>> >
>> > 1) float64 with nans
>> > 2) int64 with a bitmask for NAs
>> > 3) category type for one of these
>> >
>> > Just want to get a feel for the extensibility and offer an NA
>> > singleton Python object (a la None) for getting and setting NAs across
>> > these 3 dtypes.
>> >
>> > If we end up going down this route, any way to place a moratorium on
>> > invasive work on pandas internals (outside bug fixes)?
>> >
>> > Pedantic aside: I'd rather avoid shipping thirdparty C/C++ libraries
>> > like googletest and friends in pandas if we can. Cloudera folks have
>> > been working on a portable C++ library toolchain for Impala and other
>> > projects at https://github.com/cloudera/native-toolchain, but it is
>> > only being tested on Linux and OS X. Most google libraries should
>> > build out of the box on MSVC but it'll be something to keep an eye on.
>> >
>> > BTW thanks to the libdynd developers for pioneering the c++ lib <->
>> > python-c++ lib <-> cython toolchain; being able to build Cython
>> > extensions directly from cmake is a godsend
>> >
>> > HNY all
>> > Wes
>> >
>> > On Tue, Dec 29, 2015 at 4:17 PM, Irwin Zaid <izaid at continuum.io> wrote:
>> >> Yeah, that seems reasonable and I totally agree a Pandas wrapper layer
>> >> would
>> >> be necessary.
>> >>
>> >> I'll keep an eye on this and I'd like to help if I can.
>> >>
>> >> Irwin
>> >>
>> >>
>> >> On Tue, Dec 29, 2015 at 6:01 PM, Wes McKinney <wesmckinn at gmail.com>
>> >> wrote:
>> >>>
>> >>> I'm not suggesting a rewrite of NumPy functionality but rather pandas
>> >>> functionality that is currently written in a mishmash of Cython and
>> >>> Python.
>> >>> Happy to experiment with changing the internal compute infrastructure
>> >>> and
>> >>> data representation to DyND after this first stage of cleanup is done.
>> >>> Even
>> >>> if we use DyND a pretty extensive pandas wrapper layer will be
>> >>> necessary.
>> >>>
>> >>>
>> >>> On Tuesday, December 29, 2015, Irwin Zaid <izaid at continuum.io> wrote:
>> >>>>
>> >>>> Hi Wes (and others),
>> >>>>
>> >>>> I've been following this conversation with interest. I do think it
>> >>>> would
>> >>>> be worth exploring DyND, rather than setting up yet another rewrite
>> >>>> of
>> >>>> NumPy-functionality. Especially because DyND is already an optional
>> >>>> dependency of Pandas.
>> >>>>
>> >>>> For things like Integer NA and new dtypes, DyND is there and ready to
>> >>>> do
>> >>>> this.
>> >>>>
>> >>>> Irwin
>> >>>>
>> >>>> On Tue, Dec 29, 2015 at 5:18 PM, Wes McKinney <wesmckinn at gmail.com>
>> >>>> wrote:
>> >>>>>
>> >>>>> Can you link to the PR you're talking about?
>> >>>>>
>> >>>>> I will see about spending a few hours setting up a libpandas.so as a
>> >>>>> C++
>> >>>>> shared library where we can run some experiments and validate
>> >>>>> whether it can
>> >>>>> solve the integer-NA problem and be a place to put new data types
>> >>>>> (categorical and friends). I'm +1 on targeting
>> >>>>>
>> >>>>> Would it also be worth making a wish list of APIs we might consider
>> >>>>> breaking in a pandas 1.0 release that also features this new "native
>> >>>>> core"?
>> >>>>> Might as well right some wrongs while we're doing some invasive work
>> >>>>> on the
>> >>>>> internals; some breakage might be unavoidable. We can always
>> >>>>> maintain a
>> >>>>> pandas legacy 0.x.x maintenance branch (providing a conda binary
>> >>>>> build) for
>> >>>>> legacy users where showstopper bugs can get fixed.
>> >>>>>
>> >>>>> On Tue, Dec 29, 2015 at 1:20 PM, Jeff Reback <jeffreback at gmail.com>
>> >>>>> wrote:
>> >>>>> > Wes your last is noted as well. I *think* we can actually do this
>> >>>>> > now
>> >>>>> > (well
>> >>>>> > there is a PR out there).
>> >>>>> >
>> >>>>> > On Tue, Dec 29, 2015 at 4:12 PM, Wes McKinney
>> >>>>> > <wesmckinn at gmail.com>
>> >>>>> > wrote:
>> >>>>> >>
>> >>>>> >> The other huge thing this will enable is to do is copy-on-write
>> >>>>> >> for
>> >>>>> >> various kinds of views, which should cut down on some of the
>> >>>>> >> defensive
>> >>>>> >> copying in the library and reduce memory usage.
>> >>>>> >>
>> >>>>> >> On Tue, Dec 29, 2015 at 1:02 PM, Wes McKinney
>> >>>>> >> <wesmckinn at gmail.com>
>> >>>>> >> wrote:
>> >>>>> >> > Basically the approach is
>> >>>>> >> >
>> >>>>> >> > 1) Base dtype type
>> >>>>> >> > 2) Base array type with K >= 1 dimensions
>> >>>>> >> > 3) Base scalar type
>> >>>>> >> > 4) Base index type
>> >>>>> >> > 5) "Wrapper" subclasses for all NumPy types fitting into
>> >>>>> >> > categories
>> >>>>> >> > #1, #2, #3, #4
>> >>>>> >> > 6) Subclasses for pandas-specific types like category,
>> >>>>> >> > datetimeTZ,
>> >>>>> >> > etc.
>> >>>>> >> > 7) NDFrame as cpcloud wrote is just a list of these
>> >>>>> >> >
>> >>>>> >> > Indexes and axis labels / column names can get layered on top.
>> >>>>> >> >
>> >>>>> >> > After we do all this we can look at adding nested types
>> >>>>> >> > (arrays,
>> >>>>> >> > maps,
>> >>>>> >> > structs) to better support JSON.
>> >>>>> >> >
>> >>>>> >> > - Wes
>> >>>>> >> >
>> >>>>> >> > On Tue, Dec 29, 2015 at 12:14 PM, Phillip Cloud
>> >>>>> >> > <cpcloud at gmail.com>
>> >>>>> >> > wrote:
>> >>>>> >> >> Maybe this is saying the same thing as Wes, but how far would
>> >>>>> >> >> something
>> >>>>> >> >> like
>> >>>>> >> >> this get us?
>> >>>>> >> >>
>> >>>>> >> >> // warning: things are probably not this simple
>> >>>>> >> >>
>> >>>>> >> >> struct data_array_t {
>> >>>>> >> >>     void *primitive;  // scalar data
>> >>>>> >> >>     data_array_t *nested; // nested data
>> >>>>> >> >>     boost::dynamic_bitset isnull;  // might have to create our
>> >>>>> >> >> own
>> >>>>> >> >> to
>> >>>>> >> >> avoid
>> >>>>> >> >> boost
>> >>>>> >> >>     schema_t schema;  // not sure exactly what this looks like
>> >>>>> >> >> };
>> >>>>> >> >>
>> >>>>> >> >> typedef std::map<string, data_array_t> data_frame_t;  //
>> >>>>> >> >> probably
>> >>>>> >> >> not
>> >>>>> >> >> this
>> >>>>> >> >> simple
>> >>>>> >> >>
>> >>>>> >> >> To answer Jeff’s use-case question: I think that the use cases
>> >>>>> >> >> are
>> >>>>> >> >> 1)
>> >>>>> >> >> freedom from numpy (mostly) 2) no more block manager which
>> >>>>> >> >> frees
>> >>>>> >> >> us
>> >>>>> >> >> from the
>> >>>>> >> >> limitations of the block memory layout. In particular, the
>> >>>>> >> >> ability
>> >>>>> >> >> to
>> >>>>> >> >> take
>> >>>>> >> >> advantage of memory mapped IO would be a big win IMO.
>> >>>>> >> >>
>> >>>>> >> >>
>> >>>>> >> >> On Tue, Dec 29, 2015 at 2:50 PM Wes McKinney
>> >>>>> >> >> <wesmckinn at gmail.com>
>> >>>>> >> >> wrote:
>> >>>>> >> >>>
>> >>>>> >> >>> I will write a more detailed response to some of these things
>> >>>>> >> >>> after
>> >>>>> >> >>> the new year, but, in particular, re: missing values, can you
>> >>>>> >> >>> or
>> >>>>> >> >>> someone tell me why creating an object that contains a NumPy
>> >>>>> >> >>> array and
>> >>>>> >> >>> a bitmap is not sufficient? If we we can add a lightweight
>> >>>>> >> >>> C/C++
>> >>>>> >> >>> class
>> >>>>> >> >>> layer between NumPy function calls (e.g. arithmetic) and
>> >>>>> >> >>> pandas
>> >>>>> >> >>> function calls, then I see no reason why we cannot have
>> >>>>> >> >>>
>> >>>>> >> >>> Int32Array->add
>> >>>>> >> >>>
>> >>>>> >> >>> and
>> >>>>> >> >>>
>> >>>>> >> >>> Float32Array->add
>> >>>>> >> >>>
>> >>>>> >> >>> do the right thing (the former would be responsible for
>> >>>>> >> >>> bitmasking to
>> >>>>> >> >>> propagate NA values; the latter would defer to NumPy). If we
>> >>>>> >> >>> can
>> >>>>> >> >>> put
>> >>>>> >> >>> all the internals of pandas objects inside a black box, we
>> >>>>> >> >>> can
>> >>>>> >> >>> add
>> >>>>> >> >>> layers of virtual function indirection without a performance
>> >>>>> >> >>> penalty
>> >>>>> >> >>> (e.g. adding more interpreter overhead with more abstraction
>> >>>>> >> >>> layers
>> >>>>> >> >>> does add up to a perf penalty).
>> >>>>> >> >>>
>> >>>>> >> >>> I don't think this is too scary -- I would be willing to
>> >>>>> >> >>> create a
>> >>>>> >> >>> small POC C++ library to prototype something like what I'm
>> >>>>> >> >>> talking
>> >>>>> >> >>> about.
>> >>>>> >> >>>
>> >>>>> >> >>> Since pandas has limited points of contact with NumPy I don't
>> >>>>> >> >>> think
>> >>>>> >> >>> this would end up being too onerous.
>> >>>>> >> >>>
>> >>>>> >> >>> For the record, I'm pretty allergic to "advanced C++"; I
>> >>>>> >> >>> think it
>> >>>>> >> >>> is a
>> >>>>> >> >>> useful tool if you pick a sane 20% subset of the C++11 spec
>> >>>>> >> >>> and
>> >>>>> >> >>> follow
>> >>>>> >> >>> Google C++ style it's not very inaccessible to intermediate
>> >>>>> >> >>> developers. More or less "C plus OOP and easier object
>> >>>>> >> >>> lifetime
>> >>>>> >> >>> management (shared/unique_ptr, etc.)". As soon as you add a
>> >>>>> >> >>> lot
>> >>>>> >> >>> of
>> >>>>> >> >>> template metaprogramming C++ library development quickly
>> >>>>> >> >>> becomes
>> >>>>> >> >>> inaccessible except to the C++-Jedi.
>> >>>>> >> >>>
>> >>>>> >> >>> Maybe let's start a Google document on "pandas roadmap" where
>> >>>>> >> >>> we
>> >>>>> >> >>> can
>> >>>>> >> >>> break down the 1-2 year goals and some of these
>> >>>>> >> >>> infrastructure
>> >>>>> >> >>> issues
>> >>>>> >> >>> and have our discussion there? (obviously publish this
>> >>>>> >> >>> someplace
>> >>>>> >> >>> once
>> >>>>> >> >>> we're done)
>> >>>>> >> >>>
>> >>>>> >> >>> - Wes
>> >>>>> >> >>>
>> >>>>> >> >>> On Fri, Dec 25, 2015 at 2:14 PM, Jeff Reback
>> >>>>> >> >>> <jeffreback at gmail.com>
>> >>>>> >> >>> wrote:
>> >>>>> >> >>> > 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 at 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 at python.org
>> >>>>> >> >>> >> https://mail.python.org/mailman/listinfo/pandas-dev
>> >>>>> >> >>> >
>> >>>>> >> >>> >
>> >>>>> >> >>> _______________________________________________
>> >>>>> >> >>> Pandas-dev mailing list
>> >>>>> >> >>> Pandas-dev at python.org
>> >>>>> >> >>> https://mail.python.org/mailman/listinfo/pandas-dev
>> >>>>> >> _______________________________________________
>> >>>>> >> Pandas-dev mailing list
>> >>>>> >> Pandas-dev at python.org
>> >>>>> >> https://mail.python.org/mailman/listinfo/pandas-dev
>> >>>>> >
>> >>>>> >
>> >>>>>
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> Pandas-dev mailing list
>> >>>>> Pandas-dev at python.org
>> >>>>> https://mail.python.org/mailman/listinfo/pandas-dev
>> >>>>>
>> >>>>
>> >>
>> _______________________________________________
>> Pandas-dev mailing list
>> Pandas-dev at python.org
>> https://mail.python.org/mailman/listinfo/pandas-dev
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