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

[Jeff Reback]

https://github.com/pydata/pandas/pull/11500.

I annotated in the shared google doc as well.

There is a section on some pandas 1.0 things to do.

On Tue, Dec 29, 2015 at 6:18 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
> >
> >
>
>
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