Page 2 of 5

Finally, GitHub listens! The importance of attention to detail in UX

I like using GitHub, as do apparently a lot of other people. When doing pull requests, I’ve been annoyed for a long time by the amount of clicking necessary to get the git URL to the contributor’s repository (which must be added as a remote, etc.):

  • Click on new contributor
  • Locate pandas fork on their list of repositories
  • Copy git link from their fork, git remote add and I’m in business
  • But, GitHub is about using git, so why should I have to go fishing to get the git link? Earlier in the Pull Request UI, there was a help box that contained the git url (which I used), but then they took it away! So fishing it was. This might seem very minor, but after adding many dozens of remotes like that the annoyance had accumulated. It seemed obvious to me that the branch names should just be links:

    Each time GitHub tweaked the UI, I complained on Twitter, like so:

    Fffffff @ changes the pull request UI again but the branch names still are not links to the repository?
    Wes McKinney

    Finally, someone noticed:

    @ Great idea. Links added as of a few minutes ago.
    Cameron McEfee

    Small victories, I guess. Enjoy.

    Latest Table of Contents for Python for Data Analysis

    Making some progress on Python for Data Analysis

    Intro to Python for Financial Data Analysis at General Assembly

    I taught a class this past Monday, June 18, at General Assembly. Here are the (very brief) slides and a link to the IPython notebooks. You’ll need at least pandas 0.8.0b2, though unfortunately I identified a few bugs during the class that have since been fixed. Look out for the final release of pandas 0.8.0 any day now.

    Easy, high performance time zone handling in pandas 0.8.0

    Making time zone handling palatable is surprisingly difficult to get right. The generally agreed-upon “best practice” for storing timestamps is to use UTC. Otherwise, you have to worry about daylight savings time ambiguities or non-existent times. The misery of time zone handling is well documented, and summarized nicely last year by Armin Ronacher. When you work in UTC, most of your troubles go away; converting a single timestamp or array of timestamps between time zones becomes in essence a “free” operation since the time zone is simply metadata for the box containing the invariant UTC timestamp.

    But it’s not all fun and games. The Python datetime API in this area is generally considered to be severely lacking. It’s so bad that 77-line modules with half a dozen convenience functions can get 245 watchers on GitHub. I often write that much code before I finish my first cup of coffee in the morning :) But, for most applications you can suffer through the API and use pytz, which is an adequate solution in most cases. pytz notably ships the Olson timezone database which is the key piece of information for powering time zone conversions.

    But what about pandas? Among other things, pandas is really good for time series data, including very large time series data in the millions of observations. I don’t want to make pandas users suffer because of Python’s datetime API, so I’m happy to provide a better one (a bit more on this later). The biggest issue is: as with many non-scientific Python libraries, pytz and other tools have a fatal illness known as TMP, a.k.a. Too Much (pure) Python. Let me explain:

    So, localizing a single datetime.datetime value takes 33 microseconds, or ~33 seconds per million timestamps. Localize serves a couple of important, but annoying functions: checking for ambiguities (“fall back”) and non-existent times (“spring forward”) at DST transition times.

    Now, one major problem that I found while examining pytz code is how many temporary datetime.datetime objects are created during a single call to tz.localize. How many do you think?

    15. 15 temporary datetime.datetime objects

    Don’t believe me? Look for yourself. Just following what is going on inside the function is enough to make your head hurt. The code is vastly complicated by the fact that tz-aware datetimes are not comparable with tz-naive datetimes.

    Obviously, there must be a better and faster way. Some might argue that I should improve pytz, but the problem is that the implementation of time zone logic is dependent on the representation of the timestamps. Over the last few months I have stopped using datetime.datetime in pandas in favor of 64-bit integer timestamps via NumPy’s datetime64 data type. Storing large arrays of datetime.datetime values is disastrously inefficient in terms of memory and performance in all time series operations. Obviously I can’t force this design decision on most Python programmers who are not engaged in high-performance data analysis work.

    Making time zone handling fast and easy

    So, here are my requirements for pandas’s time zone capabilities:

  • All operations must be vectorized and be as fast as possible on large arrays of irregular, not necessarily ordered 64-bit timestamps
  • API must be as simple and non-crappy as possible without sacrificing functionality.
  • pandas 0.8.0 has a new Timestamp data type which is a subclass of datetime.datetime providing nanosecond resolution support and, in my opinion, a strictly superior interface for working with dates and time:

    Timestamps can be created as local or converted to local using tz_localize. Conversions from one time zone to another use tz_convert:

    Wonder what time it is right now in London (it’s 8:50 PM in New York as I type this)?

    So that’s nice. Compared with datetime.datetime, Timestamp doesn’t get in your way as much. Timestamps are equal if and only if their UTC timestamps are equal:

    This makes sense, because they refer to the same moment in time. Also, adding timedeltas will do the right thing around DST transitions:

    OK, great. Scalar operations. I could have done all this with pytz. I’m really interested in vector operations on large time series.

    Localizing all of 1.8 million timestamps (without taking advantage of the fact that this range is regular and lacks any DST transitions, which you cannot assume in the general, irregular case) would have taken about 1 full minute if we were working with pytz and datetime.datetime objects. Here it takes about 390 ms using a vectorized Cython routine of my devising:

    What’s nice about working in UTC is that time zone conversions are now nearly free and do not copy any data (the DatetimeIndex is immutable):

    Scalar values are converted to Timestamp objects with the right hour, minute, second:

    Anyway, this is just a flavor of some of the things you can do in the almost-released version of pandas. Lots more easy-to-use and high-performance data analysis tooling to come.

    Mastering high performance data algorithms I: Group By

    I’m on my way back from R/Finance 2012. Those guys did a nice job of organizing the conference and was great to meet everyone there.

    As part of pandas development, I have had to develop a suite of high performance data algorithms and implementation strategies which are the heart and soul of the library. I get asked a lot why pandas’s performance is much better than R and other data manipulation tools. The reasons are two-fold: careful implementation (in Cython and and C, so minimizing the computational friction) and carefully-thought-out algorithms.

    Here are some of the more important tools and ideas that I make use of on a day-to-day basis:

  • Hash tables I use klib. It’s awesome and easy to use. I have this for critical algorithms like unique, factorize (unique + integer label assignment)
  • O(n) sort, known in pandas as groupsort, on integers with known range. This is a variant of counting sort; if you have N integers with known range from 0 to K – 1, this can be sorted in O(N) time. Combining this tool with factorize (hash table-based), you can categorize and sort a large data set in linear time. Failure to understand these two algorithms will force you to pay O(N log N), dominating the runtime of your algorithm.
  • Vectorized data movement and subsetting routines: take, put, putmask, replace, etc.
  • Let me give you a prime example from a commit yesterday of me applying these ideas to great effect. Suppose I had a time series (or DataFrame containing time series) that I want to group by year, month, and day:

    This is a small dataset, but imagine you have millions of observations and thousands or even millions of groups. How does that look algorithmically? I guarantee if you take a naive approach, you will crash and burn when the data increases in size. I know, because I did just that (take a look at the vbenchmarks). Laying down the infrastructure for doing a better job is not simple. Here are the steps for efficiently aggregating data like this:

  • Factorize the group keys, computing integer label arrays (O(N) per key)
  • Compute a “cartesian product number” or group index for each observation (since you could theoretically have K_1 * … * K_p groups observed, where K_i is the number of unique values in key i). This is again O(n) work.
  • If the maximum number of groups is large, “compress” the group index by using the factorize algorithm on it again. imagine you have 1000 uniques per key and 3 keys; most likely you do not actually observe 1 billion key combinations but rather some much smaller number. O(n) work again
  • For simple aggregations, like mean, go ahead and aggregate the data in one linear sweep without moving anything around.
  • Otherwise, for arbitrary user-defined aggregations, we have two options for chopping up the data set: sorting it (O(N) using groupsort!) or create index arrays indicating which observations belong to each group. The latter would be preferable in many cases to sorting a large data set (reordering the data, even though linear time, can be very costly).
  • I worked on speeding up the latter part of this last bullet point yesterday. The resulting code looked like this:

    The details of lib.indices_fast aren’t that interesting; it chops up np.arange(len(group_index)).take(sorter), the sorted indirect indices, to produce the index dictionary. Running %lprun to get a line profiling on a large-ish data set:

    You might say, well, this seems like a lot of work and maybe we should just zip the keys (forming an array of Python tuples) and do a dumb algorithm? The speed difference ends up being something like an order of magnitude or more faster by being careful in this way and working with indirect integer index arrays.

    Anyway, in conclusion, it’s these kinds of algorithms and ideas why pandas is perhaps the best-performing open-source data analysis toolkit for in memory data (I’m going to get to out-of-core data processing and “big data” eventually, just hang tight). It goes beyond language features and data structure internals (though this naturally also has a major impact, a lot of the things I do are easy to express in Python but would be very awkward or impossible to do in, say, R. Maybe I should write a whole article on this.); carefully thought-out algorithms are a major piece of the puzzle.

    A O(n log n) NA-friendly time series “as of” using array operations

    In time series data, it’s fairly common to need to compute the last known value “as of” a particular date. However, missing data is the norm, so it’s a touch more complicated than doing a simple binary search. Here is an implementation using array operations that takes these things into account:

    Some algorithmic notes. First, let’s run this through line_profiler on a large time series to see where time is being taken up:

    The main trickiness here is this step:

    Since the indices returned by searchsorted are relative to the filtered values, you need to remap to what would have been the indices in the original array, and this does exactly that. Lastly, you might be interested in breaking up stamps[mask].searchsorted(where, side='right') to see how much time is spend in stamps[mask] versus searchsorted:

    So, this could definitely be sped up by a factor or 2 or more by doing the whole operation in C or Cython, but it’s nice to be able to express it concisely and speedily with array operations.

    Why I’m not on the Julia bandwagon (yet)

    EDIT (2013-07-11): The following article may or may not been accurate anymore since it’s been more than a year. I recommend running your own benchmarks on array expressions with the latest Julia builds and draw your own conclusions.

    I apologize in advance for writing what might be the first neutral to negative article about Julia that you’ve read. For those of you who don’t know, Julia is a new dynamic language with an LLVM-based JIT compiler designed for technical computing. It’s intent is to try to bridge the gap between high level languages and low level ones in terms of giving you the power, flexibility, and general-purpose usefulness of a Lisp or Python-like language but with low-level performance. It has MATLAB-like syntax but has a lot of the features of Lisp (macros, metaprogramming) and Python that we’ve all come to love. Before anything else, I’d like to say that I’m very impressed with the project, the team, and overall their technology choices.

    Many people are very excited about Julia, and one of the main reasons is because of a number of very impressive benchmarks. This is great and I’m also impressed with the benchmarks, but they are in my opinion fairly misleading and don’t represent the reality of a lot of technical computing.

    I compiled Julia in advance of Stefan Karpinski’s (one of the creators) talk in New York this week. I ran a few benchmarks and then proceeded to raise hell on the Julia mailing list.

    TL;DR: if you’re an R, MATLAB, or scientific Python user, it’s probably not time to start programming in Julia if the highest performance code is your goal for minimum effort. It may be in a couple of years when the JIT-compiler improves. However, Julia may soon be a reasonable free computational alternative to MATLAB. I say this even in a perfect world where Julia had all the libraries that Python and R have; as far as a sole language for building large scientific production applications in industry right now, I think Python + Cython are pretty much the only game in town.

    Why am I complaining about the benchmarks? Well, one of the first things I did was look at the performance of array operations. In scientific and technical computing, we work a lot with arrays, right? In fact, convention wisdom is “don’t write loops”. Many people have told me that I should be unrolling array operations into loops and my experience is that this is the way to get the best performance. Keep in mind that I am a performance-obsessed tool maker, and in libraries like pandas I am in control of the vast majority of the areas of people’s applications that would otherwise be bottlenecks.

    Here’s a simple benchmark: compute the inner product of two arrays, in other words, the sum of the element-wise product. Julia code (I disabled the garbage collector to remove its effect from the equation):

    And the result:

    Lastly, you might recognize that an array inner product can be expressed as a matrix operation and carried out by BLAS in Fortran:

    So most of you know me as a Python hacker. So I’ll run the same array benchmark with NumPy:

    OK, so NumPy is faster than Julia for array operations, temporary array and all. But how much faster can you go by eliminating temporaries like Julia’s inner function? Here is Cython code:

    And the timing:

    So Julia is about 2.5 times slower than Cython in this trivial benchmark (straight C wouldn’t that much faster). You can do even better by using the Intel MKL in NumPy:

    So anyway, Julia is not a magic solution. It _is_ pretty good though, especially if you never want to have to compile code. Having to unroll array expressions into loops is still not good and I don’t think it’s a habit that should be encouraged. And the JIT will get better over time. The Julia devs are spending a lot of their time I believe more fruitfully on language design, libraries and features, and “big science” topics like distributed computing and parallel processing problems which I think will be very impactful. But if you’re looking for a high performance alternative to R for implementing scientific algorithms with low effort, the sweet spot (at least in my opinion) between performance and fast development time is Cython. But if a perfect marriage of Python, Julia, and Cython is made, perhaps we can all have our cake and eat it too.

    The need for an embedded array expression compiler for NumPy

    Yesterday I found myself adding some additional Cython methods for doing fast grouped aggregations in pandas. To my disappointment, I found myself duplicating a lot of code and not having much alternative beyond cooking up some kind of ad hoc code generation framework. So, here’s the problem: array of data (possibly with NAs), array of labels, with the number of distinct labels known (range from 0 to N – 1). Aggregate data based on label using some function in {sum, min, max, product, mean, variance, …}. Here is a Cython function to add all the values in a group:

    Now suppose we want to take the product of the values:

    However, the amount of code duplication here is kind of ridiculous; the only changes are: different reduction function, and different starting values. Ideally I would like to write code that looks like:

    IMHO there is plenty of information here to generate C or LLVM that is at least as fast as the above code. However, this isn’t completely extensible– suppose we wanted to compute group variance. In that case what you actually need is

    The only thing you care about here is the variance result. Ideally, you want the generated code to:

  • Only make one pass over the data
  • Have no unnecessary temporary variables, except the X and XX arrays; the full reductions are necessary for computing the variance.
  • Under this framework, something like mean becomes even more trivial:

    Now, I can live with creating a C/Cython code generation framework, enabling me to write code like the above and get code that’s at least as good as something hand-written. But it would be best, of course, if the translation / machine code generation were dynamic to remove the compile step. Is there an existing toolchain here that can help with the machine code generation? Perhaps Numba is the place to do this with LLVM: Numba.

    Perhaps the eventual result of something like this is an embedded JIT-ing APL-like language with Python syntax running inside CPython. You would like to make Numexpr (which is an awesome project!) effective obsolete while you’re at it. In the meantime, I’ll keep thinking about the best way forward.

    vbench Lightning Talk Slides from PyCon 2012

    I prepared this lightning talk for PyCon on vbench, my performance monitoring tool, but didn’t have a chance to give it. If I get energetic I might make a screencast of it, but here are the slides:

    Even easier frequency tables in pandas 0.7.0

    I put in a little work on a new crosstab function in the main pandas namespace. It’s basically a convenient shortcut to calling pivot_table to make it easy to compute cross-tabulations for a set of factors using pandas DataFrame or even vanilla NumPy arrays!

    Here’s an example:

    This makes it very easy to produce an easy-on-the-eyes frequency table. crosstab can also take NumPy arrays. Suppose we had 1 million draws from a normal distribution, and we wish to produce a histogram-like table showing the number of draws whose absolute values fall into the bins defined by [0, 1, 2, 3]. Also, let’s divide things up by sign. Using crosstab this becomes dead simple.

    Of course since the result is a DataFrame you can customize the indexes:

    © 2015 Wes McKinney

    Theme by Anders NorenUp ↑