The PyToolz project extends itertools and functools to provide a set of standard functions for iterators, functions, and dictionaries.

tl;dr – PyToolz provides good functions for core data structures. These functions work together well. Here is a partial API:

groupby, unique, isiterable, intersection, frequencies,
get, concat, isdistinct, interleave, accumulate
first, second, nth, take, drop, rest, last,
memoize, curry, compose, merge, assoc

Why?

Two years ago I started playing with functional programming. One powerful feature of functional languages oddly stuck out as having very little to do with FP in general. In particular modern functional languages often have really killer standard libraries for dealing with iterators, functions, and dictionaries. This standard function set doesn’t depend on macros, monads, or any other mind bending language feature understandable only to LISP-ers or Haskell-ites. This feature only requires higher order functions and lazy iterators, both of which Python does quite well.

This is well known. The libraries itertools and functools are supposed to fill this niche in the Python ecosystem. Personally I’ve found these libraries to be useful but often incomplete (although the Python 3 versions are showing signs of improvement.) To fill these gaps we started hacking together the libraries itertoolz and functoolz which were modeled largely after the Clojure standard library. These projects were eventually merged into a single codebase, named toolz which is available for your hacking pleasure at http://github.com/pytoolz/toolz/.

Official

The official description of Toolz from the docs is as follows:

The Toolz project provides a set of utility functions for iterators, functions, and dictionaries. These functions are designed to interoperate well, forming the building blocks of common data analytic operations. They extend the standard libraries itertools and functools and borrow heavily from the standard libraries of contemporary functional languages.

Toolz provides a suite of functions which have the following virtues:

  • Composable: They interoperate due to their use of core data structures.
  • Pure: They don’t change their inputs or rely on external state.
  • Lazy: They don’t run until absolutely necessary, allowing them to support large streaming data sets.

This gives developers the power to write powerful programs to solve complex problems with relatively simple code which is easy to understand without sacrificing performance. Toolz enables this approach, commonly associated with functional programming, within a natural Pythonic style suitable for most developers.

This project follows in the footsteps of the popular projects Underscore.js for JavaScript and and Enumerable for Ruby.

Examples

Word counting is a common example used to show off data processing libraries. The Python version that leverages toolz demonstrates how the algorithm can be deconstructed into the three operations of splitting, stemming, and frequency counting:

>>> from toolz import *

>>> def stem(word):
... """ Stem word to primitive form """
...     return word.lower().rstrip(",.!:;'-\"").lstrip("'\"")

>>> wordcount = compose(frequencies, partial(map, stem), str.split)  # Function
>>> sentence = "This cat jumped over this other cat!"                # Data

>>> wordcount(sentence)
{'this': 2, 'cat': 2, 'jumped': 1, 'over': 1, 'other': 1}

There are many solutions to the wordcounting problem. What I like about this solution is that it breaks down the wordcounting problem into a composition of three fundamental operations.

  1. Splitting a text into words – (str.split)
  2. Stemming those words to a base form so that 'Hello!' is the same as 'hello' – (partial(map, stem))
  3. Counting occurrences of each base word – (frequencies)

Toolz provides both common operations for iterators (like frequencies for counting occurrences) and common operations for functions (like compose for function composition). Using these together, programmers can describe a number of data analytic solutions clearly and concisely.

Here is another example performing analytics on the following directed graph

>>> from toolz.curried import *
>>> a, b, c, d, e, f, g = 'abcdefg'

>>> edges = [(a, b), (b, a), (a, c), (a, d), (d, a), (d, e),
...          (e, f), (d, f), (f, d), (d, g), (e, g)]

>>> # Nodes
>>> set(concat(edges))
{'a', 'b', 'c', 'd', 'e', 'f', 'g'}

>>> # Out degree
>>> countby(first, edges)
{'a': 3, 'b': 1, 'd': 4, 'e': 2, 'f': 1}

>>> # In degree
>>> countby(second, edges)
{'a': 2, 'b': 1, 'c': 1, 'd': 2, 'e': 1, 'f': 2, 'g': 2}

>>> # Out neighbors
>>> valmap(compose(list, map(second)),
...        groupby(first, edges))
{'a': ['b', 'c', 'd'],
 'b': ['a'],
 'd': ['a', 'e', 'f', 'g'],
 'e': ['f', 'g'],
 'f': ['d']}

>>> # In neighbors
>>> valmap(compose(list, map(first)),
...        groupby(second, edges))
{'a': ['b', 'd'],
 'b': ['a'],
 'c': ['a'],
 'd': ['a', 'f'],
 'e': ['d'],
 'f': ['e', 'd'],
 'g': ['d', 'e']}

Learning a small set of higher order functions like groupby, map, and valmap gives a surprising amount of leverage over this kind of data. Additionally the streaming nature of many (but not all) of the algorithms allows toolz to perform well even on datasets that do not fit comfortably into memory.

I routinely process large network datasets at my work and find toolz to be invaluable in this context.

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