convtools 0.4.0

convtools is a python library to declaratively define fast conversions from python objects to python objects, including processing collections and doing complex aggregations and joins.

convtools is a python library to declaratively define fast conversions from python objects to python objects, including processing collections and doing complex aggregations and joins.

Description

The speed of convtools comes from the approach of generating code & compiling conversion functions, which don’t have any generic code like superfluous loops, ifs, etc.

So you can follow the DRY principle by storing and reusing the code on the python expression level, but at the same time be able to run the gen_converter and get the compiled code which doesn’t care about being DRY and is generated to be highly specialized for the specific need.

Thanks to pipes & labels it’s possible to define multiple pipelines of data processing, including branching and merging of them.

Conversions are not limited to simple data transformations, there are GroupBy & Aggregate conversions with many useful reducers:

• from common Sum, Max

• and less widely supported First/Last, Array/ArrayDistinct

• to DictSum-like ones (for nested aggregation) and MaxRow/MinRow (for finding an object with max/min value and further processing)

Also there are higher-level conversions - JOINS (inner, left, right, outer, cross), which processes 2 iterables and returns a generator of joined pairs.

Every conversion:

• contains the information of how to transform an input

• can be piped into another conversion (same as wrapping)

• can be labeled to be reused further in the conversions chain

• has a method gen_converter returning a function compiled at runtime

• despite being compiled at runtime, is debuggable with pdb due to linecache populating.

Installation:

pip install convtools

An example:

import re
from itertools import chain

# the suggested way of importing convtolls
from convtools import conversion as c

# Let's say we need to count words across all files
input_data = [
    "war-and-peace-1.txt",
    "war-and-peace-2.txt",
    "war-and-peace-3.txt",
    "war-and-peace-4.txt",
]
def read_file(filename):
    with open(filename) as f:
        for line in f:
            yield line

# iterate an input and read file lines
extract_strings = c.generator_comp(
    c.call_func(read_file, c.this())
)

# 1. make ``re`` pattern available to the code to be generated
# 2. call ``finditer`` method of the pattern and pass the string
#    as an argument
# 3. pass the result to the next conversion
# 4. iterate results, call ``.group()`` method of each re.Match
#    and call ``.lower()`` on each result
split_words = (
    c.naive(re.compile(r'\w+')).call_method("finditer", c.this())
    .pipe(
        c.generator_comp(
            c.this().call_method("group", 0).call_method("lower")
        )
    )
)

# ``extract_strings`` is the generator of strings
# so we iterate it and pass each item to ``split_words`` conversion
vectorized_split_words = c.generator_comp(
    c.this().pipe(
        split_words
    )
)

# flattening the result of ``vectorized_split_words``, which is
# a generator of generators of strings
flatten = c.call_func(
    chain.from_iterable,
    c.this(),
)

# aggregate the input, the result is a single dict
# words are keys, values are count of words
dict_word_to_count = c.aggregate(
    c.reduce(
        c.ReduceFuncs.DictCount,
        (c.this(), c.this()),
        default=dict
    )
)

# take top N words by:
#  - call ``.items()`` method of the dict (the result of the aggregate)
#  - pass the result to ``sorted``
#  - take the slice, using input argument named ``top_n``
#  - cast to a dict
take_top_n = (
    c.this().call_method("items")
    .pipe(sorted, key=lambda t: t[1], reverse=True)
    .pipe(c.this()[:c.input_arg("top_n")])
    .as_type(dict)
)

# the resulting pipeline is pretty self-descriptive, except the ``c.if_``
# part, which checks the condition (first argument),
# and returns the 2nd if True OR the 3rd (input data by default) otherwise
pipeline = (
    extract_strings
    .pipe(flatten)
    .pipe(vectorized_split_words)
    .pipe(flatten)
    .pipe(dict_word_to_count)
    .pipe(
        c.if_(
            c.input_arg("top_n").is_not(None),
            c.this().pipe(take_top_n),
        )
    )
# Define the resulting converter function signature.
# In fact this isn't necessary if you don't need to specify default values
).gen_converter(debug=True, signature="data_, top_n=None")

# check the speed yourself :)
# e.g. take a look in txt format and tune the ``extract_strings``
# conversion as needed
pipeline(input_data, top_n=3)

Generated code:

def aggregate(data_):
    _none = v467_576
    agg_data_v0_ = _none
    for row_ in data_:

        if agg_data_v0_ is _none:
            agg_data_v0_ = {row_: 1}
        else:
            if row_ not in agg_data_v0_:
                agg_data_v0_[row_] = 1
            else:
                agg_data_v0_[row_] += 1

    result_ = dict() if agg_data_v0_ is _none else agg_data_v0_

    return result_


def converter459_881(data_, top_n=None):
    pipe459_557 = (read_file376_398(i458_940) for i458_940 in data_)
    pipe459_694 = from_iterable401_690(pipe459_557)
    pipe459_916 = (
        (i397_760.group(0).lower() for i397_760 in v379_129.finditer(i456_473))
        for i456_473 in pipe459_694
    )
    pipe459_431 = from_iterable401_690(pipe459_916)
    pipe459_970 = aggregate469_287(pipe459_431)
    return (
        dict(
            (
                sorted(pipe459_970.items(), key=lambda418_804, reverse=True)[
                    (slice(None, top_n, None))
                ]
            )
        )
        if (top_n is not None)
        else pipe459_970
    )

Documentation

convtools on Read the Docs