python-plyr 0.7

Mapping tools for nested containers.

Plyr: computing on nested containers

plyr [/plaɪ'ə/], derived from applier, is a python C-extension, that implements a map-like logic, which computes a specified function on the lower-most non-container data of arbitrarily nested built-in python containers, i.e. dicts, lists, tuples. It automatically unpacks nested containers in order to call the same function on their underlying non-container objects and then reassembles the structures. See the docstring of plyr.apply for details.

plyr happens to coincide with a similarly named library for R statistical computations language, which streamlines dataframe and vector/matrix transformations.

the Essential Example

Below we provide an example, which, we hope, illustrates the cases plyr might be useful in.

import plyr
from collections import namedtuple

nt = namedtuple('nt', 'u,v')

# add the leaf data in a pair of nested objects
plyr.apply(
    lambda u, v: u + v,
    [{'a': (1, 2, 3), 'z': 3.1415}, nt([1, 'u'], 'abc')],
    [{'a': (4, 6, 8), 'z': 2.7128}, nt([4, 'v'], 'xyz')],
    # _star=True,  # (default) call fn(d1, d2, **kwargs)
)
# output: [{'a': (5, 8, 11), 'z': 5.8543}, nt(u=[5, 'uv'], v='abcxyz')]

# join strings in a pair of tuples
plyr.apply(
    ' -->> '.join,
    ('abc', 'uvw', 'xyz',),
    ('123', '456', '789',),
    _star=False,  # call fn((d1, d2,), **kwargs)
)
# output: ('abc -->> 123', 'uvw -->> 456', 'xyz -->> 789')

By default .apply performs safety checks to ensure identical structure if multiple nested objects are given. If the arguments have identical structure by design, then these integrity checks may be turned off by specifying _safe=False. Please refer to the docs of plyr.apply.

plyr.ragged is a special version of .apply which implements leaf broadcasting semantics. When processing multiple nested objects it allows one structure to subsume the other structures: any intermediate leaf data is broadcasted deeper into the hierarchy of the other nested structures. Please refer to ./doc/mapping_structures.ipynb for details.

Serializing and deserializing

Serialization and deserialization of nested objects can be done by these procedures:

import plyr


def flatten(struct, *, flat=None):
    """Get a flat depth-first representation of the nested object.

    Parameters
    ----------
    struct : nested object
        The nested object to serialize.

    flat : list, or None
        A flat iterable container to serially append the leaf data to.

    Returns
    -------
    flat : list
        The container populated with the leaves in depth-first structure order.

    struct : nested object
        The skeletal structure of the nested object with arbitrary leaf data.
    """
    if not isinstance(flat, list):
        flat = []

    return flat, plyr.apply(flat.append, struct)


def unflatten(flat, struct, *, raises=True):
    """Place the data from iterable into the specified nested structure.

    Parameters
    ----------
    flat : iterable
        The iterable that supplies the data for the nested object in fifo
        depth-first order.

    struct : nested object
        The desired structure of the nested object (leaf data is intact).

    Returns
    -------
    result : nested object
        The nested object with the specified hierarchy populated by the data
        from the iterable.

    Details
    -------
    Raises `StopItertaion` if the iterable is exhausted before the structure is
    completely rebuilt. Data consumed by an unsuccessful `unflatten` is LOST.
    """
    # use `next(it, None)` to fill missing leaves with `None`-s.
    if not raises:
        return plyr.apply(lambda *a, it=iter(flat): next(it, None), struct)

    return plyr.apply(lambda *a, it=iter(flat): next(it), struct)
    # return plyr.ragged(lambda it, *a: next(it), iter(flat), struct)

The following snippet shows how to represent a given nested object as a flat list and then undo the process.

o = [1, (2, 3), {'a': (4, 5), 'z': {'a': 6}}, 7]

flat, skel = flatten(o)
assert o == unflatten(flat, skel)

flat
# output: ([1, 2, 3, 4, 5, 6, 7]

This example demonstrates how to unpack a stream of data into nested objects.

stream, _ = iter(range(13)), None

struct = ({'foo': _, 'bar': [_, _]}, _)

objects = [unflatten(stream, struct) for _ in range(3)]

Other Examples

Below we perform something fancy with numpy. Specifically, we stack outputs from some experiments (dicts of arrays), to get the standard deviation between the results.

import plyr
import numpy as np


# some computations
def experiment(j):
    return dict(
        a=float(np.random.normal()),
        u=np.random.normal(size=(5, 2)) * 0.1,
        z=np.random.normal(size=(2, 5)) * 10,
    )


# run 10 replications of an experiment
results = [experiment(j) for j in range(10)]

# stack and analyze the results (np.stack needs an iterable argument)
res = plyr.apply(np.stack, *results, axis=0, _star=False)

# get the shapes
shapes = plyr.apply(lambda x: x.shape, res)

# compute the std along the replication axis
plyr.apply(np.std, res, axis=0)

You may notice that .apply is very unsophisticated: it applies the specified function to the leaf data regardless of its type, and every dict, list, or tuple is always treated as a nested container.