handpick 0.7.2

Inspect nested data structures.

Handpick

Handpick is a tool to traverse nested data structures and pick all objects that meet certain criteria.

The pick function

The pick generator function is the main component of the package. It performs the recursive traversal of a (presumably nested) data structure and applies the picking criteria provided in the form of a predicate function (see below for various examples). Picked objects are retrieved lazily by a generator.

Simple predicate functions

The predicate function is passed to pick as the second positional argument. In simple cases, lambda functions can be used as predicates. For example:

from handpick import pick

data = [[1, 'Py'], [2, ['', 3.0]], 4]

two_or_more = pick(data, lambda n: n >= 2)
non_empty_strings = pick(data, lambda s: isinstance(s, str) and s)

>>> list(two_or_more)
[2, 3.0, 4]
>>> list(non_empty_strings)
['Py']

Non-callable predicates

If predicate is not callable, equality will be used as the picking criteria. For example:

from handpick import pick

data = [1, [1.0, [2, 1.]], [{'1': 1}, [3]]]

ones = pick(data, 1)    # equivalent to pick(data, lambda n: n == 1)

>>> list(ones)
[1, 1.0, 1.0, 1]

Handling dictionary keys

When inspecting mappings (dictionaries), you can configure whether or not pick will inspect dictionary keys by specifying the dict_keys keyword argument. Default is False, which means only values will be inspected. For example:

from handpick import pick

data = {'key': {'name': 'foo'}, '_key': {'_name': '_bar'}}

default = pick(data, lambda s: s.startswith('_'))
keys_included = pick(data, lambda s: s.startswith('_'), dict_keys=True)

>>> list(default)
['_bar']
>>> list(keys_included)
['_key', '_name', '_bar']

Predicates

The predicate decorator

The predicate decorator wraps a function in a object that can be combined with other predicates using the operators & (and) and | (or), as well as negated using the operator ~ (not).

Combining predicates

For example:

from handpick import pick, predicate

@predicate
def is_int(n):
    return isinstance(n, int)

@predicate
def is_even(n):
    return n % 2 == 0

data = [[4, [5.0, 1], 3.0], [[15, []], {17: [7, [8], 0]}]]

# compound predicate
non_even_int = is_int & ~is_even

odd_integers = pick(data, non_even_int)

>>> list(odd_integers)
[1, 15, 7]

Combining predicates with functions

In addition, the & and | operations are supported between predicates and regular undecorated functions. For example:

from handpick import pick, predicate

@predicate
def is_list(obj):
    return isinstance(obj, list)

data = [('1', [2]), {('x',): [(3, [4]), '5']}, ['x', ['6']], {7: ('x',)}]

# compound predicate
short_list = (lambda obj: len(obj) < 2) & is_list

short_lists = pick(data, short_list)

>>> list(short_lists)
[[2], [4], ['6']]

Built-in predicates

Handpick provides some predefined predicates to be used in common scenarios. For example:

from handpick import pick, ALL, NO_CONTAINERS

data = [[], [0], [['1'], b'2']]

# pick all objects encountered during recursive traversal of data
everything = pick(data, ALL)

# pick only objects that are not containers of other objects
only_values = pick(data, NO_CONTAINERS)

>>> list(everything)
[[], [0], 0, [['1'], b'2'], ['1'], '1', b'2']
>>> list(only_values)
[0, '1', b'2']

Note: Strings and bytes-like objects are not regarded as containers of other objects by the NO_CONTAINERS built-in predicate.

Predicate factories

The is_type and not_type functions can be used to create predicates based on an object’s type. For example:

from handpick import pick, is_type, not_type

data = [[1.0, [2, True]], [False, [3]], ['4', {5, True}]]

integers_only = pick(data, is_type(int) & not_type(bool))

>>> list(integers_only)
[2, 3, 5]

Useful functions

The flat function

This function can be used to flatten a nested data structure. Values are retrieved lazily by a generator. For example:

from handpick import flat

data = [[], [0], [[[], 1], [2, [3, [4]], []], [5]]]
flat_data = flat(data)

>>> list(flat_data)
[0, 1, 2, 3, 4, 5]

When flattening a mapping (dictionary), only its values are inspected. For example:

>>> list(flat({1: 2, 3: {4: 5}}))
[2, 5]

Note: flat(data) is a shortcut for pick(data, NO_CONTAINERS).

The max_depth function

This function returns the maximum nested depth of a data structure. For example:

from handpick import max_depth

nested_list = [0, [1, [2]]]
nested_dict = {0: {1: {2: {3: {4: 4}}}}}

>>> max_depth(nested_list)
2
>>> max_depth(nested_dict)
4

Note: Empty containers constitute a level of nested depth as well as non-empty ones. For example:

>>> max_depth([0, [1, []]])
2

API reference

handpick.pick(data, predicate, dict_keys=False, strings=False, bytes_like=False)

Pick objects from data based on predicate.

Traverse data recursively and yield all objects for which predicate(obj) is true.

data should be an iterable container.

predicate should be a callable taking one argument and returning a Boolean value. If predicate is not callable, equality will be used as the picking criteria, i.e. objects for which obj == predicate is true will be yielded.

When traversing a mapping, only its values are inspected by default. If dict_keys is set to True, both keys and values of the mapping are inspected.

By default, strings are not regarded as containers of other objects and therefore not visited by the recursive algorithm. This can be changed by setting strings to True. Strings of length 0 or 1 are never visited.

By default, bytes-like sequences (bytes and bytearrays) are not regarded as containers of other objects and therefore not visited by the recursive algorithm. This can be changed by setting bytes_like to True.

@handpick.predicate(func)

Decorator wrapping a function with a predicate object.

The decorated function can be combined with other predicates using the operators & (and) and | (or), as well as negated using the operator ~ (not).

Predicate objects are intended to be used as the predicate argument to the pick function.

handpick.ALL

Predicate that returns True for all objects.

handpick.NO_CONTAINERS

Predicate that returns False for all iterable objects except strings and bytes-like objects.

handpick.NO_LIST_DICT

Predicate that returns False for instances of list and dict.

handpick.is_type(type_or_types)

Predicate factory. Return a predicate that returns True if object is an instance of specified type(s).

type_or_types must be a type or tuple of types.

handpick.not_type(type_or_types)

Predicate factory. Return a predicate that returns True if object is not an instance of specified type(s).

type_or_types must be a type or tuple of types.

handpick.flat(data)

Flatten data.

Yield a sequence of objects from a (presumably nested) data structure data. Only non-iterable objects, strings and bytes-like objects are yielded.

When traversing a mapping, only its values are inspected.

handpick.max_depth(data)

Return maximum nested depth of data.

data should be an iterable container. Depth is counted from zero, i.e. the direct elements of data are in depth 0.