mappingtools 0.7.0

MappingTools. Do stuff with Mappings and more

MappingTools

Do stuff with Mappings and more

This library provides utility functions for manipulating and transforming data structures which have or include Mapping-like characteristics. Including inverting dictionaries, converting class like objects to dictionaries, creating nested defaultdicts, and unwrapping complex objects.

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Overview

The MappingTools library is organized into several namespaces, each containing specific functionalities for manipulating and transforming data structures. Below is a brief description of the main namespaces within the library:

• collectors - This namespace contains classes and functions for collecting and categorizing data items into mappings.
  • AutoMapper - A Mapping-like class that automatically generates and assigns unique, minified strings values for any new keys accessed.
  • CategoryCounter - Extends a dictionary to count occurrences of data items categorized by multiple categories.
  • MappingCollector - Collects key-value pairs into an internal mapping based on different modes (ALL, COUNT, DISTINCT, FIRST, LAST).
  • MeteredDict - A dictionary that tracks changes made to it.
  • nested_defaultdict - Creates a nested defaultdict with specified depth and factory.
• operators - This namespace provides functions that perform operations on mappings.
  • distinct - Yields distinct values for a specified key across multiple mappings.
  • flattened - Converts a nested mapping structure into a single-level dictionary by flattening the keys into tuples.
  • inverse - generates an inverse Mapping by swapping keys and values.
  • keep - Yields subsets of mappings by retaining the specified keys.
  • remove - Yields subsets mappings with the specified keys removed.
  • stream - Generates items from a mapping, optionally applying a factory function to each key-value pair.
  • stream_dict_records - Generates dictionary records from a mapping with customizable key and value names.
• transformers - This namespace includes functions that reshape objects while maintaining the consistency of their structure.
  • listify - Transforms complex objects into a list of dictionaries with key and value pairs.
  • minify - The minify function is used to shorten the keys of an object using a specified alphabet.
  • simplify - Converts objects to strictly structured dictionaries.
  • strictify - Applies a strict structural conversion to an object using optional converters for keys and values.
  • stringify - Converts an object into a string representation by recursively processing it based on its type.

Examples

Collectors

Collectors are classes that collect data items into a Mapping.

AutoMapper

A Mapping that automatically generates and assigns unique, minified string values for any new keys accessed. The values are generated using the specified alphabet.

from mappingtools.collectors import AutoMapper

auto_mapper = AutoMapper()
print(auto_mapper['example_key'])
print(auto_mapper['another_key'])
print(auto_mapper['example_key'])
auto_mapper
# output:
# 'A'
# 'B'
# 'A'
# AutoMapper({'example_key': 'A', 'another_key': 'B'})
greek_auto_mapper = AutoMapper(alphabet='αβγ')
print(greek_auto_mapper['first'])
print(greek_auto_mapper['second'])
print(greek_auto_mapper['first'])
print(greek_auto_mapper['third'])
print(greek_auto_mapper['fourth'])
print(greek_auto_mapper['fifth'])
greek_auto_mapper
# output:
# 'α'
# 'β'
# 'γ'
# 'αα'
# 'αβ'
# AutoMapper({'first': 'α', 'second': 'β', 'third': 'γ', 'fourth': 'αα', 'fifth': 'αβ'})

CategoryCounter

The CategoryCounter class extends a dictionary to count occurrences of data items categorized by multiple categories. It maintains a total count of all data items and allows categorization using direct values or functions.

from mappingtools.collectors import CategoryCounter

counter = CategoryCounter()

for fruit in ['apple', 'banana', 'apple']:
    counter.update({fruit: 1}, type='fruit', char_count=len(fruit), unique_char_count=len(set(fruit)))

print(counter.total)
# Output: Counter({'apple': 2, 'banana': 1})

print(counter)
# output: CategoryCounter({'type': defaultdict(<class 'collections.Counter'>, {'fruit': Counter({'apple': 2, 'banana': 1})}), 'char_count': defaultdict(<class 'collections.Counter'>, {5: Counter({'apple': 2}), 6: Counter({'banana': 1})}), 'unique_char_count': defaultdict(<class 'collections.Counter'>, {4: Counter({'apple': 2}), 3: Counter({'banana': 1})})})

MappingCollector

A class designed to collect key-value pairs into an internal mapping based on different modes. It supports modes like ALL, COUNT, DISTINCT, FIRST, and LAST, each dictating how key-value pairs are collected.

from mappingtools.collectors import MappingCollector, MappingCollectorMode

collector = MappingCollector(MappingCollectorMode.ALL)
collector.add('a', 1)
collector.add('a', 2)
collector.collect([('b', 3), ('b', 4)])
print(collector.mapping)
# output: {'a': [1, 2], 'b': [3, 4]}

MeteredDict

A dictionary that tracks changes made to it.

from mappingtools.collectors import MeteredDict

metered_dict = MeteredDict()
metered_dict['a'] = 1
metered_dict['b'] = 2
_ = metered_dict['a']

metered_dict
# output: {'a': 1, 'b': 2}

metered_dict.summaries()
# output: {'a': {'get': {'count': 1, 'first': datetime.datetime(2025, 10, 26, 9, 3, 52, 347825, tzinfo=datetime.timezone.utc), 'last': datetime.datetime(2025, 10, 26, 9, 3, 52, 347825, tzinfo=datetime.timezone.utc), 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'get_default': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'set': {'count': 1, 'first': datetime.datetime(2025, 10, 26, 9, 3, 52, 347806, tzinfo=datetime.timezone.utc), 'last': datetime.datetime(2025, 10, 26, 9, 3, 52, 347806, tzinfo=datetime.timezone.utc), 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'set_default': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'pop': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}}, 'b': {'get': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'get_default': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'set': {'count': 1, 'first': datetime.datetime(2025, 10, 26, 9, 3, 52, 347820, tzinfo=datetime.timezone.utc), 'last': datetime.datetime(2025, 10, 26, 9, 3, 52, 347820, tzinfo=datetime.timezone.utc), 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'set_default': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}, 'pop': {'count': 0, 'first': None, 'last': None, 'duration': datetime.timedelta(0), 'frequency': 0.0}}}

nested_defaultdict

Creates a nested defaultdict with specified depth and factory.

from mappingtools.collectors import nested_defaultdict

nested_dd = nested_defaultdict(1, list)
nested_dd[0][1].append('value')
print(nested_dd)
# output: defaultdict(<function nested_defaultdict.<locals>.factory at ...>, {0: defaultdict(<function nested_defaultdict.<locals>.factory at ...>, {1: ['value']})})

Operators

Operators are functions that perform operations on Mappings.

distinct

Yields distinct values for a specified key across multiple mappings.

from mappingtools.operators import distinct

mappings = [
    {'a': 1, 'b': 2},
    {'a': 2, 'b': 3},
    {'a': 1, 'b': 4}
]
distinct_values = list(distinct('a', *mappings))
print(distinct_values)
# output: [1, 2]

flattened

The flattened function takes a nested mapping structure and converts it into a single-level dictionary by flattening the keys into tuples.

from mappingtools.operators import flatten

nested_dict = {
    'a': {'b': 1, 'c': {'d': 2}},
    'e': 3
}
flat_dict = flatten(nested_dict)
print(flat_dict)
# output: {('a', 'b'): 1, ('a', 'c', 'd'): 2, ('e',): 3}

inverse

Swaps keys and values in a dictionary.

from mappingtools.operators import inverse

original_mapping = {'a': {1, 2}, 'b': {3}}
inverted_mapping = inverse(original_mapping)
print(inverted_mapping)
# output: defaultdict(<class 'set'>, {1: {'a'}, 2: {'a'}, 3: {'b'}})

keep

Yields subsets of mappings by retaining only the specified keys.

from mappingtools.operators import keep

mappings = [
    {'a': 1, 'b': 2, 'c': 3},
    {'a': 4, 'b': 5, 'd': 6}
]
keys_to_keep = ['a', 'b']
output = list(keep(keys_to_keep, *mappings))
print(output)
# output: [{'a': 1, 'b': 2}, {'a': 4, 'b': 5}]

remove

Yields mappings with specified keys removed. It takes an iterable of keys and multiple mappings, and returns a generator of mappings with those keys excluded.

from mappingtools.operators import remove

mappings = [
    {'a': 1, 'b': 2, 'c': 3},
    {'a': 4, 'b': 5, 'd': 6}
]
keys_to_remove = ['a', 'b']
output = list(remove(keys_to_remove, *mappings))
print(output)
# output: [{'c': 3}, {'d': 6}]

stream

Takes a mapping and an optional item factory function, and generates items from the mapping. If the item factory is provided, it applies the factory to each key-value pair before yielding.

from collections import namedtuple

from mappingtools.operators import stream


def custom_factory(key, value):
    return f"{key}: {value}"


my_mapping = {'a': 1, 'b': 2, 'c': 3}

for item in stream(my_mapping, custom_factory):
    print(item)

# output:
# a: 1
# b: 2
# c: 3


MyTuple = namedtuple('MyTuple', ['key', 'value'])
data = {'a': 1, 'b': 2}

for item in stream(data, MyTuple):
    print(item)


# output:
# MyTuple(key='a', value=1)
# MyTuple(key='b', value=2)


def record(k, v):
    return {'key': k, 'value': v}


for item in stream(data, record):
    print(item)

# output:
# {'key': 'a', 'value': 1}
# {'key': 'b', 'value': 2}

stream_dict_records

generates dictionary records from a given mapping, where each record contains a key-value pair from the mapping with customizable key and value names.

from mappingtools.operators import stream_dict_records

mapping = {'a': 1, 'b': 2}
records = stream_dict_records(mapping, key_name='letter', value_name='number')
for record in records:
    print(record)
# output:
# {'letter': 'a', 'number': 1}
# {'letter': 'b', 'number': 2}

Transformers

Transformers are functions that reshape an object, while maintaining the consistency of the structure.

listify

Transforms complex objects into a list of dictionaries with key and value pairs.

from mappingtools.transformers import listify

wrapped_data = {'key1': {'subkey': 'value'}, 'key2': ['item1', 'item2']}
unwrapped_data = listify(wrapped_data)
print(unwrapped_data)
# output: [{'key': 'key1', 'value': [{'key': 'subkey', 'value': 'value'}]}, {'key': 'key2', 'value': ['item1', 'item2']}]

minify

The minify function is used to shorten the keys of an object using a specified alphabet. This function can be particularly useful for reducing the size of data structures, making them more efficient for storage or transmission.

from mappingtools.transformers import minify

data = [
    {
        'first_name': 'John',
        'last_name': 'Doe',
        'age': 30,
        'address': {
            'street': '123 Main St',
            'city': 'New York',
            'state': 'CA'
        }
    },
    {
        'first_name': 'Jane',
        'last_name': 'Smith',
        'age': 25,
        'address': {
            'street': '456 Rodeo Dr',
            'city': 'Los Angeles',
            'state': 'CA'
        }
    }
]

# Minify the dictionary keys
minified_dict = minify(data)

print(minified_dict)
# [{'A': 'John', 'B': 'Doe', 'C': 30, 'D': {'E': '123 Main St', 'F': 'New York', 'G': 'CA'}}, {'A': 'Jane', 'B': 'Smith', 'C': 25, 'D': {'E': '456 Rodeo Dr', 'F': 'Los Angeles', 'G': 'CA'}}]

simplify

Converts objects to strictly structured dictionaries.

from collections import Counter
from dataclasses import dataclass
from datetime import datetime
from typing import Mapping

from mappingtools.transformers import simplify

data = {'key1': 'value1', 'key2': ['item1', 'item2']}
simplified_data = simplify(data)
print(simplified_data)
# Output: {'key1': 'value1', 'key2': ['item1', 'item2']}

counter = Counter({'a': 1, 'b': 2})
print(counter)
# Output: Counter({'b': 2, 'a': 1})

simplified_counter = simplify(counter)
print(simplified_counter)


# output: {'a': 1, 'b': 2}


@dataclass
class SampleDataClass:
    a: int
    b: int
    aa: str
    bb: str
    c: list[int]
    d: Mapping
    e: datetime


sample_datetime = datetime(2024, 7, 22, 21, 42, 17, 314159)
sample_dataclass = SampleDataClass(1, 2, '11', '22', [1, 2], {'aaa': 111, 'bbb': '222'}, sample_datetime)
print(sample_dataclass)
# output: SampleDataClass(a=1, b=2, aa='11', bb='22', c=[1, 2], d={'aaa': 111, 'bbb': '222'}, e=datetime.datetime(2024, 7, 22, 21, 42, 17, 314159))

simplified_sample_dataclass = simplify(sample_dataclass)
print(simplified_sample_dataclass)
# output: {'a': 1, 'aa': '11', 'b': 2, 'bb': '22', 'c': [1, 2], 'd': {'aaa': 111, 'bbb': '222'}, 'e': datetime.datetime(2024, 7, 22, 21, 42, 17, 314159)}

strictify

Applies a strict structural conversion to an object using optional converters for keys and values.

Example 1

from mappingtools.transformers import strictify


def uppercase_key(key):
    return key.upper()


def double_value(value):
    return value * 2


data = {'a': 1, 'b': 2}
result = strictify(data, key_converter=uppercase_key, value_converter=double_value)
print(result)
# output: {'A': 2, 'B': 4}

Example 2

from mappingtools.transformers import strictify

stringify

Converts an object into a string representation by recursively processing it based on its type.

from mappingtools.transformers import stringify

data = {'key1': 'value1', 'key2': 'value2'}
result = stringify(data)

print(result)
# output: "key1=value1, key2=value2"

data = [1, 2, 3]
result = stringify(data)

print(result)
# output: "[1, 2, 3]"

Compatibility

This project exposes mappingtools._compat.UTC as a stable UTC tzinfo object that works across Python versions. On Python 3.11+ it maps to datetime.UTC; on earlier versions it maps to datetime.timezone.utc.

Use it in your code or tests when you need a timezone-aware UTC datetime:

from mappingtools._compat import UTC
from datetime import datetime

dt = datetime.now(tz=UTC)

Development

Ruff

ruff check src

ruff check tests

Test

Standard (cobertura) XML Coverage Report

python -m pytest tests --cov=src --cov-branch --doctest-modules --cov-report=xml

HTML Coverage Report

python -m pytest tests --cov=src --cov-branch --doctest-modules --cov-report=html