# MappingTools
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> 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.
<table>
<tr style="vertical-align: middle;">
<td>Package</td>
<td>
<img alt="PyPI - Version" class="off-glb" loading="lazy" src="https://img.shields.io/pypi/v/mappingtools.svg?logo=pypi&logoColor=lightblue">
<img alt="PyPI - Status" class="off-glb" loading="lazy" src="https://img.shields.io/pypi/status/mappingtools.svg?logo=pypi&logoColor=lightblue">
<img alt="PyPI - Python Version" class="off-glb" loading="lazy" src="https://img.shields.io/pypi/pyversions/mappingtools.svg?logo=python&label=Python&logoColor=lightblue">
<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dd/mappingtools.svg?logo=pypi&logoColor=lightblue">
<img alt="Libraries.io SourceRank" src="https://img.shields.io/librariesio/sourcerank/pypi/mappingtools.svg?logo=Libraries.io&label=SourceRank">
</td>
</tr>
<tr>
<td>Code</td>
<td>
<img alt="GitHub" src="https://img.shields.io/github/license/erivlis/mappingtools">
<img alt="GitHub repo size" src="https://img.shields.io/github/repo-size/erivlis/mappingtools.svg?label=Size&logo=git">
<img alt="GitHub last commit (by committer)" src="https://img.shields.io/github/last-commit/erivlis/mappingtools.svg?&logo=git">
<a href="https://github.com/erivlis/mappingtools/graphs/contributors"><img alt="Contributors" src="https://img.shields.io/github/contributors/erivlis/mappingtools.svg?&logo=git"></a>
</td>
</tr>
<tr>
<td>Tools</td>
<td>
<a href="https://www.jetbrains.com/pycharm/"><img alt="PyCharm" src="https://img.shields.io/badge/PyCharm-FCF84A.svg?logo=PyCharm&logoColor=black&labelColor=21D789&color=FCF84A"></a>
<a href="https://github.com/astral-sh/uv"><img alt="uv" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json" style="max-width:100%;"></a>
<a href="https://github.com/astral-sh/ruff"><img alt="Ruff" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" style="max-width:100%;"></a>
<!--a href="https://squidfunk.github.io/mkdocs-material/"><img src="https://img.shields.io/badge/Material_for_MkDocs-526CFE?&logo=MaterialForMkDocs&logoColor=white&labelColor=grey"></a-->
<a href="https://hatch.pypa.io"><img alt="Hatch project" class="off-glb" loading="lazy" src="https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg"></a>
</td>
</tr>
<tr>
<td>CI/CD</td>
<td>
<a href="https://github.com/erivlis/mappingtools/actions/workflows/test.yml"><img alt="Test" src="https://github.com/erivlis/mappingtools/actions/workflows/test.yml/badge.svg"></a>
<a href="https://github.com/erivlis/mappingtools/actions/workflows/publish.yml"><img alt="Publish" src="https://github.com/erivlis/mappingtools/actions/workflows/test-beta.yml/badge.svg"></a>
<a href="https://github.com/erivlis/mappingtools/actions/workflows/publish.yml"><img alt="Publish" src="https://github.com/erivlis/mappingtools/actions/workflows/publish.yml/badge.svg"></a>
<!--a href="https://github.com/erivlis/mappingtools/actions/workflows/publish-docs.yaml"><img alt="Publish Docs" src="https://github.com/erivlis/mappingtools/actions/workflows/publish-docs.yaml/badge.svg"></a-->
</td>
</tr>
<tr>
<td>Scans</td>
<td>
<a href="https://codecov.io/gh/erivlis/mappingtools"><img alt="Coverage" src="https://codecov.io/gh/erivlis/mappingtools/graph/badge.svg?token=POODT8M9NV"/></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Quality Gate Status" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=alert_status"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Security Rating" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=security_rating"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Maintainability Rating" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=sqale_rating"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Reliability Rating" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=reliability_rating"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Lines of Code" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=ncloc"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Vulnerabilities" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=vulnerabilities"></a>
<a href="https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools"><img alt="Bugs" src="https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=bugs"></a>
<a href="https://app.codacy.com/gh/erivlis/mappingtools/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade"><img alt="Codacy Quality" src="https://app.codacy.com/project/badge/Grade/8b83a99f939b4883ae2f37d7ec3419d1"></a>
<a href="https://app.codacy.com/gh/erivlis/mappingtools/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage"><img alt="Codacy Coverage" src="https://app.codacy.com/project/badge/Coverage/8b83a99f939b4883ae2f37d7ec3419d1"/></a>
<a href="https://www.codefactor.io/repository/github/erivlis/mappingtools/overview/main"><img src="https://www.codefactor.io/repository/github/erivlis/mappingtools/badge/main" alt="CodeFactor" /></a>
<a href="https://app.deepsource.com/gh/erivlis/mappingtools/" target="_blank"><img alt="DeepSource" title="DeepSource" src="https://app.deepsource.com/gh/erivlis/mappingtools.svg/?label=active+issues&show_trend=true&token=5hN3svCsdJtdULGTc68MngKa"/></a>
<a href="https://app.deepsource.com/gh/erivlis/mappingtools/" target="_blank"><img alt="DeepSource" title="DeepSource" src="https://app.deepsource.com/gh/erivlis/mappingtools.svg/?label=resolved+issues&show_trend=true&token=5hN3svCsdJtdULGTc68MngKa"/></a>
<a href="https://snyk.io/test/github/erivlis/mappingtools"><img alt="Snyk" src="https://snyk.io/test/github/erivlis/mappingtools/badge.svg"></a>
</td>
</tr>
</table>
## 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.
- **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).
- **MinifyingMapper** - A mapping-like map that shortens its keys using a specified alphabet.
- **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.
#### 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.
<!-- name: test_category_counter -->
```python
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.
<!-- name: test_mapping_collector -->
```python
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]}
```
#### MinifyingMapper
A mapping-like map that shortens its keys using a specified alphabet.
<!-- name: test_minifying_mapper -->
```python
from mappingtools.collectors import MinifyingMapper
# TBD
```
#### nested_defaultdict
Creates a nested defaultdict with specified depth and factory.
<!-- name: test_nested_defaultdict -->
```python
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
Operators are functions that perform operations on Mappings.
#### distinct
Yields distinct values for a specified key across multiple mappings.
<!-- name: test_distinct -->
```python
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.
<!-- name: test_flattened -->
```python
from mappingtools.operators import flattened
nested_dict = {
'a': {'b': 1, 'c': {'d': 2}},
'e': 3
}
flat_dict = flattened(nested_dict)
print(flat_dict)
# output: {('a', 'b'): 1, ('a', 'c', 'd'): 2, ('e',): 3}
```
#### inverse
Swaps keys and values in a dictionary.
<!-- name: test_inverse -->
```python
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.
<!-- name: test_keep -->
```python
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.
<!-- name: test_remove -->
```python
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.
<!-- name: test_stream -->
```python
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.
<!-- name: test_stream_dict_records -->
```python
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.
<!-- name: test_listify -->
```python
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.
<!-- name: test_minify -->
```python
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.
<!-- name: test_simplify -->
```python
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.
<!-- name: test_strictify -->
##### Example 1
```python
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}
```
#### `stringify`
#### stringify
Converts an object into a string representation by recursively processing it based on its type.
<!-- Name: test_stringify -->
```python
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]"
```
## Development
### Ruff
```shell
ruff check src
ruff check tests
```
### Test
#### Standard (cobertura) XML Coverage Report
```shell
python -m pytest tests -n auto --cov=src --cov-branch --doctest-modules --cov-report=xml
```
#### HTML Coverage Report
```shell
python -m pytest tests -n auto --cov=src --cov-branch --doctest-modules --cov-report=html
```
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"author": null,
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35.35 581.94 36.49Q581.23 37.63 581.40 39.40L582.33 48Q582.58 50.40 581.91 52.02Q581.25 53.65 579.79 54.59Q578.33 55.52 576.21 55.94Q574.10 56.35 571.50 56.35L571.50 52.23Q573.20 52.23 574.54 51.91Q575.88 51.60 576.59 50.79Q577.30 49.98 577.15 48.55L576.23 39.38Q575.98 36.92 576.61 35.42Q577.25 33.92 578.73 33.14Q580.20 32.35 582.43 32.02Q580.20 31.68 578.73 30.84Q577.25 30 576.61 28.50Q575.98 27 576.23 24.63L577.15 15.45Q577.30 14.02 576.59 13.23Q575.88 12.42 574.55 12.10Q573.23 11.77 571.50 11.77L571.50 7.65Z\"/></g></g></svg>\n\n> Do stuff with Mappings and more\n\nThis library provides utility functions for manipulating and transforming data structures which have or include\nMapping-like characteristics. Including inverting dictionaries, converting class like objects to dictionaries,\ncreating nested defaultdicts, and unwrapping complex objects.\n\n<table>\n <tr style=\"vertical-align: middle;\">\n <td>Package</td>\n <td>\n <img alt=\"PyPI - Version\" class=\"off-glb\" loading=\"lazy\" src=\"https://img.shields.io/pypi/v/mappingtools.svg?logo=pypi&logoColor=lightblue\">\n <img alt=\"PyPI - Status\" class=\"off-glb\" loading=\"lazy\" src=\"https://img.shields.io/pypi/status/mappingtools.svg?logo=pypi&logoColor=lightblue\">\n <img alt=\"PyPI - Python Version\" class=\"off-glb\" loading=\"lazy\" src=\"https://img.shields.io/pypi/pyversions/mappingtools.svg?logo=python&label=Python&logoColor=lightblue\">\n <img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dd/mappingtools.svg?logo=pypi&logoColor=lightblue\">\n <img alt=\"Libraries.io SourceRank\" src=\"https://img.shields.io/librariesio/sourcerank/pypi/mappingtools.svg?logo=Libraries.io&label=SourceRank\">\n </td>\n </tr>\n <tr>\n <td>Code</td>\n <td>\n <img alt=\"GitHub\" src=\"https://img.shields.io/github/license/erivlis/mappingtools\">\n <img alt=\"GitHub repo size\" src=\"https://img.shields.io/github/repo-size/erivlis/mappingtools.svg?label=Size&logo=git\">\n <img alt=\"GitHub last commit (by committer)\" src=\"https://img.shields.io/github/last-commit/erivlis/mappingtools.svg?&logo=git\">\n <a href=\"https://github.com/erivlis/mappingtools/graphs/contributors\"><img alt=\"Contributors\" src=\"https://img.shields.io/github/contributors/erivlis/mappingtools.svg?&logo=git\"></a>\n </td>\n </tr>\n <tr>\n <td>Tools</td>\n <td>\n <a href=\"https://www.jetbrains.com/pycharm/\"><img alt=\"PyCharm\" src=\"https://img.shields.io/badge/PyCharm-FCF84A.svg?logo=PyCharm&logoColor=black&labelColor=21D789&color=FCF84A\"></a>\n <a href=\"https://github.com/astral-sh/uv\"><img alt=\"uv\" src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json\" style=\"max-width:100%;\"></a>\n <a href=\"https://github.com/astral-sh/ruff\"><img alt=\"Ruff\" src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\" style=\"max-width:100%;\"></a>\n <!--a href=\"https://squidfunk.github.io/mkdocs-material/\"><img src=\"https://img.shields.io/badge/Material_for_MkDocs-526CFE?&logo=MaterialForMkDocs&logoColor=white&labelColor=grey\"></a-->\n <a href=\"https://hatch.pypa.io\"><img alt=\"Hatch project\" class=\"off-glb\" loading=\"lazy\" src=\"https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg\"></a>\n </td>\n </tr>\n <tr>\n <td>CI/CD</td>\n <td>\n <a href=\"https://github.com/erivlis/mappingtools/actions/workflows/test.yml\"><img alt=\"Test\" src=\"https://github.com/erivlis/mappingtools/actions/workflows/test.yml/badge.svg\"></a>\n <a href=\"https://github.com/erivlis/mappingtools/actions/workflows/publish.yml\"><img alt=\"Publish\" src=\"https://github.com/erivlis/mappingtools/actions/workflows/test-beta.yml/badge.svg\"></a>\n <a href=\"https://github.com/erivlis/mappingtools/actions/workflows/publish.yml\"><img alt=\"Publish\" src=\"https://github.com/erivlis/mappingtools/actions/workflows/publish.yml/badge.svg\"></a>\n <!--a href=\"https://github.com/erivlis/mappingtools/actions/workflows/publish-docs.yaml\"><img alt=\"Publish Docs\" src=\"https://github.com/erivlis/mappingtools/actions/workflows/publish-docs.yaml/badge.svg\"></a-->\n </td>\n </tr>\n <tr>\n <td>Scans</td>\n <td>\n <a href=\"https://codecov.io/gh/erivlis/mappingtools\"><img alt=\"Coverage\" src=\"https://codecov.io/gh/erivlis/mappingtools/graph/badge.svg?token=POODT8M9NV\"/></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Quality Gate Status\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=alert_status\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Security Rating\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=security_rating\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Maintainability Rating\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=sqale_rating\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Reliability Rating\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=reliability_rating\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Lines of Code\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=ncloc\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Vulnerabilities\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=vulnerabilities\"></a>\n <a href=\"https://sonarcloud.io/summary/new_code?id=erivlis_mappingtools\"><img alt=\"Bugs\" src=\"https://sonarcloud.io/api/project_badges/measure?project=erivlis_mappingtools&metric=bugs\"></a>\n <a href=\"https://app.codacy.com/gh/erivlis/mappingtools/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade\"><img alt=\"Codacy Quality\" src=\"https://app.codacy.com/project/badge/Grade/8b83a99f939b4883ae2f37d7ec3419d1\"></a>\n <a href=\"https://app.codacy.com/gh/erivlis/mappingtools/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage\"><img alt=\"Codacy Coverage\" src=\"https://app.codacy.com/project/badge/Coverage/8b83a99f939b4883ae2f37d7ec3419d1\"/></a>\n <a href=\"https://www.codefactor.io/repository/github/erivlis/mappingtools/overview/main\"><img src=\"https://www.codefactor.io/repository/github/erivlis/mappingtools/badge/main\" alt=\"CodeFactor\" /></a>\n <a href=\"https://app.deepsource.com/gh/erivlis/mappingtools/\" target=\"_blank\"><img alt=\"DeepSource\" title=\"DeepSource\" src=\"https://app.deepsource.com/gh/erivlis/mappingtools.svg/?label=active+issues&show_trend=true&token=5hN3svCsdJtdULGTc68MngKa\"/></a>\n <a href=\"https://app.deepsource.com/gh/erivlis/mappingtools/\" target=\"_blank\"><img alt=\"DeepSource\" title=\"DeepSource\" src=\"https://app.deepsource.com/gh/erivlis/mappingtools.svg/?label=resolved+issues&show_trend=true&token=5hN3svCsdJtdULGTc68MngKa\"/></a>\n <a href=\"https://snyk.io/test/github/erivlis/mappingtools\"><img alt=\"Snyk\" src=\"https://snyk.io/test/github/erivlis/mappingtools/badge.svg\"></a>\n </td>\n </tr>\n</table>\n\n## Overview\n\nThe **_MappingTools_** library is organized into several namespaces, each containing specific functionalities for\nmanipulating and transforming data structures. Below is a brief description of the main namespaces within the library:\n\n- **collectors** - This namespace contains classes and functions for collecting and categorizing data items into\n mappings.\n - **CategoryCounter** - Extends a dictionary to count occurrences of data items categorized by multiple categories.\n - **MappingCollector** - Collects key-value pairs into an internal mapping based on different modes (ALL, COUNT,\n DISTINCT, FIRST, LAST).\n - **MinifyingMapper** - A mapping-like map that shortens its keys using a specified alphabet.\n - **nested_defaultdict** - Creates a nested `defaultdict` with specified depth and factory.\n- **operators** - This namespace provides functions that perform operations on mappings.\n - **distinct** - Yields distinct values for a specified key across multiple mappings.\n - **flattened** - Converts a nested mapping structure into a single-level dictionary by flattening the keys into\n tuples.\n - **inverse** - generates an inverse Mapping by swapping keys and values.\n - **keep** - Yields subsets of mappings by retaining the specified keys.\n - **remove** - Yields subsets mappings with the specified keys removed.\n - **stream** - Generates items from a mapping, optionally applying a factory function to each key-value pair.\n - **stream_dict_records** - Generates dictionary records from a mapping with customizable key and value names.\n- **transformers** - This namespace includes functions that reshape objects while maintaining the consistency of their\n structure.\n - **listify** - Transforms complex objects into a list of dictionaries with key and value pairs.\n - **minify** - The minify function is used to shorten the keys of an object using a specified alphabet.\n - **simplify** - Converts objects to strictly structured dictionaries.\n - **strictify** - Applies a strict structural conversion to an object using optional converters for keys and values.\n - **stringify** - Converts an object into a string representation by recursively processing it based on its type.\n\n## Examples\n\n### Collectors\n\nCollectors are classes that collect data items into a Mapping.\n\n#### CategoryCounter\n\nThe CategoryCounter class extends a dictionary to count occurrences of data items categorized by multiple categories.\nIt maintains a total count of all data items and allows categorization using direct values or functions.\n\n<!-- name: test_category_counter -->\n\n```python\nfrom mappingtools.collectors import CategoryCounter\n\ncounter = CategoryCounter()\n\nfor fruit in ['apple', 'banana', 'apple']:\n counter.update({fruit: 1}, type='fruit', char_count=len(fruit), unique_char_count=len(set(fruit)))\n\nprint(counter.total)\n# Output: Counter({'apple': 2, 'banana': 1})\n\nprint(counter)\n# 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})})})\n```\n\n#### MappingCollector\n\nA class designed to collect key-value pairs into an internal mapping based on different modes.\nIt supports modes like ALL, COUNT, DISTINCT, FIRST, and LAST, each dictating how key-value pairs are\ncollected.\n\n<!-- name: test_mapping_collector -->\n\n```python\nfrom mappingtools.collectors import MappingCollector, MappingCollectorMode\n\ncollector = MappingCollector(MappingCollectorMode.ALL)\ncollector.add('a', 1)\ncollector.add('a', 2)\ncollector.collect([('b', 3), ('b', 4)])\nprint(collector.mapping)\n# output: {'a': [1, 2], 'b': [3, 4]}\n```\n\n#### MinifyingMapper\n\nA mapping-like map that shortens its keys using a specified alphabet.\n\n<!-- name: test_minifying_mapper -->\n\n```python\nfrom mappingtools.collectors import MinifyingMapper\n\n# TBD\n```\n\n#### nested_defaultdict\n\nCreates a nested defaultdict with specified depth and factory.\n\n<!-- name: test_nested_defaultdict -->\n\n```python\nfrom mappingtools.collectors import nested_defaultdict\n\nnested_dd = nested_defaultdict(1, list)\nnested_dd[0][1].append('value')\nprint(nested_dd)\n# output: defaultdict(<function nested_defaultdict.<locals>.factory at ...>, {0: defaultdict(<function nested_defaultdict.<locals>.factory at ...>, {1: ['value']})})\n```\n\n### `Operators`\n### Operators\n\nOperators are functions that perform operations on Mappings.\n\n#### distinct\n\nYields distinct values for a specified key across multiple mappings.\n\n<!-- name: test_distinct -->\n\n```python\nfrom mappingtools.operators import distinct\n\nmappings = [\n {'a': 1, 'b': 2},\n {'a': 2, 'b': 3},\n {'a': 1, 'b': 4}\n]\ndistinct_values = list(distinct('a', *mappings))\nprint(distinct_values)\n# output: [1, 2]\n```\n\n#### flattened\n\nThe flattened function takes a nested mapping structure and converts it into a single-level dictionary by flattening the\nkeys into tuples.\n\n<!-- name: test_flattened -->\n\n```python\nfrom mappingtools.operators import flattened\n\nnested_dict = {\n 'a': {'b': 1, 'c': {'d': 2}},\n 'e': 3\n}\nflat_dict = flattened(nested_dict)\nprint(flat_dict)\n# output: {('a', 'b'): 1, ('a', 'c', 'd'): 2, ('e',): 3}\n```\n\n#### inverse\n\nSwaps keys and values in a dictionary.\n\n<!-- name: test_inverse -->\n\n```python\nfrom mappingtools.operators import inverse\n\noriginal_mapping = {'a': {1, 2}, 'b': {3}}\ninverted_mapping = inverse(original_mapping)\nprint(inverted_mapping)\n# output: defaultdict(<class 'set'>, {1: {'a'}, 2: {'a'}, 3: {'b'}})\n```\n\n#### keep\n\nYields subsets of mappings by retaining only the specified keys.\n\n<!-- name: test_keep -->\n\n```python\nfrom mappingtools.operators import keep\n\nmappings = [\n {'a': 1, 'b': 2, 'c': 3},\n {'a': 4, 'b': 5, 'd': 6}\n]\nkeys_to_keep = ['a', 'b']\noutput = list(keep(keys_to_keep, *mappings))\nprint(output)\n# output: [{'a': 1, 'b': 2}, {'a': 4, 'b': 5}]\n```\n\n#### remove\n\nYields mappings with specified keys removed. It takes an iterable of keys and multiple mappings, and returns a generator\nof mappings with those keys excluded.\n\n<!-- name: test_remove -->\n\n```python\nfrom mappingtools.operators import remove\n\nmappings = [\n {'a': 1, 'b': 2, 'c': 3},\n {'a': 4, 'b': 5, 'd': 6}\n]\nkeys_to_remove = ['a', 'b']\noutput = list(remove(keys_to_remove, *mappings))\nprint(output)\n# output: [{'c': 3}, {'d': 6}]\n```\n\n#### stream\n\nTakes a mapping and an optional item factory function, and generates items from the mapping.\nIf the item factory is provided, it applies the factory to each key-value pair before yielding.\n\n<!-- name: test_stream -->\n\n```python\nfrom collections import namedtuple\n\nfrom mappingtools.operators import stream\n\n\ndef custom_factory(key, value):\n return f\"{key}: {value}\"\n\n\nmy_mapping = {'a': 1, 'b': 2, 'c': 3}\n\nfor item in stream(my_mapping, custom_factory):\n print(item)\n\n# output:\n# a: 1\n# b: 2\n# c: 3\n\n\nMyTuple = namedtuple('MyTuple', ['key', 'value'])\ndata = {'a': 1, 'b': 2}\n\nfor item in stream(data, MyTuple):\n print(item)\n\n\n# output:\n# MyTuple(key='a', value=1)\n# MyTuple(key='b', value=2)\n\n\ndef record(k, v):\n return {'key': k, 'value': v}\n\n\nfor item in stream(data, record):\n print(item)\n\n# output:\n# {'key': 'a', 'value': 1}\n# {'key': 'b', 'value': 2}\n```\n\n#### stream_dict_records\n\ngenerates dictionary records from a given mapping, where each record contains a key-value pair from the mapping with\ncustomizable key and value names.\n\n<!-- name: test_stream_dict_records -->\n\n```python\nfrom mappingtools.operators import stream_dict_records\n\nmapping = {'a': 1, 'b': 2}\nrecords = stream_dict_records(mapping, key_name='letter', value_name='number')\nfor record in records:\n print(record)\n# output:\n# {'letter': 'a', 'number': 1}\n# {'letter': 'b', 'number': 2}\n```\n\n### Transformers\n\nTransformers are functions that reshape an object, while maintaining the consistency of the structure.\n\n#### listify\n\nTransforms complex objects into a list of dictionaries with key and value pairs.\n\n<!-- name: test_listify -->\n\n```python\nfrom mappingtools.transformers import listify\n\nwrapped_data = {'key1': {'subkey': 'value'}, 'key2': ['item1', 'item2']}\nunwrapped_data = listify(wrapped_data)\nprint(unwrapped_data)\n# output: [{'key': 'key1', 'value': [{'key': 'subkey', 'value': 'value'}]}, {'key': 'key2', 'value': ['item1', 'item2']}]\n```\n\n#### minify\n\nThe minify function is used to shorten the keys of an object using a specified alphabet.\nThis function can be particularly useful for reducing the size of data structures, making\nthem more efficient for storage or transmission.\n\n<!-- name: test_minify -->\n\n```python\nfrom mappingtools.transformers import minify\n\ndata = [\n {\n 'first_name': 'John',\n 'last_name': 'Doe',\n 'age': 30,\n 'address': {\n 'street': '123 Main St',\n 'city': 'New York',\n 'state': 'CA'\n }\n },\n {\n 'first_name': 'Jane',\n 'last_name': 'Smith',\n 'age': 25,\n 'address': {\n 'street': '456 Rodeo Dr',\n 'city': 'Los Angeles',\n 'state': 'CA'\n }\n }\n]\n\n# Minify the dictionary keys\nminified_dict = minify(data)\n\nprint(minified_dict)\n# [{'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'}}]\n```\n\n#### simplify\n\nConverts objects to strictly structured dictionaries.\n\n<!-- name: test_simplify -->\n\n```python\nfrom collections import Counter\nfrom dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import Mapping\n\nfrom mappingtools.transformers import simplify\n\ndata = {'key1': 'value1', 'key2': ['item1', 'item2']}\nsimplified_data = simplify(data)\nprint(simplified_data)\n# Output: {'key1': 'value1', 'key2': ['item1', 'item2']}\n\ncounter = Counter({'a': 1, 'b': 2})\nprint(counter)\n# Output: Counter({'b': 2, 'a': 1})\n\nsimplified_counter = simplify(counter)\nprint(simplified_counter)\n\n\n# output: {'a': 1, 'b': 2}\n\n\n@dataclass\nclass SampleDataClass:\n a: int\n b: int\n aa: str\n bb: str\n c: list[int]\n d: Mapping\n e: datetime\n\n\nsample_datetime = datetime(2024, 7, 22, 21, 42, 17, 314159)\nsample_dataclass = SampleDataClass(1, 2, '11', '22', [1, 2], {'aaa': 111, 'bbb': '222'}, sample_datetime)\nprint(sample_dataclass)\n# 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))\n\nsimplified_sample_dataclass = simplify(sample_dataclass)\nprint(simplified_sample_dataclass)\n# 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)}\n```\n\n#### strictify\n\nApplies a strict structural conversion to an object using optional converters for keys and values.\n\n<!-- name: test_strictify -->\n\n##### Example 1\n\n```python\nfrom mappingtools.transformers import strictify\n\n\ndef uppercase_key(key):\n return key.upper()\n\n\ndef double_value(value):\n return value * 2\n\n\ndata = {'a': 1, 'b': 2}\nresult = strictify(data, key_converter=uppercase_key, value_converter=double_value)\nprint(result)\n# output: {'A': 2, 'B': 4}\n```\n\n#### `stringify`\n#### stringify\n\nConverts an object into a string representation by recursively processing it based on its type.\n\n<!-- Name: test_stringify -->\n\n```python\nfrom mappingtools.transformers import stringify\n\ndata = {'key1': 'value1', 'key2': 'value2'}\nresult = stringify(data)\n\nprint(result)\n# output: \"key1=value1, key2=value2\"\n\ndata = [1, 2, 3]\nresult = stringify(data)\n\nprint(result)\n# output: \"[1, 2, 3]\"\n```\n\n## Development\n\n### Ruff\n\n```shell\nruff check src\n\nruff check tests\n```\n\n### Test\n\n#### Standard (cobertura) XML Coverage Report\n\n```shell\npython -m pytest tests -n auto --cov=src --cov-branch --doctest-modules --cov-report=xml\n```\n\n#### HTML Coverage Report\n\n```shell\npython -m pytest tests -n auto --cov=src --cov-branch --doctest-modules --cov-report=html\n```\n",
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