Metadata-Version: 2.1
Name: convtools
Version: 0.42.3
Summary: convtools allows to define and reuse conversions for processing collections and csv tables, complex aggregations and joins.
Author: Nikita Almakov
Author-email: nikita.almakov@gmail.com
License: mit
Project-URL: Documentation, https://convtools.readthedocs.io/en/latest/
Project-URL: Source, https://github.com/westandskif/convtools
Project-URL: Bug Reports, https://github.com/westandskif/convtools/issues
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt

# convtools

**convtools** is a specialized Python library designed for defining data
transformations dynamically using a declarative approach. It automatically
generates custom Python code for the user in the background.

### ChatGPT's take on it :)

	Introducing ConvTools, the revolutionary Python library for dynamic data
	transformations! With its declarative approach, transforming data has never
	been easier. Say goodbye to manual coding, ConvTools automatically
	generates the code for you, saving you time and effort. Whether you're
	working with large datasets or need to make real-time updates, ConvTools is
	the solution you've been searching for. Get ready to experience the power
	of efficient and effortless data transformation!

[![License](https://img.shields.io/github/license/westandskif/convtools.svg)](https://github.com/westandskif/convtools/blob/master/LICENSE.txt)
[![codecov](https://codecov.io/gh/westandskif/convtools/branch/master/graph/badge.svg)]( https://codecov.io/gh/westandskif/convtools)
[![Tests status](https://github.com/westandskif/convtools/workflows/tests/badge.svg)](https://github.com/westandskif/convtools/actions/workflows/pytest.yml)
[![Docs status](https://readthedocs.org/projects/convtools/badge/?version=latest)](https://convtools.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://badge.fury.io/py/convtools.svg)](https://pypi.org/project/convtools/)
[![Twitter](https://img.shields.io/twitter/url?label=convtools&style=social&url=https%3A%2F%2Ftwitter.com%2Fconvtools)](https://twitter.com/convtools)
[![Downloads](https://static.pepy.tech/badge/convtools)](https://pepy.tech/project/convtools)
[![Python versions](https://img.shields.io/pypi/pyversions/convtools.svg)](https://pypi.org/project/convtools/)

____

## Installation

`pip install convtools`

## Documentation

**[convtools.readthedocs.io](https://convtools.readthedocs.io/en/latest/)**


## Group by example

```python
from convtools import conversion as c

input_data = [
    {"a": 5, "b": "foo"},
    {"a": 10, "b": "foo"},
    {"a": 10, "b": "bar"},
    {"a": 10, "b": "bar"},
    {"a": 20, "b": "bar"},
]

conv = (
    c.group_by(c.item("b"))
    .aggregate(
        {
            "b": c.item("b"),
            "a_first": c.ReduceFuncs.First(c.item("a")),
            "a_max": c.ReduceFuncs.Max(c.item("a")),
        }
    )
    .pipe(
        c.aggregate({
            "b_values": c.ReduceFuncs.Array(c.item("b")),
            "mode_a_first": c.ReduceFuncs.Mode(c.item("a_first")),
            "median_a_max": c.ReduceFuncs.Median(c.item("a_max")),
        })
    )
    .gen_converter()
)

assert conv(input_data) == {
    'b_values': ['foo', 'bar'],
    'mode_a_first': 10,
    'median_a_max': 15.0
}

```

##### Built-in reducers like `c.ReduceFuncs.First`
    * Sum
    * SumOrNone
    * Max
    * MaxRow
    * Min
    * MinRow
    * Count
    * CountDistinct
    * First
    * Last
    * Average
    * Median
    * Percentile
    * Mode
    * TopK
    * Array
    * ArrayDistinct
    * ArraySorted

    DICT REDUCERS ARE IN FACT AGGREGATIONS THEMSELVES, BECAUSE VALUES GET REDUCED.
    * Dict
    * DictArray
    * DictSum
    * DictSumOrNone
    * DictMax
    * DictMin
    * DictCount
    * DictCountDistinct
    * DictFirst
    * DictLast

    AND LASTLY YOU CAN DEFINE YOUR OWN REDUCER BY PASSING ANY REDUCE FUNCTION
    OF TWO ARGUMENTS TO ``c.reduce``.


## What's the point if there are tools like Pandas / Polars?

* convtools doesn't need to wrap data in a container to provide functionality,
  it simply runs the python code it generates on **any input**
* convtools is lightweight (_though optional `black` is highly recommended for
  pretty-printing generated code out of curiosity_)
* convtools fosters building pipelines on top of iterators, allowing for stream
  processing
* convtools supports nested aggregations
* convtools is a set of primitives for code generation, so it's just different.

## Reporting a Security Vulnerability

See the [security policy](https://github.com/westandskif/convtools/security/policy).
