Metadata-Version: 2.4
Name: py_exp_calc
Version: 1.2.1
Summary: Python package for amplifying operations on main Python data structures.
Home-page: https://github.com/seoanezonjic/py_exp_calc
Author: seoanezonjic
Author-email: seoanezonjic@uma.es
License: MIT
Project-URL: Documentation, https://github.com/seoanezonjic/py_exp_calc
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst; charset=UTF-8
License-File: LICENSE.txt
Requires-Dist: importlib-metadata; python_version < "3.8"
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: statsmodels
Requires-Dist: py_cmdtabs
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"
Dynamic: license-file

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===========
py_exp_calc
===========


    Python package for amplifying operations on main Python data structures.


First, this package can enhance operations on various data structures such as lists, numpy arrays, and dictionaries. It allows not only an increase in the available operations within these data structures but also conversion between them. Some of the main operations are:

* Lists: Flattening, ensuring unique elements (uniq), adding tags to list elements and performing set operations such as intersection, union, and difference between lists.
* Dictionaries: Enhanced operations for nested dictionaries, such as the dig method from Ruby, addition and removal of key-value pairs and expanding dictionaries by adding values in list format.
* Numpy arrays: Matrix normalization and filtering.

Secondly, advanced data analysis operations have been added to the toolkit, including statistical analysis and clustering. To facilitate these operations, two command-line interfaces (CLIs) have been designed:

* clusterize: Applies hierarchical clustering to a given matrix.
* inference_analyzer: Executes inference analysis on a set of n observations divided into m categories.
