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@ -20,7 +20,7 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
- [Command-line Interface Development](#command-line-interface-development)
- [Command-line Tools](#command-line-tools)
- [Computer Vision](#computer-vision)
- [Configuration](#configuration)
- [Configuration Files](#configuration-files)
- [Cryptography](#cryptography)
- [Data Analysis](#data-analysis)
- [Data Validation](#data-validation)
@ -248,7 +248,6 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
* [mypy](https://github.com/python/mypy) - Check variable types during compile time.
* [pyre-check](https://github.com/facebook/pyre-check) - Performant type checking.
* [typeshed](https://github.com/python/typeshed) - Collection of library stubs for Python, with static types.
* [pydantic](https://github.com/pydantic/pydantic) - Data validation using Python type hints.
* Static Type Annotations Generators
* [MonkeyType](https://github.com/Instagram/MonkeyType) - A system for Python that generates static type annotations by collecting runtime types.
* [pytype](https://github.com/google/pytype) - Pytype checks and infers types for Python code - without requiring type annotations.
@ -302,45 +301,41 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php).
* [pytesseract](https://github.com/madmaze/pytesseract) - A wrapper for [Google Tesseract OCR](https://github.com/tesseract-ocr).
* [tesserocr](https://github.com/sirfz/tesserocr) - Another simple, Pillow-friendly, wrapper around the `tesseract-ocr` API for OCR.
## Configuration
## Configuration Files
*Libraries for storing and parsing configuration options.*
* [configobj](https://github.com/DiffSK/configobj) - INI file parser with validation.
* [configparser](https://docs.python.org/3/library/configparser.html) - (Python standard library) INI file parser.
* [configobj](https://github.com/DiffSK/configobj) - INI file parser with validation.
* [hydra](https://github.com/facebookresearch/hydra) - Hydra is a framework for elegantly configuring complex applications.
* [profig](https://profig.readthedocs.io/en/latest/) - Config from multiple formats with value conversion.
* [python-decouple](https://github.com/henriquebastos/python-decouple) - Strict separation of settings from code.
* [python-decouple](https://github.com/HBNetwork/python-decouple) - Strict separation of settings from code.
## Cryptography
* [cryptography](https://cryptography.io/en/latest/) - A package designed to expose cryptographic primitives and recipes to Python developers.
* [cryptography](https://github.com/pyca/cryptography) - A package designed to expose cryptographic primitives and recipes to Python developers.
* [paramiko](https://github.com/paramiko/paramiko) - The leading native Python SSHv2 protocol library.
* [passlib](https://passlib.readthedocs.io/en/stable/) - Secure password storage/hashing library, very high level.
* [pynacl](https://github.com/pyca/pynacl) - Python binding to the Networking and Cryptography (NaCl) library.
## Data Analysis
*Libraries for data analyzing.*
* [AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) - Pandas on AWS.
* [Blaze](https://github.com/blaze/blaze) - NumPy and Pandas interface to Big Data.
* [Open Mining](https://github.com/mining/mining) - Business Intelligence (BI) in Pandas interface.
* [Optimus](https://github.com/ironmussa/Optimus) - Agile Data Science Workflows made easy with PySpark.
* [Orange](https://orange.biolab.si/) - Data mining, data visualization, analysis and machine learning through visual programming or scripts.
* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
* [pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
* [aws-sdk-pandas](https://github.com/aws/aws-sdk-pandas) - Pandas on AWS.
* [datasette](https://github.com/simonw/datasette) - An open source multi-tool for exploring and publishing data.
* [optimus](https://github.com/hi-primus/optimus) - Agile Data Science Workflows made easy with PySpark.
## Data Validation
*Libraries for validating data. Used for forms in many cases.*
* [Cerberus](https://github.com/pyeve/cerberus) - A lightweight and extensible data validation library.
* [colander](https://docs.pylonsproject.org/projects/colander/en/latest/) - Validating and deserializing data obtained via XML, JSON, an HTML form post.
* [jsonschema](https://github.com/Julian/jsonschema) - An implementation of [JSON Schema](http://json-schema.org/) for Python.
* [cerberus](https://github.com/pyeve/cerberus) - A lightweight and extensible data validation library.
* [colander](https://github.com/Pylons/colander) - Validating and deserializing data obtained via XML, JSON, an HTML form post.
* [jsonschema](https://github.com/python-jsonschema/jsonschema) - An implementation of [JSON Schema](http://json-schema.org/) for Python.
* [schema](https://github.com/keleshev/schema) - A library for validating Python data structures.
* [Schematics](https://github.com/schematics/schematics) - Data Structure Validation.
* [valideer](https://github.com/podio/valideer) - Lightweight extensible data validation and adaptation library.
* [schematics](https://github.com/schematics/schematics) - Data Structure Validation.
* [voluptuous](https://github.com/alecthomas/voluptuous) - A Python data validation library.
* [pydantic](https://github.com/pydantic/pydantic) - Data validation using Python type hints.
## Data Visualization