PyYAML-based python module to produce pretty and readable YAML-serialized data
Python module to produce formatted YAML-serialized data
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A fast multidimensional array facility for Python
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Utilities to provide lightweight pipelining in Python
Joblib is a set of tools to provide lightweight pipelining in Python.
Interpreter of the Python3 programming language
Python combines remarkable power with very clear syntax. It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. There are interfaces to many system calls and libraries, as well as to various windowing systems. New built-in modules are easily written in C or C++ (or other languages, depending on the chosen implementation). Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces. This package Provides python version 3.
Sequential model-based optimization toolbox
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.
A Python module for machine learning built on top of SciPy
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.