Python

General

• Project site: https://www.python.org/
• Downloads: https://www.python.org/downloads/

• Python2 vs Python3: https://learn.onemonth.com/python-2-vs-python-3/

Important Libraries

• NumPy (1.16): http://www.numpy.org/

Manual: https://docs.scipy.org/doc/numpy/user/quickstart.html

Reference: https://docs.scipy.org/doc/numpy/reference/

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.

• Pandas (0.24.2): https://pandas.pydata.org/

Manual: http://pandas.pydata.org/pandas-docs/stable/

Quick start: http://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.

• scikit-learn: https://scikit-learn.org/stable/

Simple and efficient tools for data mining and data analysis

• StatsModels: http://www.statsmodels.org/stable/index.html

A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.

• TensorFlow: https://www.tensorflow.org

An open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

• Keras ( The Python Deep Learning library): https://keras.io

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

• mpi4py: requires MPI libraries.

• Dask:

Extending Python

Virtual environments

$ python -m venv my_environment