Python

General

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

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.

Pandas is well suited for many different kinds of data:

-- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet

-- Ordered and unordered (not necessarily fixed-frequency) time series data.

-- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

-- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure