NumPy

NumPy is the basic package of Python scientific computing, which provides:

Fast and efficient multi-dimensional array object ndarray；;

Functions that directly perform mathematical operations on arrays and perform element-level calculations on arrays;

Linear algebraic operation, random number generation;

Tools for integrating C, C++ and Fortran codes into Python, etc.

It is designed for strict digital processing. It is mainly used by many large financial companies and core scientific computing organizations, such as Lawrence Livermore, and used by NASA to deal with some tasks that were originally completed by C++, Fortran or Matlab.

panda

Pandas mainly provides a large number of data structures and functions for processing structured data quickly and conveniently.

Matplotlib

Matplotlib is the most popular Python library for charting data.

IPython

IPython is a part of Python scientific computing standard toolset, which is an enhanced Python Shell to improve the speed of writing, testing and debugging Python code. It is mainly used for interactive data processing and data visualization using matplotlib.

SciPy

SciPy is a software package dedicated to solving various standard problem domains in scientific computing. Mainly includes the following packages:

Scipy.integrate: numerical integration program and differential equation solver;

Scipy.linalg: It extends the linear algebraic routines and matrix decomposition functions provided by numpy.linalg;

Scipy.optimize: function optimizer and root search algorithm;

Scipy.signal: signal processing tool;

Scipy.sparse: Solver of sparse matrix and sparse linear system;

Scipy。 Special: a wrapper for Specfun, a Fortran library that implements many common mathematical functions.

Scipy.stats: standard continuous and discrete probability distribution, various statistical test methods and better descriptive statistics;

Scipy.weave: A tool to speed up array calculation by using inline C++ code.