Introduction to data Analysis _ numpy

Numpy (Numerical Python)

Numpy: provides a basic library for doing scientific calculations in Python, focusing on numerical calculations, mainly for multi-dimensional array (matrix) processing. For storing and processing large matrices, it is much more efficient than Python's own nested list structure. Itself is developed by the C language, is a very basic extension, Python most of the rest of the scientific computing extension is based on this.

Ndarray, the basic package of high performance scientific computing and data analysis, multi-dimensional array (matrix), with vector operation ability, fast, space-saving matrix operation, no cycle, can complete similar vector operations in Matlab linear algebra, random number generation import numpy as npScipy

Scipy: based on Numpy, it provides a toolset for doing scientific calculations in Python, a Python toolkit designed for science and engineering. It is mainly used in statistics, optimization, integration, linear algebra module, Fourier transform, signal and image processing, ordinary differential equation solving, sparse matrix and so on. It is relatively more used in the department of mathematics or engineering, and has little to do with data processing. We can know it, but we won't explain it here.

On the basis of NumPy library, many common library functions such as linear algebra, ordinary differential equation solving, signal processing and image processing are added. Numpy is enough for import scipy as sp reference learning materials.

Introduction to Python, NumPy and SciPy: http://cs231n.github.io/python-numpy-tutorial

Getting started with NumPy and SciPy: https://docs.scipy.org/doc/numpy-dev/user/quickstart.html