Introduction to NumPy and SciPy

NumPy and SciPy are open-source add-on modules to Python that provide common mathematical and numerical routines in pre-compiled, fast functions. These are growing into highly mature packages. The NumPy (Numeric Python) package provides basic routines for manipulating large arrays and matrices of numeric data. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms, like minimization, Fourier transformation, regression, and other applied mathematical techniques. 

What is the relationship between Numpy and Scipy?

In an ideal world Links to an external site., NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera. All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

Installation

If you installed Python3 on your platform, then you should be ready to go. If not, then you will have to install these add-ons manually after installing Python, - in the order of NumPy and then SciPy. Use pip or the following wheels Links to an external site..


Usage

There are several ways to import NumPy. The standard approach is to use a simple import statement.

import numpy

However, for large amounts of calls to NumPy functions, it can become tedious to write numpy.X over and over again. Instead, it is common to import under the briefer name np:

import numpy as np


Additional Resources