SciPy: Numerical Algorithms for Python

Learning Objective: Learn how to find and use numerical algorithms in the SciPy package.



In [1]:

    
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np

Overview

The SciPy framework builds on top NumPy and provides a large number of numerical algorithms for working with data. Some of the topics that SciPy covers are:

Special functions (scipy.special)
Integration/ODEs (scipy.integrate)
Optimization (scipy.optimize)
Interpolation (scipy.interpolate)
Fourier Transforms (scipy.fftpack)
Signal Processing (scipy.signal)
Linear Algebra (scipy.linalg)
Sparse Eigenvalue Problems (scipy.sparse)
Statistics (scipy.stats)
Multi-dimensional image processing (scipy.ndimage)
File IO (scipy.io)

This notebook is not a complete tour of SciPy. Rather it focuses on the most important parts of the package for processing data.

In many cases, you will want to import specific names from scipy subpackages. However, as a start, it is helpful to do the following import:



In [2]:

    
import scipy as sp

Approach

One of the most important skills in data science is to be able to find Python functions and classes in a module and learn how to use them yourself. Here are some recommended steps on how to go about this:

Find the online documentation for the package you are using.
Try to find the subpackage or even the function that looks like will do the job.
Import the module, function or class and use tab completion and ? to explore it.
Try using the function or class for an extremely simple case where you know the answer.
Then try using for your real problem.

Resources

SciPy Website
SciPy Reference Documentation
Python Scientific Lecture Notes, Edited by Valentin Haenel, Emmanuelle Gouillart and Gaël Varoquaux.
Lectures on Scientific Computing with Python, J.R. Johansson.
Introduction to Scientific Computing in Python, Jake Vanderplas.