Intro to NumPy, SciPy, Matplotlib

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David Wright
February 27 2021

What We’ll Be Covering Today

  1. What are NumPy, SciPy, and Matplotlib?
  2. Basic usage and functionality
  3. Demos

What are NumPy, SciPy, and Matplotlib?

NumPy

numpy-logo.png

  • NumPy is the fundamental package for scientific computing in Python.
  • Python library that provides the following:
    • Multidimensional array object (ndarray)
    • Various derived objects (such as masked arrays and matrices)

    • Assortment of routines for fast operations on arrays


SciPy

scipy.png

  • Built on NumPy
  • Provides numerical routines, such as:
    • Numerical integration
    • Interpolation
    • Optimization
    • Linear algebra
    • Statistics

Matplotlib

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  • Library for creating visualizations in Python
    • Static,
    • Animated,
    • and Interactive visualizations

Basic Usage and Functionality

NumPy Basics

  • Basic data type is ndarray
import numpy as np
x = np.array([[1,2,3],[4,5,6]])
print(type(x))
print(x.shape)
print(x)

<class 'numpy.ndarray'>
(2, 3)
[[1 2 3]
 [4 5 6]]
  • Pre-compiled C code runs behind the scenes
    • Gives us speed and memory efficiency

NumPy Basics

As an example, I’ll show how matrix multiplication can be done very easily with NumPy 

import numpy as np
np.set_printoptions(suppress=True)
np.set_printoptions(precision=3)

x = np.array([1,0])
th = np.pi / 2
y = np.array([[np.cos(th), -np.sin(th)],
              [np.sin(th),  np.cos(th)]])
rot = np.matmul(y,x)
print(rot)

[0. 1.]
  • Note: by default, the * operator performs element-wise multiplication on NumPy arrays

SciPy Basics

  • SciPy is split into a number of subpackages
Subpackage Description
cluster Clustering algorithms
constants Physical and mathematical constants
fftpack Fast Fourier Transform routines
integrate Integration and ordinary differential equation solvers
interpolate Interpolation and smoothing splines
io Input and Output
linalg Linear Algebra
ndimage N-dimensional image processing
odr Orthogonal distance regression

SciPy Basics

Subpackage Description
optimize Optimization and root-finding routines
signal Signal processing
sparse Sparse matrices and associated routines
spatial Spatial data structures and algorithms
special Special functions
stats Statistical distributions and functions
  • Standard practice is to import only the subpackages you need
from scipy import optimize

Matplotlib Basics

  • The basic usage is as follows 

    import matplotlib.pyplot as plt
plt.plot(#your-data)
  • Matplotlib has many different plotting options
    • Histograms
    • Bar Charts
    • Errorbar
    • Scatter
    • 3D
    • Contours, and more

Python Demos

Visit the link below to get an online instance of a Jupyter Notebook with some demos.


The End

Acknowledgements

  • Snippets of Dr. Joseph Harrington’s Python demos were used with his permission
  • ThinkPython was used as a reference

Further Reading