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Python NumPy: Scientific computing with Python
Rating: 3.6 out of 5(23 ratings)
179 students

Python NumPy: Scientific computing with Python

The fundamental package for scientific computing with Python
Created byStone River eLearning
Last updated 2/2017
English
English [Auto],

What you'll learn

  • Express fully why Numpy should be used
  • Ability to install Numpy
  • Understanding of how to use Numpy

Course content

2 sections • 12 lectures • 1h 5m total length

  • Module Introduction1:13

    Explore NumPy for scientific computing in Python, compare it with SciPy, learn installation, and master basic functions for arrays, including eigenvalues, determinants, and covariance, with a project walkthrough.

  • What is Numpy3:12

    Explore how numpy enables powerful array calculations and mathematical computations, and how SciPy provides open-source tools for advanced numerical work and easy installation on macOS and Windows.

  • Basic Mathematics6:23

    Learn basic numpy operations by generating random numbers, computing square roots and powers, and creating and expanding matrices with seeding, appending, and computing the sign of a matrix.

  • Tour of Numpy I8:02

    Take a tour of numpy by creating arrays with np, applying vectorized operations to all elements, and generating ranges with arange and linspace while inspecting types and dimensions.

  • Tour of Numpy II10:17

    Create and print multi-dimensional NumPy arrays, reshape a range into a 4 by 6 matrix, and slice elements; examine shape, dimensions, zeros, and the identity matrix with zero-based indexing.

  • Matrices6:56

    Learn how to create matrices with NumPy: zeros, ones, and full constants; build identity matrices and random arrays; and explore array construction, shapes, and simple dtype notes.

  • Deviations3:18

    Compute the standard deviation of a two by two matrix in numpy. Then compute the standard deviation of the rows and of the columns using axis 0 and axis 1.

  • Eigen Values5:18

    Learn how to compute eigenvalues for two matrices using numpy, exploring the determinant condition for a matrix minus lambda times the identity and practical code for diagonal and complex cases.

  • Covariance9:55

    Explore covariance by computing the covariance of two gaussian arrays with different sigmas, using a normalized centralized gaussian function in numpy, and printing the results.

  • Project (Determinants)9:33

    Construct three matrices, compute each determinant with NumPy, and compare the product det(A) det(C) with det(AC) using the Benay Koski formula to show efficiency.

  • Module Summary1:03

    Summarize the course by exploring eigenvalues, determinants, and covariance matrices, then combine matrices, analyze deviations and diagonals, and complete a module project with a walkthrough for Python.

Requirements

  • Some Python experience
  • Some experience working with matrices will be helpful

Description

At the end of this course, you will have a thorough understanding of Numpy' s features and when to use them. Numpy is mainly used in matrix computing. We'll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which Numpy is helpful.  There are a few computational computing libraries available for Python. It's important to know when to choose one over the other.  Through rigorous exercises, you'll experience where Numpy is powerful and develop and understanding of the scenarios in which Numpy is most useful.

  • Express fully why Numpy should be used
  • Ability to install Numpy 
  • Understanding of how to use Numpy


Who this course is for:

  • Those wanting to know about Python libraries helpful for computational computing

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