Python for Data Science: Advanced Operations with NumPy Arrays


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 13-video course explores advanced operations in NumPy, a Python library, and covers array operations such as image manipulation, fancy indexing, views, and broadcasting. To take this Skillsoft Aspire course, you should already have basic experience with NumPy arrays, and be comfortable with array creation, indexing and slicing, and applying both aggregate and universal functions to your array data. You will learn about the several options available in NumPy to split arrays. You will learn how to use NumPy to work with digital images, which are multidimensional arrays. Next, learn to manipulate a color image, which is a three-dimensional array using NumPy, and to perform slicing operations to view sections of the image, and how to use SciPy package for image manipulation. Learners explore the concepts of both shallow copies and deep copies in NumPy. You will learn how to use masks, an array of index values, to access multiple elements of an array simultaneously, referred to as Sansi indexing. Finally, this course covers broadcasting to perform operations between mismatched arrays.



Expected Duration (hours)
1.1

Lesson Objectives

Python for Data Science: Advanced Operations with NumPy Arrays

  • Course Overview
  • identify different ways in which arrays can be split up
  • describe how grayscale and color images can be represented as multi-dimensional arrays
  • perform some basic image manipulation after converting images to arrays
  • create a view into a NumPy array and learn of the relationship between views and their base arrays
  • compare deep copies of arrays with views and know when to use each of them
  • use fancy indexing with arrays using an index mask
  • use fancy indexing to analyze real-world data
  • apply boolean masks to access array elements which fulfil a specific condition
  • use structured arrays in order to store heterogeneous data
  • describe how operations can be performed between arrays of mismatched shapes using broadcasting
  • perform operations between arrays of mismatched shapes by applying broadcasting rules
  • utilize NumPy to perform multi-dimensional array operations
  • Course Number:
    it_dspydsdj_02_enus

    Expertise Level
    Intermediate