Python for Data Science: Advanced Operations with NumPy Arrays


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore advanced array operations such as image manipulation, fancy indexing, views and broadcasting.



Expected Duration (hours)
1.1

Lesson Objectives

Python for Data Science: Advanced Operations with NumPy Arrays

  • 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