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