Doing more with Python Numpy

The course covers three key areas in Numpy:

Numpy Arrays as Data Structures – Developing an in–depth understanding along the lines of:

Intuition of Arrays as Data Containers

Visualizing 2D/3D and higher dimensional Arrays

Array Indexing and Slicing – 2D/3D Arrays

Performing basic/advanced operations using Numpy Arrays

Useful Numpy Functions – Basic to Advanced usage of the below Numpy functions and how they perform compared to their counterpart methods

numpy where() function

Comparison with Apply + Lambda

Performance on Large DataFrames

Varied uses in new variable creation

numpy select() function

Apply conditions on single and multiple numeric variables

Apply conditions on categorical variable

Array Broadcasting – Developing an intuition of How Arrays with dissimilar shapes interact and how to put it to use

Intuition of Broadcasting concept on 2D/3D Arrays

Under what scenarios can we use Broadcasting to replace some of the computationally expensive methods like For loops and Cross–join Operations, etc. especially when working on a large Datasets

The course also covers the topic – How to time your codes/processes , which will equip you to:

Track time taken by any code block (using Two different methods) and also apply to your own processes/codes

Prepare for the upcoming Chapter Useful Numpy Functions , where we not only compare performance of Numpy functions with other conventionally used methods but also monitor how they perform on large Datasets