Doing more with Python Numpy

[yanfan001]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 33 lectures (4h 17m) | Size: 1.2 GB

Tap full potential of Numpy Library by putting Arrays, Numpy's functions and Broadcasting to work

What you'll learn:
Develop understanding of how Arrays work and what advantages they offer over other Data Structures
Use Arrays as Data containers for common data operations
Compare time performance of your process codes versus a suitable Numpy function
In-depth understanding to use numpy's where() and select() functions to replace conventionally used methods
Apply Array Broadcasting in your line of work to replace Nested For loops and Cross-join operations

Requirements
Basic knowledge of Python (including Data Types and Structures, Control Flow, Functions, etc.)
Basic knowledge of Pandas

Description
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

Who this course is for
Anyone who wants to learn in more depth, about Numpy Arrays and Array Broadcasting and put them to practical use

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