Doing more with Python Numpy

Doing more with Python Numpy, available at $49.99, has an average rating of 4.4, with 33 lectures, 8 quizzes, based on 18 reviews, and has 108 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to learn in more depth, about Numpy Arrays and Array Broadcasting and put them to practical use It is particularly useful for Anyone who wants to learn in more depth, about Numpy Arrays and Array Broadcasting and put them to practical use.

Enroll now: Doing more with Python Numpy

Summary

Title: Doing more with Python Numpy

Price: $49.99

Average Rating: 4.4

Number of Lectures: 33

Number of Quizzes: 8

Number of Published Lectures: 33

Number of Published Quizzes: 7

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 41

Original Price: ₹1,199

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone who wants to learn in more depth, about Numpy Arrays and Array Broadcasting and put them to practical use

Target Audiences

Anyone who wants to learn in more depth, about Numpy Arrays and Array Broadcasting and put them to practical use

The course covers three key areas in Numpy:

Numpy Arrays as Data Structures – Developing an in-depth understanding along the lines of:
1. Intuition of Arrays as Data Containers
2. Visualizing 2D/3D and higher dimensional Arrays
3. Array Indexing and Slicing – 2D/3D Arrays
4. 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
1. numpy where() function
  1. Comparison with Apply + Lambda
  2. Performance on Large DataFrames
  3. Varied uses in new variable creation
2. numpy select() function
  1. Apply conditions on single and multiple numeric variables
  2. Apply conditions on categorical variable
Array Broadcasting – Developing an intuition of “How Arrays with dissimilar shapes interact” and how to put it to use
1. Intuition of Broadcasting concept on 2D/3D Arrays
2. 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

Course Curriculum

Chapter 1: Introduction to Numpy Library and Arrays

Lecture 1: Overview of Numpy Library

Lecture 2: Overview of Numpy Arrays (sample)

Lecture 3: Overview of Numpy Arrays

Chapter 2: Numpy Arrays

Lecture 1: Array Basics Part 1 of 2

Lecture 2: Array Basics Part 2 of 2

Lecture 3: Arrays as Data Containers (sample)

Lecture 4: Arrays as Data Containers

Lecture 5: Visualizing Arrays

Lecture 6: Array Indexing and Slicing Part 1 of 2

Lecture 7: Array Indexing and Slicing Part 2 of 2

Lecture 8: 3D Array Indexing and Slicing

Lecture 9: Basic Array Operations

Chapter 3: Timing the code

Lecture 1: Chapter Introduction : Timing the Code

Lecture 2: Timing Codes : Popular Methods

Lecture 3: Comparing how Arrays perform versus a List (for the same operation)

Lecture 4: Comparing Binning methods performance : Numpy digitige() Function versus Others

Chapter 4: Numpy Functions

Lecture 1: Chapter Introduction : Numpy Functions

Lecture 2: np.where() function overview

Lecture 3: np.where() function performance versus Apply + Lambda

Lecture 4: np.where() performance with increasing DataFrame size

Lecture 5: Various uses of np.where() Function

Lecture 6: np.select() function overview

Lecture 7: np.select() function : Application in Flooring/Capping (Basic)

Lecture 8: np.select() function : Application in Flooring/Capping (Advanced)

Lecture 9: np.select() function : Application on a categorical variable

Chapter 5: Array Broadcasting

Lecture 1: Chapter Introduction : Array Broadcasting

Lecture 2: Broadcasting Intuition : 2D Arrays Example

Lecture 3: Laying down Broadcasting rules for a 2D Array

Lecture 4: Practical Application 01 : Simple operations on Multiple Variables

Lecture 5: Practical Application 02 : Flooring/Capping with different threshold values

Lecture 6: Practical Application 03 : Broadcasting as an Alternate to Cross-join

Lecture 7: Broadcasting Intuition : 3D Array Example

Lecture 8: Practical Application 04 : Finding closest centroid

Instructors

Gaurav Singh
Data Science Professional – BFSI and Life Sciences

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 6 votes
5 stars: 11 votes

Frequently Asked Questions

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