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

Course content

5 sections • 33 lectures • 4h 17m total length

Overview of Numpy Library3:13
Overview of Numpy Arrays (sample)3:55
Overview of Numpy Arrays12:17
Compare lists and NumPy arrays for vectorized arithmetic across many values, highlighting axis-based indexing, memory efficiency, and seamless interaction with lists, tuples, and other data structures through broadcasting.

Array Basics Part 1 of 212:10
Explore numpy array basics by creating one-dimensional, two-dimensional, and three-dimensional arrays with np.array, np.arange, and np.random.randint; inspect size, ndim, shape, and dtype.
Quiz 2.1
Array Basics Part 2 of 210:39
Quiz 2.2
Arrays as Data Containers (sample)2:05
Arrays as Data Containers7:40
Visualizing Arrays4:18
Array Indexing and Slicing Part 1 of 24:30
Array Indexing and Slicing Part 2 of 211:13
Quiz 2.3
3D Array Indexing and Slicing13:14
Learn 3d array indexing and slicing with a 60-point 2018–2020 sales dataset across five products, reshaping a 1d array into 3d and extracting subsets by axis.
Quiz 2.4
Basic Array Operations11:08
Chapter 02 Assignment

Chapter Introduction : Timing the Code1:07
Timing Codes : Popular Methods2:39
Quiz 3.1
Comparing how Arrays perform versus a List (for the same operation)10:17
Compare performance of adding a value to a list versus a numpy array using the time module and %timeit, showing numpy faster and offering insights into average times and standard deviations.
Quiz 3.2
Comparing Binning methods performance : Numpy digitige() Function versus Others13:36
Quiz 3.3

Chapter Introduction : Numpy Functions1:24
np.where() function overview1:37
Use numpy.where to replace negative values in column B with zeros, creating a new variable C, and explore its behavior on dataframes of varying sizes.
np.where() function performance versus Apply + Lambda13:10
Compare numpy.where and apply lambda by building a dummy DataFrame and generating a 1/0 flag, then time both methods with the time module to contrast performance.
np.where() performance with increasing DataFrame size9:53
Various uses of np.where() Function13:03
np.select() function overview2:36
np.select() function : Application in Flooring/Capping (Basic)5:38
Apply np.select() to floor and cap outliers in a DataFrame column, setting negatives to 0 and values above 100 to 100. Compare with np.where and extend to multiple variables.
np.select() function : Application in Flooring/Capping (Advanced)10:42
np.select() function : Application on a categorical variable6:55
Use numpy's np.select to map grocery items to product categories with a dictionary-based approach and isin conditions on a DataFrame, replacing unknown with a default label.

Chapter Introduction : Array Broadcasting1:36
Learn array broadcasting across 2d and 3d arrays and its rules. Apply it to dataframe arithmetic, with numpy select for flooring and capping, and compare to cross-join alternatives.
Broadcasting Intuition : 2D Arrays Example3:08
Laying down Broadcasting rules for a 2D Array8:14
Practical Application 01 : Simple operations on Multiple Variables13:54
Practical Application 02 : Flooring/Capping with different threshold values8:37
Explore applying flooring and capping with different thresholds via numpy broadcasting on a subset of variables in a data frame, using threshold lists and conditional masks.
Practical Application 03 : Broadcasting as an Alternate to Cross-join15:55
Broadcasting Intuition : 3D Array Example8:20
Practical Application 04 : Finding closest centroid9:01

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

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

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