Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

python.tar.gz

python3.6

Live3.6

If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

# What is Broadcasting in NumPy?
The term *broadcasting* describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing array operations. 

# General rules for Broadcasting
When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions and works its way forward. Two dimensions of size `N`  are compatible when
1. they are equal, or
2. one of them is the singular value 
3. one is N * N  and other is N * 1
4. one is N * N and other is 1 * N    

# Examples
The concept of broadcasting is explained through code and figure in the following examples:-

## When one operand is N*N and other is 1 * 1
```python
Z1 = np.arange(9).reshape(3,3)
Z2 = 1 
Z1 + Z2
```
```
  
   ┌───┬───┬───┐   ┌───┐   ┌───┬───┬───┐   ┏━━━┓───┬───┐   ┌───┬───┬───┐
   │ 0 │ 1 │ 2 │ + │ 1 │ = │ 0 │ 1 │ 2 │ + ┃ 1 ┃ 1 │ 1 │ = │ 1 │ 2 │ 3 │
   ├───┼───┼───┤   └───┘   ├───┼───┼───┤   ┗━━━┛───┼───┤   ├───┼───┼───┤
   │ 3 │ 4 │ 5 │           │ 3 │ 4 │ 5 │   │ 1 │ 1 │ 1 │   │ 4 │ 5 │ 6 │
   ├───┼───┼───┤           ├───┼───┼───┤   ├───┼───┼───┤   ├───┼───┼───┤
   │ 6 │ 7 │ 8 │           │ 6 │ 7 │ 8 │   │ 1 │ 1 │ 1 │   │ 7 │ 8 │ 9 │ 
   └───┴───┴───┘           └───┴───┴───┘   └───┴───┴───┘   └───┴───┴───┘


```

This lesson explains broadcasting in NumPy in detail.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Broadcasting in NumPy

What is Broadcasting in NumPy?

General rules for Broadcasting