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

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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

*Indexing* means to refer to any value in an array.
Each item in a numpy array is stored at a specific index. To access value at a specific index write:
```python
Z=np.arrange(9)
Z[0] #get the value at index 0
```
# Get the First Value
To get the first value of a matrix, write: `Z[0,0]`. 

```
   ┏━━━┓───┬───┐   ┏━━━┓
   ┃ 0 ┃ 1 │ 2 │ → ┃ 0 ┃ (scalar)
   ┗━━━┛───┼───┤   ┗━━━┛
Z  │ 3 │ 4 │ 5 │  
   ├───┼───┼───┤
   │ 6 │ 7 │ 8 │
   └───┴───┴───┘
```

import numpy as np
Z = np.arange(9).reshape(3,3)
print(Z[0,0])

# Get the Last Value
To get the last value of a matrix, write: `Z[-1,-1]`.
```
   ┌───┬───┬───┐
   │ 0 │ 1 │ 2 │
   ├───┼───┼───┤
 Z │ 3 │ 4 │ 5 │
   ├───┼───┏━━━┓   ┏━━━┓
   │ 6 │ 7 ┃ 8 ┃ → ┃ 8 ┃ (scalar)
   └───┴───┗━━━┛   ┗━━━┛
```

import numpy as np
Z = np.arange(9).reshape(3,3)
print(Z[-1,-1])

# Get a row from a Grid
To get a row from a grid, write: `Z[row_index`].

To get the first row from a grid, write: `Z[1]`.

```
   ┌───┬───┬───┐   
   │ 0 │ 1 │ 2 │ 
   ┏━━━┳━━━┳━━━┓   ┏━━━┳━━━┳━━━┓
Z  ┃ 3 ┃ 4 ┃ 5 ┃ → ┃ 3 ┃ 4 ┃ 5 ┃
   ┗━━━┻━━━┻━━━┛   ┗━━━┻━━━┻━━━┛
   │ 6 │ 7 │ 8 │      (view)
   └───┴───┴───┘
```

import numpy as np
Z = np.arange(9).reshape(3,3)
print(Z[1])

# Get a Column from a Grid
To get the column from a grid, use
  `Z[:,column_index]`

To get the second column from a grid, use
  `Z[:,2]`
   
``` 
   ┌───┬───┏━━━┓   ┏━━━┓
   │ 0 │ 1 ┃ 2 ┃   ┃ 2 ┃
   ├───┼───┣━━━┫   ┣━━━┫
Z  │ 3 │ 4 ┃ 5 ┃ → ┃ 5 ┃ (view)
   ├───┼───┣━━━┫   ┣━━━┫
   │ 6 │ 7 ┃ 8 ┃   ┃ 8 ┃
   └───┴───┗━━━┛   ┗━━━┛
```

This lesson will help you learn indexing in NumPy.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Indexing in NumPy

Get the First Value

Get the Last Value

Get a row from a Grid

Get a Column from a Grid