Week 8 - NumPy (Part I) - Introduction to NumPy

Liaw Bei Le · May 15, 2021

Learning Python NumPy Practices Cheatsheets Reflections

Introduction to NumPy:

np.array( )
Indexing
Conditions
np.zeros( ) , np.ones( )
np.arange( )
np.linspace( )
Python Lists vs NumPy Arrays

Cheatsheets

NumPy Guide

What I learnt:

Introduction to NumPy

NumPy: stands for Numerical Python

is a Python library
used to perform a wide variety of mathematical operations on arrays
also has functions for working in domain of linear algebra, fourier transform, and matrices.
to import NumPy,
```
  import numpy as np
```

Array:

Elements are all of the same type, i.e. array dtype.

An array can be indexed by a tuple of nonnegative integers, by booleans, by another array, or by integers.

Rank of the array: the number of dimensions

Shape of the array: a tuple of integers giving the size of the array along each dimension

np.array( ):
Initialize NumPy arrays from Python lists, using nested lists for two- or higher-dimensional data. e.g.:
```
a = np.array([1, 2, 3, 4, 5, 6])
```
```
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
```
To access the elements in the array, use indexing:
```
print(a[0])
```
Output:
```
[1, 2, 3, 4]
```

Conditions
Example 1:

a>3

Output:

array([False, False, False, True])

Example 2:

a[a>3]

Output:

array([4])

np.zeros( ):
Easily create an array filled with 0’s. e.g.:
```
np.zeros(2)
```
Output:
```
array([0., 0.])
```
np.ones( ):
Easily create an array filled with 1’s. e.g.:
```
np.ones(2)
```
Output:
```
array([1., 1.])
```
np.arange( ):
Create an array with a range of elements. e.g.:
```
np.arange(4)
```
Output:
```
array([0, 1, 2, 3])
```
np.arange(first number, last number, step size):
Create an array that contains a range of evenly spaced intervals. e.g.:
```
np.arange(2, 9, 2)
```
Output:
```
array([2, 4, 6, 8])
```
np.linspace( ):
Create an array with values that are spaced linearly in a specified interval. e.g.:
```
np.linspace(0, 10, num=5)
```
Output:
```
array([ 0. ,  2.5,  5. ,  7.5, 10. ])
```

Python Lists vs NumPy Arrays

For python lists, elements within lists in the same indexes cannot do math operations with one another quickly. It is possible using numpy arrays.

Without NumPy, using Python lists:

a_list=[1,2,3]
b_list=[2,4,6]
print(a_list+b_list)
print(a_list/b_list)

Output:

[1,2,3,2,4,6]

Output:

error

With NumPy, using Python lists:

np_list_1=np.array(a)
np_list_2=np.array(b)
print(np_list_1 + np_list_2)
print(np_list_1 / np_list_2)

Output:

array([3,6,9])

Output:

array([0.5,0.5,0.5])

Thoughts

NumPy looks like it would come in handy in efficiently doing numerical calculations, while Python lists have limitations in doing the same.

NumPy actually has more features, which I will probably learn and then share about them in future posts, so do keep a look out!

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