Section 4: NumPy - NumPy Arrays

13. Introduction of NumPy 

Section Goal

- Understand Numpy

- Create arrays with Numpy

- Retrieve information from a NumPy array through slicing and indexing

- Learn basic NumPy operations

- Test NumPy skills with exercise questions. 

 

 

What is NumPy?

- Python library for creating N-dimensional arrays

- Ability to quickly broadcast functions 

- Built-in linear algebra, statistical distributions, trigonometric, and random number capabilities

 

Why use NumPy?

- While NumPy structures look similar to standard Python lists, they are much more efficient

- The broadcasting capabilities are also extremely useful for quickly applying functions to our data sets

 

NumPy Arrays

Numpy를 library로 import하기

# NumPy Arrays
NumPy arrays are the main way we will use NumPy throughout the course. NumPy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-dimensional (1D) arrays and matrices are 2D (but you should note a matrix can still have only one row or one column).

# Why use Numpy array? Why not just a list?
There are lot's of reasons to use a Numpy array instead of a "standard" python list object. Our main reasons are:
* Memory Efficiency of Numpy Array vs list
* Easily expands to N-dimensional objects
* Speed of calculations of numpy array
* Broadcasting operations and functions with numpy
* All the data science and machine learning libraries we use are built with Numpy

 

 

# Creating NumPy arrays from Objects

From a Python List 

We can create an array by directly converting a list or list of lists : 

# Built-in Methods to create arrays

There are lots of built-in ways to generate arrays.

 

arange

Return evenly spaced values within a given interval  [reference]

zeros and ones

Generate arrays of zeros or ones.  [reference]

linspace

Return evenly spaced numbers over a specified interval. [reference]

 

eye

Creates an identity matrix  [reference]

# Random

Numpy also has lots of ways to create random number arrayas: 

 

rand

Creates an array of the given shaped and populates it with random samples from a uniform distribution over [0,1)  [reference]

randn

Returns a sample(or samples) from the "standard normal" distribution  [σ = 1]. Unlike rand which is uniform, values closer to zero are more likely to appear. [reference]

 

randint 

Returns random integers from low(inclusive) to high(exclusive). [reference]

seed

can be used to set the random state, so that the same "random" results can be reproduced [reference]

# Arrays 

Let's discuss some useful attributes and methods for an array:

Reshape

Returns an array containing the same data with a new shape. [reference]

max, min, argmax, argmin

These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax

Shape

Shape is an attribute that arrays have (not a method): [reference]

dtype

You can also grab the data type of the object in the array: [reference]