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NumPy is the core library used for scientific computing in Python. It provides a high$-$performance, multidimensional array object, and tools for working with these arrays. It is used extensively in machine learning and will be the main library used in our class.

Introduction to NumPy Arrays

A numpy array is a grid of values, all of the same type $($i$.$e$.$ integer, fioat or other$),$ and is indexed by a tuple $($i$.$e$.$ sequence$)$ of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.

We can initialize numpy arrays from nested Python lists, and access elements using square brackets. Here are a few examples of common usage:

os

$[1]$ import numpy as np

$$ have to import the numpy library before we can use it # write the import line above at the top of every script!

os

#array basics:

$a=np.$array$([1,2,3]$ # create a $1-$dimension array $(1,2,3)$

print$("$a $1$D array:"$)$

print $[]$

print$($type$($a$))$ # print the class

print$("$shape:$",$ a$.$shape$)$ # print the shape

print$()$ #calling print$()$ with no arguments writes a new line

#changing values in an array:

print$($a$[0],$ a$[1],$ a$[2])$ # print the items at array indicies $0,1$ and $2($please hote indexing $a[0]=5$

print$($a$[0],$ a$[1],$ a$[2])$

print$()$

#$2$D arrays:

$b=np.$array$([[1,2,3],[4,5,6]$ # create a $2-$dimension array

print$("$a $2$D array:"$)$

print$($b$)$

print$($type$($a$))$ # print the class

print$("$shape:$",$ b$.$shape$)$

print$()$

print$($b$[0,0],$ b$[0,1],$ b$[1,0])$

a $10$ array:

$[123]$