Numpy Basic (Part 2)

Shape and Reshape

Something really useful is be able to change or create an array in a specific shape, we will start with the same array that before:

np.random.seed(101)
arr =  np.random.randint(0, 100,10)
#array([95, 11, 81, 70, 63, 87, 75,  9, 77, 40])

Find the shape

now we can use shape to find what shape our array has in this moment

arr.shape
# (10,)

this mean that is an array, or, better a vector with 10 items.

How to reshape

we can use the function reshape to change the previous vector

Syntax

arr.reshape((2,5))
# array([[95, 11, 81, 70, 63],
#       [87, 75,  9, 77, 40]])

we need to reshape to values that make sense, in this case 2 rows and 5 columns does, because \(2*5 = 10\)

if i try to create a shape that is not correct i will get a valueError exception, like here

 arr.reshape((2,10))
 ---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-35-ef8eb80c29ad> in <module>()
----> 1 arr.reshape((2,10))

ValueError: cannot reshape array of size 10 into shape (2,10)

Indexing

it is important to be able to get back rows, columns or slice of the matrix we create, in this case we enter to scope of indexing.

mat = np.arange(0,100).reshape(10,10)
# array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
#       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
#       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
#       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
#       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
#       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
#       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
#       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
#       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
#       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

Find a single digit in the matrix

let say that i want to get the element in row = 0 and col = 1

Syntax

mat[row,col]
# 1

we don't need to define the variables 'row' and 'col' we can simply

mat[5,5]
# 55

How to slice a matrix

let say that we want

1. Get all the values in a row or column we want to get all the values in the row 1

Syntax

mat[:,1]
# array([ 1, 11, 21, 31, 41, 51, 61, 71, 81, 91])

now for the column

mat[1,:]
# array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

we can change the shape of the new array

mat[1,:].reshape(10,1)
# array([[10],
#       [11],
#       [12],
#       [13],
#       [14],
#       [15],
#       [16],
#       [17],
#       [18],
#       [19]])

2. Get all the values from n rows and m columns We want to get back part of the original matrix

mat[:3,:3]
# array([[ 0,  1,  2],
#       [10, 11, 12],
#       [20, 21, 22]])

we can replace part of the matrix as well

mat[0:4,0:4] = 0
#array([[ 0,  0,  0,  0,  4,  5,  6,  7,  8,  9],
#       [ 0,  0,  0,  0, 14, 15, 16, 17, 18, 19],
#       [ 0,  0,  0,  0, 24, 25, 26, 27, 28, 29],
#       [ 0,  0,  0,  0, 34, 35, 36, 37, 38, 39],
#       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
#       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
#       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
#       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
#       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
#       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])