Numpy Intro

From Great Learning.

single dimensional array

import numpy as np


n1=np.array([10,20,30,40])
n1

array([10, 20, 30, 40])

multi-dimensional array

n2=np.array([[10,20,30,40],[40,30,20,10]])
n2

array([[10, 20, 30, 40],
[40, 30, 20, 10]])

type(n1)
type(n2)

numpy.ndarray

n3=np.zeros((1,2))
n3

array(0., 0.)

n4=np.zeros((5,5))
n4

array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])

n5=np.full((2,2),10)
n5

array([[10, 10],
[10, 10]])

n6=np.arange(10,20)
n6

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

n7=np.arange(1,100,10)
n7

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

n8=np.random.randint(1,100,5)
n8

array([40, 16, 83, 96, 6])

n9=np.array([[1,2,3],[4,5,6]])
n9

array([[1, 2, 3],
[4, 5, 6]])

n9.shape

(2, 3)

n9.shape = (3,2)
n9

array([[1, 2],
[3, 4],
[5, 6]])

joining numpy arrays - vstack

n1=np.array([10,20,30])
n2=np.array([40,50,60])

np.vstack((n1,n2))

array([[10, 20, 30],
[40, 50, 60]])

joining numpy arrays - hstack

np.hstack((n1,n2))

array([10, 20, 30, 40, 50, 60])

joining numpy arrays - column_stack

np.column_stack((n1,n2))

array([[10, 40],
[20, 50],
[30, 60]])

numpy intersection & difference

n1=np.array([10,20,30,40,50,60])
n2=np.array([50,60,70,80,90])

np.intersect1d(n1,n2)

array([50, 60])

np.setdiff1d(n1,n2)

array([10, 20, 30, 40])

np.setdiff1d(n2,n1)

array([70, 80, 90])

array mathematics

n1=np.array([10,20])
n2=np.array([30,40])

np.sum([n1,n2])

100

np.sum([n1,n2],axis=0)

array([40, 60])

np.sum([n1,n2],axis=1)

array([30, 70])

can also be used for other mathematical operations

import numpy as np
n1=np.array([10,20,30])
n1=n1+1
n1

array([11, 21, 31])

mean

# mean
n1=np.array([10,20,30,40,50,60])
np.mean(n1)

35.0

standard deviation

np.std(n1)

17.07825127659933

median

np.median(n1)

35.0

save numpy array

np.save('my_numpy', n1)

# loading array
n2=np.load('my_numpy.npy')
n2

array([10, 20, 30, 40, 50, 60])