Numpy in python

 In this post lets see about Numpy module it is same as list. We are going to use it in data science.Numpy is used for mathematical calculations. Install Numpy by

pip install numpy 

Type this in your cmd and hit enter.

To declare Numpy:

import numpy as np

x = [[1,2,5,3],[1,2,5,3],[1,2,5,3],[1,2,5,3]]

print(x)

y = np.array(x)

print(y)

Output:

[[1, 2, 5, 3], [1, 2, 5, 3], [1, 2, 5, 3], [1, 2, 5, 3]]

[[1 2 5 3] [1 2 5 3] [1 2 5 3] [1 2 5 3]]

This is how the numpy works. Numpy works both on single dimension and 2D array.

Methods for Numpy:

import numpy as np

x=[1,2,3,4]

print(x)

print('----------------------------')

y =[[1,2,3],[4,5,6],[7,8,9]]

z=np.array(y)

print(z)#print in 2 dimension

print('----------------------------') 

# to print zero matrix

print(np.zeros(2))

print('-------------------------')

#with two rows and two column 

print(np.zeros((2,2)))

# add operation

print("add",np.zeros((2,2))+5)

print('--------------------------')

print("mul",np.zeros((2,2))*5)

print('--------------------------')

print(np.ones((3,4)))

print('--------------------------')

print(np.eye(3))#diagonal matrix

these are some basic methods used in numpy

Output:

[1, 2, 3, 4] ---------------------------- [[1 2 3] [4 5 6] [7 8 9]] ---------------------------- [0. 0.] ------------------------- [[0. 0.] [0. 0.]] add [[5. 5.] [5. 5.]] -------------------------- mul [[0. 0.] [0. 0.]] -------------------------- [[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]] -------------------------- [[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]

To print 2D matrix:

import numpy as np

a= np.arange(10)

print(a)

a= np.arange(10).reshape(2,5)

print(a)

[0 1 2 3 4 5 6 7 8 9] [[0 1 2 3 4] [5 6 7 8 9]]

To print random number:

-rand to print random floating point
-randb to print random -ve num also
-randint to print integers

import numpy as np

print(np.random.rand(4))

print(np.random.randn(4))

print(np.random.randint(10))

[0.24395678 0.85441042 0.20545385 0.31506932] [-0.59030679 0.66762296 0.17482717 1.988936 ] 8

To print linear element:

If you want to print 100 element with Linear space that is the difference between each number should be same. Then we use linspace method

import numpy as np

b = np.linspace(0,10)

print(b)

b = np.linspace(0,10,5)

print(b)

[ 0. 0.20408163 0.40816327 0.6122449 0.81632653 1.02040816 1.2244898 1.42857143 1.63265306 1.83673469 2.04081633 2.24489796 2.44897959 2.65306122 2.85714286 3.06122449 3.26530612 3.46938776 3.67346939 3.87755102 4.08163265 4.28571429 4.48979592 4.69387755 4.89795918 5.10204082 5.30612245 5.51020408 5.71428571 5.91836735 6.12244898 6.32653061 6.53061224 6.73469388 6.93877551 7.14285714 7.34693878 7.55102041 7.75510204 7.95918367 8.16326531 8.36734694 8.57142857 8.7755102 8.97959184 9.18367347 9.3877551 9.59183673 9.79591837 10. ] [ 0. 2.5 5. 7.5 10. ]

Indexing and slicing:

import numpy as np

y = np.arange(1,20)

print(y)

print(y[0])

print(y[7])

print(y[10:])

print(y[:10])

print(y[3:8])

print(y[-5])

print(y[-5:-1])

print(y[-1])

x = y[8:12]=30

print(y)

z= y.copy()

print("y copyed to z")

print(z)

Output:

[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] 1 8 [11 12 13 14 15 16 17 18 19] [ 1 2 3 4 5 6 7 8 9 10] [4 5 6 7 8] 15 [15 16 17 18] 19 [ 1 2 3 4 5 6 7 8 30 30 30 30 13 14 15 16 17 18 19] y copyed to z [ 1 2 3 4 5 6 7 8 30 30 30 30 13 14 15 16 17 18 19]

Hope you understand the program and try yourself for more clear view ,feel free to comment your thoughts!!