Python Numpy

Numpy is the python library working with numerical data,this library consists of multiple libraries and a multidimensional array.It is an open source project.

Why Numpy

We have seen that python using the list aims at the serve of array but it is the slow process Numpy provides very fast processing of list (array). Array in numpy is called ndarray.

Numpy provides the function to deal with n dimension array very quickly and easy

Operation in Numpy

Arithmetic Operations

Program

import numpy as abc
  
  
#initialization of array
a =abc.arange(4).reshape(2, 2) #reshape gives the matrix dimension
  
print('first array:') 
print(a)#print array
  
print('second array:') 
a1 = abc.array([20, 40]) #initialization array a1
print(a1)
  
print('add array') 
print(abc.add(a, a1))#add two array
  
print('subtract arrays:') 
print(abc.subtract(a, a1))#similarly subtract array
  
print('multiply array:')
print(abc.multiply(a, a1))#multiply two array 
  
print('divide array:')
print(abc.divide(a, a1))#divide two array

Output

first array:
[[0 1]
 [2 3]]

second array:
[20 40]

add array:
[[20 41]
[22 43]]

subtract two array:
[[-20 -39]
 [-18 -37]]

multiply array:
[[  0  40]
 [ 40 120]]

divide array:
[[0    0.025]
 [0.1   0.075]]

Note :numpy consists of the array funciton dimension .

Numpy Power()

import numpy as abc
#import numpy and creates objects abc
  
arr = abc.array([5, 10, 15]) #initialization of array  
print('simple array:') 
print(arr)
  
print('power function:') 
print(np.power(arr, 20))#invokes the power functions

Output

simple array:
[ 5 10 15]
power function:
[95367431640625   7766279631452241920  4664335276710460609]

Numpy mod()

import numpy as abc 
#import numpy and creates the objects abc  
  
a = abc.array([10, 25, 18]) #creates two array a and b
b = abc.array([8,9,11])
  
print('first array:') 
print(a) 
                        #print array a and b
print(‘second array:') 
print(b)
  
print('mod function:') 
print(abc.mod(a,b)) # invokes mod function
  
print('remainder function:') 
print(abc.remainder(a,b))#invokes remainder function

Mod and remainder function libraries consist by the numpy.

Output

first array:
[10 25 18]

second array:
[ 8  9 11]

mod function:
[2 7 7]

remainder function:
[2 7 7]

Slicing

Array slicing is one of the important concepts of Python, It makes little bit confusing to the beginners to understand the concept of the Slicing. Now we will see the Slicing concept along with the demonstration.

Syntax data[from:to]
data[start:stop:increment]

One Dimensional Slicing
Program

#importing numpy
from numpy import array
#array define
data = array([45,53,53,34,53,52,452])
print(data[:1])

Output

[ 45 ]

Program

#simple slicing
from numpy import array
#array define
data = array([45,53,53,34,53,52,452])
print(data[-3:])

Output

[ 53  52 452]

Two Dimensional Slicing
Program

import numpy as np

data= np.array([[23,234,23423,42,34], [2,653,6,35,33]])

print(data[1, 1:3])

Output

[653   6]

Program

import numpy as np
data= np.array([[23,234,23423,42,34], [2,653,6,35,33]])
print(data[0:1, 1:3])

Output

[[  234 23423]]

Numpy Indexing
One Dimensional Indexing

Program

import numpy as np
arr = np.array([11,22,33,44,55,66,77,88])
print(arr[1])

Output

[2]

Program

import numpy as np
arr = np.array([11,22,33,44,55,66,77,88])
print(arr[-5])

Output

[44]

Two Dimensional Indexing

Program 1


import numpy as np
arrays = np.array([[11,22,33,44,55], [66,77,88,99,100]])
print('3nd element on 2st dim: ', arrays[1, 2])

Output

3nd element on 2st dim:  88

Program 2

import numpy as np

arrays = np.array([[11,22,33,44,55], [66,77,88,99,100]])

print('Backward printing 1nd element on 1st dim: ', arrays[-1,-1])

Output

3nd element on 2st dim:  100

Data Science

Python Numpy