CS3362 - Data Science Lab

 CS3362 - Data Science Lab 

Numpy - array manipulation

 # numpy

import numpy as np

import random

# Creating a random 2d array object

M1 = np.random.randint(10, size=(2,2))

M2= np.random.randint(10, size=(2,2))

print("Generated array object")

print(M1)

print(M2)

# Array operations

print("Array operations")

print("Sum")

print(np.add(M1,M2))

print("Difference")

print(np.subtract(M1,M2))

print("Product")

print(np.dot(M1,M2))

print("Division")

np.set_printoptions(precision=2)

print(np.divide(M1,M2))

"""

Sample output

>python 8_5_numpy2.py

Generated array object

[[8 3]

 [0 7]]

[[8 5]

 [5 6]]

 

Array operations

Sum

[[16  8]

 [ 5 13]]

Difference

[[ 0 -2]

 [-5  1]]

Product

[[79 58]

 [35 42]]

Division

[[1.   0.6 ]

 [0.   1.17]]

 

"""

Numpy - Creating arrays

 # numpy

import numpy as np

import random

# Creating a random 2d array object of size 2 x 3

M1 = np.random.randint(10, size=(2,3))

print("Generated array object")

print(M1)

# About array object

print("About array object")

print("Type : ", type(M1))

  

# Array dimensions (axes)

print("Number of dimensions : ", M1.ndim)

  

# Shape shape of array

print("Shape of array: ", M1.shape)

  

# Size (total number of elements)

print("Size of array (total number of elements) : ", M1.size)

  

# Data type of elements in array

print("Data type : ", M1.dtype)

# Manipulation of array data

print("Data manipulation")

print("Sum of all elements : ",np.sum(M1))

print("Sum of column elements")

print(np.sum(M1,axis=0))

print("Sum of row elements")

print(np.sum(M1,axis=1))

# Transpose of the 2d array

print("Transpose")

print(M1.T)

"""

Sample output

>python 8_5_numpy1.py

Generated array object

[[1 6 9]

 [0 6 3]]

 

About array object

Type :  <class 'numpy.ndarray'>

Number of dimensions :  2

Shape of array:  (2, 3)

Size of array (total number of elements) :  6

Data type :  int32

Data manipulation

Sum of all elements :  25

Sum of column elements

[ 1 12 12]

Sum of row elements

[16  9]

Transpose

[[1 0]

 [6 6]

 [9 3]]

 

"""