## Blog Pages

### Datatypes in Numpy

import numpy as np

x = np.array([101, 202])
print(x.dtype)

x = np.array([11.75, 21.75])
print(x.dtype)

x = np.array([1, 2], dtype=np.int64)
print(x.dtype)

x = np.array([1, 2], dtype=np.complex128)
print(x.dtype)

### 2D-array representation using with & without numpy implementation

import numpy as np

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print ("Using Numpy\n",a)

b=[[1,2,3,4], [5,6,7,8], [9,10,11,12]]
print ("Without Numpy\n", b)

### Matrix representation and version detailsof Numpy

import numpy as np
print("\nVersion of Numpy is ",np.__version__)
x =  np.arange(25, 50).reshape(5,5)
print("\n Matrix representation in Numpy\n",x)

### Different Set Operations

A = {1,2,3,4,5,6,7,8};
B = {5,10,15,20,25,30,35,40};

print("Union of A and B is",A | B)

print("\nIntersection of A and B is",A & B)

print("\nDifference of A and B is",A - B)

print("\nSymmetric difference of A and B is",A ^ B)

### Use pass for an empty block

```#Credits to NPTEL MOOC, Programming, Data Structures & Algorithms in
#Python by Madhavan Mukund, Chennai Mathematical Institute ```

while(True):
try:
userdata = input("Enter a number: ")
usernum = int(userdata)
except ValueError:
print("Not a number. Try again")
except NameError:
pass
else:
break