Statistical and Extrema operations on Numpy Array

import numpy as np
x = np.array([11, 13, 121, 181, 99, 100])
print('Numpy Array Elements',x)
print ('Minimum Value in array',x.min())
print ('Maximum Value in array',x.max())
print ('Index of Minimum Value',x.argmin())
print ('Index of Maximum Value',x.argmax())
print ('Mean of Array Values',x.mean())
print ('Median of Array Values',np.median(x))
print ('Standard deviation of Array Values',x.std())

Elementwise sum of two array elemets

import numpy as np
x = [[11,22],[33,44]]
y = [[55,66],[77,88]]
x1 = np.array([[11,22],[33,44]], dtype=np.int32)
y1 = np.array([[55,66],[77,88]], dtype=np.int32)
print("ADD USING LIST\n",x + y)
print("ADD NUMPY ARRAY\n",np.add(x1, y1))

Datatypes in Numpy

import numpy as np

x = np.array([101, 202])  

x = np.array([11.75, 21.75]) 

x = np.array([1, 2], dtype=np.int64) 

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

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)