Python -Zufallszahl
from random import randint
print(randint(1,3))
#Possible Outputs#
#1
#2
#3
DatMADCoder
from random import randint
print(randint(1,3))
#Possible Outputs#
#1
#2
#3Name = input("Enter name of student: ")
Address = input("Enter Full Address: ")
Mobile = int(input("Enter Mobile NUmber: "))
Colg = input("Enter name of College: ")
Course = input("Enter the Course Name: ")
Sub1 = input("Enter the core subject1: ")
Sub2 = input("Enter the core subject2: ")
print()
print("Name:", Name, "Address:", Address, "Mobile:", Mobile, "College:", Colg, "Course:", Course, "
Sub1:", Sub1, " Sub2:", Sub2)
print()
print()
print("Name:", Name)
print("Address:", Address)
print("Mobile:", Mobile)
print("College:", Colg)
print("Course:", Course)
print("Sub1:", Sub1)
print("Sub2:", Sub2)
list1=[]
i=int(input('Enter the length of the list : '))
for x in range(0,i):
y=float(input('Enter the numbers for the list : '))
list1.append(y)
print(f'Your Inputted List is : {list1}')
#To Find the largest number
max1=max(list1)
#To Find the position of the largest number
index1=list1.index(max1)
print(f'The largest number of the list is {max1}, and the position is{index1}.')
Name = input("Enter name of student: ")
Address = input("Enter Full Address: ")
Mobile = int(input("Enter Mobile NUmber: "))
Colg = input("Enter name of College: ")
Course = input("Enter the Course Name: ")
Sub1 = input("Enter the core subject1: ")
Sub2 = input("Enter the core subject2: ")
print()
print("Name:", Name, "Address:", Address, "Mobile:", Mobile, "College:", Colg, "Course:", Course, "
Sub1:", Sub1, " Sub2:", Sub2)
print()
print()
print("Name:", Name)
print("Address:", Address)
print("Mobile:", Mobile)
print("College:", Colg)
print("Course:", Course)
print("Sub1:", Sub1)
print("Sub2:", Sub2)
import numpy as np
import sys
def inverse(a):
n = len(a) #defining the range through which loops will run
#constructing the n X 2n augmented matrix
P = np.eye(n)
a = np.concatenate((a, P), axis=1)
#main loop for gaussian elimination begins here
for k in range(n):
if abs(a[k][k]) < 1.0e-12:
for i in range(k+1, n):
if abs(a[i][k]) > abs(a[k][k]):
for j in range(k, 2*n):
a[k][j], a[i][j] = a[i][j], a[k][j] #swapping of rows
break
pivot = a[k][k] #defining the pivot
if pivot == 0: #checking if matrix is invertible
print("This matrix is not invertible.")
return
else:
for j in range(k, 2*n): #index of columns of the pivot row
a[k][j] /= pivot
for i in range(n): #index the subtracted rows
if i == k or a[i][k] == 0: continue
factor = a[i][k]
for j in range(k, 2*n): #index the columns for subtraction
a[i][j] -= factor * a[k][j]
for i in range(len(a)): #displaying the matrix
for j in range(n, len(a[0])):
print(a[i][j], end = " ")
print()
a = np.array([[0, 2, 1], [1, 1, 2], [2, 1, 1]])
# n = len(a)
# a = np.concatenate((a, np.eye(n)), axis=1)
print(inverse(a))