sahasourav17

#include<stdio.h>
int main()
{
    int n , k ,w;
    int i ,sum = 0;
    int dollar;
    scanf("%d %d %d",&k,&n,&w);
    for(i = 1;i <= w;i++)
    {
        sum = sum + i*k;

sahasourav17

import pyqrcode

def qrcode():
    q = pyqrcode.create(input())
    q.png('gen_qrcode.png',scale=7)
    print('qrcode generated')

if __name__=='__main__':
    qrcode()

sahasourav17

#import needed libraries
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
import matplotlib.pyplot as plt

#set the hyper-parameters
learning_rate = 0.001
training_epochs = 1000

sahasourav17

#Function to check two strings are anagrams or not
def anagrams(s1,s2):

    """
    we first check if both strings have the same length
    because if they're not it's impossible for them to
    be anagrams.
    """

    if len(s1) != len(s2):

sahasourav17

from collections import Counter

def anagrams(s1,s2):
    """
    As seen in method 2, we first check if both strings have the 
    same length because if they're not it's impossible for them
    to be anagrams.
    """
    if len(s1) != len(s2):
        return False

sahasourav17

def anagrams(s1,s2):
    if len(s1) != len(s2):
        return False
    return sorted(s1) == sorted(s2)

sahasourav17

#import uniform distribution
from scipy.stats import uniform
import seaborn as sns
import matplotlib.pyplot as plt

#taking random variables from uniform distribution
data = uniform.rvs(size = 10000,loc = 5,scale = 10)

#plotting the uniform data
ax = sns.distplot(data,

sahasourav17

#importing required libraries
from scipy.stats import norm
import seaborn as sns
import matplotlib.pyplot as plt

#creating the normal distribution
data = norm.rvs(size=10000,loc=0,scale=1)

#plotting the  data
ax = sns.distplot(data,

sahasourav17

#importing required libraries
from scipy.stats import expon
import seaborn as sns
import matplotlib.pyplot as plt

#creating the exponential distribution
data = expon.rvs(loc=0,scale=10,size=10000)

#plotting the  data
ax = sns.distplot(data,

sahasourav17

#importing required libraries
from scipy.stats import binom
import seaborn as sns
import matplotlib.pyplot as plt

#creating the binomial distribution
data = binom.rvs(n=20,p=0.7,size=10000)

#plotting the  data
ax = sns.distplot(data,