siddydutta

/*
The Magic Wand
You are a wizard who possesses a magical wand that can be used to change the values of an array. Your wand has two powers: Increase and Decrease. With each use of the wand, you can either increase or decrease any element of the array by 1.
One day, a group of villagers came to you with a problem. They had an array of positive integers of size N and a set of queries of size M. For each query, queries[i], they wanted to make all the elements of the array equal to queries[i] using your magic wand.
To help the villagers, you decided to use your magic wand to perform the operations. However, each time you perform an operation, the cost of using your wand increases. The cost of using your wand for an operation on an element is equal to the absolute difference between the value of the element and the desired value after the operation.


Example:
If you want to change an element from 5 to 3, it will cost you 2. If you want to change an element from 7 to 8, it will cost you 1.
You can perform any num

siddydutta

# -*- coding: utf-8 -*-
import os
import requests

'''
Environment variables should contain the following mappings:
AMADEUS_API_KEY = YOUR_API_KEY
AMADEUS_API_SECRET = YOUR_API_SECRET
'''

siddydutta

"""
A Python template to solve problems involving binary search.
Template based on this post: https://leetcode.com/problems/kth-smallest-number-in-multiplication-table/discuss/769704/Python-Clear-explanation-Powerful-Ultimate-Binary-Search-Template.-Solved-many-problems.
"""
def binary_search(array) -> int:
	def condition(value) -> bool:
		# Conditional logic based on question
		# Returns true if current value satisfies requirements
		# binary search will take care of finding the minimal val
		return

siddydutta

Azure Fundamentals Part 1: Describe core Azure concepts

Cloud computing is the delivery of computing services over the internet by using a pay-as-you-go pricing model. You typically pay only for the cloud services you use, which helps you:
	Lower your operating costs.
	Run your infrastructure more efficiently.
	Scale as your business needs change.

Types of Cloud Models
	Public cloud
		No capital expenditures to scale up.

siddydutta

# -*- coding: utf-8 -*-
'''
This is a program for University of Virginia's "Continous Delivery and DevOps"
course on Coursera. This script demonstrates the System Test example using
the Selenium webdriver.
'''


from random import randint
from selenium import webdriver

siddydutta

// HackerRank's NodeJS environment allows the `request` package.
const request = require('request');

function fetch(url) {
    return new Promise((resolve, reject) => {
        request(url, function (error, response, body) {
            if (error)
                reject(error)
            else
                resolve(body)

siddydutta

"""
A Python template to solve most problems involving substrings using a two-pointer approach.
Template based on this post: https://leetcode.com/problems/minimum-window-substring/discuss/26808/Here-is-a-10-line-template-that-can-solve-most-'substring'-problems
"""
def substring(string: str) -> int:
  counter = int()  # To check if the substring is valid
  left, right = 0, 0  # Two pointers, right pointer is exclusive
  length = int()  # Length of the substring
  map = dict()  # Intialize the hash map here, collections.Counter is useful
  

siddydutta

# -*- coding: utf-8 -*-
"""
Questions based on the session on Dynamic Programming by Apaar Kamal.
https://join.codingminutes.com/dp-08


Alice and Bob are playing a game. Alice plays first.
We have a pile of n stones.

Q1.

siddydutta

# -*- coding: utf-8 -*-
import cv2
import numpy as np


def convulate(image, kernel, padding=False, paddingType=cv2.BORDER_CONSTANT):
    '''
    @param image: 2D array of type <numpy.ndarray> for given image.
    @param kernel: 2D array of type <numpy.ndarray> for given kernel.
    @param padding: Type <bool> to indicate if padding required.

siddydutta

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

raw_data = pd.read_csv('data.csv',encoding='latin1',thousands=',')

# Top 5 Countries Affected
data = raw_data[['Country,Other','TotalCases','TotalDeaths','TotalRecovered']]
most_affected = data.sort_values('TotalCases', ascending=False)[:5]
plt.figure(figsize=(10,5))