Which of the following allow a computer to host multiple flask sites on the same network at the same time?

Which of the following ferrous alloy types is most easily melted?

What is the typical carbon concentration range for medium-carbon steels?

The success of the steam engine in the Industrial Revolution made Britain dependent upon

Answer (A) and (B).  Each prompt should be 2-4 sentences.  Make sure to identify and explain.  This should take about 8 minutes to answer.  A.  Describe ONE  specific example of a policy or action in England, France, Austria-Hungary or Russia that supported mass politics and unified the nation.   B.  Describe ONE  specific example of a policy or action in England, France, Austria-Hungary or Russia that ignored mass politics and divided the nation.  

Question 3: Assume that there exists a file named “numbersWords.txt” in the local disk. Each line of this file consists of some integer numbers (int) and words (str). Write a program that finds the largest number in this file, and prints out this number as well as the number of the line this number was in. Below is a sample input and output: Input – numbersWords.txt: random 346 numbers and 233 words are in 306 This file you 15663 need to find 4 the largest number here 592 Output: The largest number: 15663 Found in line: 3

Question 4: Download the following file to your local machine: NHL_data.csv The data set contains statistics for some of the National Hockey League teams across the years. Question: Perform the tasks listed below using the Pandas Library. Notice that these tasks are divided into 3 cells. Please only use the designated cell area in the Jupyter file to write down your corresponding code. After importing the file at the beginning, you can continue working on the same data (i.e., you don’t need to re-import the data for each individual cell). Cell #1: Import the downloaded data into the Python environment as a Pandas DataFrame. Drop the column titled “GamesPlayed” from the DataFrame. Select the first 10 observations and the first 5 columns from the data and display the values. Do NOT assign the selection to anything — simply display the values on the screen. Cell #2: Display all observations that had more than 40 “Shots”. Show all the columns for these observations. Select and display all observations in Year 2011 that had more “Wins” than the average number of “Wins” for that particular year (2011). Cell #3: Notice that the “SeasonWinner” column indicates the season winner for a given year (The value of that column is “Y” if winner of the “Year”). Find out which team won the season the most across the years. Do the necessary operation in Pandas and print out a single output (can be a table or a string) that states the team’s name and the number of season wins.   

A Jupyter template for the exam has been provided to you above. Complete the template and use this text box area to submit the Secret Gist ID of your code (saved in your GitHub account). Use the next question (file upload question) to attach the .ipynb file.

Question 1: Note: This question has 2 steps. Step 1: Write a function that takes two inputs: 1) a string consisting of a single letter (e.g., ‘e’) 2) a long string consisting of some letters (e.g., ‘HELLo WORld’) and counts how many times the single letter (1st input) occurs in the long string (2nd input). The function should ignore capitalization – i.e., uppercase ‘E’ should be counted as ‘e’, etc. The output of the function should be the frequency count of the letter. Step 2: Write a loop that iteratively calls the function you developed in the first step to count how many times each letter in a given list (e.g., ) occurs in the long string consisting of some letters (2nd input of Step 1). Below is a sample output after completing both steps. longString = ‘HELLo WORld’ aList = Output (after defining the function and running the loop):  The letter a occurs 0 times in the long string. The letter e occurs 1 times in the long string. The letter o occurs 2 times in the long string.

Which of the following is one of the limitations of Numpy arrays?