4.Question 4What does this code generate?12sigma = daily_return_df['FB'].std() 1 pointStandard deviation of Facebook daily returnsDispersion of Facebook daily returns away from the meanMean of Facebook daily returnsVariance of Facebook daily returns5.Question 5What will this code generate?

What will the following code generate?portfolio_df = pd.DataFrame({'stock ticker symbols' : ['FB', 'TSLA', 'T'],'price per share [$]' : [20, 30, 40],'Number of stocks' : [1, 1, 1]})stocks_dollar_value = portfolio_df['price per share [$]'] * portfolio_df['Number of stocks']print('Total portfolio value = {}'.format(stocks_dollar_value.sum()))A.Total portfolio value = 90B.Total portfolio value = 100C.Total portfolio value = 2,400D.Total portfolio value = 600

What will the following code generate?my_list = [20, 3, 10]total = 1for i in my_list:   total = total * itotalA.500B.600C.33

2.Question 2What does the following code return?device_id = "uu0ktt0vwugjyf2"print(device_id[2:5])1 point"u0kt""0ktt""0kt""u0k"

Which line of code generates 10,000 normally distributed numbers with a mean of 50 and a standard deviation of 2?test_data1 = np.normal.random(50, 2, 10000)test_data1 = np.random.normal (2, 50, 10000)test_data1 = np.random.normal(50, 2, 10000)test_data1 = np.normal.random(2, 50, 10000)

Q.7 What will be output for the following code?import pandas as pdimport numpy as nps = pd.Series(np.random.randn(4))print s.ndim1. 02. 13. 24. 3