Peform this in Python . Utilize vectorized calculations and not use any form of loops. Make a data frame consisting of 20 and 10 columns. Each column j should consist of 20 values from a normal...

Peform this in
Python. Utilize vectorized calculations and not use any form of loops.  Make a data frame consisting of 20 and 10 columns. Each
column j should consist of 20 values from a normal distribution with mean (j-1) and standard deviation 0.5j. For example, the third column should be normal(mean=2, sd=1.5). Using this data frame, do each of the following (using code, of course):

• Find the mean and standard deviation for each column.


• Write code that counts the number of columns for which the sample mean and sample standard deviation are within 20% of the values used to generate the data.


• Write code that writes the columns from part b to a new data frame.


• For each value in the new data frame, subtract its column mean and divide by the column standard deviation.