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.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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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. 
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