# NaN

The problem to solve is as follows: use this data frame:exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’],
‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]}
labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’]For this data frame, write the code in python using Pandas and answer the following questions:Get the first three rows of data
Select the ‘name’ and ‘score’ columns
Select ‘name’ and ‘score’ columns in rows 1, 3, 5, 6
select the rows where the number of attempts in the examination is greater than 2
count the number of rows and columns
select the rows where the score is missing, i.e. is NaN
select the rows the score is between 15 and 20 (inclusive)
select the rows where number of attempts in the examination is less than 2 and score greater than 15
calculate the sum of the examination attempts by the students
calculate the mean score for each different student