Q.Write the python statements to print the sum of the english marks scored by Mishti.
[Table: The chapter's case study (Program 3.1) stores unit test marks of 4 students (maximum marks 25 in each subject) in a DataFrame df created from the dictionary marksUT = {'Name':['Raman','Raman','Raman','Zuhaire','Zuhaire','Zuhaire','Ashravy','Ashravy','Ashravy','Mishti','Mishti','Mishti'], 'UT':[1,2,3,1,2,3,1,2,3,1,2,3], 'Maths':[22,21,14,20,23,22,23,24,12,15,18,17], 'Science':[21,20,19,17,15,18,19,22,25,22,21,18], 'S.St':[18,17,15,22,21,19,20,24,19,25,25,20], 'Hindi':[20,22,24,24,25,23,15,17,21,22,24,25], 'Eng':[21,24,23,19,15,13,22,21,23,22,23,20]}.]

To calculate the sum of English marks scored by Mishti, we first need to filter the DataFrame to include only Mishti's records and then sum her scores in the 'Eng' column.

Here are the Python statements:

import pandas as pd

# Create the DataFrame from the given dictionary
marksUT = {'Name':['Raman','Raman','Raman','Zuhaire','Zuhaire','Zuhaire','Ashravy','Ashravy','Ashravy','Mishti','Mishti','Mishti'],
           'UT':[1,2,3,1,2,3,1,2,3,1,2,3],
           'Maths':[22,21,14,20,23,22,23,24,12,15,18,17],
           'Science':[21,20,19,17,15,18,19,22,25,22,21,18],
           'S.St':[18,17,15,22,21,19,20,24,19,25,25,20],
           'Hindi':[20,22,24,24,25,23,15,17,21,22,24,25],
           'Eng':[21,24,23,19,15,13,22,21,23,22,23,20]}
df = pd.DataFrame(marksUT)

# Filter the DataFrame for rows where 'Name' is 'Mishti'
mishti_data = df[df['Name'] == 'Mishti']

# Select the 'Eng' column from Mishti's data and calculate the sum
sum_english_marks_mishti = mishti_data['Eng'].sum()

# Print the result
print(f"The sum of English marks scored by Mishti is: {sum_english_marks_mishti}")

Explanation of Key Lines:

1. df = pd.DataFrame(marksUT): This line creates a Pandas DataFrame named df from the provided marksUT dictionary. This is the initial step to organize the data into a tabular structure. …

The case-study DataFrame df (built in Program 3-1 from the marksUT dictionary) stores 12 rows -- one row per student per Unit Test. To get a single student's total in one subject, we need two operations chained together: first filter the rows down to that student, then aggregate one column of the filtered result.

import pandas as pd

marksUT = {
    'Name': ['Raman', 'Raman', 'Raman', 'Zuhaire', 'Zuhaire', 'Zuhaire',
              'Ashravy', 'Ashravy', 'Ashravy', 'Mishti', 'Mishti', 'Mishti'],
    'UT': [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
    'Maths': [22, 21, 14, 20, 23, 22, 23, 24, 12, 15, 18, 17],
    'Science': [21, 20, 19, 17, 15, 18, 19, 22, 25, 22, 21, 18],
    'S.St': [18, 17, 15, 22, 21, 19, 20, 24, 19, 25, 25, 20],
    'Hindi': [20, 22, 24, 24, 25, 23, 15, 17, 21, 22, 24, 25],
    'Eng': [21, 24, 23, 19, 15, 13, 22, 21, 23, 22, 23, 20]
}
df = pd.DataFrame(marksUT)

Step 1 -- filter to Mishti's rows. The same pattern the textbook itself uses (Program 3-3, filtering to Mishti to find her minimum marks): compare the Name column to 'Mishti', which produces a Boolean Series the same length as df -- True for the three rows belonging to Mishti (index 9, 10, 11), False for the other nine:

mask = df['Name'] == 'Mishti'
print(mask)

Output:

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
9      True
10     True
11     True
Name: Name, dtype: bool

Passing that mask back into df[...] keeps only the rows where it is True:

dfMishti = df[df['Name'] == 'Mishti']
print(dfMishti)

Output:

    Name  UT  Maths  Science  S.St  Hindi  Eng
9   Mishti   1     15       22    25     22   22
10  Mishti   2     18       21    25     24   23
11  Mishti   3     17       18    20     25   20

Step 2 -- select the Eng column and sum it. From dfMishti, ['Eng'] picks a single Series of three values, and .sum() adds them:

sum_english_marks_mishti = dfMishti['Eng'].sum()
print(f"The sum of English marks scored by Mishti is: {sum_english_marks_mishti}")

Output:

The sum of English marks scored by Mishti is: 65
``` …