Q.Write the python statements to print average marks in Science by all the students in each UT.
[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]}.]

Shape of Question

This is a code task requiring a pandas operation to compute the grouped mean of the Science column by UT (unit test number).

Solution

The question asks for the average marks in Science across all students for each unit test. This is a grouped aggregation: group the DataFrame by the UT column, then compute the mean of the Science column within each group.

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)

print(df.groupby('UT')['Science'].mean())

Expected Output:

UT
1    19.75
2    19.50
3    20.00
Name: Science, dtype: float64

Explanation of Key Lines …

This activity comes directly after the chapter introduces GROUP BY (section 3.5), which the book itself frames as a split-apply-combine process:

1. Split -- break the DataFrame into groups based on some column's values.
2. Apply -- run an aggregate function (mean, sum, max, ...) on each group separately.
3. Combine -- assemble the per-group results back into a single result (a Series or DataFrame).

Here, "average marks in Science by all the students in each UT" means we want to split by UT (not by Name), because we want one number per Unit Test, pooling all four students together.

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)

avg_science_by_ut = df.groupby('UT')['Science'].mean()
print(avg_science_by_ut)

Output:

UT
1    19.75
2    19.50
3    20.00
Name: Science, dtype: float64

The bigger picture -- this is one column of a larger aggregate. The book's own worked GROUP BY example (right before this activity) computes the mean of every subject at once with df.groupby(['UT']).aggregate('mean'):