Q.Consider the data of unit test marks given at program 3.1, write the python statements to print name wise UT marks in mathematics.
[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]}.]

This is a code task — you are asked to write Python statements that print name-wise UT marks in Mathematics from the given DataFrame.

The DataFrame df has one row per student per UT (3 UTs per student, 12 rows total). "Name wise" means you need to group by Name and show all Maths marks for each student. The cleanest way is to use groupby() and then print the 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)

# Group by Name and collect Maths marks into a list
grouped = df.groupby('Name')['Maths'].apply(list)

# Print name-wise
for name, marks in grouped.items():
    print(f"{name}: {marks}")

Output:

Ashravy: [23, 24, 12]
Mishti: [15, 18, 17]
Raman: [22, 21, 14]
Zuhaire: [20, 23, 22]
``` …

The case-study DataFrame df stores data in long format: each row is one student's marks in one Unit Test, so a given student's Maths marks are spread across three separate rows.

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)

"Name wise UT marks in mathematics" is exactly what reshaping solves: we want the data turned around so that Name becomes the row label, UT becomes the column label, and Maths fills in the cells -- a wide format. This is the identical pattern the chapter just taught with pivot1 = df.pivot(index='Store', columns='Year', values='Total_sales(Rs)') for the Store/Year sales example; here Name plays the role Store played, and UT plays the role Year played.

name_wise_maths = df.pivot(index='Name', columns='UT', values='Maths')
print(name_wise_maths)

Output:

UT        1   2   3
Name
Ashravy  23  24  12
Mishti   15  18  17
Raman    22  21  14
Zuhaire  20  23  22

Why pivot() works cleanly here: pivot() requires that every (index, columns) pair be unique -- i.e. there must be exactly one row for each (Name, UT) combination, otherwise pandas raises a ValueError (it wouldn't know which value to place in a cell with more than one match). Since each student has exactly one row per Unit Test in this DataFrame, (Name, UT) pairs are all unique, so the pivot succeeds without needing an aggregate function.

Alternative: groupby() with apply(list). If instead of a readable grid you want each student's three Maths marks collected as a Python list (useful if you plan to process them further in code), groupby() is the tool:

name_wise_list = df.groupby('Name')['Maths'].apply(list)
print(name_wise_list)

Output:

Name
Ashravy    [23, 24, 12]
Mishti     [15, 18, 17]
Raman      [22, 21, 14]
Zuhaire    [20, 23, 22]
Name: Maths, dtype: object
``` …