Q.Can you write a shortened code to get the output of Program 3.4?
[Context: Program 3.4 — Write the python statement to print the total marks secured by raman in each subject. The textbook's code is:

dfRaman=df[df['Name']=='Raman']
print("Marks obtained by Raman in each test are:
", dfRaman)
dfRaman[['Maths','Science','S.St','Hindi','Eng']].sum()]

To provide a shortened code for Program 3.4, we first understand its objective: to print the total marks secured by 'Raman' in each subject. The original code from the textbook performs two main actions: first, it filters the DataFrame for 'Raman' and prints the individual test scores; second, it calculates and prints the sum of marks for 'Raman' across specified subjects.

The problem statement for Program 3.4 explicitly asks for "the total marks secured by raman in each subject". The second print statement in the textbook's code directly addresses this requirement by summing Raman's scores for each subject across all available tests. The first print statement, showing individual test scores, serves more as an intermediate display.

Therefore, a "shortened code" to get the relevant output of Program 3.4 (i.e., the total marks per subject) can be achieved by chaining the pandas operations into a single, concise statement.

Let's assume we have a DataFrame df with student data, including multiple entries for the same student (e.g., for different tests), as shown below:

import pandas as pd

# Assume 'df' is a DataFrame with student data
data = {
    'Name': ['Raman', 'Suman', 'Raman', 'Priya', 'Raman'],
    'Test': [1, 1, 2, 1, 3],
    'Maths': [80, 70, 85, 90, 78],
    'Science': [75, 80, 70, 88, 82],
    'S.St': [90, 85, 92, 95, 89],
    'Hindi': [85, 90, 88, 82, 91],
    'Eng': [70, 75, 72, 80, 77]
}
df = pd.DataFrame(data)

The original Program 3.4 code would produce the following output for the sum of marks:

Maths      243
Science    227
S.St       271
Hindi      264
Eng        219
dtype: int64

Shortened Code

The shortened code combines the filtering, column selection, and aggregation into a single line using method chaining.

# Shortened code to print the total marks secured by Raman in each subject
print(df[df['Name'] == 'Raman'][['Maths', 'Science', 'S.St', 'Hindi', 'Eng']].sum())

Explanation of Key Lines

1. df[df['Name'] == 'Raman']: This part filters the DataFrame df to select only the rows where the 'Name' column has the value 'Raman'. This creates a temporary DataFrame containing all of Raman's records. …

Program 3.4 in the textbook is written as three separate statements:

dfRaman = df[df['Name'] == 'Raman']
print("Marks obtained by Raman in each test are:\n", dfRaman)
dfRaman[['Maths', 'Science', 'S.St', 'Hindi', 'Eng']].sum()

Using the chapter's own case-study data (marksUT from Program 3-1):

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)

...the full three-statement version produces:

dfRaman = df[df['Name'] == 'Raman']
print("Marks obtained by Raman in each test are:\n", dfRaman)

Output:

Marks obtained by Raman in each test are:
    Name  UT  Maths  Science  S.St  Hindi  Eng
0  Raman   1     22       21    18     20   21
1  Raman   2     21       20    17     22   24
2  Raman   3     14       19    15     24   23

dfRaman[['Maths', 'Science', 'S.St', 'Hindi', 'Eng']].sum()

Output:

Maths      57
Science    60
S.St       50
Hindi      66
Eng        68
dtype: int64

What "shortened" really means here. Program 3.4's purpose is the final Series of subject totals -- the intermediate print(dfRaman) line only exists to let you see Raman's three raw rows before they're summed; it is not part of the actual answer. So the "output of Program 3.4" a shortened version needs to reproduce is that final totals Series, and the filter + column-selection + .sum() can all be chained into a single expression without ever creating the named variable dfRaman:

print(df[df['Name'] == 'Raman'][['Maths', 'Science', 'S.St', 'Hindi', 'Eng']].sum())

Output:

Maths      57
Science    60
S.St       50
Hindi      66
Eng        68
dtype: int64

Key lines explained:

• df[df['Name'] == 'Raman'] -- filters df down to Raman's three rows (index 0, 1, 2), using the same Boolean-mask mechanism as the original code's dfRaman = df[df['Name'] == 'Raman']. …