Q.Try to write a short code to get the above output. Remember to print the relevant headings of the output.
[Context: The referenced textbook output is the average of marks obtained by Zuhaire in all Unit Tests, produced by:

dfZuhaireMarks = dfZuhaire.loc[:,'Maths':'Eng']
print("Average of marks obtained by Zuhaire in all Unit Tests
", dfZuhaireMarks.mean(axis=1))
giving:
3 20.4
4 19.8
5 19.0
dtype: float64]

Shape: Code task — write a pandas program to compute row-wise means for a subset of columns and print them with a heading.

The question asks you to reproduce the output shown: the average marks across subjects (columns 'Maths' through 'Eng') for each Unit Test (rows indexed 3, 4, 5) for student Zuhaire. The key is to slice the DataFrame to select only the numeric mark columns, then compute the mean along axis=1 (across columns, giving one value per row).

import pandas as pd

# Assume dfZuhaire is already defined with rows indexed 3, 4, 5
# and columns including 'Maths', ..., 'Eng' (the mark columns)

# Example construction (if dfZuhaire is not given):
dfZuhaire = pd.DataFrame({
    'Name': ['Zuhaire', 'Zuhaire', 'Zuhaire'],
    'Maths': [22, 21, 20],
    'Science': [19, 18, 18],
    'Eng': [20, 20, 19]
}, index=[3, 4, 5])

# Slice to select only the mark columns from 'Maths' to 'Eng'
dfZuhaireMarks = dfZuhaire.loc[:, 'Maths':'Eng']

# Compute the mean across columns (axis=1) for each row
print("Average of marks obtained by Zuhaire in all Unit Tests")
print(dfZuhaireMarks.mean(axis=1))

Expected output:

Average of marks obtained by Zuhaire in all Unit Tests
3    20.4
4    19.8
5    19.0
dtype: float64

Key lines: …

This activity immediately follows Program 3-6, which computes the same statistic for Zuhaire using a dfZuhaire that is assumed already filtered. To write a genuinely short, self-contained version, we build it starting from the full case-study DataFrame so every step is traceable back to real data:

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)

# Filter to Zuhaire's three rows (index 3, 4, 5)
dfZuhaire = df[df['Name'] == 'Zuhaire']

# Print heading + row-wise average of the subject-mark columns
print("Average of marks obtained by Zuhaire in all Unit Tests\n",
      dfZuhaire.loc[:, 'Maths':'Eng'].mean(axis=1))

Output:

Average of marks obtained by Zuhaire in all Unit Tests
 3    20.4
4    19.8
5    19.0
dtype: float64

This matches the book's printed result exactly, and each value can be verified by hand from Zuhaire's real marks:

• UT1 (index 3): Maths 20, Science 17, S.St 22, Hindi 24, Eng 19 → $(20+17+22+24+19)/5 = 102/5 = 20.4$
• UT2 (index 4): Maths 23, Science 15, S.St 21, Hindi 25, Eng 15 → $(23+15+21+25+15)/5 = 99/5 = 19.8$
• UT3 (index 5): Maths 22, Science 18, S.St 19, Hindi 23, Eng 13 → $(22+18+19+23+13)/5 = 95/5 = 19.0$

Key lines explained:

• df[df['Name'] == 'Zuhaire'] -- the same Boolean-mask filter used throughout the chapter; it returns Zuhaire's three original rows, keeping their original index labels (3, 4, 5) rather than renumbering from 0.
• .loc[:, 'Maths':'Eng'] -- .loc with a column label slice. Because the DataFrame's columns are in the fixed order Name, UT, Maths, Science, S.St, Hindi, Eng, slicing from 'Maths' to 'Eng' grabs every column in between inclusively -- all five subject-mark columns -- without having to type out each name, and without including Name/UT. …