Q.Write the statement to display the first and third quartiles of all subjects.

dfSubject = df[['Maths','Science','S.St','Hindi','Eng']]
print(dfSubject.quantile([.25, .75]))

Output:

       Maths  Science  S.St   Hindi  Eng
0.25   16.50  18.00    18.75  20.75  19.75
0.75   22.25  21.25    22.50  24.00  23.00 …

What a "quantile" is, briefly

A quantile cuts the sorted data at a given fraction: q=0.25 (the first quartile, Q1) is the value below which 25% of the data falls; q=0.75 (the third quartile, Q3) is the value below which 75% falls. q=0.5 is the median — the same value DataFrame.median() returns. In fact, calling df.quantile() with no argument at all defaults to q=0.5.

The book's own two-step version (over the WHOLE df)

Before this program, the chapter shows .quantile() used one fraction at a time, over every numeric column including UT:

print(df.quantile(q=.25))

UT          1.00
Maths      16.50
Science    18.00
S.St       18.75
Hindi      20.75
Eng        19.75
Name: 0.25, dtype: float64

print(df.quantile(q=.75))

UT          3.00
Maths      22.25
Science    21.25
S.St       22.50
Hindi      24.00
Eng        23.00
Name: 0.75, dtype: float64

Two separate calls, two separate results — and a UT quartile that isn't actually meaningful (UT is a test number, 1/2/3, not a mark).

This program's version — subjects only, in one call

Since the question specifically asks for the quartiles "of all subjects" (not of UT), select just the five subject columns first, then request both quantiles in a single call by passing a list:

dfSubject = df[['Maths','Science','S.St','Hindi','Eng']]
dfQ = dfSubject.quantile([.25, .75])
print(dfQ)

Output:

       Maths  Science  S.St   Hindi  Eng
0.25   16.50  18.00    18.75  20.75  19.75
0.75   22.25  21.25    22.50  24.00  23.00
``` …