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 🧠 ASSERTION–REASON QUESTIONS (Q101–Q110)

⚖️ Q101–Q110: Assertion & Reason Questions

📋 Directions & Evaluation Key

• Option A: Both A and R are true, and R is the correct explanation of A
• Option B: Both A and R are true, but R is not the correct explanation of A
• Option C: A is true, R is false
• Option D: A is false, R is true

QUESTION 101

Assertion (A): Pandas Series can store data of different data types.

Reason (R): Pandas Series is built on NumPy array.

✅ Correct Option: A

QUESTION 102

Assertion (A): A Pandas Series is mutable.

Reason (R): Values of a Series can be changed after creation.

✅ Correct Option: A

QUESTION 103

Assertion (A): dropna() removes missing values from a Series.

Reason (R): Missing values are represented by NaN in Pandas.

✅ Correct Option: A

QUESTION 104

Assertion (A): s.size and s.count() always return the same value.

Reason (R): s.count() ignores NaN values.

✅ Correct Option: D

QUESTION 105

Assertion (A): iloc[] uses integer-based indexing.

Reason (R): iloc[] uses labels for indexing.

✅ Correct Option: C

QUESTION 106

Assertion (A): loc[] can use string labels.

Reason (R): loc[] is label-based indexing.

✅ Correct Option: A

QUESTION 107

Assertion (A): fillna() replaces missing values in a Series.

Reason (R): Missing values cannot be replaced in Pandas.

✅ Correct Option: C

QUESTION 108

Assertion (A): Arithmetic operations in Series are vectorized.

Reason (R): Operations are applied element-wise.

✅ Correct Option: A

QUESTION 109

Assertion (A): value_counts() returns frequency of unique values.

Reason (R): It sorts the output in descending order by default.

✅ Correct Option: B

QUESTION 110

Assertion (A): astype() is used to change data type of Series.

Reason (R): Data type of Series cannot be modified.

✅ Correct Option: C

🧪 Q111–Q120: Output-Based Coding Questions

QUESTION 111

import pandas as pd
s = pd.Series([10, 20, 30])
print(s + 5)

🖥️ Console Output View:

0 15 1 25 2 35 dtype: int64

QUESTION 112

s = pd.Series([1, 2, None, 4])
print(s.count())

🖥️ Console Output View:

3

QUESTION 113

s = pd.Series([5, 10, 15], index=['a','b','c'])
print(s.loc['b'])

🖥️ Console Output View:

10

QUESTION 114

s = pd.Series([10, 20, 30])
print(s.iloc[0])

🖥️ Console Output View:

10

QUESTION 115

s = pd.Series([2, 4, 6])
print(s.mean())

🖥️ Console Output View:

4.0

QUESTION 116

s = pd.Series([1, 1, 2, 3])
print(s.unique())

🖥️ Console Output View:

[1 2 3]

QUESTION 117

s = pd.Series([10, 20, 30])
print(s.idxmax())

🖥️ Console Output View:

2

QUESTION 118

s = pd.Series([5, 10, 15])
print(s.tail(2))

🖥️ Console Output View:

1 10 2 15 dtype: int64

QUESTION 119

s = pd.Series([1, 2, 3])
print(s.cumsum())

🖥️ Console Output View:

0 1 1 3 2 6 dtype: int64

QUESTION 120

s = pd.Series([10, 20, 30])
print(s > 15)

🖥️ Console Output View:

0 False 1 True 2 True dtype: bool

📘 Q121–Q125: Case-Study Based Questions

📖 Case Scenario Background

A school teacher tracks and processes the examination marks securements of multiple student blocks within a single isolated structural Pandas Series array instance:

import pandas as pd
marks = pd.Series([78, 85, None, 90, 85])

QUESTION 121

How many valid non-null marks are present inside the tracking array?

✅ Answer: 4

QUESTION 122

Which function will clean out and remove all missing marks entries completely?

✅ Answer: dropna()

QUESTION 123

Which function will replace missing structural marks fields with a default score of 0?

✅ Answer: fillna(0)

QUESTION 124

Which analytical aggregate function calculates and gives the mathematical average marks?

✅ Answer: mean()

QUESTION 125

Which distribution function calculates and shows the frequency counts of marks?

✅ Answer: value_counts()