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 🧠 ASSERTION–REASON QUESTIONS (Q101–Q110)

Directions:
Choose the correct option:
A. Both A and R are true, and R is the correct explanation of A
B. Both A and R are true, but R is not the correct explanation of A
C. A is true, R is false
D. A is false, R is true

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Q101

Assertion (A): Pandas Series can store data of different data types.
Reason (R): Pandas Series is built on NumPy array.

✅ Ans: A

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Q102

A: A Pandas Series is mutable.
R: Values of a Series can be changed after creation.

✅ Ans: A

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Q103

A: dropna() removes missing values from a Series.
R: Missing values are represented by NaN in Pandas.

✅ Ans: A

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Q104

A: s.size and s.count() always return the same value.
R: s.count() ignores NaN values.

✅ Ans: D

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Q105

A: iloc[] uses integer-based indexing.
R: iloc[] uses labels for indexing.

✅ Ans: C

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Q106

A: loc[] can use string labels.
R: loc[] is label-based indexing.

✅ Ans: A

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Q107

A: fillna() replaces missing values in a Series.
R: Missing values cannot be replaced in Pandas.

✅ Ans: C

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Q108

A: Arithmetic operations in Series are vectorized.
R: Operations are applied element-wise.

✅ Ans: A

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Q109

A: value_counts() returns frequency of unique values.
R: It sorts the output in descending order by default.

✅ Ans: B

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Q110

A: astype() is used to change data type of Series.
R: Data type of Series cannot be modified.

✅ Ans: C

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🧪 OUTPUT-BASED QUESTIONS (Q111–Q120)

Q111

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

✅ Output:

0    15
1    25
2    35
dtype: int64

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Q112

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

✅ Output: 3

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Q113

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

✅ Output: 10

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Q114

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

✅ Output: 10

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Q115

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

✅ Output: 4.0

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Q116

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

✅ Output:

[1 2 3]

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Q117

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

✅ Output: 2

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Q118

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

✅ Output:

1    10
2    15
dtype: int64

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Q119

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

✅ Output:

0    1
1    3
2    6
dtype: int64

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Q120

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

✅ Output:

0    False
1     True
2     True
dtype: bool

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📘 CASE-STUDY QUESTIONS (Q121–Q125)

Case Study

A teacher stores marks of students in a Pandas Series.

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

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Q121. How many valid marks are present?

✅ Ans: 4

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Q122. Which function will remove missing marks?

✅ Ans: dropna()

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Q123. Which function will replace missing marks with 0?

✅ Ans: fillna(0)

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Q124. Which function gives average marks?

✅ Ans: mean()

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Q125. Which function shows frequency of marks?

✅ Ans: value_counts()