CBSE Class 12

Q-1

Which Python library is primarily used for data analysis and handling tabular data?

Easy Python Libraries

Answer: Pandas

Explanation: Pandas is specifically designed for data analysis and handling structured data.

Q-2

Which data structure in Pandas is one-dimensional?

Easy Pandas Series

Answer: Series

Explanation: A Pandas Series is a one-dimensional labeled array.

Q-3

How many elements are present in the following Series?

Easy Pandas Series

Sample Data

s = [10, 20, 30, 40]

Reference Code


import pandas as pd
s = pd.Series([10, 20, 30, 40])

Answer: 4

Explanation: The Series is created from a list containing four values.

Q-4

Which attribute is used to display the values of a Pandas Series?

Easy Pandas Series

Answer: values

Explanation: The values attribute returns the data stored in the Series.

Q-5

Which object represents rows and columns in Pandas?

Easy Pandas DataFrame

Answer: DataFrame

Explanation: A DataFrame is a two-dimensional structure with rows and columns.

Q-6

How many columns are present in the given DataFrame?

Easy Pandas DataFrame

Sample Data

Name	Age	Marks
Anu	16	85
Ravi	17	78

Reference Code


import pandas as pd
df = pd.DataFrame({
    'Name': ['Anu', 'Ravi'],
    'Age': [16, 17],
    'Marks': [85, 78]
})

Answer: 3

Explanation: The DataFrame has three columns: Name, Age, and Marks.

Q-7

Which function is used to read data from a CSV file into a DataFrame?

Easy CSV Import Export

Answer: read_csv()

Explanation: The read_csv() function is used to read CSV files into Pandas DataFrames.

Q-8

Which function is used to write a DataFrame to a CSV file?

Easy CSV Import Export

Answer: to_csv()

Explanation: The to_csv() function writes a DataFrame to a CSV file.

Q-9

Which library provides the ndarray data structure?

Easy Pandas vs NumPy

Answer: NumPy

Explanation: NumPy provides the ndarray data structure for numerical computing.

Q-10

Which statement correctly distinguishes a Pandas Series from a NumPy ndarray?

Easy Pandas vs NumPy

Answer: Series supports labels while ndarray does not

Explanation: A Pandas Series has index labels, whereas a NumPy ndarray does not support labels.

Q-11

What will be the index values of the Series created by the code?

Medium Pandas Series

Sample Data

Data values: [100, 200, 300]

Reference Code


import pandas as pd
s = pd.Series([100, 200, 300])

Answer: [0, 1, 2]

Explanation: By default, Pandas assigns integer index values starting from 0.

Q-12

What will be the output of the following statement?

Medium Pandas Series

Sample Data

s = [10, 20, 30]

Reference Code


import pandas as pd
s = pd.Series([10, 20, 30])
print(s[1])

Answer: 20

Explanation: Index position 1 refers to the second element of the Series, which is 20.

Q-13

How many rows and columns does the DataFrame have?

Medium Pandas DataFrame

Sample Data

Name	Maths	Science
Anu	80	85
Ravi	75	78
Meena	90	88

Reference Code


import pandas as pd
df = pd.DataFrame({
    'Name': ['Anu', 'Ravi', 'Meena'],
    'Maths': [80, 75, 90],
    'Science': [85, 78, 88]
})

Answer: 3 rows and 3 columns

Explanation: The DataFrame has 3 rows (students) and 3 columns (Name, Maths, Science).

Q-14

Which column will be returned by the following statement?

Medium Pandas DataFrame

Sample Data

DataFrame with columns: Name, Age, Marks

Reference Code


print(df['Marks'])

Answer: Only Marks column as Series

Explanation: Accessing a single column using df['column'] returns a Series.

Q-15

Which function is used to display the first five rows of a DataFrame?

Medium Pandas DataFrame

Answer: head()

Explanation: The head() function displays the first five rows by default.

Q-16

What will be the shape of the DataFrame?

Medium Pandas DataFrame

Sample Data

A	B
1	4
2	5
3	6
4	7

Reference Code


import pandas as pd
df = pd.DataFrame({'A':[1,2,3,4],'B':[4,5,6,7]})
print(df.shape)

Answer: (4, 2)

Explanation: The DataFrame has 4 rows and 2 columns, so shape is (4, 2).

Q-17

Which parameter is used to prevent the index from being written to a CSV file?

Medium CSV Import Export

Answer: index=False

Explanation: Setting index=False prevents the index from being written to the CSV file.

Q-18

What type of object is returned by read_csv()?

Medium CSV Import Export

Answer: DataFrame

Explanation: The read_csv() function returns a Pandas DataFrame.

Q-19

Which statement about Pandas Series and NumPy ndarray is correct?

Medium Pandas vs NumPy

Answer: Only Series supports labeled indexing

Explanation: Pandas Series supports labeled indexing, while NumPy ndarray does not.

Q-20

Which attribute is used to check the data type of elements stored in a Series?

Medium Pandas Series

Answer: dtype

Explanation: The dtype attribute shows the data type of elements in a Series.

Q-21

What will be the output of the following code?

Hard Pandas Series

Sample Data

Series values: [5, 10, 15]

Reference Code


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

Answer: 10

Explanation: Label-based indexing is used. Index 'b' corresponds to value 10.

Q-22

What will be returned by the following statement?

Hard Pandas DataFrame

Sample Data

DataFrame with columns: Name, Marks

Reference Code


print(type(df['Marks']))

Answer: <class 'pandas.core.series.Series'>

Explanation: Accessing a single column using df['Marks'] returns a Pandas Series.

Q-23

What will be the shape of the DataFrame after execution?

Hard Pandas DataFrame

Sample Data

A	B	C
1	4	7
2	5	8

Reference Code


import pandas as pd
df = pd.DataFrame({'A':[1,2],'B':[4,5],'C':[7,8]})
df2 = df[['A','C']]
print(df2.shape)

Answer: (2, 2)

Explanation: Two rows remain and only columns A and C are selected, giving shape (2, 2).

Q-24

What will be the result of the following operation?

Hard Pandas Series

Sample Data

Series values: [10, 20, 30]

Reference Code


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

Answer: Each value increased by 5

Explanation: Pandas Series supports vectorized operations; 5 is added to every element.

Q-25

Which parameter ensures that the first column in a CSV file becomes the index?

Hard CSV Import Export

Answer: index_col=0

Explanation: index_col=0 tells Pandas to use the first column as the index.

Q-26

What happens when a Pandas Series is created from a dictionary?

Hard Pandas Series

Answer: Dictionary keys become index labels

Explanation: When a Series is created from a dictionary, keys become the index.

Q-27

Which operation will result in alignment based on index labels?

Hard Pandas Series

Sample Data

s1 index: [a, b, c] s2 index: [b, c, d]

Reference Code


s3 = s1 + s2

Answer: Index-based alignment before addition

Explanation: Pandas aligns data based on index labels before performing operations.

Q-28

Which statement correctly distinguishes Pandas DataFrame from NumPy ndarray?

Hard Pandas vs NumPy

Answer: DataFrame supports labeled rows and columns

Explanation: A DataFrame supports both row and column labels, unlike ndarray.

Q-29

Which attribute returns both row and column labels of a DataFrame?

Hard Pandas DataFrame

Answer: axes

Explanation: The axes attribute returns row index and column labels.

Q-30

Which statement about Pandas Series and NumPy ndarray is TRUE?

Hard Pandas vs NumPy

Answer: Series supports heterogeneous data types

Explanation: A Pandas Series can store heterogeneous data types, unlike ndarray.

Q-31

What will be the output of the following code?

Medium Pandas Series

Sample Data

Series values: [2, 4, 6, 8]

Reference Code


import pandas as pd
s = pd.Series([2, 4, 6, 8])
print(s[1:3])

Answer: 4, 6

Explanation: Slicing uses start index inclusive and end index exclusive, so values at positions 1 and 2 are returned.

Q-32

Which type of object is returned by the following expression?

Medium Pandas DataFrame

Sample Data

DataFrame with columns: Name, Age, Marks

Reference Code


result = df[['Marks']]

Answer: DataFrame

Explanation: Using double square brackets returns a DataFrame, even when selecting a single column.

Q-33

What will be the shape of the DataFrame?

Medium Pandas DataFrame

Sample Data

A	B	C
1	2	3
4	5	6

Reference Code


import pandas as pd
df = pd.DataFrame([[1,2,3],[4,5,6]], columns=['A','B','C'])
print(df.shape)

Answer: (2, 3)

Explanation: The DataFrame has 2 rows and 3 columns.

Q-34

Which method is most suitable to display only the last 3 rows of a DataFrame?

Medium Pandas DataFrame

Answer: tail(3)

Explanation: tail(3) displays the last three rows of a DataFrame.

Q-35

Which statement correctly describes the output of df.info()?

Medium Pandas DataFrame

Answer: Data types and non-null counts

Explanation: info() shows column names, data types, and non-null counts.

Q-36

What will be the index of the resulting Series?

Medium Pandas Series

Sample Data

Original Series index: [a, b, c]

Reference Code


import pandas as pd
s = pd.Series([10, 20, 30], index=['a','b','c'])
print(s[['a','c']])

Answer: ['a', 'c']

Explanation: When selecting by labels, the original index labels are preserved.

Q-37

Which parameter is used to prevent column headers from being written to a CSV file?

Medium CSV Import Export

Answer: header=False

Explanation: header=False prevents column names from being written to the CSV file.

Q-38

What happens if a Series is created with fewer index labels than values?

Medium Pandas Series

Answer: Error is raised

Explanation: The number of index labels must match the number of values; otherwise, an error occurs.

Q-39

Which statement about df.axes is correct?

Medium Pandas DataFrame

Answer: Returns both row and column labels

Explanation: df.axes returns a list containing the index and column labels.

Q-40

Which operation will return a NumPy ndarray from a Pandas object?

Medium Pandas vs NumPy

Answer: df.values

Explanation: The values attribute returns the underlying NumPy ndarray.

Q-41

What will be the output of the following code?

Hard Pandas Series

Sample Data

Series s: Index → a b c Values → 10 20 30

Reference Code


import pandas as pd
s = pd.Series([10, 20, 30], index=['a','b','c'])
print(s[1])

Answer: 20

Explanation: Integer-based indexing is positional here. Index position 1 refers to the second value, 20.

Q-42

What will be the output of the following code?

Hard Pandas Series

Sample Data

Series s: Index → a b c Values → 10 20 30

Reference Code


import pandas as pd
s = pd.Series([10, 20, 30], index=['a','b','c'])
print(s['b':'c'])

Answer: 20, 30

Explanation: Label-based slicing in Pandas is inclusive of both start and end labels.

Q-43

What will be the shape of the resulting DataFrame?

Hard Pandas DataFrame

Sample Data

Name	Maths	Science
A	80	70
B	60	75
C	90	85

Reference Code


import pandas as pd
df = pd.DataFrame({
 'Name':['A','B','C'],
 'Maths':[80,60,90],
 'Science':[70,75,85]
})
df2 = df[['Maths','Science']]
print(df2.shape)

Answer: (3, 2)

Explanation: All three rows remain, and only two columns are selected, giving shape (3, 2).

Q-44

What will be the output of the following operation?

Hard Pandas Series

Sample Data

Series s1 index: a, b, c Series s2 index: b, c, d

Reference Code


import pandas as pd
s1 = pd.Series([10,20,30], index=['a','b','c'])
s2 = pd.Series([1,2,3], index=['b','c','d'])
print(s1 + s2)

Answer: Values added only for matching indices

Explanation: Pandas aligns Series by index labels. Only matching indices (b, c) are added; others become NaN.

Q-45

What will be the data type of the object returned?

Hard Pandas DataFrame

Sample Data

DataFrame df with columns: Name, Marks

Reference Code


result = df.loc[:, 'Marks']

Answer: Series

Explanation: Selecting a single column using .loc returns a Pandas Series.

Q-46

What will be the output of the following code?

Hard Pandas DataFrame

Sample Data

A	B
1	4
2	5
3	6

Reference Code


import pandas as pd
df = pd.DataFrame({'A':[1,2,3],'B':[4,5,6]})
print(df.iloc[1:3, 0])

Answer: 2, 3

Explanation: iloc slicing is positional and end-exclusive. Rows 1 and 2 from column 0 give values 2 and 3.

Q-47

What happens when the following CSV file is read?

Hard CSV Import Export

Sample Data

CSV file marks.csv: Roll,Marks 1,80 2,75

Reference Code


import pandas as pd
df = pd.read_csv('marks.csv', index_col=0)

Answer: Roll becomes index

Explanation: index_col=0 makes the first column (Roll) the DataFrame index.

Q-48

Which rows will be displayed by the following statement?

Hard Pandas DataFrame

Sample Data

Roll	Marks
1	40
2	55
3	70
4	85

Reference Code


print(df[df['Marks'] >= 60])

Answer: Roll 3 and 4

Explanation: Boolean indexing selects rows where Marks are 60 or more: Roll 3 and 4.

Q-49

What will be the output of the following code?

Hard Pandas vs NumPy

Sample Data

Series s: Values → 10, 20, 30

Reference Code


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

Answer: NumPy ndarray

Explanation: The values attribute returns the underlying NumPy ndarray.

Q-50

Which statement best explains the behaviour of the following code?

Hard Pandas Series

Sample Data

Series s with index a, b, c

Reference Code


s = pd.Series([1,2,3], index=['a','b','c'])
print(s + 1)

Answer: Addition happens element-wise using index alignment

Explanation: Pandas supports vectorized operations; scalar addition is applied to each element.

Informatics Practices

02 Data Handling using Pandas - I