CBSE Class 12

Q-1

Which statistical value is represented by the result of the following code?

Easy Descriptive Statistics

Sample Data

Data values: [10, 20, 30, 40]

Reference Code


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

A Median

B Mean

C Mode

D Range

Answer: Mean

Explanation: The mean() function calculates the average value of the data.

Q-2

What does the following function return?

Easy Descriptive Statistics

Sample Data

Series values: [5, 15, 25]

Reference Code


print(s.count())

A Sum of values

B Number of non-missing values

C Total number of rows including missing values

D Maximum value

Answer: Number of non-missing values

Explanation: count() returns the number of non-NULL (non-missing) values.

Q-3

Which operation is being performed by the following code?

Easy Data Aggregations

Sample Data

Name	Score
A	80
B	70
C	90

Reference Code


print(df['Score'].sum())

A Finding average score

B Finding total score

C Finding highest score

D Finding lowest score

Answer: Finding total score

Explanation: The sum() function adds all values in the column.

Q-4

What will be the order of rows after executing the following code?

Easy Sorting DataFrame

Sample Data

Name	Marks
A	65
B	85
C	75

Reference Code


print(df.sort_values(by='Marks'))

A B, C, A

B A, C, B

C C, A, B

D Original order

Answer: A, C, B

Explanation: sort_values() arranges rows in ascending order of Marks by default.

Q-5

Which result is produced by the following GROUP BY operation?

Easy GROUP BY Functions

Sample Data

Class	Marks
XI	70
XI	80
XII	90

Reference Code


df.groupby('Class').mean()

A Average marks for each class

B Total marks for each class

C Highest marks for each class

D All individual records

Answer: Average marks for each class

Explanation: groupby().mean() calculates average values for each group.

Q-6

What is the effect of the following statement?

Easy Altering the Index

Sample Data

DataFrame index: 0, 1, 2

Reference Code


df.set_index('RollNo', inplace=True)

A Creates a new DataFrame

B Deletes the RollNo column

C Changes index to RollNo

D Sorts data by RollNo

Answer: Changes index to RollNo

Explanation: set_index() replaces the existing index with the specified column.

Q-7

Which operation is being performed by the following code?

Easy Other DataFrame Operations

Sample Data

A	B
1	4
2	5

Reference Code


print(df.describe())

A Sorting the DataFrame

B Displaying structure information

C Displaying statistical summary

D Removing missing values

Answer: Displaying statistical summary

Explanation: describe() provides a statistical summary of numerical columns.

Q-8

Which rows are removed by the following statement?

Easy Handling Missing Values

Sample Data

Name	Marks
A	80
B	NaN
C	70

Reference Code


df.dropna()

A Rows with Marks less than 75

B Rows with missing values

C Rows with highest values

D No rows are removed

Answer: Rows with missing values

Explanation: dropna() removes rows containing missing (NaN) values.

Q-9

Which method is used to replace missing values with a specified value?

Easy Handling Missing Values

A dropna()

B fillna()

C replace()

D isnull()

Answer: fillna()

Explanation: fillna() replaces missing values with a given value.

Q-10

Which operation is being performed by the following code?

Easy Pandas MySQL Integration

Reference Code


df.to_sql('student', con=conn, if_exists='replace')

A Reading data from MySQL

B Exporting DataFrame to MySQL

C Sorting database records

D Deleting MySQL table

Answer: Exporting DataFrame to MySQL

Explanation: to_sql() writes DataFrame data into a MySQL table.

Q-11

Which statistical measure is returned by the following code?

Medium Descriptive Statistics

Sample Data

Data values: [12, 15, 18, 20]

Reference Code


import pandas as pd
s = pd.Series([12, 15, 18, 20])
print(s.median())

A Mean

B Median

C Mode

D Standard Deviation

Answer: Median

Explanation: median() returns the middle value (or average of two middle values) of the sorted data.

Q-12

What does the following code compute?

Medium Descriptive Statistics

Sample Data

Name	Score
A	50
B	70
C	90

Reference Code


print(df['Score'].std())

A Average score

B Spread of score values

C Highest score

D Difference between max and min

Answer: Spread of score values

Explanation: std() computes the standard deviation, which measures the spread of data values.

Q-13

Which result is produced by the following aggregation?

Medium Data Aggregations

Sample Data

Dept	Salary
IT	40000
IT	30000
HR	35000

Reference Code


df.groupby('Dept')['Salary'].sum()

A Average salary per department

B Total salary per department

C Highest salary per department

D Individual salaries

Answer: Total salary per department

Explanation: groupby().sum() adds salaries within each department group.

Q-14

How will the DataFrame rows be ordered after execution?

Medium Sorting DataFrame

Sample Data

Name	Age
A	18
B	16
C	17

Reference Code


print(df.sort_values(by='Age', ascending=False))

A B, C, A

B A, C, B

C C, A, B

D Original order

Answer: A, C, B

Explanation: Rows are sorted in descending order of Age: 18, 17, 16.

Q-15

Which output is generated by the following group operation?

Medium GROUP BY Functions

Sample Data

Class	Marks
XI	60
XI	80
XII	70

Reference Code


df.groupby('Class').count()

A Number of non-missing values per class

B Total marks per class

C Average marks per class

D Original DataFrame

Answer: Number of non-missing values per class

Explanation: count() returns the number of non-missing values for each group.

Q-16

What will be the effect of the following statement?

Medium Altering the Index

Sample Data

Current DataFrame index: RollNo

Reference Code


df.reset_index(inplace=True)

A Deletes the index permanently

B Moves index back to a column

C Sorts rows by index

D Removes duplicate rows

Answer: Moves index back to a column

Explanation: reset_index() converts the index into a column and restores default integer index.

Q-17

Which information is displayed by the following method?

Medium Other DataFrame Operations

Reference Code


print(df.info())

A Statistical summary

B Row-wise data

C Column names, data types, and non-null counts

D Sorted DataFrame

Answer: Column names, data types, and non-null counts

Explanation: info() displays structure-related information of the DataFrame.

Q-18

Which rows remain after executing the following code?

Medium Handling Missing Values

Sample Data

Name	Marks
A	80
B	NaN
C	70
D	NaN

Reference Code


df.dropna(subset=['Marks'])

A Only rows with NaN values

B Rows A and C

C Rows B and D

D All rows

Answer: Rows A and C

Explanation: dropna(subset=...) removes rows where specified column has missing values.

Q-19

What does the following statement achieve?

Medium Handling Missing Values

Reference Code


df.fillna(df.mean())

A Deletes missing values

B Replaces missing values with column means

C Sorts the DataFrame

D Converts data types

Answer: Replaces missing values with column means

Explanation: fillna(df.mean()) fills missing values using the mean of each column.

Q-20

Which operation is being performed by the following code?

Medium Pandas MySQL Integration

Reference Code


df = pd.read_sql('SELECT * FROM student', con=conn)

A Exporting DataFrame to MySQL

B Importing MySQL table into DataFrame

C Sorting MySQL records

D Deleting MySQL table

Answer: Importing MySQL table into DataFrame

Explanation: read_sql() reads data from a MySQL query into a Pandas DataFrame.

Q-21

Which statistic is computed by the following code, and on which data?

Medium GROUP BY Functions

Sample Data

City	Temp
A	30
A	32
B	28
B	26

Reference Code


import pandas as pd
result = df.groupby('City')['Temp'].mean()

A Overall mean temperature

B Mean temperature per city

C Maximum temperature per city

D Count of records per city

Answer: Mean temperature per city

Explanation: Grouping by City and applying mean() computes the average temperature for each city.

Q-22

What does the following code return?

Medium Descriptive Statistics

Sample Data

Numeric column with values including missing entries

Reference Code


stats = df['Score'].describe()

A Only mean and standard deviation

B Statistical summary including count, mean, std, min, quartiles, max

C Only minimum and maximum values

D Row-wise descriptive data

Answer: Statistical summary including count, mean, std, min, quartiles, max

Explanation: describe() returns a comprehensive statistical summary for numeric data.

Q-23

How does the following aggregation behave?

Medium Data Aggregations

Sample Data

Dept	Salary
IT	50000
IT	NaN
HR	40000

Reference Code


df.groupby('Dept')['Salary'].count()

A Counts total rows per department

B Counts non-missing salary values per department

C Sums salary per department

D Ignores departments with missing values

Answer: Counts non-missing salary values per department

Explanation: count() counts only non-missing values within each group.

Q-24

What will be the order of rows after sorting?

Medium Sorting DataFrame

Sample Data

Name	Score
A	70
B	85
C	70

Reference Code


sorted_df = df.sort_values(by=['Score','Name'])

A B, A, C

B A, C, B

C C, A, B

D A, B, C

Answer: A, C, B

Explanation: Rows are sorted by Score ascending, and ties are resolved by Name alphabetically.

Q-25

What is the effect of the following index operation?

Medium Altering the Index

Sample Data

Current index is default integer index

Reference Code


df.set_index('ID', inplace=False)

A Permanently changes the index of df

B Returns a new DataFrame with ID as index

C Deletes the ID column from df

D Sorts df by ID

Answer: Returns a new DataFrame with ID as index

Explanation: With inplace=False, set_index returns a new DataFrame without modifying the original.

Q-26

Which information is best inferred from the following output?

Medium Other DataFrame Operations

Sample Data

DataFrame with mixed numeric columns

Reference Code


print(df.corr())

A Row-wise differences

B Correlation between numeric columns

C Frequency of values

D Sorted order of columns

Answer: Correlation between numeric columns

Explanation: corr() computes pairwise correlation between numeric columns.

Q-27

Which rows are affected by the following operation?

Medium Handling Missing Values

Sample Data

Name	Marks
A	NaN
B	60
C	NaN

Reference Code


df.fillna(0)

A Only rows with Marks < 60

B Only rows with missing values

C All rows

D No rows

Answer: Only rows with missing values

Explanation: fillna(0) replaces missing values while leaving existing values unchanged.

Q-28

What will be the result of the following chained operation?

Medium GROUP BY Functions

Sample Data

Dept	Sales
A	100
A	200
B	150

Reference Code


df.groupby('Dept')['Sales'].mean().sort_values(ascending=False)

A Departments sorted by total sales

B Departments sorted by average sales descending

C Original order with averages

D Row-wise sorted sales

Answer: Departments sorted by average sales descending

Explanation: The code computes mean sales per department and sorts them in descending order.

Q-29

What is the primary effect of the following code?

Medium Pandas MySQL Integration

Sample Data

Existing DataFrame with table ‘employees’ in MySQL

Reference Code


df.to_sql('employees', con=conn, if_exists='append')

A Replaces the existing table

B Deletes existing records

C Adds DataFrame rows to the existing table

D Creates a temporary table

Answer: Adds DataFrame rows to the existing table

Explanation: if_exists='append' adds rows to the existing table without deleting data.

Q-30

Which conclusion is correct based on the following code?

Medium Other DataFrame Operations

Sample Data

DataFrame with numeric column ‘Marks’

Reference Code


filtered = df[df['Marks'] > df['Marks'].mean()]

A Rows with marks equal to the mean are selected

B Rows above the average marks are selected

C Rows below the average marks are selected

D All rows are selected

Answer: Rows above the average marks are selected

Explanation: Boolean indexing selects rows where Marks exceed the column mean.

Q-31

What will be the result of the following code?

Hard GROUP BY Functions

Sample Data

Dept	Salary
IT	40000
IT	NaN
HR	30000
HR	30000

Reference Code


result = df.groupby('Dept')['Salary'].mean()

A IT: 20000, HR: 30000

B IT: 40000, HR: 30000

C IT: NaN, HR: 30000

D IT: 80000, HR: 60000

Answer: IT: 40000, HR: 30000

Explanation: mean() ignores NaN values. IT has one valid value (40000), HR average is (30000+30000)/2.

Q-32

How many rows will remain after executing the following code?

Hard Handling Missing Values

Sample Data

Name	Marks
A	80
B	NaN
C	70
D	NaN

Reference Code


df2 = df.dropna()

A 4

B 3

C 2

D 1

Answer: 2

Explanation: dropna() removes all rows containing at least one missing value. Only A and C remain.

Q-33

Which rows will appear in the final output?

Hard Sorting DataFrame

Sample Data

Student	Score
A	60
B	80
C	70
D	90

Reference Code


result = df.sort_values('Score').tail(2)

A A and C

B B and D

C C and D

D A and D

Answer: C and D

Explanation: After sorting ascending, tail(2) selects the two highest scores: 70 and 90 (C and D).

Q-34

What is the shape of the resulting DataFrame?

Hard GROUP BY Functions

Sample Data

Dept	Year	Sales
A	2022	100
A	2023	150
B	2022	120

Reference Code


result = df.groupby(['Dept','Year']).sum()

A (3, 1)

B (2, 2)

C (3, 3)

D (2, 1)

Answer: (3, 1)

Explanation: There are three unique (Dept, Year) groups, and one aggregated column (Sales).

Q-35

What will be the effect of the following operation?

Hard Altering the Index

Sample Data

Index currently set to ‘RollNo’

Reference Code


df.reset_index(drop=True, inplace=True)

A Moves index to a column

B Deletes the existing index and creates a new default index

C Sorts rows by RollNo

D Removes duplicate rows

Answer: Deletes the existing index and creates a new default index

Explanation: reset_index(drop=True) removes the current index and restores a default integer index.

Q-36

Which statistical value is affected MOST by an extreme outlier?

Hard Descriptive Statistics

Sample Data

Values: [10, 12, 14, 1000]

Reference Code


s.mean(), s.median()

A Median only

B Mean only

C Both equally

D Neither

Answer: Mean only

Explanation: Mean is highly sensitive to extreme values, while median is more robust.

Q-37

Which rows are selected by the following chained operation?

Hard Other DataFrame Operations

Sample Data

Name	Marks
A	40
B	60
C	80
D	100

Reference Code


result = df[df['Marks'] > df['Marks'].median()]

A A and B

B B and C

C C and D

D A and D

Answer: C and D

Explanation: Median is (60+80)/2 = 70. Rows with Marks > 70 are C and D.

Q-38

What will be the output index type after this operation?

Hard Altering the Index

Sample Data

Grouped DataFrame with multi-level index

Reference Code


result = df.groupby(['Dept','Year']).sum().reset_index()

A MultiIndex

B Single integer index

C Index based on Dept only

D Index based on Year only

Answer: Single integer index

Explanation: reset_index() removes the MultiIndex and replaces it with a default integer index.

Q-39

What is the primary risk of the following operation?

Hard Pandas MySQL Integration

Sample Data

Existing MySQL table ‘sales’

Reference Code


df.to_sql('sales', con=conn, if_exists='replace')

A Duplicate records

B Loss of existing table data

C Index mismatch

D Type conversion error

Answer: Loss of existing table data

Explanation: if_exists='replace' drops the existing table and recreates it, causing data loss.

Q-40

Which conclusion is correct for the following operation?

Hard Descriptive Statistics

Sample Data

A	B
1	10
2	NaN
3	30

Reference Code


result = df.mean()

A NaN propagates and mean is NaN

B Mean ignores NaN values

C Mean replaces NaN with 0

D Mean cannot be computed

Answer: Mean ignores NaN values

Explanation: Pandas statistical functions ignore NaN values by default.

Q-41

What will be the output of the following chained operation?

Hard GROUP BY Functions

Sample Data

Dept	Sales
A	100
A	200
B	150
B	NaN

Reference Code


result = df.groupby('Dept')['Sales'].mean().fillna(0)

A A: 150, B: 0

B A: 300, B: 150

C A: 150, B: 150

D A: 0, B: 150

Answer: A: 150, B: 150

Explanation: Mean ignores NaN. A mean = (100+200)/2 = 150; B mean = 150. fillna(0) has no effect here.

Q-42

How many rows will remain after executing the following code?

Hard Other DataFrame Operations

Sample Data

ID	Score
1	45
2	55
3	NaN
4	75

Reference Code


df2 = df[df['Score'] > df['Score'].mean()]

A 1

B 2

C 3

D 4

Answer: 2

Explanation: Mean ignores NaN: (45+55+75)/3 ≈ 58.33. Only Score 75 is above the mean.

Q-43

What will be the resulting index after the following operation?

Hard GROUP BY Functions

Sample Data

Data grouped by two columns

Reference Code


result = df.groupby(['Dept','Year']).sum()

A Default integer index

B Single-level index with Dept

C MultiIndex with Dept and Year

D Index removed

Answer: MultiIndex with Dept and Year

Explanation: Grouping by multiple columns creates a MultiIndex by default.

Q-44

Which rows will be selected by the following sorting logic?

Hard Sorting DataFrame

Sample Data

Name	Marks
A	70
B	90
C	80
D	60

Reference Code


result = df.sort_values('Marks', ascending=False).head(2)

A A and C

B B and C

C B and A

D C and D

Answer: B and C

Explanation: Sorting descending gives 90, 80, 70, 60. head(2) selects B and C.

Q-45

What is the effect of the following operation on missing values?

Hard Handling Missing Values

Sample Data

A	B
1	NaN
2	5
3	NaN

Reference Code


result = df.fillna(df.median())

A Replaces NaN with column means

B Replaces NaN with column medians

C Drops rows with NaN

D Leaves NaN unchanged

Answer: Replaces NaN with column medians

Explanation: fillna(df.median()) fills missing values using the median of each column.

Q-46

What will be the number of rows in the resulting DataFrame?

Hard GROUP BY Functions

Sample Data

Dept	Year	Profit
A	2022	50
A	2023	60
A	2023	70

Reference Code


result = df.groupby(['Dept','Year'])['Profit'].count().reset_index()

A 1

B 2

C 3

D 4

Answer: 2

Explanation: There are two unique (Dept, Year) groups: (A,2022) and (A,2023). reset_index does not change row count.

Q-47

Which statement correctly explains the result of the following code?

Hard Descriptive Statistics

Sample Data

Numeric DataFrame with missing values

Reference Code


stats = df.describe()

A Includes NaN rows in all calculations

B Ignores NaN values in statistics

C Drops columns with NaN

D Returns only count and mean

Answer: Ignores NaN values in statistics

Explanation: describe() ignores NaN values when computing statistics.

Q-48

What will be the primary outcome of the following database operation?

Hard Pandas MySQL Integration

Sample Data

Existing MySQL table ‘students’ with records

Reference Code


df.to_sql('students', con=conn, if_exists='append', index=False)

A Existing table is replaced

B New rows are added without index column

C Index column becomes primary key

D Table structure is dropped

Answer: New rows are added without index column

Explanation: append adds new rows and index=False prevents writing the index as a column.

Q-49

Which rows will remain after the following two-step operation?

Hard Other DataFrame Operations

Sample Data

ID	Score
1	40
2	60
3	NaN
4	80

Reference Code


df2 = df.dropna()
df3 = df2[df2['Score'] >= df2['Score'].mean()]

A Only ID 4

B ID 2 and ID 4

C ID 1, 2, and 4

D Only ID 2

Answer: ID 2 and ID 4

Explanation: After dropna, scores are 40, 60, 80. Mean = 60. Rows with Score ≥ 60 are IDs 2 and 4.

Q-50

Which conclusion is correct about the following chained computation?

Hard Descriptive Statistics

Sample Data

Value
10
20
NaN
40

Reference Code


result = df['Value'].mean() * df['Value'].count()

A Equals sum of all values including NaN

B Equals sum of non-missing values

C Equals median multiplied by count

D Produces incorrect result due to NaN

Answer: Equals sum of non-missing values

Explanation: mean() and count() both ignore NaN. Their product equals the sum of non-missing values.

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Informatics Practices

03 Data Handling using Pandas - II