Forward College - Python Aptitude Quiz

1. What is the output of the following code?

x = [1, 2, 3] y = x y.append(4) print(len(x)

1. What is the output of the following code?

A

3

B

None

C

Error

D

4

2. Which of the following is the correct way to create a dictionary in Python?

A

dict = ('name', 'John')

B

dict = {'name': 'John', 'age': 25}

C

dict = {1, 2, 3}

D

dict = [key: value]

3. What does the enumerate() function return when iterating over a list?

A

Only the index of each element

B

Only the value of each element

C

A tuple containing the index and value

D

A dictionary of index-value pairs

4. What is the result of: [x**2 for x in range(5) if x % 2 == 0]

A

[0, 4, 16]

B

[0, 1, 4, 9, 16]

C

[0, 2, 4]

D

[1, 9, 25]

5. Which method is used to remove and return the last element from a list?

A

discard()

B

pop()

C

delete()

D

remove()

6. Which Pandas function is used to read a CSV file?

A

pd.import_csv()

B

pd.read_csv()

C

pd.csv_reader()

D

pd.load_csv()

7. What does df.dropna() do in Pandas?

A

Converts NaN to empty strings

B

Removes rows containing NaN values

C

Fills NaN values with zeros

D

Drops all columns from the DataFrame

8. How do you select rows where the 'age' column is greater than 30 in a DataFrame df?

A

df.filter(age > 30)

B

df.where('age' > 30)

C

df[df['age'] > 30]

D

df.select(age > 30)

9. What is the difference between df.loc[] and df.iloc[]?

A

They are identical in functionality

B

loc uses labels, iloc uses integer position

C

loc is for columns, iloc is for rows

D

loc uses integer position, iloc uses labels

10. Which method is used to combine two DataFrames vertically (row-wise)?

A

pd.merge()

B

pd.join()

C

pd.concat()

D

pd.append()

11. What does df.groupby('category').mean() return?

A

A single mean value for the entire DataFrame

B

Mean of all numeric columns grouped by unique values in 'category'

C

An error because groupby requires multiple columns

D

The mean of the 'category' column

12. How do you rename columns in a Pandas DataFrame?

A

df.set_columns(['new_name1', 'new_name2'])

B

df.rename(columns={'old': 'new'})

C

Both A and B are valid methods

D

df.columns = ['new_name1', 'new_name2']

13. Which function is used to display a plot in Matplotlib?

A

plt.render()

B

plt.show()

C

plt.display()

D

plt.draw()

14. What does plt.figure(figsize=(10, 6)) do?

A

Creates a figure with 10 rows and 6 columns

B

Sets the figure size to 10 inches wide and 6 inches tall

C

Creates 10 figures with 6 subplots each

D

Sets the DPI to 10 and resolution to 6

15. Which function creates a bar chart in Matplotlib?

A

plt.barplot()

B

plt.bar()

C

plt.bars()

D

plt.barchart()

16. Which Seaborn function is best for visualizing the distribution of a single variable?

A

sns.scatterplot()

B

sns.histplot() or sns.kdeplot()

C

sns.barplot()

D

sns.lineplot()

17. What parameter in Seaborn functions is used to color data points by a categorical variable?

A

style

B

color

C

palette

D

hue

18. Scenario: A student is trying to remove all "Done" items from their task list. Their current for loop is skipping items because the list size changes during iteration. Prompt: Rewrite the assignment for tasks using a list comprehension to correctly filter out all items that are "Done". tasks = ["Done", "Done", "Pending", "Done", "Pending"] # Write a single line of code to redefine 'tasks' correctly: tasks =

18. Scenario: A student is trying to remove all "Done" items from their task list. Their current for loop is skipping items because the list size changes during iteration. Prompt: Rewrite the assignment for tasks using a list comprehension to correctly filter out all items that are "Done". tasks = ["Done", "Done", "Pending", "Done", "Pending"] # Write a single line of code to redefine 'tasks' correctly: tasks =

A

tasks = [task for task in tasks if task == "Done"]

B

tasks = [task for task in tasks if task != "Done"]

C

tasks = [tasks.pop(i) for i in range(len(tasks)) if tasks[i] != "Done"]

D

tasks = tasks.remove("Done")

19. The goal is to filter the DataFrame to show only rows where 'Score' is greater than 80 AND 'Attendance' is greater than 90. The code below causes a ValueError. How should the last line be written to fix the error? import pandas as pd df = pd.DataFrame({ 'Student': ['Sam', 'Mia', 'Jay'], 'Score': [85, 92, 78], 'Attendance': [95, 88, 92] }) # BUG: This line throws a ValueError passed = df[(df['Score'] > 80) and (df['Attendance'] > 90)]

19. The goal is to filter the DataFrame to show only rows where 'Score' is greater than 80 AND 'Attendance' is greater than 90. The code below causes a ValueError. How should the last line be written to fix the error? import pandas as pd df = pd.DataFrame({ 'Student': ['Sam', 'Mia', 'Jay'], 'Score': [85, 92, 78], 'Attendance': [95, 88, 92] }) # BUG: This line throws a ValueError passed = df[(df['Score'] > 80) and (df['Attendance'] > 90)]

A

passed = df[(df['Score'] > 80) and (df['Attendance'] > 90)]

B

passed = df[df['Score'] > 80 | df['Attendance'] > 90]

C

passed = df[df['Score'] > 80 & df['Attendance'] > 90]

D

passed = df[(df['Score'] > 80) & (df['Attendance'] > 90)]

20. Review the code snippet below. The developer intends to access the first row of the DataFrame (the row containing "Apple"). What will be the actual result of running this code ? import pandas as pd data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Price': [1.2, 0.8, 2.5]} # Note the custom index starting at 10 df = pd.DataFrame(data, index=[10, 20, 30]) # Attempting to get the first row print(df.loc[0])

20. Review the code snippet below. The developer intends to access the first row of the DataFrame (the row containing "Apple"). What will be the actual result of running this code ? import pandas as pd data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Price': [1.2, 0.8, 2.5]} # Note the custom index starting at 10 df = pd.DataFrame(data, index=[10, 20, 30]) # Attempting to get the first row print(df.loc[0])

A

It prints the first column.

B

It prints the row for "Banana".

C

It raises a KeyError.

D

It prints the row for "Apple"