Question 1

You discover that a text-based column in your DataFrame supposed to contain only alphanumeric characters has special characters in some entries. This constitutes "bad data" for your specific use-case. How can you most effectively clean this column?

Options :

A : Convert the entire DataFrame to a NumPy array and handle text cleaning

B : Use Regular Expressions with df.str.replace() to remove all special characters at once

C : Use df.replace() to replace each special character individually

D : Drop the column to avoid any complications

Answer: B

Question 2

Your Python script is taking significantly longer than expected to complete. Which Python standard library could provide you with granular insights into the time taken by different parts of your code?

Options :

A : os

B : cProfile

C : sys

D : pickle

Answer: B

Question 3

You have the following DataFrame named sales_data:

sales_data = pd.DataFrame({

'Year': [2021, 2021, 2022, 2022],

'Quarter': [1, 2, 1, 2],

'Revenue': [1000, 1100, 1200, 1300]

})

You want to reshape the DataFrame such that the Revenue for each Quarter becomes its own column, and the Year remains as the index. Which of the following code snippets accomplishes this?

Options :

A : sales_data.pivot(index='Year', columns='Quarter', values='Revenue')

B : sales_data.melt(id_vars=['Year'], value_vars=['Revenue'])

C : pd.crosstab(index=sales_data['Year'], columns=sales_data['Quarter'], values=sales_data['Revenue'], aggfunc='sum')

D : sales_data.groupby(['Year', 'Quarter']).Revenue.sum().unstack()

Answer: A

Question 4

When would you prefer to use t-SNE over PCA for visualizing high-dimensional data?

Options :

A : When you need to capture linear relationships in the data.

B : When computational efficiency is crucial.

C : When you want to visualize clusters or groups in the data.

D : When the dataset has fewer than three features.

Answer: C

Question 5

You need to collect data from an Excel file with multiple sheets. One of the sheets is named "Sales" and another is named "Inventory". You want to load the "Sales" sheet into a DataFrame named df_sales and the "Inventory" sheet into a DataFrame named df_inventory. Which of the following code snippets accomplishes this?

Options :

A : xls = pd.Excel('file.xlsx')

df_sales = xls.parse('Sales')

df_inventory = xls.parse('Inventory')

B : df_sales = pd.read_excel('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_excel('file.xlsx', sheet_name='Inventory')

C : df_sales = pd.read_csv('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_csv('file.xlsx', sheet_name='Inventory')

D : df_sales = pd.read_excel('file.xlsx').parse('Sales')

df_inventory = pd.read_excel('file.xlsx').parse('Inventory')

Answer: B