Question 1

You have the following Pandas DataFrame with some missing values:

import pandas as pd

import numpy as np

df = pd.DataFrame(

{"A": [1, np.nan, 3], "B": [4, 5, np.nan], "C": [7, 8, 9]}

)

How would you fill the missing values in column 'A' with the mean value of that column?

Options :

A : df['A'] = df['A'].fillna(df['C'].mean())

B : df['A'].fillna(df['A'].mean(), inplace=True)

C : df.fillna({'A': df['A'].mean()})

D : df['A'].apply(lambda x: x.fillna(x.mean()))

Answer: B

Question 2

Which of the following libraries in Python is not commonly used for web scraping to collect data?

Options :

A : Scrapy

B : Selenium

C : Pandas

D : BeautifulSoup

Answer: C

Question 3

You have a DataFrame named df that has three columns: 'A', 'B', 'C'. The DataFrame has 100 rows. Your task is to create a DataFrame where each column 'A', 'B', 'C' is transformed into 3 columns each: 'A_min', 'A_max', 'A_mean', 'B_min', 'B_max', 'B_mean', 'C_min', 'C_max', 'C_mean'. Which of the following code snippets accomplishes this task?

Options :

A : new_df = df.groupby(['A', 'B', 'C']).agg(['min', 'max', 'mean'])

B : new_df = df.apply(['min', 'max', 'mean'])

C : new_df = df.pivot(columns=['A', 'B', 'C'], values=['min', 'max', 'mean'])

D : new_df = df.aggregate(

{

'A': ['min', 'max', 'mean'],

'B': ['min', 'max', 'mean'],

'C': ['min', 'max', 'mean'],

}

)

Answer: D

Question 4

You are tasked with merging two datasets, df1 and df2, on a common column 'id'. df1 has a column 'value1' and df2 has a column 'value2'. After merging, you need to create a new column called 'total_value' that sums 'value1' and 'value2'. Which of the following code snippets accomplishes this?

Options :

A : df_merged = pd.concat([df1, df2], keys='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

B : df_merged = df1.merge(df2)

df_merged['total_value'] = df1['value1'] + df2['value2']

C : df_merged = df1.join(df2, on='id')

df_merged['total_value'] = df1['value1'] + df2['value2']

D : df_merged = pd.merge(df1, df2, on='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

Answer: D

Question 5

What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?

Options :

A : PCA is non-linear while t-SNE is linear

B : PCA is more computationally efficient for large datasets

C : PCA retains local structure while t-SNE focuses on global structure

D : PCA gives a more accurate representation of distances between clusters

Answer: B