Question 1

Consider the Python code snippet below:

numbers = [1, 2, 3, 4, 5]

filtered_numbers = list(filter(lambda x: x % 2 == 1, numbers))

result = len(filtered_numbers)

What will be the value of result after executing the code?

Options :

A : 3

B : 4

C : 1

D : 5

E : 2

Answer: A

Question 2

Which of the following techniques should be avoided to ensure that your Python data scripting code is easily understandable and follows best practices?

Options :

A : Using descriptive variable names

B : Refactoring repetitive code into reusable functions

C : Documenting functions with docstrings

D : Using single-letter variable names for main variables

Answer: D

Question 3

In a regression analysis of employee job satisfaction against years of experience, the p-value for the 'years of experience' variable is found to be 0.12. How should you interpret this result at a 0.05 significance level?

Options :

A : A p-value of 0.12 confirms that years of experience and job satisfaction are positively correlated.

B : Years of experience is not a statistically significant predictor of job satisfaction.

C : Years of experience is a statistically significant predictor of job satisfaction.

D : A p-value of 0.12 means that years of experience has a 12% impact on job satisfaction.

Answer: B

Question 4

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 5

In a Pandas DataFrame, what would be the most efficient way to group the data by one column and apply multiple aggregate functions?

Options :

A : df.groupby('column').agg(['sum', 'mean'])

B : df.groupby('column').apply(lambda x: x.sum())

df.groupby('column').apply(lambda x: x.mean())

C : df.groupby('column').sum()

df.groupby('column').mean()

D : df['column'].sum()

df['column'].mean()

Answer: A

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