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Question 1

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A

Question 2

Your dataset consists of geographical data points for different species of birds. You want to visualize the distribution of these species on a geographical map. Which Python library would be most appropriate for this kind of visualization?

Options :

A : Plotly

B : Geopandas

C : Seaborn

D : Matplotlib

Answer: B

Question 3

You are working with a logistic regression model for a binary classification problem. You notice that the training error is significantly lower than the validation error. What could you do to improve the model's performance on the validation set?

Options :

A : Increase the model-s learning rate.

B : Add more features to the model.

C : Remove all features and retrain the model.

D : Regularize the model by introducing L1 or L2 penalties.

Answer: D

Question 4

You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

Options :

A : Perform clustering on the dataset

B : Apply a linear regression model to reduce dimensions

C : Use a scatter plot to visualize all the features

D : Standardize the dataset

Answer: D

Question 5

You have a DataFrame df that contains duplicate rows. Your task is to remove duplicates based on two columns: 'A' and 'B'. What is the correct line of code to remove such duplicates while keeping the first occurrence?

Options :

A : df.drop_duplicates(subset=['A', 'B'], keep=False)

B : df.drop_duplicates()

C : df.unique(subset=['A', 'B'])

D : df.drop_duplicates(subset=['A', 'B'], keep='first')

Answer: D