You have a dataset of a retail company's sales transactions, which includes details like customer ID, transaction amount, transaction date, and store ID. You're tasked with summarizing monthly revenue for each store. What would be the most appropriate data aggregation technique?

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?

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?

You have developed a logistic regression model for a classification task. After training, you notice that both the training and validation errors are high. Which of the following might be the problem with your model?

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?