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?
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?
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?
You are building a data pipeline that requires you to load data from an API, transform it, and then save it to a database. Which of the following is the best approach to structure your script for maintainability and reusability?
When dealing with a categorical feature that has high cardinality in a dataset, which of the following approaches is most advisable for one-hot encoding?