Pandas and Numpy Interview Questions for Data/ Business Analysts

Question 1: You have a NumPy array representing student grades. How would you filter the array to extract only the grades above a certain threshold?

Question 2: Explain the difference between 'drop_duplicates()' and 'duplicated()' methods in Pandas. Provide a code example demonstrating their usage.

Question 3: Describe the usage of 'at' and 'iat[]' in Pandas for accessing DataFrame elements. How do they differ from 'loc[]' and 'iloc[]'?

Question 4: You have a DataFrame containing sales data with columns 'ProductID', 'Category', and 'Sales'. How would you use the 'groupby()' method to calculate total sales for each category?

Question 5: Discuss the benefits of using list comprehensions over traditional for loops in Python. Can you provide an example where list comprehensions improve code readability and efficiency?

Question 6: What is the purpose of the 'apply()' function in Pandas? Can you provide a scenario where it's advantageous over other methods?

Question 7: You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?

Question 8: Describe how to perform array indexing and slicing in NumPy. Provide examples demonstrating different indexing and slicing techniques.

Question 9: You have a DataFrame containing columns 'Name', 'Age', and 'Gender'. How would you filter the DataFrame to extract only the rows where Age is greater than 30 and Gender is 'Female'?

Question 10: Describe the benefits of using the 'pd.merge()' function over other methods like 'DataFrame.join()' for merging DataFrames in Pandas. Can you provide a scenario where 'pd.merge()' is advantageous?


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