Data Analysis with NumPy and Pandas

Test your combined skills in data manipulation and numerical computation.

1. What is the primary data structure in Pandas used for tabular data?
2. Which NumPy function creates an array filled with zeros?
3. Which of the following are core data structures in Pandas?
4. A Pandas DataFrame is a 2-dimensional labeled data structure with columns that can have different data types.
5. What is the NumPy function used to reshape an array into a specified shape? (function name only)
6. How can you access the 'age' column in a Pandas DataFrame named 'df'?
7. Which NumPy functions compute statistical measures of an array?
8. The NumPy operation arr1 * arr2 performs matrix multiplication when arr1 and arr2 are 2D arrays.
9. What Pandas method generates descriptive statistics (count, mean, std, etc.) for a DataFrame?
10. What does the Pandas method df.dropna() do by default?
11. Which of the following are valid ways to create a Pandas DataFrame?
12. A Pandas Series can contain elements of different data types.
13. What is the output of np.array([1, 2, 3]) + np.array([4, 5, 6])? (as a space-separated string of numbers)
14. In NumPy, what does arr[1:4] return from the array arr = np.array([0, 1, 2, 3, 4, 5])?
15. Which Pandas methods are used to handle missing values?
16. The Pandas groupby() method splits data into groups based on a key and applies a function to each group.
17. What is the Pandas function to read data from an Excel file? (function name only, excluding parentheses)
18. By default, what axis does the Pandas method df.mean() operate on?
19. Which of these are attributes of a NumPy array?
20. NumPy arrays are mutable, meaning their elements can be modified after creation.
Answered 0 of 0 — 0 correct