Descriptive Statistics Part- 2 | 37/100 Days of Python Algo Trading

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Congratulations on reaching this stage!

If you’re here, it means you’re truly dedicated to mastering Python. Directly below, you’ll find a quiz designed to help you reinforce what you’ve just learned. This isn’t just about recalling facts; it’s about deeply understanding the concepts. Take a moment to answer the questions and see how well you can apply the knowledge from the video. You’re doing great—every question you tackle brings you one step closer to becoming a Python expert!

Day 37: Descriptive Statistics Part- 2

1. What is the primary step in addressing quality issues in a smartphone dataset used for algorithmic trading?
2. Which of the following is an example of a tidiness issue in a smartphone dataset?
3. How can missing values in a smartphone dataset be effectively handled?
4. What is the best way to detect duplicate entries in a smartphone dataset?
5. Which pandas function can be used to fill missing values with the mean in a smartphone dataset?
6. How do you handle inconsistent data entries in a smartphone dataset?
7. Which data quality issue arises when there are multiple representations of the same data?
8. What is the role of the 'melt' function in addressing tidiness issues in a dataset?
9. How can you check for outliers in a numerical column of a smartphone dataset?
10. What is a common method to handle outliers in a smartphone dataset?
11. Which function in pandas is used to remove duplicate rows from a smartphone dataset?
12. What is the purpose of the 'pivot_table' function in addressing tidiness issues?
13. How can you ensure the accuracy of the data in a smartphone dataset?
14. What is the significance of the 'astype()' function in data cleaning?
15. How do you address the issue of inconsistent units in a smartphone dataset?
16. Which method is used to merge two DataFrames containing smartphone data on a common column?
17. How can you handle missing categorical data in a smartphone dataset?
18. What is a tidiness issue that involves multiple variables stored in one column?
19. How do you transform a wide-format DataFrame to long-format in pandas?
20. What is the best approach to validate data after cleaning in a smartphone dataset?

 

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