Descriptive Statistics | 36/100 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 36: Descriptive Statistics

1. What is the primary purpose of data assessing in algorithmic trading?
2. Which type of unclean data is characterized by incorrect or inconsistent data values?
3. What is the best method to summarize data for a quick overview in pandas?
4. What are the two main types of data assessment?
5. How does manual data assessment differ from automatic data assessment?
6. Which data quality dimension is most concerned with the accuracy of trading signals?
7. What is the first step in the data cleaning process?
8. Which pandas function is commonly used to identify missing values in a DataFrame?
9. How can you handle duplicate data entries in a trading dataset using pandas?
10. What is the purpose of using the 'describe()' function in pandas?
11. Which type of unclean data involves data that is duplicated or redundant?
12. How can you automate the assessment of data quality in a large trading dataset?
13. Which pandas function can be used to replace missing values with a specific value?
14. What does the 'info()' function in pandas provide about a dataset?
15. Why is it important to assess data quality before using it for algorithmic trading?
16. Which data quality dimension addresses the completeness of trading data?
17. How can you assess the consistency of data in a trading dataset?
18. What is the role of exploratory data analysis (EDA) in data assessing?
19. How can you detect outliers in a trading dataset using pandas?
20. What is the benefit of using the 'dropna()' function in data cleaning?


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