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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What command would you use to find missing values in a dataset?
💡 Hint: Look for a command that checks for null entries.
Question 2
Easy
How can you remove duplicate rows from a DataFrame?
💡 Hint: Think about a method that deals with repeated entries.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the purpose of filling missing values in a dataset?
- To inflate the data
- To ensure there are no gaps for analysis
- To remove important data
💡 Hint: Consider why we would need to fill gaps.
Question 2
Duplicates in a dataset can lead to:
- True
- False
💡 Hint: Think about how repeated entries might change statistical outcomes.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset of 1000 students with 15% missing grades. What steps would you take to clean this dataset before analysis?
💡 Hint: Consider the implications of keeping or dropping missing data.
Question 2
Imagine you're analyzing a dataset that includes ages but some are stored as strings, like '20', '25', '30'. How would you convert these to integers for accurate analysis?
💡 Hint: Check how you can convert data types in Pandas.
Challenge and get performance evaluation