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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
How can you identify missing values in a Pandas DataFrame?
💡 Hint: Look for a function that checks for nulls.
Question 2
Easy
What function can be used to fill missing values with 0?
💡 Hint: Focus on the filling function.
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 function allows you to count missing values in a DataFrame?
- df.count()
- df.isnull()
- df.fillna()
💡 Hint: Think about which function checks for nulls.
Question 2
True or False: You can fill missing values with any numeric value using df.fillna().
- True
- False
💡 Hint: Consider what the fillna function does.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with several columns and many missing entries, write a function to automatically fill these values. Discuss whether you would use the mean, median, or a static number, and justify your choice.
💡 Hint: Evaluate the nature of the data to choose the best filling method.
Question 2
In a real-world scenario where a customer dataset contains null values in the 'Age' column, explain how you would approach filling these values and why.
💡 Hint: Consider the implications of your choice on data integrity.
Challenge and get performance evaluation