9.4.1 - Handling Missing Values
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
How can you identify missing values in a Pandas DataFrame?
💡 Hint: Look for a function that checks for nulls.
What function can be used to fill missing values with 0?
💡 Hint: Focus on the filling function.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What function allows you to count missing values in a DataFrame?
💡 Hint: Think about which function checks for nulls.
True or False: You can fill missing values with any numeric value using df.fillna().
💡 Hint: Consider what the fillna function does.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.