Practice Handling Missing Values (9.4.1) - Data Analysis using Python - CBSE 12 AI (Artificial Intelligence)

Practice: Handling Missing Values

Lesson 9.4.1Data Analysis using Python

Practise this section

9 questions

Flashcard drill
2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
How can you identify missing values in a Pandas DataFrame?
Show hintHide hint
Look for a function that checks for nulls.
Question 2.
What function can be used to fill missing values with 0?
Show hintHide hint
Focus on the filling function.
3Question 3.
What function allows you to count missing values in a DataFrame?
- df.count()
- df.isnull()
- df.fillna()
Show hintHide hint
Think about which function checks for nulls.
4Question 4.
True or False: You can fill missing values with any numeric value using df.fillna().
- True
- False
Show hintHide hint
Consider what the fillna function does.
Question 5.
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.
Show hintHide hint
Evaluate the nature of the data to choose the best filling method.
Question 6.
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.
Show hintHide hint
Consider the implications of your choice on data integrity.

5 more questions in this section. Enroll free to practise them all.