Practice Handling Techniques - 5.4.2 | Data Cleaning and Preprocessing | Data Science Basic
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Handling Techniques

5.4.2 - Handling Techniques

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What function do we use to detect missing values in a pandas DataFrame?

💡 Hint: Think about what method checks for NaN values.

Question 2 Easy

Which method would remove all rows with missing data?

💡 Hint: Consider the action of dropping missing entries.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

Which function is used to drop rows with missing values in pandas?

dropna()
remove_na()
isnull()

💡 Hint: It starts with 'drop' and deals with NaN.

Question 2

True or False: Z-score is used to detect outliers.

True
False

💡 Hint: Consider what Z represents in statistical contexts.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with a 'Height' column in centimeters, write a function to find and remove outliers based on the IQR method. Explain each step taken.

💡 Hint: Remember to define Q1 and Q3 before filtering!

Challenge 2 Hard

Create a DataFrame with duplicate entries of a fictional customer dataset. Write code to identify and display these duplicates before using drop_duplicates() to remove them. Explain why this step is necessary.

💡 Hint: Think of duplicates like repeating unwanted guests at a party who mess up the fun!

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