Practice Handling Techniques (5.4.2) - Data Cleaning and Preprocessing

Practice: Handling Techniques

Lesson 5.4.2Data Cleaning and Preprocessing

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10 questions

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6 sample questions

Question 1.
What function do we use to detect missing values in a pandas DataFrame?
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Think about what method checks for NaN values.
Question 2.
Which method would remove all rows with missing data?
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Consider the action of dropping missing entries.
3Question 3.
Which function is used to drop rows with missing values in pandas?
- dropna()
- remove_na()
- isnull()
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It starts with 'drop' and deals with NaN.
4Question 4.
True or False: Z-score is used to detect outliers.
- True
- False
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Consider what Z represents in statistical contexts.
Question 5.
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.
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Remember to define Q1 and Q3 before filtering!
Question 6.
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.
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Think of duplicates like repeating unwanted guests at a party who mess up the fun!

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