Practice Handling Missing Data (4.8) - Chapter 4: Understanding Pandas for Machine Learning

Practice: Handling Missing Data

Lesson 4.8Understanding Pandas for Machine Learning

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What method in Pandas is used to check for missing values?
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Think about how to identify nulls in a DataFrame.
Question 2.
How do you fill missing values with zero in a DataFrame?
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Consider the method used to replace nulls.
3Question 3.
What method would you use to check for missing data in Pandas?
- isna()
- isnull()
- nullcheck()
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Consider what the terminology might suggest about its function.
4Question 4.
True or False: The method dropna() will keep rows that contain null values.
- True
- False
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Think about the action this function performs.
Question 5.
Suppose you have a dataset with 100 rows, and 40 of them have missing values. Discuss the implications if you choose to drop these rows versus filling them.
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Think about how critical each piece of data is for your final analysis.
Question 6.
Given the list of operations and the initial dataset, outline a sequence of steps for cleaning data that has missing entries in various columns using both filling and dropping methods.
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Consider the balance between maintaining data and ensuring quality.

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