Practice Handling Missing Data (5.4) - Data Cleaning and Preprocessing

Practice: Handling Missing Data

Lesson 5.4Data Cleaning and Preprocessing

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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 can be used to detect missing values in a DataFrame?
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Look for a method that identifies null entries.
Question 2.
What function drops rows with any missing values?
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Think of a function that removes unwanted entries.
3Question 3.
What does the command df.isnull().sum() do?
- Identifies rows with duplicates
- Counts missing values in each column
- Shows total records in DataFrame
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Focus on what it means to check for 'null'.
4Question 4.
True or False: Forward fill is used to fill missing values based on the next value in a column.
- True
- False
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Think about the direction in which the values are filled.
Question 5.
Given a dataset with 20% missing values in a key feature, evaluate the best approach to handle this. Discuss options of dropping, filling with mean, or a combination.
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Consider both the impact of losing data against the data's significance.
Question 6.
Evaluate a dataset where all rows for users under 18 are missing their income data. Discuss implications of dropping these rows versus filling values and potential biases introduced.
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Think about how data loss may disproportionately affect certain groups.

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