Practice Handling Missing Data - 5.4 | Data Cleaning and Preprocessing | Data Science Basic
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5.4 - Handling Missing Data

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What method can be used to detect missing values in a DataFrame?

💡 Hint: Look for a method that identifies null entries.

Question 2

Easy

What function drops rows with any missing values?

💡 Hint: Think of a function that removes unwanted entries.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does the command df.isnull().sum() do?

  • Identifies rows with duplicates
  • Counts missing values in each column
  • Shows total records in DataFrame

💡 Hint: Focus on what it means to check for 'null'.

Question 2

True or False: Forward fill is used to fill missing values based on the next value in a column.

  • True
  • False

💡 Hint: Think about the direction in which the values are filled.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Consider both the impact of losing data against the data's significance.

Question 2

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.

💡 Hint: Think about how data loss may disproportionately affect certain groups.

Challenge and get performance evaluation