Practice Data Type Conversion - 5.6 | Data Cleaning and Preprocessing | Data Science Basic
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5.6 - Data Type Conversion

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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 convert column types in a pandas DataFrame?

💡 Hint: Think about the function used to change the type of a variable.

Question 2

Easy

How would you convert a string date into a datetime object?

💡 Hint: It's a pandas function specifically for converting dates.

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 is the purpose of data type conversion?

  • To change values
  • To ensure consistency
  • To alter data

💡 Hint: Think about why we do any form of data cleaning.

Question 2

True or False: You cannot convert a string of numbers (like '12') to an integer.

  • True
  • False

💡 Hint: Consider the functions available in pandas.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with a 'Date' column formatted as strings. Describe the steps needed to convert this column into datetime type, and explain why this is critical for time series analysis.

💡 Hint: Focus on the specific pandas function that handles dates.

Question 2

A dataset contains a column 'Income' stored as strings. Some entries are '50000', while others are 'not available'. How would you address the conversion of this column, ensuring that only valid numbers are converted, and explain what happens to invalid entries?

💡 Hint: Think about handling missing data before conversion.

Challenge and get performance evaluation