Section 1 - Instruction

You've mastered finding missing values, handling duplicates, fixing data types, cleaning text, and working with dates. Now it's time to combine all these skills in a comprehensive data cleaning workflow.

Real-world datasets often have multiple issues that need to be addressed systematically.

Engagement Message

Ready to become a complete data cleaning expert?

Section 2 - Practice

Type

Fill In The Blanks

Markdown With Blanks

You receive a customer dataset with multiple issues. Fill in the blanks to start your cleaning workflow by checking the overall data quality.

Suggested Answers

  • shape
  • info
  • isnull
  • sum
Section 3 - Practice

Type

Sort Into Boxes

Practice Question

Your initial analysis reveals these data quality issues. Sort them into the correct cleaning method needed.

Labels

  • First Box Label: Missing Data Methods
  • Second Box Label: Other Cleaning Methods

First Box Items

  • NaN values in age
  • Empty email addresses

Second Box Items

  • Duplicate customer records
  • City names with spaces
  • Dates stored as text
  • Prices as object type
Section 4 - Practice

Type

Multiple Choice

Practice Question

You have a messy 'product_name' column with entries like ' Apple Phone ', 'APPLE PHONE', and 'apple phone'. What's the best cleaning sequence?

Sign up
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal