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Python: Cleaning Data

  • Number of Courses5 courses
  • Duration4 hours
  • Skill IQ available Skill IQ

Welcome to the "Python: Cleaning Data" path, where you'll embark on a comprehensive journey to become a skilled data cleaner and preparer using Python. Whether you're a data analyst, data scientist, or anyone dealing with messy data, this path is designed to equip you with the knowledge and tools to effectively validate, clean, and transform data for meaningful analysis and insights.

Courses in this path

Python: Cleaning Data

In this path, you'll start by validating data cleanliness with Python's assert statements, ensuring data integrity from the outset. You'll then delve into cleaning and manipulating dataframes, creating and altering indexes, and mastering the art of cleaning string data. The path concludes by equipping you with the skills to manage invalid, duplicate, and missing data efficiently. Whether you're a data analyst, scientist, or anyone dealing with messy data, this path empowers you to transform chaotic datasets into structured, reliable sources of insights and analysis.

Validate Data Cleanliness Using Asserts in Python

  • by Pinal Dave
  • 42m Duration

Cleaning and Working with Dataframes in Python

  • by Jacob Lyman (Jake)
  • 50m Duration

Create and Alter DataFrame Indexes

  • by Matt Stenzel
  • 39m Duration

Cleaning String Data in Python

  • by Victoria Clark
  • 50m Duration

Manage Invalid, Duplicate, and Missing Data in Python

  • by Axel Sirota
  • 57m Duration

Learn with the best

Pinal Dave
Pinal Dave
Pinal Dave is an SQL Server Performance Tuning Expert and independent consultant with over 22 years of hands-on experience. He holds a Master of Science degree and numerous database certifications. Pinal has authored 14 SQL Server database books and 81 Pluralsight courses. To freely share his knowledge and help others build their expertise, Pinal has also written more than 5,800 database tech articles on his blog at https://blog.sqlauthority.com.
Jacob Lyman (Jake)
Jacob Lyman (Jake)
Jacob Lyman (Jake) is a data professional currently working as an MLOps Engineer at Comet. Jake has a degree from Southern Utah University in Economics and Business Analytics. Since graduating from SUU, he has embraced the school's motto “Learning Lives Forever” throughout his career and now holds multiple professional certifications and proficiencies pertaining to Machine Learning Operations. More details on Jake can be found at www.jacoblyman.com.
Matt Stenzel
Matt Stenzel
Matt Stenzel is a Data and AI Cloud Solution Architect at Microsoft. He has 15+ years of experience in the data and analytics industry working in various roles including a software developer, data engineer, DW developer, DBA and integration architect. He has spent the last 8 years working at Microsoft helping companies across the US successfully implement Azure solutions that provide business value.
Victoria Clark
Victoria Clark
Victoria Clark is a Financial Management Consultant with a background in Finance projects and systems, especially facilitating workshops and education on developments in her field, and has a great interest in new technologies, especially how Finance is evolving due to automation and the role of Finance in the Digital Age. Born in South Africa she is currently living in the Netherlands and enjoys writing, learning new topics and spending time with family.
Axel Sirota
Axel Sirota
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.

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