Cleaning and Working with Dataframes in Python
Learn to rename columns, tidy up messy data, and convert data types for efficient analysis. Say goodbye to data headaches and hello to streamlined insights.
What you'll learn
In today's data-driven world, cleaning and organizing data has become an essential task for businesses and organizations. Messy data can lead to incorrect insights, which can lead to poor decision-making.
In this course, Cleaning and Working with Dataframes in Python, you’ll gain the ability to clean and organize messy data using the powerful pandas library in Python.
First, you’ll explore how to rename columns in a dataframe for more intuitive data access. You'll learn how to assign column names manually using the .columns dataframe attribute and how to rename an existing column in a dataframe using the rename() function.
Next, you’ll discover how to alter columns in a dataframe for a tidy data set. You'll learn how to drop a list of columns with a single call to drop(), and you'll define the purpose of the in place and axis parameters.
Finally, you’ll learn how to apply these skills to solve real-world problems. When you’re finished with this course, you’ll have the skills and knowledge of cleaning and working with dataframes - using pandas in Python - needed to clean and organize messy data and obtain accurate insights. You'll be ready to take on data cleaning challenges and become a more efficient data professional.
Table of contents
About the author
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.