Often data is in a human-readable format, but it’s not suitable for data analysis. This is where pandas can help—it’s a powerful tool for reshaping DataFrames into different formats. In this course, you’ll grow your data scientist and analyst skills as you learn how to wrangle string columns and nested data contained in a DataFrame. You’ll work with real-world data, including FIFA player ratings, book reviews, and churn analysis data, as you learn how to reshape a DataFrame from wide to long format, stack and unstack rows and columns, and get descriptive statistics of a multi-index DataFrame.
Introduction to Data ReshapingFree
Let's start by understanding the concept of wide and long formats and the advantages of using each of them. You’ll then learn how to pivot data from long to a wide format, and get summary statistics from a large DataFrame.
Converting Between Wide and Long Format
Master the technique of reshaping DataFrames from wide to long format. In this chapter, you'll learn how to use the melting method and wide to long function before discovering how to handle string columns by concatenating or splitting them.
Stacking and Unstacking DataFrames
In this chapter, you’ll level-up your data manipulation skills using multi-level indexing. You'll learn how to reshape DataFrames by rearranging levels of the row indexes to the column axis, or vice versa. You'll also gain the skills you need to handle missing data generated in the stacking and unstacking processes.
You'll finish by learning how to combine the reshaping process with grouping to produce quick data manipulations. Lastly, you'll discover how to transform list-like columns and handle complex nested data, such as nested JSON files.
Maria Eugenia Inzaugarat
Eugenia is a data scientist that enjoys not only doing machine learning projects but also telling stories with data. She obtained a Ph.D. from the University of Buenos Aires. She has taught university courses in mathematics and biology as well as online courses on Data Science. Having transitioned from an academic background into data science, Eugenia loves teaching concepts related to python programming, data science, and machine learning to help others also gain knowledge about these fields.