Preface
pandas is a library for creating and manipulating structured data with Python. What do I mean by structured? I mean tabular data in rows and columns like what you would find in a spreadsheet or database. Data scientists, analysts, programmers, engineers, and more are leveraging it to mold their data.
pandas is limited to "small data" (data that can fit in memory on a single machine). However, the syntax and operations have been adopted or inspired other projects: PySpark, Dask, Modin, cuDF, Baloo, Dexplo, Tabel, StaticFrame, among others. These projects have different goals, but some of them will scale out to big data. So there is a value in understanding how pandas works as the features are becoming the defacto API for interacting with structured data.
I, Matt Harrison, run a company, MetaSnake, that does corporate training. My bread and butter is training large companies that want to level up on Python and data skills. As such, I've taught thousands of Python and pandas users over the years. My goal in producing the second version of this book is to highlight and help with the aspects that many find confusing when coming to pandas. For all of its benefits, there are some rough edges or confusing aspects of pandas. I intend to navigate you to these and then guide you through them, so you will be able to deal with them in the real world.
If your company is interested in such live training, feel free to reach out (matt@metasnake.com).
