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 others 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 by or inspired other projects: PySpark, Dask, and cuDF, among others. These projects have different goals, but some of them will scale out to big data. So, there is value in understanding how pandas works as the features are becoming the de facto API for interacting with structured data.

I, Will Ayd, have been a core maintainer of the pandas library since 2018. During that time, I have had the pleasure of contributing to and collaborating on a host of other open source projects in the same ecosystem, including but not limited to Arrow, NumPy and Cython.

I also consult for a living, utilizing the same ecosystem that I contribute to. Using the best open source tooling, I help clients develop data strategies, implement processes and patterns, and train associates to stay ahead of the ever-changing analytics curve. I strongly believe in the freedom that open source tooling provides, and have proven that value to many companies.

If your company is interested in optimizing your data strategy, feel free to reach out ([e-mail]).