This tutorial is an introduction to pandas, a library providing data structures and algorithms for tabular data analysis. It's aimed at scientists and data analysts new to scientific Python. No previous experience with pandas is expected. Familiarity with the basics of Python will be helpful.
We'll work through a series of Jupyter notebooks together, with an emphasis on solving realistic problems as exercises. We'll cover
1. A definition of tabular data and pandas' data structures for tabular data
2. How pandas' alignment by row and column labels simplifies data analysis
3. groupby for analyzing subsets of a table grouped by some common factor
4. Tidy data: how to structure your data to facilitate analysis.
5. Performance: How to benchmark and profile code, and some common pandas performance pitfalls
6. pandas' special support for time-series data.
See tutorial materials here: https://scipy2018.scipy.org/ehome/299527/648136/