Summary
In this chapter, we covered the basics of data analysis with pandas: loading a dataset, selecting rows and columns, grouping and aggregating quantities, and performing complex operations efficiently.
The next natural step is to conduct statistical analyses: hypothesis testing, modeling, predictions, and so on. Several Python libraries provide such functionality beyond pandas: SciPy, statsmodels, PyMC, and more. The IPython Cookbook contains many advanced examples of such analyses.
In the next chapter, we will introduce NumPy, the library underlying the entire SciPy ecosystem.
