Summary

In this chapter, we saw how building Python packages for our analysis applications can make it very easy for others to carry out their own analyses and reproduce ours, as well as for us to create repeatable workflows for future analyses.

The stock_analysis package we created in this chapter contained classes for gathering stock data from the Internet (StockReader); visualizing individual assets or groups of them (Visualizer family); calculating metrics for single assets or groups of them for comparisons (StockAnalyzer and AssetGroupAnalyzer, respectively); and time series modeling with decomposition, ARIMA, and linear regression (StockModeler). We also got our first look at using the statsmodels package in the StockModeler class. This chapter showed us how the pandas, matplotlib, seaborn, and numpy functionality that we've covered so far in this book has come together and how these libraries can work harmoniously with other packages for custom applications. I strongly...