Python for Finance, 3rd Edition

Chapter 10. Input/Output Operations

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Financial analytics always starts and ends with data on disk. Prices, orders, risk reports, and simulation results need to be loaded from files, transformed in memory, and written back to storage so that other systems and people can work with them. This chapter shows how to perform these input/output (I/O) tasks efficiently and reliably with the Python standard library, NumPy, and pandas.

The chapter focuses on practical file-based workflows for financial data. It shows how to write and read text files and serialized Python objects, store numerical arrays efficiently with NumPy, use pandas for CSV, SQL, JSON, and HDF5 I/O, and structure examples so that they can later be measured with tools such as %timeit in a notebook.

To follow along, you need a working Python environment with numpy, pandas, and matplotlib installed, basic familiarity with Path from the pathlib module and with Series and DataFrame objects, and read access to data/eod_data.csv or its ...