Chapter 4: Persisting Time Series Data to Files

In this chapter, you will be using the pandas library to persist your time series DataFrames to a different file format, such as CSV, Excel, and pickle files. When performing analysis or data transformations on DataFrames, you are essentially leveraging pandas' in-memory analytics capabilities, which offer great performance. But being in-memory means that the data can easily be lost since it is not persisting on disk.

When working with DataFrames, there will be a need to persist your data for future retrieval, creating backups, or for sharing your data with others. The pandas library is bundled with a rich set of writer functions to persist your in-memory DataFrames (or series) to disk in various file formats. These writer functions allow you to store data to a local drive or to a remote server location such as a cloud storage filesystem, including Google Drive, AWS S3, and Dropbox.

In this chapter, you will explore writing...