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In this chapter, you will use the pandas library to persist your time series DataFrames to different file formats, such as CSV, Excel, Parquet, and pickle files. When performing analysis or data transformations on DataFrames, you essentially leverage pandas' in-memory analytics capabilities, offering great performance. However, being in memory means the data can easily be lost since it has not yet persisted in disk storage.
When working with DataFrames, you will need to persist your data for future retrieval, creating backups, or sharing your data with others. The pandas library is bundled with a rich set of writer functions to persist your in-memory DataFrame (or series) to disk in various file formats. These writer functions allow you to store data on a local drive or a remote server location, such as a cloud storage filesystem, including Google Drive, AWS S3, Azure Blob Storage, and Dropbox.
In this chapter, you will...
