Saving Metadata with DataFrames

Saving Metadata with DataFrames

Pandas lacks a dedicated mechanism for saving metadata to a DataFrame. However we have seen that recent libraries, such as Arrow and Parquet, do provide direct support for persisting DataFrames and metadata together in highly portable and performant files

Metadata is important — okay, perhaps not exactly a life & death issue for most Python data engineers, but its power and utility should not be overlooked. It enriches data with essential context, such as when, where and how it was created. Collection and storage of metadata should be considered a key feature to include in data processing applications. But how best to do this? If using the popular data analysis toolkit Pandas, how can metadata be stored with the ubiquitous DataFrame? Unfortunately Pandas doesn’t have great support for metadata. There’s no conventional way to attach metadata to a DataFrame, or for portable storage of combined data & metadata. The essential challenge is information coupling; how to ensure data & metadata remain linked together, not only during a single Python session, but also as they get persisted and passed from one system to the next. This latter question is our focus here; how can data & metadata be stored together in a portable and durable format? Naively we might try to save DataFrames as “pickle” files, perhaps adding metadata as a custom attribute or using the experimental attrs. This feels natural, straightforward and initially appears to work. But this approach has serious drawbacks. Pickles can be very specific to the particular version of Python and Pandas used to create them, rendering them unusable by different versions. They are not readily portable to non-Python programs, nor particularly optimised for performance.

arrow pandas parquet metadata python

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