faloworlds.blogg.se - Python json to csv

If you need to share data with other organizations, or even other teams within your organization, you need to limit yourself to data formats you know they will be able to process. “Best” is situation-specificĭifferent use cases imply different requirements. While there is no one true answer that works for everyone, this article will try to help you narrow down the field and make an informed decision.

Some data formats do a better job at this than others.

You also want to make sure the loaded data has all the right types: numeric types, datetimes, and so on.

Ideally you’d want a file format that’s fast, efficient, small, and broadly supported.

You don’t want loading the data to be slow, or use lots of memory: that’s pure overhead.

There are plenty of data formats supported by Pandas, from CSV, to JSON, to Parquet, and many others as well. Before you can process your data with Pandas, you need to load it (from disk or remote storage).