Consistent iterable API for reading and writing to external datasets
Project description
Python libraries for consuming (input) and generating (output) external data resources in various formats.
Importing data
The basic process is broken into several steps which are handed by various submodules.
loaders
Load an external resource from the local filesystem or from the web into a file-like object.
parsers
Parse the file (using the standard python library for that file type) and convert the recordset into a simple list of dictionaries.
mappers
Rename field names and values if needed and optionally convert the dictionaries into other object types (such as a namedtuple).
Traversing data
The base library then provides a collection object (an “IO”) that can be used to easily navigate the dataset. It also provides a number of convenience classes for common IO use cases. For example, CsvNetIO provides an IO with loaders.NetLoader, parsers.CsvParser, and mappers.TupleMapper pre-mixed into the class.
Exporting data
The export process uses the same submodules to apply the above steps in reverse:
mappers
Convert the mapped object back into a simple dictionary and map the field names and values back to the format expected by the file.
parsers
Convert the dictionary list back into the source format and write out to the file.
loaders
Prepare the file-like object for writing and perform any needed wrap-up operations.
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