Summary

yo_fluq focuses on a fluent, lazy, expandable way of writing data processing pipelines. The typical example is

(Query
    .en(orders)
    .take(1000)
    .where(lambda order: order['is_shipped'])
    .select(lambda order: order['payment_information'])
    .group_by(lambda payment: payment['customer_id'])
    .to_dictionary(
        lambda group: group.key,
        lambda group: Query
                        .en(group.value)
                        .select(lambda payment: payment['value'])
                        .sum()
        )
)

This way of writing code is typical for C# Linq and Spark, and this project makes it available for Python as well.

Unlike pandas, yo_fluq is:

• Lazy, so it does not require to keep the whole collection in memory
• Extendable, so you can define your own filters and use them in pipelines.

Unlike asq or py_linq, well-known ports of C# LINQ to Python, yo_fluq fully supports data annotations, even in case of user-defined extensions. Therefore, in IDE like PyCharm you will see the available methods in the hints. plinq supports annotations, but does not offer extendability technique.

The library has been developed since 2017, is extensively tested and is currently available under MIT licence, at Beta development stage.

Structure

This repository contains the following modules:

• yo_fluq
  • Does not have any dependencies on other Pypi modules
  • Python port for LINQ, or pull-pipelines
  • Push-pipelines
• yo_fluq_ds
  • Aggregation of data into numpy and pandas data structures and files in pull- and push-queries
  • Querying numpy, pandas data structures, as well as files and combinatorics lazy sources
  • Several extension methods with no real structure, just something I use a lot
  • Documentation
• yo_ds
  • Experimental stuff, my own plots, etc. Highly unstable. Use at your own risk. No documentation is available.
• yo_extensions
  • Obsolete, used for backward compatibility. Will be removed at some point. Do not use in new projects.