fastkafka VS python-tabulate

Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier.

After a short discussion, we concluded we were just too spoiled to use low-level libraries that were nothing more than just tiny wrappers around C++ libs and that we could just build our own. So, we shamelessly made one by reusing beloved paradigms from FastAPI and we shamelessly named it FastKafka. The point was to set the expectations right - you get pretty much what you would expect: function decorators for consumers and producers with type hints specifying Pydantic classes for JSON encoding/decoding, automatic message routing to Kafka brokers and documentation generation.

FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project.

FastStream (https://github.com/airtai/faststream) is a new Python framework, born from Propan and FastKafka teams' collaboration (both are deprecated now). It extremely simplifies event-driven system development, handling all the parsing, networking, and documentation generation automatically. Now FastStream supports RabbitMQ and Kafka, but supported brokers are constantly growing (wait for NATS and Redis a bit). FastStream itself is a really great tool to build event-driven services. Also, it has a native FastAPI integration. Just create a StreamRouter (very close to APIRouter) and register event handlers the same with the regular HTTP-endpoints way:

Inspired by FastAPI, FastKafka uses the same paradigms for routing, validation, and documentation, making it easy to learn and integrate into your existing streaming data projects. Please check out the latest version adds supporting the newly released Pydantic v2.0, making it significantly faster. https://github.com/airtai/fastkafka

pandas.DataFrame().to_latex() [1] and tabulate [2] support latex table output.

[1] https://pandas.pydata.org/docs/reference/api/pandas.DataFram...

[2] https://github.com/astanin/python-tabulate/blob/master/tabul...

pandas uses tabulate

As a general piece of advice, the tabulate package is useful for neatly formatting spreadsheet-style data, as is the pandas package, although tabulate is much simpler to use.

There are ways to do it manually with padding/alignment, but using the built-in csv module to read the file and tabulate to format it is probably the easiest way.

If you're not against a third-party library, consider tabulate.

You mean output to the terminal? You could use a module like tabulate or pandas to do that for you. You could also write a quick function yourself that does the same thing; that would be a fairly easy project. Just transpose the data, calculate the max length in each column, then print row by row while padding to the max length. Probably 8 lines of code.