Modern Pandas (Part 2): Method Chaining

• data_algebra

  5 113 8.5 Python

  Codd method-chained SQL generator and Pandas data processing in Python.

  

There are a number of packages in Python specializing in variations of piped processing in Pandas. My own is this one: https://github.com/WinVector/data_algebra .

• polars

  144 26,378 10.0 Rust

  Dataframes powered by a multithreaded, vectorized query engine, written in Rust

  

I'd recommend checking out polars as an alternative to pandas - https://github.com/pola-rs/polars

It has a rather different api, and is significantly faster. Highly recommend it.

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• dataiter

  2 25 7.8 Python

  Python classes for data manipulation

Here's another alternative. I wrote Dataiter specifically as I too was frustrated with Pandas. In my experience if you design a new API from scratch (and don't try to reimplement the Pandas API as many projects have done!) and have some vision and consistent principles, it's well possible to get a good intuitive API as a result. Two relevant issues remain: You're limited by NumPy's datatypes and their problems, such as memory-hogging strings and a lack of a proper missing value (NA), and secondly, limited by the Python language, so compared to e.g. dplyr's non-standard evaluation, you'll need to use lambda functions, which are unfortunately clumsy and verbose.

https://github.com/otsaloma/dataiter

Here's a comparison of dplyr vs. Dataiter vs. Pandas, which should give quick overview of the similarieties and differences.

https://dataiter.readthedocs.io/en/latest/_static/comparison...

• mito

  18 2,223 10.0 Python

  The mitosheet package, trymito.io, and other public Mito code.

  

My team has been trying to modernize pandas from a different tact. Regardless of struggle with the syntax, it seems Pandas is very sticky, and we don't predict much migration to other data science languages. Instead of refining the syntax, we have combined it with a spreadsheet GUI (https://github.com/mito-ds/monorepo). Here, we worry less about writing perfect syntax ourselves, and let the GUI write the code for functions like pivot tables and merges that work well visually.

• chain-ops-python

  2 0 10.0

  Simple chaining of operations (a.k.a. pipe operator) in python

You don't need pandas to do chaining. It's a one-liner in pure python: https://github.com/tpapastylianou/chain-ops-python

Not to mention, it's a lot more debuggable this way (which is generally the biggest downside to most specialised chaining approaches).

• SaaSHub

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  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

Suggest a related project