dataiter
chain-ops-python
dataiter | chain-ops-python | |
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2 | 2 | |
25 | 0 | |
- | - | |
7.8 | 10.0 | |
24 days ago | over 1 year ago | |
Python | ||
MIT License | - |
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dataiter
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Modern Pandas (Part 2): Method Chaining
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...
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Polars: Lightning-fast DataFrame library for Rust and Python
Agreed, dplyr is great.
I built my own data frame implementation on top of NumPy specifically trying to accomplish a better API, similar to dplyr. It's not exactly the same naming or operations, but should feel familiar and much simpler and consistent than Pandas. And no indexes or axes.
Having done this, a couple notes on what will unavoidably differ in Python
* It probably makes more sense in Python to use classes, so method chaining instead of function piping. I wish one could syntactically skip enclosing parantheses in Python though, method chains look a bit verbose.
* Python doesn't have R's "non-standard evaluation", so you end up needing lambda functions for arguments in method chains and group-wise aggregation etc. I'd be interested if someone has a better solution.
* NumPy (and Pandas) is still missing a proper missing value (NA). It's a big pain to try to work around that.
https://github.com/otsaloma/dataiter
chain-ops-python
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Modern Pandas (Part 2): Method Chaining
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).
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More Intuitive Partial Function Application in Python
This is great. I will use it with my python chaining approach: https://github.com/tpapastylianou/chain-ops-python
My only grief is that decorator, which forces you to wrap existing functions anyway (same way I had to define lambdas in my example anyway).
Do you have any insight on that?
What are some alternatives?
dtplyr - Data table backend for dplyr
data_algebra - Codd method-chained SQL generator and Pandas data processing in Python.
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
mito - The mitosheet package, trymito.io, and other public Mito code.
dataframe-api - RFC document, tooling and other content related to the dataframe API standard
minimal-pandas-api-for-pola
db-benchmark - reproducible benchmark of database-like ops
Datamancer - A dataframe library with a dplyr like API
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars