dataiter
dataframe-api
dataiter | dataframe-api | |
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2 | 2 | |
23 | 95 | |
- | - | |
7.8 | 8.6 | |
23 days ago | about 1 month ago | |
Python | Python | |
MIT License | 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
dataframe-api
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Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
Yes, with the upcoming dataframe api protocol the implementation and API will be separated for libraries that adopt that protocol.
- Polars: Lightning-fast DataFrame library for Rust and Python
What are some alternatives?
dtplyr - Data table backend for dplyr
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
chain-ops-python - Simple chaining of operations (a.k.a. pipe operator) in python
Datamancer - A dataframe library with a dplyr like API
data_algebra - Codd method-chained SQL generator and Pandas data processing in Python.
mito - The mitosheet package, trymito.io, and other public Mito code.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
minimal-pandas-api-for-pola