dataiter
data_algebra
dataiter | data_algebra | |
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2 | 5 | |
25 | 113 | |
- | 0.0% | |
7.8 | 8.5 | |
24 days ago | 7 months ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" 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
data_algebra
- Control Pandas, Polars, or SQL from One DSL
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Modern Pandas (Part 2): Method Chaining
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 .
- Plotting Multiple Curves in Python
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Siuba – A Dplyr Port to Python
Neat. I've been working on my own "piped-Codd" style system I call the "data algebra" https://github.com/WinVector/data_algebra
I use method chaining as the composing notation.
What are some alternatives?
dtplyr - Data table backend for dplyr
siuba - Python library for using dplyr like syntax with pandas and SQL
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
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
chain-ops-python - Simple chaining of operations (a.k.a. pipe operator) in python
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
minimal-pandas-api-for-pola
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.