vaex
dataiter
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| vaex | dataiter | |
|---|---|---|
| 7 | 2 | |
| 8,171 | 23 | |
| 0.4% | - | |
| 5.4 | 7.8 | |
| 21 days ago | 16 days ago | |
| Python | Python | |
| MIT License | MIT License |
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vaex
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preprocessing millions of records - how to speed up the processing
Try vaex, vaex, using lazy evaluation and parallel calculations, you should be fine.
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High performance (for the consumer) time series storage?
I'd recommend QuestDB. Worked with it multiple times for different algorithmic trading needs and it didn't disappoint. If you want to load data fast, I'd recommend this Python library.
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Python Pandas vs Dask for csv file reading
How about vaex?
- Polars: Lightning-fast DataFrame library for Rust and Python
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For stocks, what historical data do you store and how do you store it?
You might find vaex (https://github.com/vaexio/vaex) interesting if you work with HDF5.
- I wrote one of the fastest DataFrame libraries
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A Hybrid Apache Arrow/Numpy DataFrame with Vaex Version 4.0
My guess is that should be possible, feel free to hop onto https://github.com/vaexio/vaex/discussions !
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
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
dtplyr - Data table backend for dplyr
data.table - R's data.table package extends data.frame:
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
dataframe-api - RFC document, tooling and other content related to the dataframe API standard
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
chain-ops-python - Simple chaining of operations (a.k.a. pipe operator) in python
visidata - A terminal spreadsheet multitool for discovering and arranging data
data_algebra - Codd method-chained SQL generator and Pandas data processing in Python.
umap - Uniform Manifold Approximation and Projection
mito - The mitosheet package, trymito.io, and other public Mito code.