vaex
minimal-pandas-api-for-polars
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| vaex | minimal-pandas-api-for-polars | |
|---|---|---|
| 7 | 1 | |
| 8,171 | 7 | |
| 0.4% | - | |
| 6.0 | 3.2 | |
| 13 days ago | over 2 years ago | |
| Python | Python | |
| 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 !
minimal-pandas-api-for-polars
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Polars: Lightning-fast DataFrame library for Rust and Python
https://github.com/austospumanto/minimal-pandas-api-for-pola...
pip install minimal-pandas-api-for-polars
I wrote a library that wraps polars DataFrame and Series objects to allow you to use them with the same syntax as with pandas DataFrame and Series objects. The goal is not to be a replacement for polars' objects and syntax, but rather to (1) Allow you to provide (wrapped) polars objects as arguments to existing functions in your codebase that expect pandas objects and (2) Allow you to continue writing code (especially EDA in notebooks) using the pandas syntax you know and (maybe) love while you're still learning the polars syntax, but with the underlying objects being all-polars. All methods of polars' objects are still available, allowing you to interweave pandas syntax and polars syntax when working with MppFrame and MppSeries objects.
Furthermore, the goal should always be to transition away from this library over time, as the LazyFrame optimizations offered by polars can never be fully taken advantage of when using pandas-based syntax (as far as I can tell). In the meantime, the code in this library has allowed me to transition my company's pandas-centric code to polars-centric code more quickly, which has led to significant speedups and memory savings even without being able to take full advantage of polars' lazy evaluation. To be clear, these gains have been observed both when working in notebooks in development and when deployed in production API backends / data pipelines.
I'm personally just adding methods to the MppFrame and MppSeries objects whenever I try to use pandas syntax and get AttributeErrors.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
dataframe-api - RFC document, tooling and other content related to the dataframe API standard
data.table - R's data.table package extends data.frame:
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
dtplyr - Data table backend for dplyr
visidata - A terminal spreadsheet multitool for discovering and arranging data
Datamancer - A dataframe library with a dplyr like API
umap - Uniform Manifold Approximation and Projection
db-benchmark - reproducible benchmark of database-like ops
db-benchmark - reproducible benchmark of database-like ops
dataiter - Python classes for data manipulation