polars
vaex
| polars | vaex | |
|---|---|---|
| 149 | 7 | |
| 31,773 | 8,334 | |
| 2.0% | 0.2% | |
| 10.0 | 6.4 | |
| 6 days ago | 4 months ago | |
| Rust | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
polars
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Using Polars in Rust for high-performance data analysis
If you want to get into Polars, the library is very well documented, and I’d recommend you check out their getting started tutorial, their API docs, and when you’re all set up, you can also check out their Cookbooks to learn about many of the standard operations within Polars.
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Why Polars rewrote its Arrow string data type
This is false. The polars api has used smart string for a long time.
https://github.com/pola-rs/polars/blob/32a2325b55f9bce81d019...
- Polars releases v1.0.0 – a Pandas alternative
- Polars Releases v1.0.0
- Big Data Is Dead
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
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 !
What are some alternatives?
datatable - A Python package for manipulating 2-dimensional tabular data structures
data.table - R's data.table package extends data.frame:
DataFrames.jl - In-memory tabular data in Julia
dtplyr - Data table backend for dplyr
modin - Modin: Scale your Pandas workflows by changing a single line of code
umap - Uniform Manifold Approximation and Projection
datafusion - Apache DataFusion SQL Query Engine
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
Apache Arrow - Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
PyO3 - Rust bindings for the Python interpreter
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow