polars
datatable
polars | datatable | |
---|---|---|
149 | 9 | |
31,656 | 1,820 | |
1.6% | 0.0% | |
10.0 | 1.6 | |
5 days ago | 4 months ago | |
Rust | C++ | |
GNU General Public License v3.0 or later | Mozilla Public License 2.0 |
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
datatable
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Cheat Sheets for data.table to Python's pandas syntax?
Aside from that, there is a Python translation of data.table (see documentation here), which might be worth looking into. However, it hasn't had any major updates in a while: the last release 2 years ago ...
- Any advice on using Pandas as a data analyst?
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Need advice on whether to store data set for regression model in SQL database or by using Python modules like Pickle or Parquet
just use HDF5 or Parquet, or CSV + https://github.com/h2oai/datatable to speed up the file reading.
- Massive R analysis of Data Science Language and Job Trends 2022
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Scikit-Learn Version 1.0
> For me I had with pandas the most issues using it's multiindex.
Yessss. I loathe indices, and have never been in a situation where I was better off with them than without them.
> Regarding fast you have something like Vaex on python sid
I've never used Vaex, but I've used datatable (https://github.com/h2oai/datatable) and polars (https://github.com/pola-rs/polars). Polars is my favorite API, but datatable was faster at reading data (Polars was faster in execution). I'll have to give Vaex a try at some point.
- Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
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Hey Reddit, here's my comprehensive course on Python Pandas, for free.
Yep. I think this is the downside to a package being entirely maintained by volunteers. In any case, Pandas is still the leading data wrangling package for Python. (I'm excited to see how datatable evolves.)
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Ditching Excel for Python in a Legacy Industry (Reinsurance)
h2o's data.table clone is fine
https://github.com/h2oai/datatable
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
sheet2dict - Simple XLSX and CSV to dictionary converter
DataFrames.jl - In-memory tabular data in Julia
db-benchmark - reproducible benchmark of database-like ops
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 🚀
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
datafusion - Apache DataFusion SQL Query Engine
skorch - A scikit-learn compatible neural network library that wraps PyTorch
Apache Arrow - Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
scientific-visualization-book - An open access book on scientific visualization using python and matplotlib
PyO3 - Rust bindings for the Python interpreter
sktime - A unified framework for machine learning with time series