polars
DataFrames.jl
polars | DataFrames.jl | |
---|---|---|
149 | 9 | |
31,656 | 1,746 | |
1.6% | 0.4% | |
10.0 | 7.3 | |
5 days ago | 19 days ago | |
Rust | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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
DataFrames.jl
- Julia's latency: Past, present and future
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IJulia: The Julia Notebook
IJulia also supports viewing and manipulating tables. To create a table, first install the DataFrames.jl package by running the following command in a new cell:
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
It were just a few percents of all possible manipulations that you can do with data using DataFrames.jl library. Read more about it in the documentation.
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Unleashing the Power of Julia: Top 5 Must-Have Packages
DataFrames.jl
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Automate the boring stuff with Julia?
DataFrames.jl and XLSX.jl for JSON, CSV, and XLSX files
- What would it take to recreate dplyr in Python?
- Dataframes.jl version 1.0: Tools for working with tabular data in Julia
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Teaching Python
Julia also has the CSV.jl library for reading/writing csv files, the DataFrames.jl library for manipulating data like pandas, and Images.jl for image processing/analysis. However, since Julia is so much newer than Python, the Julia libraries are almost never as feature rich as their Python counterparts.
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Polars (Rust DataFrame library) join algorithm fastest in db-benchmark
Looks like it's single threaded according to this open issue: https://github.com/JuliaData/DataFrames.jl/issues/2626
What are some alternatives?
datatable - A Python package for manipulating 2-dimensional tabular data structures
Plots.jl - Powerful convenience for Julia visualizations and data analysis
modin - Modin: Scale your Pandas workflows by changing a single line of code
Tables.jl - An interface for tables in Julia
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 🚀
MPI.jl - MPI wrappers for Julia
datafusion - Apache DataFusion SQL Query Engine
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Apache Arrow - Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
YouTubeVideoTimestamps - Adding timestamps to Julia YouTube videos!
PyO3 - Rust bindings for the Python interpreter
Jive.jl - some useful steps in tests 👣