siuba
sspipe
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| siuba | sspipe | |
|---|---|---|
| 25 | 1 | |
| 1,100 | 145 | |
| - | 0.0% | |
| 7.5 | 0.0 | |
| 7 months ago | almost 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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siuba
- The Design Philosophy of Great Tables (Software Package)
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Best alternative to Pandas 2023?
I don't know what's best for you, but I can recommend Siuba, a tidy interface for Python to send queries to pandas and SQL-db.
- Method Chaining in Pandas: Bad Form or a Recipe for Success?
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Happy Halloween, Pandas! 🎃🤓
You mean siuba?
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Explorer (Elixir and Polars)
For further inspiration, this is a pretty good-looking "dplyr for Python": https://github.com/machow/siuba
- Unpopular opinion: Matplotlib is a bad library
- A trick to have arbitrary infix operators in Python
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Going from R to Pandas: dplython vs dfply vs plydata
You should follow /u/the75th's advice. However, if you decide to buck that take, I'd look into siuba. I've never heard of those packages you've listed, and have doubts they'd be maintained.
- Tidyverse equivalent in Python?
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R / Tidyverse User -> Python | How to Make it Hurt Less
Check out siuba
sspipe
What are some alternatives?
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
tsflex - Flexible time series feature extraction & processing
dtale - Visualizer for pandas data structures
kindleServer - This project serve HTML files (and a few more) saved in your computer with a UI suitable for Kindle web browser. On top of that, it include a Read Mode (thanks to ReadabiliPy) to display the text in a comfortable size without have to use the 'Article Mode' in Kindle web browser.
Altair - Declarative statistical visualization library for Python
concrete-numpy - Concrete-Numpy: A library to turn programs into their homomorphic equivalent.
q - q - Run SQL directly on delimited files and multi-file sqlite databases
seaborn - Statistical data visualization in Python
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Dask - Parallel computing with task scheduling