dtale-desktop
Numba
dtale-desktop | Numba | |
---|---|---|
7 | 124 | |
156 | 9,471 | |
- | 1.1% | |
2.6 | 9.9 | |
about 3 years ago | 4 days ago | |
Python | Python | |
MIT License | BSD 2-clause "Simplified" License |
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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.
dtale-desktop
- I made a data dashboard web app which you can interactively build by writing python code IN the dashboard. It's available as a python package, and contributors are very welcome! (and I actually need some)
- I made a data dashboard web app which you can interactively build by writing python code IN the dashboard. It's available as a python package, and contributors are welcome!
- Show HN: Dtaledesktop – organize your Python scripts into a data viz dashboard
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Hi pandas lovers - I released a package for easily converting your python scripts into data visualization dashboards (dtale/fastapi/react)
It can be run both locally and as a web service -- the demo site is actually running on a kubernetes cluster. When run as a web service, it uses websocket connections to push real-time updates, ensuring that all connected users see the same thing. There are a large number of settings which can be used to configure exactly how it behaves, documented [here](https://github.com/phillipdupuis/dtale-desktop#settings).
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If you use python/pandas/dtale for analysis, I released a free and open-source GUI for organizing your random python scripts into a data visualization dashboard
Also, if you want to you can run it as a web service -- the demo site is actually running on a kubernetes cluster. When run as a web service, it uses websocket connections to push real-time updates, ensuring that all connected users see the same thing. There are a large number of settings which can be used to configure exactly how it behaves, documented here.
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Need Help to understand importlib module
Inspiration taken from here: https://github.com/phillipdupuis/dtale-desktop/blob/master/dtale_desktop/source_code_tools.py#L20, in that case it's being used to execute text users write in a web browser as python code (text sent to server > server writes it to tempfile, turns the tempfile into a module, executes the code in the module, sends the result of executing that code back)
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
dtale - Visualizer for pandas data structures
NetworkX - Network Analysis in Python
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
jupyterlab-autoplot - Magical Plotting in JupyterLab
Dask - Parallel computing with task scheduling
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
cupy - NumPy & SciPy for GPU
chord - Engaging visualisations, made easy.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
hyperglass - hyperglass is the network looking glass that tries to make the internet better.
SymPy - A computer algebra system written in pure Python