PyInstaller VS Numba

Compare PyInstaller VS Numba and see what are their differences

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PyInstaller

PyInstaller is a program that freezes (packages) Python programs into stand-alone executables...

Numba

Numba gives you the power to speed up your applications with high performance functions written...

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2021-10-20

Landing page //
2019-09-05

PyInstaller

Website: pyinstaller.org
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Numba

Website: numba.pydata.org
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PyInstaller features and specs

Cross-Platform Support
PyInstaller supports Windows, macOS, and Linux, allowing developers to create executables for multiple platforms from a single codebase.
Single Executable
PyInstaller can bundle a Python application and all its dependencies into a single executable, simplifying distribution as users do not need to install Python separately.
Easy to Use
PyInstaller has straightforward commands and a simple configuration process, making it accessible even for those with limited experience in creating executables.
Customizable
PyInstaller provides various options for customization, allowing developers to specify which files to include or exclude, add data files, and more.
Active Community
PyInstaller benefits from an active community that contributes to its development and provides support through forums and other platforms.

Possible disadvantages of PyInstaller

Executable Size
The executable files generated by PyInstaller can be large since they include the Python interpreter and all dependencies, which may not be ideal for applications with size constraints.
Compatibility Issues
While PyInstaller supports many third-party Python packages, some packages may not work out of the box, requiring additional configuration or adjustments.
Occasional Bugs
Like any software tool, PyInstaller can have bugs, especially with new or less common Python features, which may require troubleshooting or code workarounds.
Limited Optimization
The executables produced by PyInstaller may not be as optimized in terms of performance as those created by more complex methods or tools specifically designed for performance enhancements.
Dynamic Module Loading
Handling dynamic imports can be challenging with PyInstaller, requiring developers to manually specify hidden imports to ensure all dependencies are included.

Numba features and specs

Performance
Numba can significantly increase the speed of execution for numerically intensive Python code by compiling Python functions to optimized machine code using LLVM.
Ease of Use
Numba is user-friendly and requires minimal code changes. Often, just applying a decorator to functions is enough to gain performance benefits.
Integration with NumPy
Numba works well with NumPy, allowing users to compile functions that utilize NumPy arrays efficiently.
JIT Compilation
It supports Just-In-Time (JIT) compilation, enabling functions to be compiled at runtime, which allows for optimizations based on actual usage.
GPGPU Acceleration
Numba offers support for GPU acceleration, which can further enhance performance by offloading tasks to NVIDIA GPUs using CUDA.

Possible disadvantages of Numba

Limited Python Feature Support
Numba does not support all Python features and standard library modules, which can limit its applicability for certain functions or applications.
Compilation Overhead
The initial compilation of functions can add overhead, which might negate performance gains for small or simple tasks.
Debugging Difficulty
Debugging Numba-compiled code can be challenging due to the compiled nature of the code, which may obscure typical Python error messages.
Complex Code Compatibility
More complex Python constructs, such as classes and closures, are not fully supported, requiring workarounds or alternative solutions.
Dependency on LLVM
Numba heavily relies on the LLVM library for compilation, which can complicate installation and increase dependency size.

Analysis of Numba

Overall verdict

Numba is considered good, especially if your work involves numerical computations that can take advantage of its just-in-time compilation. Its ability to speed up Python code while allowing you to remain within the Python ecosystem makes it a valuable tool for performance optimization in computationally demanding applications.

Why this product is good

Numba is a just-in-time compiler for Python that is particularly effective for numerical and scientific computing. It translates Python functions to optimized machine code at runtime using the LLVM compiler infrastructure. This can significantly accelerate execution speed, especially for operations that involve loops and computationally intensive tasks. It's an attractive option for developers looking for performance optimization without having to write C or C++ code. Numba is also easy to integrate with other popular scientific computing libraries such as NumPy.

Recommended for

Data scientists and engineers working with large datasets.
Developers involved in scientific computing and numerical analysis.
Researchers needing to optimize algorithms for speed without leaving Python.
Educational purposes for those learning about compiling and performance acceleration.

PyInstaller videos

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Archivo ejecutable en Python | Windows| PyInstaller |PyQT5| Python | ¡Muy fácil!

Numba videos

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The Criminal History of RondoNumbaNine

Category Popularity

0-100% (relative to PyInstaller and Numba)

Numba

Website Builder

37 37%

Website Builder

63% 63

Website Design

43 43%

Website Design

57% 57

CMS

51 51%

CMS

49% 49

Programming Language

24 24%

Programming Language

76% 76

User comments

Share your experience with using PyInstaller and Numba. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Numba should be more popular than PyInstaller. It has been mentiond 93 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PyInstaller mentions (31)

Cosmopolitan v3.5.0
Looking forward toward somebody hooking together Python in APE [0], something like pex [1]/shiv[2]/pyinstaller[3], and the pants build system [4] to have a toolchain which spits out single-file python executables with baked-in venv and portable across mainstream OSes with native (or close enough) performance. 0 - https://news.ycombinator.com/item?id=40040342 2 - https://shiv.readthedocs.io/en/latest/ 3 -... - Source: Hacker News / 11 months ago
Playable Sandbox Now Available
Normally games made with pygame are not playable from the web. They can only be run from the command line or use PyInstaller or cx_Freeze to create a standalone executable. - Source: dev.to / over 1 year ago
Python GUIs
I have found PyInstaller [1] to work well for packaging everything into a single ZIP file that unzips to a folder with an executable binary and all accompanying files (or even a single EXE file that self-extracts when run, but that increases startup time). It knows how to package PyQt and its associated Qt libraries (or PySide, which I actually prefer) so that they can be shipped with your application. [1... - Source: Hacker News / almost 2 years ago
Advice on turning tcod python game into something I can share with others?
PyInstaller is the main way to build a Python executable. I'd recommenced bundling your program in the default one-folder mode and uploading it to Itch. Source: about 2 years ago
What's the best way to ship a Python script?
There are tools, not from Python Software Foundation (or officially supported by them), such as Pyinstaller, that will try to produce a single executable file that you can distribute for people to install. Of course, this would depend on the controls on the end user devices allowing such an installation. There can be some compatibility challenges, but if you are using reasonably standard Python it shall probably... Source: about 2 years ago

Numba mentions (93)

I Use Nim Instead of Python for Data Processing
>Not type safe That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that... - Source: Hacker News / 9 months ago
Gravitational Collapse of Spongebob
I believe it is using Numba which converts to machine code. https://numba.pydata.org/. - Source: Hacker News / about 1 year ago
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. - Source: dev.to / over 1 year ago
Mojo: The usability of Python with the performance of C
Or you use numba [1]. Then you can use a subset of plain Python. [1] https://numba.pydata.org/. - Source: Hacker News / over 1 year ago
Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse. [0]: https://numba.pydata.org/. - Source: Hacker News / almost 2 years ago