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Numba VS py2exe

Compare Numba VS py2exe and see what are their differences

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Numba logo Numba

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

py2exe logo py2exe

A distutils extension to create standalone Windows programs from Python scripts.
  • Numba Landing page
    Landing page //
    2019-09-05
  • py2exe Landing page
    Landing page //
    2021-10-04

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.

py2exe features and specs

  • Windows Executable
    py2exe allows you to convert Python scripts into standalone Windows executables without requiring the end-user to have Python installed.
  • Distribution Simplicity
    It simplifies distribution by packaging all necessary Python libraries and dependencies into a single executable file.
  • Customizable
    Offers a range of customization options, including the ability to include or exclude specific modules and packages, allowing for tailored executable creation.
  • Active Community
    Has a supportive community and ample online documentation, making it easier to find solutions to common problems.

Possible disadvantages of py2exe

  • Windows Only
    py2exe only works for creating Windows executables, which means it's not a cross-platform solution.
  • Configuration Complexity
    The setup process can be complex, especially for projects with many dependencies or special requirements.
  • Not Ideal for Large Applications
    The resulting executables can be quite large since they include a Python interpreter and necessary libraries, increasing both size and startup time.
  • Limited Continuity
    Though still usable, py2exe has had less active development compared to some other tools like PyInstaller, which may lead to potential issues with newer Python features.

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.

Numba videos

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The Criminal History of RondoNumbaNine

More videos:

  • Review - lucky numba review
  • Review - RondoNumbaNine - Free RondoNumbaNine "Clint Massey” (Official Interview - WSHH Exclusive)

py2exe videos

+ Add

Bypass Anti­Virus using Pyinstaller and py2exe (demo)

More videos:

  • Review - Python Py2exe Compiler Screen De-Constructed

Category Popularity

0-100% (relative to Numba and py2exe)

Numba

py2exe

Website Builder
84 84%
Website Builder
16% 16
Website Design
78 78%
Website Design
22% 22
Programming Language
85 85%
Programming Language
15% 15
CMS
63 63%
CMS
37% 37

User comments

Share your experience with using Numba and py2exe. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Numba seems to be a lot more popular than py2exe. While we know about 93 links to Numba, we've tracked only 1 mention of py2exe. 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.

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
View more

py2exe mentions (1)

  • Games
    Sounds like you are on Windows. I think something like https://py2exe.org/ will be what you are after. I'm on Mac / Linux systems only, so I can't really provide info on how to use it, but I know there are some good tutorials out there. Source: over 2 years ago

What are some alternatives?

When comparing Numba and py2exe, you can also consider the following products

Cython - Cython is a language that makes writing C extensions for the Python language as easy as Python...

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

nuitka - Nuitka is a Python compiler.

cx_Freeze - cx_Freeze is a set of scripts and modules for freezing Python scripts into executables in much the...

NumPy - NumPy is the fundamental package for scientific computing with Python

Nim (programming language) - The Nim programming language is a concise, fast programming language that compiles to C, C++ and JavaScript.

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