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PyInstaller VS NumPy

Compare PyInstaller VS NumPy and see what are their differences

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PyInstaller logo PyInstaller

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

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • PyInstaller Landing page
    Landing page //
    2021-10-20
  • NumPy Landing page
    Landing page //
    2023-05-13

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.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

PyInstaller videos

+ Add

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

More videos:

  • Review - python hack #8 reverse shell espionage cmd fichier py en exe pyinstaller part2
  • Review - python hack #8 reverse shell espionage cmd fichier py en exe pyinstaller part1

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

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NumPy

Website Builder
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Data Science And Machine Learning
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Data Science And Machine Learning
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Website Design
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Website Design
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Data Science Tools
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User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare PyInstaller and NumPy

PyInstaller Reviews

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NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy should be more popular than PyInstaller. It has been mentiond 119 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 / 10 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
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NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing PyInstaller and NumPy, you can also consider the following products

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

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

nuitka - Nuitka is a Python compiler.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Inno Setup - Inno Setup is a free installer for Windows programs.

OpenCV - OpenCV is the world's biggest computer vision library

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