python setup.py example

Blake 159 Published: 08/01/2024

python setup.py example

I'm sorry, but I must follow the rules. Here's an example of a Python setup.py file:

from setuptools import setup

setup(


name='MyPackage',


version='1.0',


packages=['mypackage'],


install_requires=['numpy', 'pandas', 'scikit-learn'],


description='This is my package, which contains useful functions for data analysis and machine learning.',


author='Your Name',


author_email='[email protected]',


url='https://github.com/YourUsername/MyPackage',


keywords=['machine learning', 'data analysis', 'numpy', 'pandas'],


classifiers=[


'Development Status :: 5 - Production Ready',


'Intended Audience :: Developers',


'License :: OSI Approved :: MIT License',


'Programming Language :: Python',


'Programming Language :: Python :: 3.6',


'Programming Language :: Python :: 3.7',


'Programming Language :: Python :: 3.8'


]


)

This is a basic example of how to create a setup.py file using the setuptools library in Python. A setup.py file is used to define the metadata and dependencies for your Python package, which can be installed and distributed via PyPI (Python Package Index) or other package managers.

Here's what this script does:

It specifies the name of the package (MyPackage) and its version (1.0). It specifies the packages to include in the distribution, in this case, a single package named mypackage. It specifies the dependencies required for your package to run, including NumPy, pandas, and scikit-learn. It provides a short description of the package (This is my package...). It specifies the author's name and email address (Your Name and [email protected], respectively). It provides a URL for the package's GitHub repository or other hosting platform (https://github.com/YourUsername/MyPackage). It lists relevant keywords related to your package, which can help with discoverability when searching on PyPI. Finally, it specifies the classification of the package, including its development status, intended audience, license, programming languages supported, and versioning.

This is just a simple example, but you may need to customize the script depending on the specific requirements of your package.

Python packages example github

Here is a response in English:

Python has become one of the most popular programming languages in the world due to its simplicity, flexibility, and extensive range of libraries and frameworks. One of the best ways to showcase the versatility of Python is by highlighting some of the amazing packages available on GitHub.

GitHub is an online platform that allows developers to share their code with others, making it easy for others to build upon or even fork their projects. Here are a few examples of impressive Python packages on GitHub:

NumPy: NumPy (Numerical Python) is a library for numerical computation. It provides support for large, multi-dimensional arrays and matrices, and has built-in support for a wide range of high-performance mathematical operations.

GitHub link: https://github.com/numpy/numpy

scikit-learn: scikit-learn is machine learning library that includes algorithms for classification, regression, clustering, and more. It provides tools for data preprocessing, feature selection, and model evaluation.

GitHub link: https://github.com/scikit-learn/scikit-learn

TensorFlow: TensorFlow is an open-source software library for numerical computation, particularly well-suited for large-scale Machine Learning tasks. Originally developed by the Google Brain team, it was released as an open-source project in 2016.

GitHub link: https://github.com/tensorflow/tensorflow

Keras: Keras is a high-level neural networks API that is easy to use and runs on top of TensorFlow, CNTK or Theano. It provides an interface for building models with simple commands, making it easier for beginners to get started with machine learning.

GitHub link: https://github.com/keras-team/keras

pandas: pandas is a software library written for Python that offers data structures and operations for efficiently handling "relational" or "labeled" data, which is typically the case when working with structured data such as spreadsheets and SQL tables.

GitHub link: https://github.com/pandas-dev/pandas

Flask: Flask is a micro web framework written in Python. It is designed to make getting started with building web applications as easy as possible.

GitHub link: https://github.com/pallets/flask

OpenCV: OpenCV (Open Source Computer Vision Library) is a library that provides a simple and consistent API for computer vision algorithms. Its functionality includes image processing, feature detection, object recognition, and more.

GitHub link: https://github.com/opencv/opencv_python

SciPy: SciPy (Scientific Python) is a collection of software for scientific and engineering applications in Python. It provides functions to solve equations, integrate functions, optimize parameters, and more.

GitHub link: https://github.com/scipy/scipy

Requests: Requests is a simple HTTP library that allows you to send HTTP requests using Python. It includes support for JSON data, HTTP/1.1 protocol, and SSL/TLS encryption.

GitHub link: https://github.com/psf/requests

Statsmodels: Statsmodels is a statistical analysis library for Python that provides functions for descriptive statistics, regression, time series analysis, hypothesis testing, confidence intervals, and more.

GitHub link: https://github.com/statsmodels/statsmodels

These are just a few examples of the many amazing Python packages available on GitHub. Whether you're interested in machine learning, data science, or web development, there's a package out there that can help you get started with your project.

Related Articles