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awesome-python's Introduction

Awesome Python

{Awesome Works in Progress}

High-level | Cross-Platform | Huge Community | Large Ecosystem 💙💛
Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.

Table of contents

Official Websites

YouTube, Groups and Communities

YouTube Channels 📺

Groups and Communities

Surveys

Certificates and Exams

Setup and Configuration

  • Python
  • PIP
    • How to Install PIP on Windows - PIP is the standard package manager for Python. It allows you to install and manage additional packages that are not part of the Python standard library. If you’re using Python 3.4 (or greater), then PIP comes installed with Python by default.
    • How to Upgrade PIP in Windows - X:>python -m pip install --upgrade pip
  • Third-party Dist.
  • PyPI - The Python Package Index
    • PyPI - The Python Package Index (PyPI) is a repository of software for the Python programming language.

IDE

Implementation

  • CPython - CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely-used implementation of the language.
  • Jython - Java
  • IronPython - C#
  • PyPy - RPython
  • MicroPython - MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments.
  • Stackless - Stackless Python, or Stackless, is a Python programming language interpreter, so named because it avoids depending on the C call stack for its own stack.

Integration

Python Web Framework

Hosting

  • Heroku - Cloud platform for building Pythonic apps and APIs
  • PythonAnywhere - Host, run, and code Python in the cloud!

Docker

Articles, Videos and Tutorials

Articles

Videos

Python for Kids and fun

  • KidsCanCode - Every kid should learn to code! KidsCanCode is producing a series of YouTube coding lessons based on its proven, in-class curriculum - and you can help!
  • Tynker - Learn Python as you play.
  • micro:bit - Power your imagination with code

Tutorials ⭐

Let's Talk Data

Data Life Cycle

Data Types

Statistics

Core

EDA

Essential features

Data Cleansing and Modeling

Visualization

Dashboard

Documentation

Packaging

Learning ⭐

Datasets

Libraries

  • Trending Python Projects

  • IDE

    • Jupyter - Install the Jupyter system, including the notebook, qtconsole, and the IPython kernel.

  • Core Libraries and Statistics

    • NumPy - NumPy (Numeric Python) is the fundamental package for scientific computing with Python. It contains among other things; a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities.
    • SciPy - The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration, interpolation, optimization, linear algebra and statistics.
    • Pandas - Pandas library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

  • Visualization

    • Matplotlib - Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
    • Seaborn - Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
    • Plotly - Plotly's Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
    • chartify - Chartify is a Python library that makes it easy for data scientists to create charts.
    • ggplot - ggplot is a plotting system for Python based on R's ggplot2 and the Grammar of Graphics. It is built for making profressional looking, plots quickly with minimal code.
    • folium - Make beautiful maps with Leaflet.js & Python

  • Machine Learning

    • Scikit-learn - Simple and efficient tools for data mining and data analysis
    • TensorFlow - The core open source library to help you develop and train ML models. Get started quickly by running Colab notebooks directly in your browser.
    • PyTorch - An open source deep learning platform that provides a seamless path from research prototyping to production deployment.
    • Keras - Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
    • NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
    • pythia - A modular framework for Visual Question Answering research from Facebook AI Research (FAIR) https://learnpythia.readthedocs.io/
    • NimbusML - NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices.

  • Dashboard

  • Python Datasets

    • scikit-learn - The sklearn.datasets package embeds some small toy datasets.
    • PyDataset - Provides instant access to many datasets right from Python (in pandas DataFrame structure).
    • gapminder - A Python version of Jennifer Bryan's excellent gapminder teaching package for R.

  • Apps

ML and Algorithms

  • Awesome ML - Awesome ML Resources and References

R vs Python

awesome-python's People

Contributors

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