4 Top Autocomplete Coding Tools For Python Programmers

While programming or coding, a programmer can sometimes take hours of time to solve an error. Autocomplete tools serve as a great helping hand as it helps them to complete the code faster while reducing the errors. In this article, we list down 4 autocomplete coding tools for Python programmers.

1| Kite

Kite is a powerful editor integration which allows you to work uninterrupted on the same screen. It is a free AI autocomplete engine which helps the programmers to code faster inPython with Line-of-Code completions. Kite’s Line-of-Code Completions feature uses deep learning to serve context-relevant code completions in real time. It is basically a plugin for your IDE which uses machine learning to give you useful code completions for Python. Some of the features of Kite are mentioned below.

  • It integrates with all the top Python IDEs such as Atom, PyCharm, Sublime, Vim and VS Code.  
  • It accelerates software development by automatically suggesting relevant code snippets in real time
  • Kite trains its machine learning models with thousands of publicly available code sources from highly rated developers.
  • It can predict several “words” of code at a time, powered by the most sophisticated AI engine available for modeling code.
  • It performs all processing locally on users’ computers, instead of in the cloud.

Click here for more details.


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2| Jedi

Jedi is a static analysis tool for Python that can be used in IDEs/editors. The IDE is primarily focused on auto-completion and also does statistical analysis. It is fast and is very well tested and it understands Python on a deeper level than all other static analysis frameworks for Python. Jedi has support for two different goto functions and uses a very simple API to connect with IDE’s. The core of Jedi consists of three parts, parser, Python code evaluation, and API. The general features of the Jedi are mentioned below:

  • Python 2.7 and 3.4+ support
  • Ignores syntax errors and wrong indentation
  • Can deal with complex module / function / class structures
  • Great Virtualenv support
  • Can infer function arguments from sphinx, epydoc and basic numpydoc docstrings, and PEP0484-style type hints (type hinting).

Click here for more details.

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3| Wing

Wing is a light-weighted and powerful Python Integrated Developmet Environment which is designed for productive development experience. Using this IDE you can easily navigate code as well as documentation and makes it easier to understand and work with existing code. There are several features of Wing as mentioned below:

  • Intelligent Editor: Wing’s editor speeds up interactive Python development with context-appropriate auto-completion and documentation, inline error detection and code quality analysis, auto-editing, refactoring, code folding, multi-selection, customizable code snippets, and much more.
  • Powerful Debugger: Wing’s debugger makes it easy to fix bugs and write new Python code interactively.
  • Easy Code Navigation: Wing makes it easy to get around code with goto-definition, find uses, editor symbol index, module and class browser, keyboard-driven search, etc.
  • Integrated Unit Testing: Wing supports test-driven development with the unittest, doctest, nose, pytest, and Django testing frameworks.
  • Remote Development: Wing’s quick-to-configure remote development support delivers all of Wing’s features seamlessly and securely to Python code running on a remote host, VM, or container.
  • Customisable and Extensible: Wing offers hundreds of configuration options affecting editor emulation, display themes, syntax coloring, UI layout, and much more.    

Click here for more details.

4| Finisher

Finisher is a lightweight autocompletion library for Python. There are basically two things going on in this library, autocompletion, and spellcheck. The autocompletion works by assuming that the input tokens are intended while the spell check is done by taking an input blob of text, tokenize it, try to convert all those tokens to valid tokens, then find the best match from those tokens. It can be used in situations where you do not want to add additional dependencies such as SOLR or Cloudsearch to provide auto-completion functionality.

Click here for more details.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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