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Platinum BlogWhat is the Best Python IDE for Data Science?


Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.



By Saurabh Hooda, Hackr.io

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Credit: Hitesh Choudhary via Unsplash

Created by Guido van Rossum, Python was first released back in 1991. The interpreted high-level programming language is developed for general-purpose programming. Python interpreters are available on several operating systems, including Linux, MacOS, and Windows.

Editor: Here are the most popular IDEs / Editors for Python, based on KDnuggets Poll inspired by this blog.

With a running course of almost 3 decades, Python has garnered enormous popularity among the programming community. Using IDLE or the Python Shell for writing down Python code is effectual for smaller projects, but not practical while working on full-fledged machine learning or data science projects.

In such a case, you need to use an IDE (Integrated Development Environment) or a dedicated code editor. As Python is one of the leading programming languages, there is a multitude of IDEs available. So the question is, “Which is the best IDE for Python?”

Apparently, there is no single IDE or code editor for Python that can be crowned with “THE BEST” label. This is because each of them has their own strengths and weaknesses. Furthermore, choosing among the vast number of IDEs might be time-consuming.

Worry not though, as we’ve got you covered. In order to help you pick the right one, we’ve sorted out some of the prominent IDEs for Python, specifically created for working with data science projects. These are:

 

Atom

 
Platform – Linux/macOS/Windows
Official Websitehttps://atom.io/
Type – General Text Editor

Atom is a free, open-source text and source code editor available for a number of programming languages, including Java, PHP, and Python. The text editor supports plugins written in Node.js. Although Atom is available for a number of programming language, it shows an exceptional love for Python with its interesting data science features.

One of the greatest features that Atom brings to the table is support for SQL queries. However, you need to install the Data Atom plugin first to access the feature. It provides support for Microsoft SQL Server, MySQL, and PostgreSQL. Furthermore, you can visualize results in Atom without the need of opening any other window.

Yet another Atom plugin that will benefit Python data scientists is the Markdown Preview Plus. This provides support for editing as well as visualizing Markdown files, allowing you to preview, render LaTeX equations, etc.

Advantages:

  • Active community support
  • Awesome integration with Git
  • Provides support for managing multiple projects

Disadvantages:

  • Might experience performance issues on older CPUs
  • Suffers from migration issues

 

Jupyter Notebook

 
Platform – Linux/macOS/Windows
Official Websitehttps://jupyter.org/
Type – Web-based IDE

Born out of IPython in 2014, Jupyter Netbook is a web application based on the server-client structure. It allows you to create as well as manipulate notebook documents called notebooks. For Python data scientists, Jupyter Notebook is a must-have as it offers one of the most intuitive and interactive data science environments.

In addition to operating as an IDE, Jupyter Notebook also works as an education or presentation tool. Moreover, it is a perfect tool for those just starting out with data science. You can easily see and edit the code with Jupyter Notebook, allowing you to create impressive presentations.

By using visualization libraries like Matplotlib and Seaborn, you can display the graphs in the same document as the code is in. Also, you can export your entire work to a PDF, HTML or .py file. Like IPython, Project Jupyter is an umbrella term for a bunch of projects, including Notebook itself, a Console, and a Qt console.

Advantages:

  • Allows creation of blogs and presentations from notebooks
  • Ensures reproducible research
  • Edit snippets before running them

Disadvantages:

  • Complex installation process

 

PyCharm

 
Platform – Linux/macOS/Windows
Official Websitehttps://www.jetbrains.com/pycharm/
Type – Python-specific IDE

PyCharm is a dedicated IDE for Python. PyCharm to Python is what Eclipse is to Java. The full-featured Integrated Development Environment is available both in free and paid versions, dubbed Community and Professional editions, respectively. It is one of the quickest IDEs to install with a simplistic set up thereafter, and is preferred by data scientists.

For those with a likeness for IPython or Anaconda distribution, know that PyCharm easily integrates tools like Matplotlib and NumPy. This means you can work easily with array viewers and interactive plots while working on data science projects. Other than that, the IDE extends support for JavaScript, Angular JS, etc. This makes it opportune for web development too.

Once you finish the installation, PyCharm can be readily used for editing, running, writing, and debugging the Python code. To start with a new Python project, you need to simply open a fresh file and start writing down the code. In addition to offering direct debugging and running features, PyCharm also offers support for source control and full-sized projects.

Advantages:

  • Active community support
  • De facto of Python development, both for data science and non-data-science projects
  • Easy to use by both newcomers and veterans alike
  • Faster reindexing
  • Runs, edits, and debugs Python code without any external requirement

Disadvantages:

  • Might be slow in loading
  • The default setting may require adjustment before existing projects can be used

 

Rodeo

 
Platform – Linux/macOS/Windows
Official Websitehttps://rodeo.yhat.com/
Type – Python-Specific IDE

The logo with the orange hints at the fact that this Python IDE is developed especially for carrying out data analysis. If you have some experience with RStudio, then you will know that Rodeo shares many of its traits with it. For those of you unaware of RStudio, it is the most popular integrated development environment for the R language.

Like RStudio, Rodeo’s window is divided into four divisions, namely text editor, console, environment for variable visualization, and plot/libraries/files. Amazingly, both Rodeo and RStudio shares a great degree of resemblance with MATLAB.

What’s best about Rodeo is that it offers the same level of convenience to both beginners and veterans. As the Python IDE allows you to see and explore while creating simultaneously, Rodeo is undoubtedly one of the best IDEs for those starting out with data science using Python. The IDE also boasts built-in tutorials and comes with helper material.

Advantages:

  • A great deal of customization
  • See and explore what you are creating in real-time
  • Write code faster with autocomplete and syntax highlighting features, and support for IPython

Disadvantages:

  • A lot of bugs
  • Not-so-active support
  • Plagued by memory issues

 

Spyder

 
Platform – Linux/macOS/Windows
Official Websitehttps://www.github.com/spyder-ide/spyder
Type – Python-Specific IDE

Spyder is an open-source, dedicated IDE for Python. What’s unique about the IDE is that it is optimized for data science workflows. It comes bundled with the Anaconda package manager, which is the standard distribution of Python programming language. Spyder has all the necessary IDE features, including code completion and an integrated documentation browser.

Build especially for data science projects, Spyder flaunts a smooth learning curve allowing you to learn it in a flash. The online help option allows you to look for specific information about libraries while side-by-side developing a project. Moreover, the Python-specific IDE shares resemblance with RStudio. Hence, it is opportune to go for when switching from R to Python.

Spyder’s integration support for Python libraries, such as Matplotlib and SciPy, further testifies to the fact that the IDE is meant especially for data scientists. Other than the appreciable IPython/Jupyter integration, Spyder has a unique “variable explorer” feature at its disposal. It allows displaying data using a table-based layout.

Advantages:

  • Code completion and variable exploring
  • Easy to use
  • Ideal to use for data science projects
  • Neat interface
  • Proactive community support

Disadvantages:

  • Falls short in capability for non-data-science projects
  • Too basic for advanced Python developers

 

How to Choose the Best IDE for Python?

 
Well, this depends entirely on the kind of requirements you need to fulfill. Nonetheless, here is some general advice:

  • When starting fresh with Python, go for an IDE that has fewer customizations and additional features. The less distraction is there, the better it is to get started with
  • Compare the IDE features with your expectations
  • Giving a try to several IDEs will help you understand better which one will work best against specific requirements

 
Bio: Saurabh Hooda has worked globally for telecom and finance giants in various capacities. After working for a decade in Infosys and Sapient, he started his first startup, Leno, to solve a hyperlocal book-sharing problem. He is interested in product marketing, and analytics. His latest venture Hackr.io recommends the best Data Science tutorial and online programming courses for every programming language. All the tutorials are submitted and voted by the programming community.

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