3 min read

Google has made it quite accessible for people to collaborate their documents, spreadsheets, and so on, with the Google Drive feature. What next? If you are one of those data science nerds who love coding, this roll-out from Google would be an amazing experimental ground for you. Google released its coLaboratory project, a new tool, and a boon for data science and analysis. It is designed in a way to make collaborating on data easier; similar to a Google document. This means it is capable of running code and providing simultaneous output within the document itself.

Collaboration is what sets coLaboratory apart. It allows an improved collaboration among people having distinct skill sets–one may be great at coding, while the other might be well aware of the front-end or GUI aspects of the project.

Just as you store and share a Google document or spreadsheets, you can store and share code with coLaboratory notebooks, in Google Drive. All you have to do is, click on the ‘Share’ option at the top right of any coLaboratory notebook. You can also look up to the Google Drive file sharing instructions. Thus, it sets new improvements for the ad-hoc workflows without the need of mailing documents back and forth.

CoLaboratory includes a Jupyter notebook environment that does not require any setup for using it. With this, one does not need to download, install, or run anything on their computer. All they would need is, just a browser and they can use and share Jupyter notebooks. At present, coLaboratory functions with Python 2.7 on the desktop version of Chrome only. The reason for this is, coLab with Python 2.7 has been an internal tool for Google, for many years. Although, making it available on other browsers and with an added support for other Jupyter Kernels such as R or Scala is on the cards, soon.

CoLaboratory’s GitHub repository contains two dependent tools, which one can make use of to leverage the tool onto the browser. First is the coLaboratory Chrome App and the other is coLaboratory with Classic Jupyter Kernels.  Both tools can be used for creating and storing notebooks within Google Drive. This allows a collaborative editing within the notebooks. The only difference is that Chrome App executes all the code within its browser using the PNaCl Sandbox. Whereas, the CoLaboratory classic code execution is done using the local Jupyter kernels (IPython kernel) that have a complete access to the host systems and files.

The coLaboratory Chrome App aids in setting up a collaborative environment for data analysis. This can be a hurdle at times, as requirements vary among different machines and operating systems. Also, the installation errors can be cryptic too. However, just with a single click, coLaboratory, IPython and a large set of popular scientific python libraries can be installed. Also, because of the Portable Native Client (PNaCl), coLaboratory is secure and runs at local speeds. This allows new users to set out on exploring IPython at a faster speed.

Here’s what coLaboratory brings about for the code-lovers:

  • No additional installation required the browser does it all
  • The capabilities of coding now within a document
  • Storing and sharing the notebooks on Google Drive
  • Real-time collaboration possible; no fuss of mailing documents to and fro

You can find a detailed explanation of the tool on GitHub.


A Data science fanatic. Loves to be updated with the tech happenings around the globe. Loves singing and composing songs. Believes in putting the art in smart.


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