Posted in python, Research tools

Introducing Jupyterlab for efficient bioinformatic workflows

In my future blog entries, I will be using and explaining how the Python Programming Language can be used for data visualisation. To run some of the commands and codes, I recommend downloading JupyterLab, which is a web-based interactive development environment for storing Jupyter notebooks, code, and data. The advantage of using JupyterLab is that it is free to download, open-source, flexible and supports multiple computer languages, including Julia, Python, and R. If needed, you can even download the SQL plug-in to execute SQL commands in Jupyterlab.

The process of downloading is simple:

  1. Visit the Anaconda website, and click on the download icon. Download the 64-bit Graphical Installer based on your computer OS.
  2. Open the package after download, and follow the instructions to download Anaconda into your computer.
  3. Launch the Anaconda-Navigator by clicking the icon. For Mac users, the icon should appear under the “Applications” tab
  4. Launch JupyterLab, choose Python3 notebook, which will eventually direct you to the notebook server’s URL.

You can import your .csv or..txt datafiles directly into JupyterLab to start analysing your dataset in Python. You can also export your notebook as a Jupyter Interactive Notebook (.ipynb file format) if you’d like to share the codes with another person. I believe that JupyterLab will enable more efficient workflows, regardless of tool or language.

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