Blog @ Codonomics

For any app to be launched from favourites in Ubuntu, it needs to have a ".desktop" file that can be pinned to the Ubuntu's dash as favourite. This helps in quickly launching the application at the click of a button from Ubuntu's dash in desktop. Spring Tool Suite IDE (aka STS) for ubuntu comes as a zip file that needs to be extracted to a custom location. Inside this extracted directory you'll find a linux/ubuntu executable file by the name that goes something like SpringToolSuite4 .

Note : I prefer  KVM over VirtualBox, because it is faster and made my life easier in working flawlessly without any integration/permission issues with Minikube. Note : For VirtualBox, instead of the snippet shown in the picture above I used snippet as in post - Install VirtualBox 6.0 on Ubuntu 18.10 . This is a quick reference picture. Want to copy-paste them making your life easier? Check out this snippet in github-gist . Looking for alternative to Minikube ? Try Microk8s - it's easier to install and much faster in execution.

You can copy-paste the code-snippets s to be run in your terminal from the github-gist .

This post is based on my experience of fixing this issue in Ubuntu OS. You may have to make some tweaks depending on your OS to see if this process fixes the issue. I wasted a couple of hours in fixing this. If you think this has saved your time, don't forget to share it in your circles to help save other's time. Cheers! References Github Issue : Environment setup before starting the kernel Installing Python Packages from a Jupyter Notebook Anaconda Enterprise : Working with environments IPython Docs - Kernels for different Env Andreas Muller answer in StackOverflow

Often in data-science or machine learning hackathons, you may want to concatenate the rows of the train and test datasets for easy data exploration and  wrangling. And when you want to do that, this pro-tip comes handy. Check the Pandas Documentation for more on this API.

It is so common a situation in data-science to select a subset of columns from a data-set. In Python the usual approach is to select a set of columns using List Comprehension or using pandas df.drop() method. I typically employ using the List Comprehension method of choosing a subset of columns. The drawback of this approach is that it is verbose over its drop() counterpart. However, there is a distinct advantage with this approach which is that this approach guarantees idempotency . Being a huge fanboy of idempotency coming from a mathematical background and  a hatred for verbosity and duplication, I came end-up using the utility methods shown in this blog post. Clearly, this utility function gives the advantage of brevity and idempotency.  Yay, I win! 

Service Orchestration When you think Service Orchestration, visualize the picture above. You will have an Orchestrator who controls the individual components in the live performance. An Orchestrator typically is present in the live performance to control the individuals' performance.