Although Iโ€™ve been tinkering with python for my Data Science projects since 2016, I only started coding professionally at the end of 2018. It wasnโ€™t easy to get accustomed to the workflow by any means and the rigor of a production and test driven environment was something completely different from what I was used to ๐Ÿ˜“. I had only been using all the tools and packages integrated into the amazing Anaconda Distribution which means Spyder as the IDE and the trusted old Jupyter Notebook for experimentation. I still use jupyter notebook, however, Iโ€™ve picked up VS Code as my primary editor not only due to the fact that everyone at my workplace uses that but also in my opinion itโ€™s one of the best language agnostic code editor, period ๐Ÿ˜. So this post is an assortment of all the tools and practices that Iโ€™ve picked up throughout my development journey๐Ÿ”ฅ.

Here Iโ€™m using Linux as my primary development OS and many of the instructions apply directly to MacOS also. However, if you are doing python in VS Code on MacOS or Windows, I encourage you contribute and extend this guideline. Now letโ€™s jump in ๐Ÿฆ˜.

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