We often encounter data as Relational Databases. To work with them we generally would need to write raw SQL queries, pass them to the database engine and parse the returned results as a normal array of records.
Pandas is a powerful python package which makes importing, cleaning, analyzing, and exporting the data easier. In this story I’d like to write down some of the key functions which I found them useful.
In this story I’ll show how some tasks can be made easier/efficient using few built-in python tools, I’m considering Python 3.6.5 in all my stories henceforth.
The core python packages like pandas, scikit-learn are built on top of numpy, So in this story I’m going to list some key functions of numpy package which made my computations much easier/efficient.
Will keep on appending new stuff to the story.
Why Probability and Statistics?
Hey 😃 Bored of watching video lectures? Tired of doing courses which have almost the similar content, introduction to ….. stuff? or need a bit revision of the key concepts? Welcome, you are at the right place. I have been in your shoes sometime back, so I had thought to pen down the summary of…