Data hardly ever comes in the shape or form that is immediately suitable for your specific needs. You will do yourself a huge favor by learning skills to quickly and effectively munge and transform data into any desired shape. Python, and pandas in specific, is great for this. However, it might take you some time to get comfortable with using pandas. If I speak for myself, I could often formulate what I wanted my data to look like in SQL, and from there tried to work my way towards pandas code. That is where this converter was born: I often looked up SQL statements and their pandas equivalents, and thought it would be useful to be able to interactively see what pandas code belonged to what SQL operation. That way, you'll start to understand how pandas code is built, and hopefully get a better understanding of pandas. Don't forget to check out the resources (such as this SQL to pandas cheat sheet) for more great ways to improve your Python skills, and where to get started. Below you see what is currently supported. I will try to add and improve functionality along the way. Have fun!
The following features are currently supported:
SELECTDISTINCTCOUNT(*), AVG(), SUM(), MAX(), MIN()CASE WHEN ... THEN ... ELSE ... END AS ...tip/total_bill AS tip_ratecolumn AS aliasFROMWHERE=, <>, >=, >, <=, <IS NULL, NOT NULL, IS IN, NOT INAND, ORLIKE: '%...', '...%', '%...%'BETWEENGROUP BYHAVINGCOUNT(*), AVG(), SUM(), MAX(), MIN()pd.Series(___) on its own.