🤔PandaSQL: Python Combo for Data Scientist
SQL and Pandas are the most popular data analytics tools for tabular data management, processing and analysis. They can be used independently or together in the PandaSQL, the Python-package which provides SQL syntax capabilities in the Python environment. PandaSQL allows you to query date frames Pandas uses SQL syntax.
PandaSQL is a great tool for those who know SQL and are not familiar with the Python syntax that requires Pandas. For example, in Pandas, filtering a dataframe or grouping by column column when aggregating multiple columns can be explored in a confusing way, unlike SQL. However, this simple use of SQL in Pandas has been tainted with redundant runtime. For example, to calculate the number of rows, PandaSQL requires almost 100 times more execution runtime than Pandas.
Also, Python already has a lot of names that are reserved as basic words like for, while, in, if, else, elif, import, as. SQL adds more keywords: create, like, where, having.
Thus, it is worth to try PandaSQL, but this interesting tool is not suitable for an production data analytics pipelines.
Usage examples and runtime comparison: https://towardsdatascience.com/the-downsides-of-pandasql-that-no-one-talks-about-9b63c664bef4