> what the point of pandas is here. And maybe in general? In general, if you are comparing to an SQL database, the most important is complex anaylsis/manipulat

> what the point of pandas is here. And maybe in general? In general, if you are comparing to an SQL database, the most important is complex anaylsis/manipulation. Of course SQL is great for database level analysis - sorting/filtering/etc. But SQL queries for domain-specific analysis can become very opaque quickly and debugging of complex queries is a mess. Pandas is great for more complex manipulation as it keeps available the wider python ecosystem, allows for simpler debugging of complex processes through a REPL loop, links easily to visualisation, is pretty fast for some things through leveraging things like numpy, in-memory avoids multiple database calls, etc. Obviously pandas isn't a great choice for persistent large datasets (maybe around the 0.1-1TB mark?) though, simply because it is memory stored. The use cases definitely overlap significantly, but run into problems (typically ridiculous SQL queries on one side, giant data sets on the other) and you really need to stop and think about the tooling.