Well does the R df$colname behave like panda df.colname or is it like df[“colname”] which opens up a whole new layer where it can be df[var]?
And there’s a ton of helpful things like dict unpacking of named aggs for groups bys. The dictionary can be the result of code or it can be spelled out.
Also, a side note, both pandas and R make it easy to chain or pipe a long series of ops. I try to keep the number down though. In other languages this can be frowned on as a hard to debug train wreck pattern, esp if it grows to long. Usually I define filters and functions outside the train and pass them in rather inline the filters or lambdas as they’re so much easier to debug that way.
And there’s a ton of helpful things like dict unpacking of named aggs for groups bys. The dictionary can be the result of code or it can be spelled out.
Also, a side note, both pandas and R make it easy to chain or pipe a long series of ops. I try to keep the number down though. In other languages this can be frowned on as a hard to debug train wreck pattern, esp if it grows to long. Usually I define filters and functions outside the train and pass them in rather inline the filters or lambdas as they’re so much easier to debug that way.