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Taking the first two rows is a mess in pandas? flights.groupby("month").head(2) Not only is does this have all the same keywords, but it is organized in a muc

Taking the first two rows is a mess in pandas? flights.groupby("month").head(2) Not only is does this have all the same keywords, but it is organized in a much clearer way to newcomers and labels things to look up in the API. Whereas your R code has a leading comma, .SD, and a mix of quotes and non-quotes for references to columns. You even admit the last was confusing to learn. This can all be crammed in your head, but not what I would call thoughtfully designed.

Bootvis 5y ago
I agree the example in GP is not convincing. Consider the following table of ordered events: | Date | EventType | and I want to find the count, and the first and last date of an event of a certain type happening in 2020: events[ year(Date) == 2020L, .(first_date = first(Date), last_date = last(Date), count = .N), EventType ] Using first and last on ordered data will be very fast thanks to something called GForce. When exploring data, I wouldn't need or use any whitespace. How would your Pandas approach look like?
- hervature 5y ago
  To do that, the code would look something like: mask = events["Date"].year == 2020 events[mask].groupby("EventType").agg(first_date=("Date", min), last_date=("Date", max), count=("Date", len)) Anyway, I don't understand why terseness is even desirable. We're doing DS and ML, no project never comes down to keystrokes but ability to search the docs and debug does matter.
  - Bootvis 5y ago
    It helps in quickly improving your understanding of the data by being able to answer simple but important questions quicker. In this contrived example I would want to know: - How many events by type - When did they happen - Are there any breaks in the count, why? - Some statistics on these events like average, min, max and so on. Terseness helps me in doing this fast.

Bootvis 5y ago

I agree the example in GP is not convincing. Consider the following table of ordered events: | Date | EventType | and I want to find the count, and the first and last date of an event of a certain type happening in 2020: events[ year(Date) == 2020L, .(first_date = first(Date), last_date = last(Date), count = .N), EventType ] Using first and last on ordered data will be very fast thanks to something called GForce. When exploring data, I wouldn't need or use any whitespace. How would your Pandas approach look like?

hervature 5y ago
To do that, the code would look something like: mask = events["Date"].year == 2020 events[mask].groupby("EventType").agg(first_date=("Date", min), last_date=("Date", max), count=("Date", len)) Anyway, I don't understand why terseness is even desirable. We're doing DS and ML, no project never comes down to keystrokes but ability to search the docs and debug does matter.
- Bootvis 5y ago
  It helps in quickly improving your understanding of the data by being able to answer simple but important questions quicker. In this contrived example I would want to know: - How many events by type - When did they happen - Are there any breaks in the count, why? - Some statistics on these events like average, min, max and so on. Terseness helps me in doing this fast.