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In my eyes, you both make a valid point: yes there's plenty of documentation but sometimes I just can't find what I'm looking for. I like how the book[1] is str

• EmlynC 12y ago
  Correct me if I'm wrong but perhaps you had the same issue as me. The documentation is plentiful, lots of good examples and the book, similarly, increases with a nice linear complexity from basic "how do I select a (cell | row | column) ..." to full blown how do I do timeseries analysis on a dataseries pulled in from a remote source. The issue I had was not the documentation but the language of pandas mirrors the language used in R (I think this is something Wes McKinney intentional did) and it's the burden of all that new verbage that makes the documentation harder to sift through. Some choice exampels; "melt", "stack/unstack" and "reindex" — necessary, I grant you, so that functions can be aptly named and in turn encapsulate vectorised procedures that are composable. I found that the documentation was harder to search because I lacked the domain language and the documentation, for better for worse, doesn't dawdle with educating the reader about the verbage — worked examples often provide a easier route. It reads like a mathematical proof rather than prose and I used to think that the documentation was too terse but now I appreciate that probably just succinct.