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 structured, it really helped me but it isn't complete.

Don't get me wrong, I'm grateful for all the work and I know I haven't contributed much but I think the online could be improved with more examples and recipes.

Edit: There really is no excuse, getting started is easy[2].

[1]: http://shop.oreilly.com/product/0636920023784.do

[2]: http://pandas.pydata.org/developers.html

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.