On Thu, 28 Sep 2006 17:32:06 +0200, Fredrik Lundh <[EMAIL PROTECTED]> wrote:
>Ramon Diaz-Uriarte wrote: > >> Going back to the original question, a related question: does anybody >> know why there are so few books on data structures and algorithms that >> use Python? > >Probably because Python has "better than textbook" implementations of >core structures, and "better than textbook" implementations of many core >algorithms, so lots of things can be done more efficiently by combining >existing structures and algorithms than by using "textbook" algorithms. But this answers a diiferent question: university and (academic) college data structure courses don't care how efficient a particular language's built-in data structures are, since the intent is for the students to learn how to implement data structures in general, and probably in a particular language--the "core" languasge used in their core programs--and then be able to apply that knowledge and skill to implementing at least "reasonably efficient" ones when they need to in languages that don't have any to speak of built in. And, since many students will go direct to business or industry, and may even be apprenticing there in "co-op work terms" during their education, most could care less how efficient Python's built-in data structures are. Also, it's a very rare DSA text that intends its DS code to be used directly--especially in "serious" applications. Preiss's texts, noted by the OP, are one exception, and many could be used "out of the box" in industrial strength applications. So far as "combining existing structures" is concerned, it's highly unlikely that any such combination of linear structures could be more efficient in both--if either--storage and running time than one specifically designed and implemented for non-linear structures, such as (many) trees and algorithms on them. For general graphs efficient list processing may be sufficient for an adjacncy structure for the graph itself, of course, but how does that translate to storing a subtree found using a DFS, for example, and efficiently processing it in some way? For learning DSA it's more important to have a clear, well-written and well-documented implementation in a language of interest (again, especially, the core language in one's programs) than just "using" or even inspecting and trying to learn subtle details of some particular implementation of a related DS or A in some "other" language. How many of those who devleoped and improved the very efficient data structures in Python learned and honed their skills in Python (vs. C or Pascal, for example)? wwwayne ></F> -- http://mail.python.org/mailman/listinfo/python-list
