My experience dealing with trees is always that the "tree" part is always
so simple that it isn't a big deal to re-implement it. The problem is
dealing with all of the extra stuff that you need and the details of what
happens when you do different operations. I think it makes more sense to
have an interface for a kind of thing you want to do with a tree (e.g.
sorted sets, or ordered maps) rather than the tree itself.
On Sun, Dec 16, 2012 at 6:41 PM, Guido van Rossum <guido@python.org>wrote:
I think of graphs and trees as patterns, not data structures.
In my world strings, ints and lists are 1D data types, and tree can be a
very important 2D data structure. Even if it is a pattern, this pattern is
vital for the transformation of structured data, because it allows to
represent any data structure in canonical format.
Speaking of tree as a data structure, I assume that it has a very basic
definition:
1. tree consists of nodes
2. some nodes are containers for other nodes
3. every node has properties
4. every node has 0 or 1 parent
5. every container has 1+ children
6. tree has a single starting root node
7. no child of a parent can be its ancestor
(no cyclic dependencies between elements)
List of trees is a forest. Every subtree is a complete tree.
To see which tree data type would be really useful in Python distribution
(e.g. provides a simple, extendable and intuitive interface), I see only
one way - is to scratching some itches relevant to Python and then try to
scale it to other problems. The outcome should be the answer -
what for native tree type is not suitable?
More ideas:
[ ] Much experience for working with trees can be brought from XML and
DOM manipulation practices (jQuery and friends)
[ ] every element in a tree can be accessed by its address specificator
as 'root/node[3]/last'
[ ] but it is also convenient to access tree data using node names as
'mylib.books[:1]'
[ ] and of course, you can run queries over trees
[ ] Tree is the base for any "Data Transformation Framework" as it allows
to jump from "data type conversion" to "data structure conversion and
mapping"
[ ] Trees can be converted to other trees and to more complicated
structures
[ ] Conversion can be symmetrical and non-symmetrical
[ ] Conversion can be lossy and lossless
[ ] Conversion can be lossless and non-symmetrical at the same time
Trees can be used, for example, for realtime migration of issues from one
tracker to another. For managing changesets with additional meta
information. For presenting package dependencies and working with them. For
atomic (transactional) file management. For managing operating system
capability information. For logging setup. For debugging structures in
Python. For working and converting binary file formats. For the common AST
transformation and query interface. For the understanding how 2to3 fixers
work. For the common ground of visual representation, comparison and
transformation of data structures. That's probably enough of my itches.
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Jeff Jenkins