I recommend starting with something that forces you to explore the expressive power of the syntax. Python allows many different ways of writing the same functionality, but there is often a single most elegant and fastest approach. If you're used to the idioms of other languages, you might never otherwise find or accept these better ways. I spent a weekend trudging through the first 20 or so Project Euler problems and made a simple webapp with Django on Google App Engine. This will only take you from apprentice to novice, maybe, but you can then continue to making somewhat more advanced webapps and solve more advanced Project Euler problems. After a few months I went back and solved the first 20 PE problems from scratch in an hour instead of a weekend.

如果你在科学中使用python(看起来你是这样)，其中的一部分将是学习和理解科学库，对我来说，这些将是

numpy scipy matplotlib mayavi / mlab 查科 Cython

了解如何使用正确的库和向量化代码对于科学计算是至关重要的。

我想补充一点，用常见的python方式(面向对象的方法、列表、迭代器)处理大型数字数据集的效率非常低。在科学计算中，有必要以与大多数常规python编码员处理数据的方式截然不同的方式构建代码。

谷歌最近发布了一个在线Python类(“类”在“一门课程”中)。

http://code.google.com/edu/languages/google-python-class/

我知道这并不能完全回答你的问题，但我认为这是一个很好的开始!

这不是你想要的，但我觉得这是个好建议。

学另一门语言，哪门都无所谓。每种语言都有自己的想法和习惯，你可以从中学习。了解语言之间的差异，更重要的是为什么它们不同。尝试纯函数式语言，如Haskell，看看函数的一些好处(和挑战)，没有副作用。看看如何将从其他语言中学到的一些东西应用到Python。

我认为掌握Python的过程是这样的:

Discover list comprehensions Discover generators Incorporate map, reduce, filter, iter, range, xrange often into your code Discover Decorators Write recursive functions, a lot Discover itertools and functools Read Real World Haskell (read free online) Rewrite all your old Python code with tons of higher order functions, recursion, and whatnot. Annoy your cubicle mates every time they present you with a Python class. Claim it could be "better" implemented as a dictionary plus some functions. Embrace functional programming. Rediscover the Strategy pattern and then all those things from imperative code you tried so hard to forget after Haskell. Find a balance.

使用Python尝试http://challenge.greplin.com/