Develop Reference

Standardizing a Release/Tools group on a specific language

I'm part of a six-member build and release team for an embedded software company. We also support a lot of developer tools, such as Atlassian's Fisheye, Jira, etc., Perforce, Bugzilla, AnthillPro, and a couple of homebrew tools (like my Django release notes generator).
Most of the time, our team just writes little plugins for larger apps (ex: customize workflows in Anthill), long-term utility scripts (package up a release for QA), or things like Perforce triggers (don't let people check into a specific branch unless their change description includes a bug number; authenticate against Active Directory instead of Perforce's internal passwords). That's about the scale of our problems, although we sometimes tackle something slightly more sizable.
My boss, who is reasonably technical, has asked us to standardize on one or two languages so we can more easily substitute for each other. He's advocating bash scripts and Perl, due to their universality and simplicity. I can see his point--we mostly do "glue", so why not use "glue" languages rather than saddle ourselves with something designed for much larger projects? Since some of the tools we work with are Java-based, we do need to use something that speaks JVM sometimes. (The path of least resistance for these projects is BeanShell and Groovy.) I feel a tremendous itch toward language advocacy, but I'm trying to avoid saying "We should use Python 'cause I like it and Perl is gross."
Instead, I'm trying to come up with a good approach to defining our problem set: what problems do we solve with scripts? Would we benefit from a library of common functions by our team, or are most of our projects more isolated? What is it reasonable to expect my co-workers to learn? What languages give us the most ease of development and ease of modification?
Can you folks suggest some useful ways to approach this problem, both for my own thinking process and to help me facilitate some brainstorming among my coworkers?

Two points:
"Eww Perl gross" is somewhat of an urban legend. You can write great clean self-documenting code in Perl, and your can write write-only code in pretty much any language. It's a property of a developer, not a language.
Just because you're writing glue code, doesn't mean the code has to suck like some glue hacks tend to be.
From many threads comparing Perl vs Python on SO, it appears to me that Perl's CPAN is more expansive than Python's repository, but I have no experience with Python and can't substantiate with real comparison.
BUT, one thing I do know. After 5 seconds search, CPAN has a JIRA module. Whether that's a good factor for you or not, that's up to you.

Google standardized on Python for such tasks (and many more) a bit before I joined the company; as far as I know, huge advantages such as Python's great implementations on the JVM and .NET didn't even play a role in the decision -- it was all about readability. At the time (and to some extent, even now) the theory at Google was that every engineer must be able, at need, to tweak every part of the codebase -- including of course build scripts, spiders (which were in Python at the time), and so forth. Demanding of engineers already proficient in C++ and Java to learn many more "scripting" languages (Python, Perl, Bash, Awk, Sed, and so forth) was simply unconsciounable: one had to be selected. Given that constraint, Python was the clear choice (under other constraints, Perl might also have been -- but I can't see the inevitable mix of Bash, Awk and Sed ever competing on such grounds!_) -- and that's how I ended up working there, a bit later;-).
Given that the overall potential of Python vs Ruby vs Perl vs PHP vs Bash + Awk + Sed vs ... is roughly equal, picking one is clearly a winner -- and Python has clean readability, strong implementations on JVM and .NET as big vigorishes. Seriously, I can only think of Javascript (inevitable for client-side work, now rich with strong implementations such as V8) as a possible "competitor" (unfortunately, JS inevitably carries on a lot of baggage for backwards compatibility -- unless you can use a use strict;-like constraint to help on that, it must be an important downside).

I don't think anyone is going to be able to solve your problem on Stackoverflow. Your choice of tools, methods, and process are much more affected by social constraints, e.g. what your boss wants and what you want, then technical merits. That's not necessarily bad.
The short answer is "Use what is going to be most pleasing to the developers". If everyone likes Python more than Perl, for whatever reason, they are probably going to get more done in Python. If they like Ruby more than Python, it's the same thing.
Some things to evaluate as part of your selection:
What do the developers already know?
What are they most willing to learn?
How much weekly time can your team spend learning new things (e.g. lunch seminars, formal classes, etc)?
What do most people in the community use to work with the tool you need to support? For instance, Fisheye has a Java API, and some REST examples for Perl and Python. If you're writing Fisheye extensions, Java seems to be the win there. If you're merely accessing Fisheye data, any language can use the REST stuff.
What is most of your code base in already? What can you replace and what do you have to continue to support? I find that many companies can't answer this question because every developer seems to add two new technologies they don't tell anyone about. :)
Which platforms do you need to support? Some languages have platform specific issues, and I don't mean just Windows vs. Unix. Do you have legacy hardware you have to support? Does your tool work on that stuff?
How much of the stuff you produce can benefit other parts of the company? What are other teams using?
Do the people advocating one tool know it well enough to be its champion? I ask What are five things he hate about your favorite language? If people can't name five valid things that are wrong with their language or tool, they don't have enough experience with it.
The Longer Answer
People tend to try to reduce this to a technical argument because they are afraid to admit their biases or examine why they think what they think. Your boss might favor bash and Perl because that's what he did a lot of work in when he was getting started. You might like Python because you have a personal affinity for the way Python does things. I like Perl because I like its flexibility and DWIMmery. Like any social situation, different people are going to be attracted to different parts of different things. Just because you like chocolate doesn't make vanilla evil. I could give you lots of good arguments why Perl can be useful, but that doesn't mean that something else can't give you the same value.
What problems do we solve with scripts?
That one you have to answer for yourself. :)
Would we benefit from a library of common functions by our team, or are most of our projects more isolated?
This is most likely a good thing in Python, Perl, Ruby, Java, and almost any other language that you might choose. I think this part of your requirement is language agnostic. No matter which one you choose, you'll probably want to do this.
What is it reasonable to expect my co-workers to learn?
A good developer should be able to work with several different languages at least to an apprentice level. Those languages should include ones that have vastly different assumptions about how people express problems, say, for example, the set { Smalltalk Perl C Lisp Java }.
The best developers I've hired and worked with have always wanted to use the right tool for the job instead of making the job right for the tool. They might have their favorite language, but they didn't grouse (too much) about using a different tool when it made more sense.
Many "developers", however, seem to think that they are getting paid to play with their favorite tool. You need to convert them into people who think they have a toolbox to solve problems that create business value.
And remember, you never stop learning. As a developer you don't have to choose one language then defend it with your life, forsaking all others, in sickness and in health, and so on. Good developers are going to continue to track new technologies and evaluate them for usefulness for their tasks. Just because you choose one tool over another doesn't mean you stop paying attention.
No matter what you choose, someone is going to complain. Don't look for the solution that makes everyone happy. There isn't one, short of getting rid of the developers who aren't happy.
What languages give us the most ease of development and ease of modification?
A skilled practitioner in just about any language will think that his chosen language is the easiest to develop, modify, and maintain. Unskilled practitioners tend to blame the language and the tools for their problems. Some languages have steeper learning curves, and some have bigger payouts. A person's tolerance with immediate gratification is a big factor here.
That being said, different languages have developed different cultures and different toolsets. Perl people tend to like vi or emacs, Ruby people tend to like TextMate, Java people tend to like Eclipse or IntelliJ. That's not always true, but the culture that evolves around the tools are often more important than the technical details of the tool. If your developers like a particular type of tool, they are probably going to like the language that has a culture built around that sort of tool.
Some processes and tools take more time to get used to or require more education, but they can have larger advantages when used properly. Other tools get you started sooner but might not give you a path to bigger and greater things, such as cross-team development. The trick, however, is to not code to the tools so you aren't stuck in any particular toolset.

First, it's important to note that it is very hard to convince someone they're wrong.
He's advocating bash scripts and Perl,
due to their universality and
simplicity
Bash scripts are not simple. The bash programming model is really complex and unfriendly. if statements and expressions, in particular are horrifying.
Perl may or may not be simple.
Bash is universal. Perl, however, is exactly as universal as Python. Python is pre-installed in almost all Linux distributions. So that argument is specious.
The "universality" of bash, Perl and python is exactly the same. The "simplicity", however, is not the same. You won't find it easy to to "prove" or "convince" anyone of this once they've already pronounced Perl as simple.
The Situation.
If the boss is advocating Perl, and Perl is not the answer, you will find it is very hard to convince someone they're wrong, making this effort nearly impossible.
If the boss was just throwing out ideas, then this is just difficult.
Quick Hack.
An easy thing you can do is to attempt head-to-head comparisons of Python and Perl for some randomly-chosen jobs. You can then have a code walkthrough to demonstrate the relative opacity of Perl compared with the relative clarity of Python.
Even this is fraught with terrible dangers.
Some folks really think code golf is important. Python loses at code golf. Perl wins. There's nothing worse than "Angry Co-worker with Perl Bias" who will kill you with code-golf solutions that -- because they're smaller -- can baffle management into thinking that they're clearer or "better" on some arbitrary scale.
Some folks really think explicit is "wordy" and bad. Python often loses because the assumptions are stated as actual parameter values. Some folks can (and do) complain at having to actually write things down. Read Stack Overflow for all of the Python questions where someone wants to make the try: block go away in a puff of assumptions.
If you choose random problems, you may -- accidentally -- chose something for which there's an existing piece of Perl or Python that can be downloaded and installed. A language can win just through an accident of the draw. Rather than a more in-depth comparison of language features.
Best Bet
The best you can do is the following.
Identify what folks value. You can call these "non-functional" requirements. These are quality factors. What are the foundational, core principles? Open, Accessible, Transferrable Skills, Simplicity, Cleanliness, Honesty, Integrity, Thriftiness, Reverence, Patience, Hard Work, A Sense of Perspective, Reef the Main in Winds over 20 kn, etc. This is hard. No sympathy here.
Identify the technical use cases. These are "functional" requirements. Which bits of glue and integration there are? This is hard, also. Requirements erupt of out of the woodwork when you do this. Also, when you have a Perl bigot on the team, numerous non-functional requirements will pile into this area. Your manager -- who proposed Perl -- may be the Perl bigot, and the use cases may be difficult to collect in the presence of a Perl bigot.
Identify how (a) Perl + Bash vs. (b) Python vs. (c) Java fit this core values and the functional requirements. Note that using Python means you do not need to use Bash as much. My preference, BTW, is to pare Bash down to the rock-bottom minimum.
This is a big, difficult job. It's hard to short-cut. If you find that Perl is not the answer and the Perl bigot you need to convince is the manager who proposed Perl in the first place, you may find that convincing someone that they're wrong is very hard.
Edit. I am aware that I am forbidden from using the string "Perl Bigot" to describe the manager's potential level of bias toward Perl. I, however, insist on using "Perl Bigot" to describe the manager who proposed Perl. The question provides no information on which to change this. The worst case is that (a) the manager is the Perl Bigot and (b) Perl is not the answer.

How to reverse engineer a program which has no documentation [closed]

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I have a source of python program which doesn't have any documentation or comments.
I did tried twice to understand it but most of the times I am losing my track, because there are many files.
What should be the steps to understand that program fully and quickly.

Michael Feathers' "Working Effectively with Legacy Code" is a superb starting point for such endeavors -- not particularly language-dependent (his examples are in several non-python languages, but the techniques and mindset DO extend pretty well to Python and just about any other language).
The key focus is, that you want to understand the code for a reason -- modifying it and/or porting it. So, instrumenting the legacy code -- with batteries and scaffolding of tests and tracing/logging -- is the crucial path on the long, hard slog to understanding and modifying safely and responsibly.
Feathers suggests heuristics and techniques for where to focus your efforts and how to get started when the code is a total mess (hence "legacy") - no docs, or misleading docs (describing something quite different, maybe in subtle ways, from what the code actually DOES), no tests, an untestable-without-refactoring tangle of spaghetti dependencies. This may seem an extreme case but anybody who's spent a long-ish career in programming knows it's actually more common than anyone would like;-).

In past I have used 'Python call graph' to understand the source structure
Use a debugger e.g. pdb to wak thru
the code.
Try to read code again after one day
break, that also helps

I would recommend to generate some documentation with epydoc http://epydoc.sourceforge.net/ . For sure, if no docstring exists, the result will be poor but it will give you at least one view of your application and you'lle be able to navigate in the classes more easily.
Then you can try to document by yourself when you understand something new and then regenerate the docs again. It is never too late to start something.
I hope it helps

You are lucky it's in Python which is easy to read. But it is of course possible to write tricky hard to understand code in Python as well.
The steps are:
Run the software and learn to use it, and understand it's features at least a little bit.
Read though the tests, if any.
Read through the code.
When you encounter code you don't understand, put a debug break there, and step through the code, looking at what it does.
If there aren't any tests, or the test coverage is low, write tests to increase the test coverage. It's a good way to learn the system.
Repeat until you feel you have a vague grip on the code. A vague grip is all you need if you are going to manage the code. You'll get a good grip once you start actually working with the code. For a big system that can take years, so don't try to understand it all first.
There are tools that can help you. As Stephen C says, an IDE is a good idea. I'll explain why:
Many editors analyses the code. This typically gives you code completion, but more importantly in this case, it makes it possible to just just ctrl-click on a variable to see where it comes from. This really speeds things up when you want to understand otehr peoples code.
Also, you need to learn a debugger. You will, in tricky parts of the code, have to step through them in a debugger to see what the code actually do. Pythons pdb works, but many IDE's have integrated debuggers, which make debugging easier.
That's it. Good luck.

I have had to do a lot of this in my job. What works for me may be different to what works for you, but I'll share my experience.
I start by trying to identify the data structures being used and draw diagrams showing the relationships between them. Not necessarily something formal like UML, but a sketch on paper which you understand which allows you to see the overall structure of the data being manipulated by the program. Only once I have some view of the data structures being used do I start to try to understand how the data is being manipulated.
Secondly, for a large body of software, sometimes you need to just attack bite sized pieces at first. You won't get an overall understanding straight away, but if you understand small parts in detail and keep chipping away, eventually all the pieces fall together.
I combine these two approaches, switching between them when I am getting overly frustrated or bored. Regular walks around the block are recommended :) I find this gets me good results in the end.
Good luck!

pyreverse from Logilab and PyNSource from Andy Bulka are helpful too for UML diagram generation.

I'd start with a good python IDE. See the answers for this question.

Enterprise Architect by Sparx Systems is very good at processing a source directory and generating class diagrams. It is not free, but very reasonably priced for what you get. (I am not associated with this company in any way, I've just been a satisfied user of their product for several years.)

Debugging a scripting language like ruby

I am basically from the world of C language programming, now delving into the world of scripting languages like Ruby and Python.
I am wondering how to do debugging.
At present the steps I follow is,
I complete a large script,
Comment everything but the portion I
want to check
Execute the script
Though it works, I am not able to debug like how I would do in, say, a VC++ environment or something like that.
My question is, is there any better way of debugging?
Note: I guess it may be a repeated question, if so, please point me to the answer.

Your sequence seems entirely backwards to me. Here's how I do it:
I write a test for the functionality I want.
I start writing the script, executing bits and verifying test results.
I review what I'd done to document and publish.
Specifically, I execute before I complete. It's way too late by then.
There are debuggers, of course, but with good tests and good design, I've almost never needed one.

Here's a screencast on ruby debugging with ruby-debug.

Seems like the problem here is that your environment (Visual Studio) doesn't support these languages, not that these languages don't support debuggers in general.
Perl, Python, and Ruby all have fully-featured debuggers; you can find other IDEs that help you, too. For Ruby, there's RubyMine; for Perl, there's Komodo. And that's just off the top of my head.

There is a nice gentle introduction to the Python debugger here

If you're working with Python then you can find a list of debugging tools here to which I just want to add Eclipse with the Pydev extension, which makes working with breakpoints etc. also very simple.

My question is, is there any better way of debugging?"
Yes.
Your approach, "1. I complete a large script, 2. Comment everything but the portion I want to check, 3. Execute the script" is not really the best way to write any software in any language (sorry, but that's the truth.)
Do not write a large anything. Ever.
Do this.
Decompose your problem into classes of objects.
For each class, write the class by
2a. Outline the class, focus on the external interface, not the implementation details.
2b. Write tests to prove that interface works.
2c. Run the tests. They'll fail, since you only outlined the class.
2d. Fix the class until it passes the test.
2e. At some points, you'll realize your class designs aren't optimal. Refactor your design, assuring your tests still pass.
Now, write your final script. It should be short. All the classes have already been tested.
3a. Outline the script. Indeed, you can usually write the script.
3b. Write some test cases that prove the script works.
3c. Runt the tests. They may pass. You're done.
3d. If the tests don't pass, fix things until they do.
Write many small things. It works out much better in the long run that writing a large thing and commenting parts of it out.

Script languages have no differences compared with other languages in the sense that you still have to break your problems into manageable pieces -- that is, functions. So, instead of testing the whole script after finishing the whole script, I prefer to test those small functions before integrating them. TDD always helps.

There's a SO question on Ruby IDEs here - and searching for "ruby IDE" offers more.
I complete a large script
That's what caught my eye: "complete", to me, means "done", "finished", "released". Whether or not you write tests before writing the functions that pass them, or whether or not you write tests at all (and I recommend that you do) you should not be writing code that can't be run (which is a test in itself) until it's become large. Ruby and Python offer a multitude of ways to write small, individually-testable (or executable) pieces of code, so that you don't have to wait for (?) days before you can run the thing.
I'm building a (Ruby) database translation/transformation script at the moment - it's up to about 1000 lines and still not done. I seldom go more than 5 minutes without running it, or at least running the part on which I'm working. When it breaks (I'm not perfect, it breaks a lot ;-p) I know where the problem must be - in the code I wrote in the last 5 minutes. Progress is pretty fast.
I'm not asserting that IDEs/debuggers have no place: some problems don't surface until a large body of code is released: it can be really useful on occasion to drop the whole thing into a debugging environment to find out what is going on. When third-party libraries and frameworks are involved it can be extremely useful to debug into their code to locate problems (which are usually - but not always - related to faulty understanding of the library function).

You can debug your Python scripts using the included pdb module. If you want a visual debugger, you can download winpdb - don't be put off by that "win" prefix, winpdb is cross-platform.

The debugging method you described is perfect for a static language like C++, but given that the language is so different, the coding methods are similarly different. One of the big very important things in a dynamic language such as Python or Ruby is the interactive toplevel (what you get by typing, say python on the command line). This means that running a part of your program is very easy.
Even if you've written a large program before testing (which is a bad idea), it is hopefully separated into many functions. So, open up your interactive toplevel, do an import thing (for whatever thing happens to be) and then you can easily start testing your functions one by one, just calling them on the toplevel.
Of course, for a more mature project, you probably want to write out an actual test suite, and most languages have a method to do that (in Python, this is doctest and nose, don't know about other languages). At first, though, when you're writing something not particularly formal, just remember a few simple rules of debugging dynamic languages:
Start small. Don't write large programs and test them. Test each function as you write it, at least cursorily.
Use the toplevel. Running small pieces of code in a language like Python is extremely lightweight: fire up the toplevel and run it. Compare with writing a complete program and the compile-running it in, say, C++. Use that fact that you can quickly change the correctness of any function.
Debuggers are handy. But often, so are print statements. If you're only running a single function, debugging with print statements isn't that inconvenient, and also frees you from dragging along an IDE.

There's a lot of good advice here, i recommend going through some best practices:
http://github.com/edgecase/ruby_koans
http://blog.rubybestpractices.com/
http://on-ruby.blogspot.com/2009/01/ruby-best-practices-mini-interview-2.html
(and read Greg Brown's book, it's superb)
You talk about large scripts. A lot of my workflow is working out logic in irb or the python shell, then capturing them into a cascade of small, single-task focused methods, with appropriate tests (not 100% coverage, more focus on edge and corner cases).
http://binstock.blogspot.com/2008/04/perfecting-oos-small-classes-and-short.html

How can I use Python for large scale development?

I would be interested to learn about large scale development in Python and especially in how do you maintain a large code base?
When you make incompatibility changes to the signature of a method, how do you find all the places where that method is being called. In C++/Java the compiler will find it for you, how do you do it in Python?
When you make changes deep inside the code, how do you find out what operations an instance provides, since you don't have a static type to lookup?
How do you handle/prevent typing errors (typos)?
Are UnitTest's used as a substitute for static type checking?
As you can guess I almost only worked with statically typed languages (C++/Java), but I would like to try my hands on Python for larger programs. But I had a very bad experience, a long time ago, with the clipper (dBase) language, which was also dynamically typed.

Don't use a screw driver as a hammer
Python is not a statically typed language, so don't try to use it that way.
When you use a specific tool, you use it for what it has been built. For Python, it means:
Duck typing : no type checking. Only behavior matters. Therefore your code must be designed to use this feature. A good design means generic signatures, no dependences between components, high abstraction levels.. So if you change anything, you won't have to change the rest of the code. Python will not complain either, that what it has been built for. Types are not an issue.
Huge standard library. You do not need to change all your calls in the program if you use standard features you haven't coded yourself. And Python come with batteries included. I keep discovering them everyday. I had no idea of the number of modules I could use when I started and tried to rewrite existing stuff like everybody. It's OK, you can't get it all right from the beginning.
You don't write Java, C++, Python, PHP, Erlang, whatever, the same way. They are good reasons why there is room for each of so many different languages, they do not do the same things.
Unit tests are not a substitute
Unit tests must be performed with any language. The most famous unit test library (JUnit) is from the Java world!
This has nothing to do with types. You check behaviors, again. You avoid trouble with regression. You ensure your customer you are on tracks.
Python for large scale projects
Languages, libraries and frameworks
don't scale. Architectures do.
If you design a solid architecture, if you are able to make it evolves quickly, then it will scale. Unit tests help, automatic code check as well. But they are just safety nets. And small ones.
Python is especially suitable for large projects because it enforces some good practices and has a lot of usual design patterns built-in. But again, do not use it for what it is not designed. E.g : Python is not a technology for CPU intensive tasks.
In a huge project, you will most likely use several different technologies anyway. As a SGBD (French for DBMS) and a templating language, or else. Python is no exception.
You will probably want to use C/C++ for the part of your code you need to be fast. Or Java to fit in a Tomcat environment. Don't know, don't care. Python can play well with these.
As a conclusion
My answer may feel a bit rude, but don't get me wrong: this is a very good question.
A lot of people come to Python with old habits. I screwed myself trying to code Java like Python. You can, but will never get the best of it.
If you have played / want to play with Python, it's great! It's a wonderful tool. But just a tool, really.

I had some experience with modifying "Frets On Fire", an open source python "Guitar Hero" clone.
as I see it, python is not really suitable for a really large scale project.
I found myself spending a large part of the development time debugging issues related to assignment of incompatible types, things that static typed laguages will reveal effortlessly at compile-time.
also, since types are determined on run-time, trying to understand existing code becomes harder, because you have no idea what's the type of that parameter you are currently looking at.
in addition to that, calling functions using their name string with the __getattr__ built in function is generally more common in Python than in other programming languages, thus getting the call graph to a certain function somewhat hard (although you can call functions with their name in some statically typed languages as well).
I think that Python really shines in small scale software, rapid prototype development, and gluing existing programs together, but I would not use it for large scale software projects, since in those types of programs maintainability becomes the real issue, and in my opinion python is relatively weak there.

Since nobody pointed out pychecker, pylint and similar tools, I will: pychecker and pylint are tools that can help you find incorrect assumptions (about function signatures, object attributes, etc.) They won't find everything that a compiler might find in a statically typed language -- but they can find problems that such compilers for such languages can't find, too.
Python (and any dynamically typed language) is fundamentally different in terms of the errors you're likely to cause and how you would detect and fix them. It has definite downsides as well as upsides, but many (including me) would argue that in Python's case, the ease of writing code (and the ease of making it structurally sound) and of modifying code without breaking API compatibility (adding new optional arguments, providing different objects that have the same set of methods and attributes) make it suitable just fine for large codebases.

my 0.10 EUR:
i have several python application in 'production'-state. our company use java, c++ and python. we develop with the eclipse ide (pydev for python)
unittests are the key-solution for the problem. (also for c++ and java)
the less secure world of "dynamic-typing" will make you less careless about your code quality
BY THE WAY:
large scale development doesn't mean, that you use one single language!
large scale development often uses a handful of languages specific to the problem.
so i agree to the-hammer-problem :-)
PS: static-typing & python

Here are some items that have helped me maintain a fairly large system in python.
Structure your code in layers. i.e separate biz logic, presentation logic and your persistence layers. Invest a bit of time in defining these layers and make sure everyone on the project is brought in. For large systems creating a framework that forces you into a certain way of development can be key as well.
Tests are key, without unit tests you will likely end up with an unmanagable code base several times quicker than with other languages. Keep in mind that unit tests are often not sufficient, make sure to have several integration/acceptance tests you can run quickly after any major change.
Use Fail Fast principle. Add assertions for cases you feel your code maybe vulnerable.
Have standard logging/error handling that will help you quickly navigate to the issue
Use an IDE( pyDev works for me) that provides type ahead, pyLint/Checker integration to help you detect common typos right away and promote some coding standards
Carefull about your imports, never do from x import * or do relative imports without use of .
Do refactor, a search/replace tool with regular expressions is often all you need to do move methods/class type refactoring.

Incompatible changes to the signature of a method. This doesn't happen as much in Python as it does in Java and C++.
Python has optional arguments, default values, and far more flexibility in defining method signatures. Also, duck typing means that -- for example -- you don't have to switch from some class to an interface as part of a significant software change. Things just aren't as complex.
How do you find all the places where that method is being called? grep works for dynamic languages. If you need to know every place a method is used, grep (or equivalent IDE-supported search) works great.
How do you find out what operations an instance provides, since you don't have a static type to lookup?
a. Look at the source. You don't have the Java/C++ problem of object libraries and jar files to contend with. You don't need all the elaborate aids and tools that those languages require.
b. An IDE can provide signature information under many common circumstances. You can, easily, defeat your IDE's reasoning powers. When that happens, you should probably review what you're doing to be sure it makes sense. If your IDE can't reason out your type information, perhaps it's too dynamic.
c. In Python, you often work through the interactive interpreter. Unlike Java and C++, you can explore your instances directly and interactively. You don't need a sophisticated IDE.
Example:
>>> x= SomeClass()
>>> dir(x)
How do you handle/prevent typing errors? Same as static languages: you don't prevent them. You find and correct them. Java can only find a certain class of typos. If you have two similar class or variable names, you can wind up in deep trouble, even with static type checking.
Example:
class MyClass { }
class MyClassx extends MyClass { }
A typo with these two class names can cause havoc. ["But I wouldn't put myself in that position with Java," folks say. Agreed. I wouldn't put myself in that position with Python, either; you make classes that are profoundly different, and will fail early if they're misused.]
Are UnitTest's used as a substitute for static type checking? Here's the other Point of view: static type checking is a substitute for clear, simple design.
I've worked with programmers who weren't sure why an application worked. They couldn't figure out why things didn't compile; the didn't know the difference between abstract superclass and interface, and the couldn't figure out why a change in place makes a bunch of other modules in a separate JAR file crash. The static type checking gave them false confidence in a flawed design.
Dynamic languages allow programs to be simple. Simplicity is a substitute for static type checking. Clarity is a substitute for static type checking.

My general rule of thumb is to use dynamic languages for small non-mission-critical projects and statically-typed languages for big projects. I find that code written in a dynamic language such as python gets "tangled" more quickly. Partly that is because it is much quicker to write code in a dynamic language and that leads to shortcuts and worse design, at least in my case. Partly it's because I have IntelliJ for quick and easy refactoring when I use Java, which I don't have for python.

The usual answer to that is testing testing testing. You're supposed to have an extensive unit test suite and run it often, particularly before a new version goes online.
Proponents of dynamically typed languages make the case that you have to test anyway because even in a statically typed language conformance to the crude rules of the type system covers only a small part of what can potentially go wrong.

I need a really good reason to use Python

I have been trying to make a case for using Python at my work. We use C# and ASP.NET for basically all of our development. 80% or more of our projects are web applications. It seems natural that we would look at some of the nice dynamic web languages (Ruby, Python, etc), and with things like IronRuby and IronPython, I started seriously investigating.
I love Python. It's a beautiful, expressive language. It's a joy to code in, for sure. The multitude of python modules and frameworks make it very appealing. Problem is, I cannot think of any specific problems, any specific hurdles that would require a language like Python. ASP.NET gives us RAD, it gives us a full-featured framework and all that good stuff. Also, we all already know C# and have lots of projects in C#, learning a new language just because doesn't quite work.
Can you guys help me think of something to finally convince my boss to really learn Python and start using it on projects?
Edit: I know that no problem requires only one language, I just meant, are there any specific problems in which dynamic languages excel over static languages.
Edit again: Let me also mention that my boss prompted ME to investigate this. He has put aside hours to research these languages, find a good one, learn it, and then figure out how we can use it. I'm at the last step here, I do not need a lecture on why I should consider my motivation for changing something my company does because they do it for a reason.

"Can you guys help me think of something to finally convince my boss to really learn Python and start using it on projects?"
Nope.
Nothing succeeds like success. Use Python. Be successful. Make people jealous.
When asked why you're successful, you can talk about Python. Not before.
Choose projects wisely: things where a dynamic language has significant advantages. Things where the requirements are not nailed down in detail. Things like data transformations, log-file scraping, and super-sophisticated replacements for BAT files.
Use Python to get started doing something useful while everyone else is standing around trying to get enough business and domain information to launch a project to develop a complicated MVC design.
Edit: Some Python to the Rescue stories.
Exploratory Programming
Tooling to build test cases
What's Central Here?
Control-Break Reporting
One More Cool Thing About Python Is...
In Praise of Serialization
And that's just me.
Edit: "boss prompted ME to investigate", "figure out how we can use it" changes everything.
The "finally convince my boss to really learn Python" is misleading. You aren't swimming upstream. See How Do I Make the Business Case for Python for the "convince my boss" problem. The edit says you're past this phase.
Dynamic languages offer flexibility. Exploit that. My two sets of examples above are two areas where flexibility matters.
Requirements aren't totally nailed down. With a dynamic language, you can get started. Rework won't be a deal-breaker. With Java (and C++ and C#) you are reluctant to tackle devastating design changes because it's hard to break everything and get it to compile and work again. In Python, devastating changes aren't as expensive.
Design is in flux because you can't pick components. You can write Wrappers and Facades very easily in Python. It's a scripting language. And, Python modules compose into larger aggregates very simply.
Coding is in flux because requirements and design keep changing. It's scripted -- not compiled. You just make a change to the code and you're off and running. Testing is easier because the work cycle is shorter. It isn't code-compile-build-test it's code-test.
Testing is in flux because the requirements keep changing. Same as above. The work cycle is shorter and faster.

Almost no problem requires a specific programming language, that's just not how things work.
The easiest way to get a new language into an environment like yours is to start a new work project in your own time in the new language. Make it do something you need doing, and write it on your own time. Use it yourself, and other people will probably notice it. They then say "Can you send me that program?" and boom, they're using your new language.
If you really want to something, I would probably write a site in Django, simply because its admin interface blows everyone away.
The main point to remember is that if you start using python, that's one more thing everyone else has to learn, and it's another bullet point that will need to be on every prospective employee's resume. That can get expensive, and management won't like it.

Sneaking a language in is often done by automating tedious manual tasks (especially dynamic/scripting languages like Python/Ruby etc). Set it up so something like deploying builds, or shuffling backups, or whatever is done with Python.
Then casually slip in how easy it was to do, and try to spread some of the enthusiasm around.
Acceptance and awareness should slowly grow from that, and before you know it, management is seriously considering Python for a new project.

Can you guys help me think of
something to finally convince my boss
to really learn Python and start using
it on projects?
Unfortunately, the answer is no. As Harley said, no problem is going to require a specific language. The approach Harley suggested is also a good one. Learn on your time, build an useful app on your time, and maybe, just maybe, someone at your work will want to use it, like it, love it, then want to learn more about it.
Another approach you could take is to get a better understanding of why your company uses .Net (therefore, Windows Server, and probably SQL server) for nearly all development. Once you have a good understanding of why they aren't open to other languages, then you have some fire power to build a business case for the "why not?".
Why pay licensing costs when you have tools that can accomplish the same things? There are free alternatives out there, like Python, that run on free servers.
Why not give your team the chance to grow their professional tool-belt? This is my opinion, but a good developer is a developer that isn't afraid to learn new ways of doing the same thing they've done before.
But, in the end, I wouldn't get your hopes up. Bottom line, it costs money to introduce a new language/environment into an IT shop. Whether it's software, training, or employee rollover, there is a business reason behind utilizing a single language for a company, and sticking to it.
I'm in the exact scenario you're in. I work in a .Net shop, and that's not going to change any time soon. I get by, by working on my own projects in my "free" time. I enjoy it, and it makes for a good balance.
Hope this helps.

Take a step back, and look at your approach. "I know what I want the answer to be, but I can't find any evidence to support it."
Despite the fact that Python is my current first choice language, I am afraid I find myself on the side of your boss! Sorry.
I think you should open your mind and consider all the options from the stance of your organisation's best interest, and see if you don't come to a different conclusion about the best language.
There are many factors in the choice of language, and how pretty it is is a fairly minor one. The availability of staff is a key one. The cost of retraining, availability of the libraries and meta-tools, performance, etc. etc.
Once you have taken into consideration all the different factors (and not just "Oooh! It'd be fun!") and made a balanced assessment (rather than a predetermined answer), you may find that your boss is more willing to listen.
p.s. The suggestion to secretly use Python for work code, and leaving the company with a terrible code debt in a language they are not prepared for seems very unprofessional to me.

The best leverage you're likely going to have is tools and libraries; as others have pointed out, no language is required to solve any particular program. So let's look at Things You Can Leverage Using Python:
Google App Engine
SciPy
pywinauto
django
Those are off the top of my head; finding what's applicable to your team and your company is left as an exercise for the questioner :)

Well, here's a view of why Python programmers make better Java programmers; the concepts are much the same as for your situation.
Essentially, people who learn a language because they want to show that they enjoy programming, like to learn new things, and are more likely to think outside the box.
...if a company chooses to write
its software in a comparatively
esoteric language, they'll be able to
hire better programmers, because
they'll attract only those who cared
enough to learn it. And for
programmers the paradox is even more
pronounced: the language to learn, if
you want to get a good job, is a
language that people don't learn
merely to get a job.
Not only that, but Python enforces "good looking" code and you don't have to do the whole code/compile routine. With IronPython, you can simply code in Python and use it as is; just another .NET tool.

Python got a good start in the Java world as Jython for unit testing. In fact many Java people started using it first that way. Its dynamic scripting nature makes it a great fit for unit tests. Just yesterday I was wishing I could use it or something like it for the unit tests I was writing for a VB.Net project. I'd have to say that it isn't so much about the individual language IronRuby or IronPython as it is about the style of development that they enable. You can write static language like code in either but you don't fully reap the benefits until you can start to think dynamically. Once you grasp those concepts you'll start to slowly change the way you code and your projects will require less classes and less code to implement. Testing, particularly unit tests will become a must since you give up the warm blanket known as a compiler with type safety checks for other efficiencies.

The language is almost never the key to success. Good programmers can be successful in a variety of languages, and you'll find successful projects in almost any language. You won't find the failures that much because those projects just go away never to be heard of again. If you're looking for a new language because you don't have good programmers, even the best language in the world isn't going to help.
And, you haven't said anything about the sort of work you're doing. Python might be a good choice because it has well-supported and widely-used libraries that are critical for you. On the other hand, C# might have better support for the stuff that you want to do. A tool outside of context has no intrinsic merit. You might love screwdrivers, but that doesn't help you row a boat.
If you want to use Python, or any other language, just because you like it, be honest with yourself and those around you. It looks like you've made a decision to switch, don't know why you are switching, and now need to rationalize it with reasons that had nothing to do with your desire to switch. If you had a good reason, you wouldn't be asking here :)
That's not entirely a bad thing, though. Programming is a human enterprise. If the programmers (at whatever level) insanely love a particular language, no matter how stupid the reason, they are probably going to produce more. It's just not a technological solution though.
Good luck, :)

I am pretty sure (100%) that you don't need to use Python for MS Windows at least.
In cases of other platforms you can use any language you like.