Python – Preferring Python over C for Algorithmic Programming

algorithmscprogramming practicespythonself-improvement

I've been studying a bit of algorithms and have been looking at sites like SPOJ.pl TopCoder etc. I've seen that programmers prefer C or C++ usually for most algorithmic programming contests.

Now I've been having some trouble lately. I know both a bit of C and Python and when trying to write a code I seem to prefer Python over C for most algorithms. Everytime I sit down to write a code in C I give up after about 15 minutes because I find it too cumbersome and tend to move over to python. Passing matrices Pointers and so on seem to be useless time wasted that I could actually be utilizing to think about the algorithm itself.

Now I know and have heard from a lot of people that C is a very important language and is the bread and butter of a lot of programmers out there.

What I wanted to know was whether this approach of mine has any drawbacks/consequences/Disadvantages etc.

This is not a Python vs C debate; This a question about how this specific practice of preferring python over C because of the ease of use will affect me or any other programmer/computer Scientist in the long run.

I'd love to hear from people who've used these languages in the industry/and or to develop large software/libraries etc.

Best Answer

In my experience, when people have excess difficulty coding algorithms in C, it's often because they are tightly coupling their data structure management with their algorithm instead of creating appropriate abstractions. For example, manually manipulating linked list pointers everywhere instead of making push() and pop() functions. They are too accustomed to having those abstractions provided to them.

While this problem is much more evident with lower level abstractions, failure to recognize tight coupling and create appropriate abstractions is a problem at any level. Practicing these skills in C until you can make an algorithm that looks clean and readable will carry over to any language you use.

The other problem I occasionally see among python programmers is difficulty adapting for performance at scale. Granted, performance isn't usually the primary concern, but the most pythonic way to implement an algorithm for a relatively small data structure can grind your system to a halt when you're working with gigabytes or more of data. Becoming a good C programmer helps you be more aware of those kinds of issues in any language.

Can you learn those skills in other languages? Sure, but C helps by making it a lot more obvious when you get it wrong.

That being said, I use python for algorithmic programming when I have a choice, even though I'm just as comfortable in C. Python has language features that make it very nice for that kind of programming, and performance differences are usually negligible. I can't speak to why other programmers who know both would choose C. I imagine a lot of them do it simply to set themselves apart from the crowd.

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Python – Confusion regarding def function within Python

It's not about Python.

But nevertheless, to answer you straightly. Lets say you have a function:

def doSomethingWithParmas(params):
    params.doSomething()

Now you can call it later on with:

print doSomethingWithParams(myParams)

or/and

print doSomethingWithParams(yourParams)

It's how functions work, you can call it with whatever params you like, all that function cares, is that they are some kind of params that it can handle.

EDIT: A remark: I have never coded a line of Python in my life.

Python – Strategy for Keeping Up with Language Changes

This is an unsolved problem in our field. There's no way to be sure that your code will indefinitely work. Even if your code was truly perfect in the forwards-compatible sense (and if it is, please come work for my company! ;) ), if it runs on, uses, or is used by any other software which gets a bug or changes in any way, your code may not work.

So I can't give you a list of things to do that, if you follow them, will guarantee success. But what you can do is minimize the risk of future breakages and minimize their impacts. A more knowledgeable Pythonist would be able to give you advice more specific to Python, so I will have to be more general:

write unit tests. Even for things that you know don't need them.
using popular/well-designed/stable libraries and technologies, avoiding unpopular (and thus likely to soon be unsupported) ones
avoid writing code that exploits implementation details. Code to interfaces, not implementations. Code against multiple implementations of the same interface. For example, run your code in CPython, Jython, and IronPython and see what happens. This will give you some great feedback about your code. This might not be helpful for Python3 though -- last I heard, some implementations were still in Python2.
write simple, clear code that is explicit about its assumptions
write modular, composable code. If some code must do something dangerous (in the future-proof sense), separate it so that even if it has to change, the rest of the code doesn't.
have a specification of some form. This is similar to the points about unit tests, if you use tests as a spec, and interfaces, which can also be used as specs. (I mean interface in the general sense, not the Java keyword sense).

Doing any of these things may/will increase the amount of work you have to do. I think that makes sense -- a lot of these points can also be made for how to write good code, which is quite difficult (in my opinion). Sometimes you may need to violate some of these suggestions. That is perfectly acceptable, but be aware of the costs.

It's great that the Python team is thinking about this, and for sure they are far more talented and skilled than I will ever be. Still, I would estimate there's a 100% that somebody's code somewhere will stop working the way the want it's intended to when Python is upgraded.

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