Tayseer On Programming

Dynamic languages are very productive. This is why they are very popular in the UNIX community, because they form an essential part of their culture. In the Windows community very few people used them. People believed that dynamic languages are slow. In 2006, the situation changed dramatically. I think there are two reasons for this:

1. Ruby on rails: Rails was created to optimize programmer's happiness, thus leading to much higher productivity.
2. C# 3.0: A lot of the new features in C# 3.0 are inspired by dynamic languages like Python.

I will try to explain what makes Python unique. Almost everything I mention here applies for other dynamic languages as well.

This is not meant to be a Python tutorial. A lot of Python tutorials and free books are available and easy to find and the syntax is very clear that you don't need a tutorial to read the examples here.

Interactive
You can just launch the interpreter, write code and see the result immediately. No need for a lengthy compile-run-debug loop, thus reducing developing time.

Strongly typed: Python is

1. Interpreted: It is parsed and executed at runtime.
2. Strongly typed: It does type checking.
3. Dynamic: Everything happens at runtime (including type checking).

>>> def add(x, y):
...   return x + y

will work if

1. x and y are of the same type (or compatible type).
2. This type provides the + operation.

If one of the above conditions is broken, Python will raise an exception at runtime, stating the exact error.

>>> add(3, 5)
8

>>> add("hello", " world")
'hello world'

>>> add(3, " world")
Traceback (most recent call last):
 File "<stdin>", line 1, in ?
 File "<stdin>", line 2, in add
TypeError: unsupported operand type(s) for +: 'int' and 'str'

Readability
Python is known for its readability. You can learn the syntax of Python in a day. Try to read this

if x in (1, 2, 3, 7, 4):
 print 'Not found'
else:
 print 'found'

Rich data types
Python has a lot of data-types built-in, which makes your life a lot easier. Take a dictionary for example

>>> d = {'name': 'Bond. James Bond!!', 3: 'Whatever'}

You can use any hashable object as a dictionary key. To access a value just use

>>> d['name']
'Bond, James Bond!!'

For example, how to implement a graph in Python? Very easy, just use a 2D dictionary

graph = {'A': {'B': 14}, 'B': {'C': 3, 'D': 9}, 'C': None, 'D': None}

which represents this graph

To get the value of the edge from B-C, use

>>> graph['B']['C']
3

This was simple, right?

The story doesn't end here. You have also built-in data structures: list, tuple, set, etc. Learn how to use them instead of building your own inefficient variants.

Objects have types, variables don't!!
As I mentioned before, Python is a strongly typed language, which means that you cannot treat one type as another. For example

>>> x = 0
>>> x.keys()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
AttributeError: 'int' object has no attribute 'keys'

But, you can do the following

>>> x = 0
>>> x = {'name': 'Bond, James Bond!!'}

The variable x has no type, but the object that x points to has a type. You can change the object that x points to using the assignment
operator. This simple fact may be a little hard to understand at first, but to clarify it I will show a counter example. In C# you can write

class Base {}
class Derived : Base{
  public void MyMethod() {}
}

Base b = new Derived();
b.MethodCall();

This will not work because the reference b is of type Base, though the object itself has this method. You have to rewrite the last line to become

((Derived)b).MethodCall();

Ok, how this can be useful? How many times you used 2 variables to point to the same
piece of information? Like

string personIdString = context.Request.Params["personId"];
int personId = int.Parse(personIdString);

In Python, this can be written as

personId = context.Request.Params['personId']
personId = int(personId)

Returning multiple values
This is a stupid problem: If you want to return more than one value, you have to use out parameters.

Python solves this by using tuples. Tuples are simply constant lists, so how can we use them?

def return_many_values():
  return (1, 2, 4, 8)

The above function returns a single object: The tuple object. The caller can unpack the tuple using this

(a, b, c, d) = return_many_values()

After executing the last statement a = 1, b = 2, c = 4, d = 8.

Because this is a very common idiom, you can ignore the braces.

def return_many_values():
  return 1, 2, 4, 8

a, b, c, d = return_many_values()

Keyword arguments
Don't you wish that you can pass parameters by name? Something like

Factorial(x=10)

1. It is much easier to read code: No need to search on MSDN to know what the parameters mean.
2. Much less errors: You remember the parameters by name, not by order.
3. The parameters can have default values, so you can send only the needed parameters.
4. They can be send out-of-order: CreateWindow(x=0, y=10) is the same as CreateWindow(y=10, x=0)

Batteries included
An essential part of Python's philosophy is the 'Battaries included' concept: The default installation should provide most of the libraries need for common tasks: Threading, Mail (SMTP, IMAP, POP3), Regex, GUI, Complex numbers, compression (Tar, Zip, …) and a lot of other useful libraries.

Not limited
If you are a Java programmer, you have suffered difficulties using platform-specific features, for example COM components. It is an essential part of Python culture that the programmers are consenting adults: they know what they should do, so the language must help them doing what they want. This is why Python is not isolated from the platform. For example, you have

1. CPython: The default Python implementation, written in C.
2. Jython: Implementation on top of JVM.
3. IronPython: Implementation on top of CLR.
4. win32all: A library for CPython to use Win32 APIs & COM components.
5. Carbon: MacOS X specific APIs.

You can write cross-platform code in Python, but you can also write platform-specific components for best use of your platform.

Everything is an object
Classes are objects. Functions are objects. This makes your life easier for 2 reasons

1. You can add members at runtime:
1. You can add members to an existing class: and all new instances will have this member.
2. You can add members to an existing object: and this member will be specific to this object. This can be useful in GUI applications if you
   want to attach a data item to an existing widget.
3. You can add members to a function: I will explain how to use this in a minute how to do this.
2. You can change objects at runtime: I will explain this with an example.

You are required to implement the factorial function with caching. If you are doing it in C#, it will look like

public class AdvancedMath
{
  static SortedDictionary<int, int> _cache = new SortedDictionary<int, int>();
  static int Factorial(int x)
  {
    if (_cache.ContainsKey(x))
      return _cache[x];

    if (x == 0)
      return 1;
    int result = x * Factorial(x - 1);
    _cache.Add(x, result);
    return result;
  }
}

What is wrong with this code?

1. It couples the caching to the calculation.
2. The cache contains all intermediate values: This can be good or bad, depending on your usage.
3. Factorial(100) = 0. Overflow.

We can solve the first problem by separating the caching & the calculation into 2 different functions

static SortedDictionary<int, int> _cache = new SortedDictionary<int, int>();

static int Factorial(int x)
{
  return Caching(x, _FactorialImpl);
}
  
delegate int MathDelegate(int x);
static int Caching(int x, MathDelegate mathDelegate)
{
  if (_cache.ContainsKey(x))
    return _cache[x];
  int result = mathDelegate(x);
  _cache.Add(x, result);
  return result;
}
  
static int _FactorialImpl(int x)
{
  if (x == 0)
    return 1;
  return x * _FactorialImpl(x - 1);
}

This solves the first 2 problems, but not the third. Besides, the Caching() function is not generic enough: It handles only integers! Of course, you can use objects instead of integers, but how can you cache functions with more than 1 parameter! I will be happy if you send me the generic solution in C#, but I like the generic solution in Python

# This function takes any function as a parameter & returns an 
# equivalent function, but with caching
def cached(old_fn):
  def new_fn(*args):# *args means that it can accept any
                    # number of arguments. They will
                    # be contained in the tuple `args`
    if new_fn.cache.has_key(args):
      return new_fn.cache[args]
    result = old_fn(*args) # We are passing all the
                           # arguments to the original
                           # function. If the number of
                           # arguments is not correct,
                           # it will throw an exception
    new_fn.cache[args] = result
    return result
  new_fn.cache = {} # the cache is a member of the 
                    # function itself
  return new_fn

def factorial(n):
  if n == 0:
    return 1
  return n * factorial(n - 1)

factorial = cached(factorial)

Python solution has many advantages over the C# solution

1. The caching is decoupled from the calculation.
2. The caching contains only final values.
3. The cache is not coupled to the function itself and we are not using a global cache, which is a cleaner design.
4. factorial(100) = 93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000L.
   This is due to the fact that an integer recognizes an overflow (because it is an object, not just stupid 4 bytes) and converts itself to a long, which can represent large numbers.
5. It can handle any number of arguments.

The power of introspection
You can call a function at runtime by its name. This allows us to call yet undefined functions. I will illustrate this with an example.

This is the standard C# idiom for SAX parsing of an XML document

try
{
  XmlTextReader rdr = new XmlTextReader();
  rdr.WhitespaceHandling = WhitespaceHandling.None;

  while(rdr.Read())
  {
    switch (rdr.Name)
    {
      case "scoresheet":
      if (rdr.IsStartElement())
      {
      // handle element start
      }
      else
      {
      // handle element end
      }
      break;
      // handle other cases here
      default:
      // handle 'unexpected element' error
    }
  }
}
catch (XmlException ex)
{
  // handle error
}

You have to write a huge switch statement with a lot of nesting to read a complex XML document, or you can modify it a little bit to forward to an observer class.

The problem with this design is inflexibility: you have to maintain the mapping yourself within the switch statement. With Python, you can make a more extensible solution

This is the main driver

from xml.sax import parse as sax_parse
parser = MySaxParser()
sax_parse(file_name, parser)

The parser will be something like

# This is the base class that you will inherit
class BaseSaxHandler(xml.sax.handler.ContentHandler):
  def startElement(self, tag, attributes):
    tag = str(tag) # convert unicode -> str
    
    # get a method in this object named 'start_' + tag, or
    # return None if the method doesn't exist
    handler = getattr(self, 'start_' + tag.lower(), None)
    if handler:
      handler(attributes)
    else:
      self.error_unknow_start_tag(tag, attributes)
	  
  def endElement(self, tag):
    tag = str(tag)
    handler = getattr(self, 'end_' + tag.lower(), None)
    if handler:
      handler()
    else:
      self.error_unknown_end_tag(tag)

  def error_unknown_start_tag(self, tag, attributes):
    print 'Unknown start tag:', tag

  def error_unknown_end_tag(self, tag):
    print 'Unknown end tag:', tag

class MySaxParser(BaseSaxHandler):
  def start_channel(self, attributes):
    # Look Ma, no switch :)
    print '<channel>'

  def end_channel(self):
    print '</channel>'

What are the benefits?

1. No need to repeat the switch statement.
2. Isolated error handling.
3. Extensions are easy to do: You can make MySaxParserV2 which inherits MySaxParser and add/override methods to handle new/existing tags.

Intropection can make your life easier when implementing proxies and web services. I suggest you read the book Dive Into Python for more in-depth explanation.

Used by the most successul sites

1. Google
2. Industrial Light & Magic: Makers of 'Star Wars' movies.
3. YouTube.com
4. Reddit.com: One of the most popular social bookmarking sites.
5. YouOs.com: Online OS.
6. And many others

References

• Python download link. I suggest using version 2.4.
• Dive Into Python: For programmers who want to learn Python
• Fredrik Lundh's articles
• Python cookbook
• Django: There are a lot of Python web frameworks, but this is the best one.
• Join the Python mailing list