Quick Python for Programmers
This book assumes you're an experienced programmer, and it's best if you have learned Python through another book. For everyone else, this chapter gives a fast introduction to the language.
This is not an introductory book. I am assuming that you have worked your way through at least Learning Python (by Mark Lutz & David Ascher; Oreilly, 1999) or an equivalent text before coming to this book.
This brief introduction is for the experienced programmer (which is what you should be if you're reading this book). You can refer to the full documentation at www.Python.org.
I find the HTML page A Python Quick Reference to be incredibly useful.
In addition, I'll assume you have more than just a grasp of the syntax of Python. You should have a good understanding of objects and what they're about, including polymorphism.
On the other hand, by going through this book you're going to learn a lot about object-oriented programming by seeing objects used in many different situations. If your knowledge of objects is rudimentary, it will get much stronger in the process of understanding the designs in this book.
Scripting vs. Programming
Python is often referred to as a scripting language, but scripting languages tend to be limiting, especially in the scope of the problems that they solve. Python, on the other hand, is a programming language that also supports scripting. It is marvelous for scripting, and you may find yourself replacing all your batch files, shell scripts, and simple programs with Python scripts. But it is far more than a scripting language.
The goal of Python is improved productivity. This productivity comes in many ways, but the language is designed to aid you as much as possible, while hindering you as little as possible with arbitrary rules or any requirement that you use a particular set of features. Python is practical; Python language design decisions were based on providing the maximum benefits to the programmer.
Python is very clean to write and especially to read. You will find that it's quite easy to read your own code long after you've written it, and also to read other people's code. This is accomplished partially through clean, to-the-point syntax, but a major factor in code readability is indentation - scoping in Python is determined by indentation. For example:
# QuickPython/if.py response = "yes" if response == "yes": print("affirmative") val = 1 print("continuing...")
The '#' denotes a comment that goes until the end of the line, just like C++ and Java '//' comments.
First notice that the basic syntax of Python is C-ish as you can see in the if statement. But in a C if, you would be required to use parentheses around the conditional, whereas they are not necessary in Python (it won't complain if you use them anyway).
The conditional clause ends with a colon, and this indicates that what follows will be a group of indented statements, which are the "then" part of the if statement. In this case, there is a "print" statement which sends the result to standard output, followed by an assignment to a variable named val. The subsequent statement is not indented so it is no longer part of the if. Indenting can nest to any level, just like curly braces in C++ or Java, but unlike those languages there is no option (and no argument) about where the braces are placed - the compiler forces everyone's code to be formatted the same way, which is one of the main reasons for Python's consistent readability.
Python normally has only one statement per line (you can put more by separating them with semicolons), thus no terminating semicolon is necessary. Even from the brief example above you can see that the language is designed to be as simple as possible, and yet still very readable.
With languages like C++ and Java, containers are add-on libraries and not integral to the language. In Python, the essential nature of containers for programming is acknowledged by building them into the core of the language: both lists and associative arrays (a.k.a. maps, dictionaries, hash tables) are fundamental data types. This adds much to the elegance of the language.
In addition, the for statement automatically iterates through lists rather than just counting through a sequence of numbers. This makes a lot of sense when you think about it, since you're almost always using a for loop to step through an array or a container. Python formalizes this by automatically making for use an iterator that works through a sequence. Here's an example:
# QuickPython/list.py list = [ 1, 3, 5, 7, 9, 11 ] print(list) list.append(13) for x in list: print(x)
The first line creates a list. You can print the list and it will look exactly as you put it in (in contrast, remember that I had to create a special Arrays2 class in Thinking in Java in order to print arrays in Java). Lists are like Java containers - you can add new elements to them (here, append( ) is used) and they will automatically resize themselves. The for statement creates an iterator x which takes on each value in the list.
You can create a list of numbers with the range( ) function, so if you really need to imitate C's for, you can.
Notice that there aren't any type declarations - the object names simply appear, and Python infers their type by the way that you use them. It's as if Python is designed so that you only need to press the keys that absolutely must. You'll find after you've worked with Python for a short while that you've been using up a lot of brain cycles parsing semicolons, curly braces, and all sorts of other extra verbiage that was demanded by your non-Python programming language but didn't actually describe what your program was supposed to do.
To create a function in Python, you use the def keyword, followed by the function name and argument list, and a colon to begin the function body. Here is the first example turned into a function:
# QuickPython/myFunction.py def myFunction(response): val = 0 if response == "yes": print("affirmative") val = 1 print("continuing...") return val print(myFunction("no")) print(myFunction("yes"))
Notice there is no type information in the function signature - all it specifies is the name of the function and the argument identifiers, but no argument types or return types. Python is a structurally-typed language, which means it puts the minimum possible requirements on typing. For example, you could pass and return different types from the same function:
# QuickPython/differentReturns.py def differentReturns(arg): if arg == 1: return "one" if arg == "one": return True print(differentReturns(1)) print(differentReturns("one"))
The only constraints on an object that is passed into the function are that the function can apply its operations to that object, but other than that, it doesn't care. Here, the same function applies the '+' operator to integers and strings:
# QuickPython/sum.py def sum(arg1, arg2): return arg1 + arg2 print(sum(42, 47)) print(sum('spam ', "eggs"))
When the operator '+' is used with strings, it means concatenation (yes, Python supports operator overloading, and it does a nice job of it).
The above example also shows a little bit about Python string handling, which is the best of any language I've seen. You can use single or double quotes to represent strings, which is very nice because if you surround a string with double quotes, you can embed single quotes and vice versa:
# QuickPython/strings.py print("That isn't a horse") print('You are not a "Viking"') print("""You're just pounding two coconut halves together.""") print('''"Oh no!" He exclaimed. "It's the blemange!"''') print(r'c:\python\lib\utils')
Note that Python was not named after the snake, but rather the Monty Python comedy troupe, and so examples are virtually required to include Python-esque references.
The triple-quote syntax quotes everything, including newlines. This makes it particularly useful for doing things like generating web pages (Python is an especially good CGI language), since you can just triple-quote the entire page that you want without any other editing.
The 'r' right before a string means "raw," which takes the backslashes literally so you don't have to put in an extra backslash in order to insert a literal backslash.
Substitution in strings is exceptionally easy, since Python uses C's printf() substitution syntax, but for any string at all. You simply follow the string with a '%' and the values to substitute:
# QuickPython/stringFormatting.py val = 47 print("The number is %d" % val) val2 = 63.4 s = "val: %d, val2: %f" % (val, val2) print(s)
As you can see in the second case, if you have more than one argument you surround them in parentheses (this forms a tuple, which is a list that cannot be modified - you can also use regular lists for multiple arguments, but tuples are typical).
All the formatting from printf() is available, including control over the number of decimal places and alignment. Python also has very sophisticated regular expressions.
Like everything else in Python, the definition of a class uses a minimum of additional syntax. You use the class keyword, and inside the body you use def to create methods. Here's a simple class:
# QuickPython/SimpleClass.py class Simple: def __init__(self, str): print("Inside the Simple constructor") self.s = str # Two methods: def show(self): print(self.s) def showMsg(self, msg): print(msg + ':', self.show()) # Calling another method if __name__ == "__main__": # Create an object: x = Simple("constructor argument") x.show() x.showMsg("A message")
Both methods have self as their first argument. C++ and Java both have a hidden first argument in their class methods, which points to the object that the method was called for and can be accessed using the keyword this. Python methods also use a reference to the current object, but when you are defining a method you must explicitly specify the reference as the first argument. Traditionally, the reference is called self but you could use any identifier you want (if you do not use self you will probably confuse a lot of people, however). If you need to refer to fields in the object or other methods in the object, you must use self in the expression. However, when you call a method for an object as in x.show( ), you do not hand it the reference to the object - that is done for you.
Here, the first method is special, as is any identifier that begins and ends with double underscores. In this case, it defines the constructor, which is automatically called when the object is created, just like in C++ and Java. However, at the bottom of the example you can see that the creation of an object looks just like a function call using the class name. Python's spare syntax makes you realize that the new keyword isn't really necessary in C++ or Java, either.
All the code at the bottom is set off by an if clause, which checks to see if something called __name__ is equivalent to __main__. Again, the double underscores indicate special names. The reason for the if is that any file can also be used as a library module within another program (modules are described shortly). In that case, you just want the classes defined, but you don't want the code at the bottom of the file to be executed. This particular if statement is only true when you are running this file directly; that is, if you say on the command line:
However, if this file is imported as a module into another program, the __main__ code is not executed.
Something that's a little surprising at first is that while in C++ or Java you declare object level fields outside of the methods, you do not declare them in Python. To create an object field, you just name it - using self - inside of one of the methods (usually in the constructor, but not always), and space is created when that method is run. This seems a little strange coming from C++ or Java where you must decide ahead of time how much space your object is going to occupy, but it turns out to be a very flexible way to program. If you declare fields using the C++/Java style, they implicitly become class level fields (similar to the static fields in C++/Java)
Because Python is dynamically typed, it doesn't really care about interfaces - all it cares about is applying operations to objects (in fact, Java's interface keyword would be wasted in Python). This means that inheritance in Python is different from inheritance in C++ or Java, where you often inherit simply to establish a common interface. In Python, the only reason you inherit is to inherit an implementation - to re-use the code in the base class.
If you're going to inherit from a class, you must tell Python to bring that class into your new file. Python controls its name spaces as aggressively as Java does, and in a similar fashion (albeit with Python's penchant for simplicity). Every time you create a file, you implicitly create a module (which is like a package in Java) with the same name as that file. Thus, no package keyword is needed in Python. When you want to use a module, you just say import and give the name of the module. Python searches the PYTHONPATH in the same way that Java searches the CLASSPATH (but for some reason, Python doesn't have the same kinds of pitfalls as Java does) and reads in the file. To refer to any of the functions or classes within a module, you give the module name, a period, and the function or class name. If you don't want the trouble of qualifying the name, you can say
from module import name(s)
Where "name(s)" can be a list of names separated by commas.
You inherit a class (or classes - Python supports multiple inheritance) by listing the name(s) of the class inside parentheses after the name of the inheriting class. Note that the Simple class, which resides in the file (and thus, module) named SimpleClass is brought into this new name space using an import statement:
# QuickPython/Simple2.py from SimpleClass import Simple class Simple2(Simple): def __init__(self, str): print("Inside Simple2 constructor") # You must explicitly call # the base-class constructor: Simple.__init__(self, str) def display(self): self.showMsg("Called from display()") # Overriding a base-class method def show(self): print("Overridden show() method") # Calling a base-class method from inside # the overridden method: Simple.show(self) class Different: def show(self): print("Not derived from Simple") if __name__ == "__main__": x = Simple2("Simple2 constructor argument") x.display() x.show() x.showMsg("Inside main") def f(obj): obj.show() # One-line definition f(x) f(Different())
you don't have to explicitly call the base-class constructor if the argument list is the same. Show example.
(Reader) The note above is confusing. Did not understand. IMHO one still needs to invoke the base-class constructor if the argument is the same. Probably one needs to state that in case the base class constructor functionality continues to be adequate for the derived class, then a new constructor need not be declared for the derived class at all.
Simple2 is inherited from Simple, and in the constructor, the base-class constructor is called. In display( ), showMsg( ) can be called as a method of self, but when calling the base-class version of the method you are overriding, you must fully qualify the name and pass self in as the first argument, as shown in the base-class constructor call. This can also be seen in the overridden version of show( ).
In __main__, you will see (when you run the program) that the base-class constructor is called. You can also see that the showMsg( ) method is available in the derived class, just as you would expect with inheritance.
The class Different also has a method named show( ), but this class is not derived from Simple. The f( ) method defined in __main__ demonstrates weak typing: all it cares about is that show( ) can be applied to obj, and it doesn't have any other type requirements. You can see that f( ) can be applied equally to an object of a class derived from Simple and one that isn't, without discrimination. If you're a C++ programmer, you should see that the objective of the C++ template feature is exactly this: to provide weak typing in a strongly-typed language. Thus, in Python you automatically get the equivalent of templates - without having to learn that particularly difficult syntax and semantics.
(Reader) I am not sure if I agree with the remark about templates. One of the big objective of templates has always been type safety along with genericity. What python gives us is the genericity. IMHO the analogy with template mechanism is not appropriate.
Suggest Further Topics for inclusion in the introductory chapter