We will be using the interactive interpreter for this chapter, so it is essential that you are comfortable using it. To get started, open a terminal and run the command python. You should see this:

[paul@caitlin ~]$ python
Python 2.3.4 (#1, Oct 26 2004, 16:42:40)
[GCC 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>

The >>> is where you type your input, and you can set and get a variable like this:

>>> python = 'great'
>>> python
'great'
>>>

On line one, the variable python is set to the text great, and on line two that value is read back from the variable simply by typing the name of the variable you want to read. Line three shows Python printing the variable; on line four, you are back at the prompt to type more commands. Python remembers all the variables you use while in the interactive interpreter, which means you can set a variable to be the value of another variable.

When you are done, press Ctrl+D to exit. At this point, all your variables and commands are forgotten by the interpreter, which is why complex Python programs are always saved in scripts!

The way Python handles numbers is more precise than some other languages. It has all the normal operators—such as + for addition, - for subtraction, / for division, and * for multiplication—but it adds % for modulus (division remainder), ** for raise to the power, and // for floor division. It is also very specific about which type of number is being used, as this example shows:

>>> a = 5
>>> b = 10
>>> a * b
50
>>> a / b
0
>>> b = 10.0
>>> a / b
0.5
>>> a // b
0.0

The first division returns 0 because both a and b are integers (whole numbers), so Python calculates the division as an integer, giving 0. By converting b to 10.0, Python considers it to be a floating-point number and so the division is now calculated as a floating-point value, giving 0.5. Even with b being floating-point, using //—floor division—rounds it down.

Using **, you can easily see how Python works with integers:

>>> 2 ** 30
1073741824
>>> 2 ** 31
2147483648L

The first statement raises 2 to the power of 30 (that is, 2 × 2 × 2 × 2 × 2 × ...), and the second raises 2 to the power of 31. Notice how the second number has a capital L on the end of it—this is Python telling you that it is a long integer. The difference between long integers and normal integers is slight but important: Normal integers can be calculated using simple instructions on the CPU, whereas long integers—because they can be as big as you need them to be—need to be calculated in software and therefore are slower.

When specifying big numbers, you need not put the L at the end—Python will figure it out for you. Furthermore, if a number starts off as a normal number and then exceeds its boundaries, Python automatically converts it to a long integer. The same is not true the other way around: If you have a long integer and then divide it by another number so that it could be stored as a normal integer, it remains a long integer:

>>> num = 999999999999999999999999999999999L
>>> num = num / 1000000000000000000000000000000
>>> num
999L

You can convert between number types using typecasting, like this:

>>> num = 10
>>> int(num)
10
>>> float(num)
10.0
>>> long(num)
10L
>>> floatnum = 10.0
>>> int(floatnum)
10
>>> float(floatnum)
10.0
>>> long(floatnum)
10L

You need not worry whether you are using integers or long integers; Python handles it all for you, so you can concentrate on getting the code right.

Python stores strings as an immutable sequence of characters—a jargon-filled way of saying “it is a collection of characters that, once set, cannot be changed without creating a new string.” Sequences are important in Python. There are three primary types, of which strings are one, and they share some properties. Mutability makes much sense when you learn about lists in the next section.

As you saw in the previous example, you can assign a value to strings in Python with just an equal sign, like this:

>>> mystring = 'hello';
>>> myotherstring = "goodbye";
>>> mystring
'hello'
>>> myotherstring;
'goodbye'
>>> test = "Are you really Bill O'Reilly?"
>>> test
"Are you really Bill O'Reilly?"

The first example encapsulates the string in single quotation marks and the second and third in double quotation marks. However, printing the first and second strings shows them both in single quotation marks because Python does not distinguish between the two. The third example is the exception—it uses double quotation marks because the string itself contains a single quotation mark. Here, Python prints the string with double quotation marks because it knows the string contains the single quotation mark.

Because the characters in a string are stored in sequence, you can index into them by specifying the character you are interested in. Like most other languages, these indexes are zero-based, which means you need to ask for character 0 to get the first letter in a string. For example:

>>> string = "This is a test string"
>>> string
'This is a test string'
>>> string[0]
'T'
>>> string [0], string[3], string [20]
('T', 's', 'g')

The last line shows how, with commas, you can ask for several indexes at the same time. You could print the entire first word using this:

>>> string[0], string[1], string[2], string[3]
('T', 'h', 'i', 's')

However, for that purpose you can use a different concept: slicing. A slice of a sequence draws a selection of indexes. For example, you can pull out the first word like this:

>>> string[0:4]
'This'

The syntax there means “take everything from position 0 (including 0) and end at position 4 (excluding it).” So, [0:4] copies the items at indexes 0, 1, 2, and 3. You can omit either side of the indexes, and it will copy either from the start or to the end:

>>> string [:4]
'This'
>>> string [5:]
'is a test string'
>>> string [11:]
'est string'

You can also omit both numbers, and it will give you the entire sequence:

>>> string [:]
'This is a test string'

Later you will learn precisely why you would want to do that, but for now there are a number of other string intrinsics that will make your life easier. For example, you can use the + and * operators to concatenate (join) and repeat strings, like this:

>>> mystring = "Python"
>>> mystring * 4
'PythonPythonPythonPython'
>>> mystring = mystring + " rocks! "
>>> mystring * 2
'Python rocks! Python rocks! '

In addition to working with operators, Python strings come with a selection of built-in methods. You can change the case of the letters with capitalize() (uppercases the first letter and lowercases the rest), lower() (lowercases them all), title() (uppercases the first letter in each word), and upper() (uppercases them all). You can also check whether strings match certain cases with islower(), istitle(), and isupper(); that also extends to isalnum() (returns true if the string is letters and numbers only) and isdigit() (returns true if the string is all numbers).

This example demonstrates some of these in action:

>>> string
'This is a test string'
>>> string.upper()
'THIS IS A TEST STRING'
>>> string.lower()
'this is a test string'
>>> string.isalnum()
False
>>> string = string.title()
>>> string
'This Is A Test String'

Why did isalnum() return false—our string contains only alphanumeric characters, doesn’t it? Well, no. There are spaces in there, which is what is causing the problem. More importantly, we were calling upper() and lower() and they were not changing the contents of the string—they just returned the new value. So, to change our string from This is a test string to This Is A Test String, we actually have to assign it back to the string variable.

Python’s built-in list data type is a sequence, like strings. However, Python’s lists are mutable, which means they can be changed. Lists are like arrays in that they hold a selection of elements in a given order. You can cycle through them, index into them, and slice them:

>>> mylist = ["python", "perl", "php"]
>>> mylist
['python', 'perl', 'php']
>>> mylist + ["java"]
['python', 'perl', 'php', 'java']
>>> mylist * 2
['python', 'perl', 'php', 'python', 'perl', 'php']
>>> mylist[1]
'perl'
>>> mylist[1] = "c++"
>>> mylist[1]
'c++'
>>> mylist[1:3]
['c++', 'php']

The brackets notation is important: You cannot use parentheses (( and )) or braces ({ and }) for lists. Using + for lists is different from using + for numbers. Python detects you are working with a list and appends one list to another. This is known as operator overloading, and it is one of the reasons Python is so flexible.

Lists can be nested, which means you can put a list inside a list. However, this is where mutability starts to matter, and so this might sound complicated! If you recall, the definition of an immutable string sequence is a collection of characters that, once set, cannot be changed without creating a new string. Lists are mutable, as opposed to immutable, which means you can change your list without creating a new list.

This becomes important because Python, by default, copies only a reference to a variable rather than the full variable. For example:

>>> list1 = [1, 2, 3]
>>> list2 = [4, list1, 6]
>>> list1
[1, 2, 3]
>>> list2
[4, [1, 2, 3], 6]

Here we have a nested list. list2 contains 4, and then list1, and then 6. When you print the value of list2, you can see it also contains list1. Now, proceeding on from that:

>>> list1[1] = "Flake"
>>> list2
[4, [1, 'Flake', 3], 6]

In line one, we set the second element in list1 (remember, sequences are zero-based!) to be Flake rather than 2; then we print the contents of list2. As you can see, when list1 changed, list2 was updated also. The reason for this is that list2 stores a reference to list1 as opposed to a copy of list1; they share the same value.

We can show that this works both ways by indexing twice into list2, like this:

>>> list2[1][1] = "Caramello"
>>> list1
[1, 'Caramello', 3]

The first line says, “get the second element in list2 (list1) and the second element of that list, and set it to be 'Caramello'.” Then list1’s value is printed, and you can see it has changed. This is the essence of mutability: We are changing our list without creating a new list. On the other hand, editing a string creates a new string, leaving the old one unaltered. For example:

>>> mystring = "hello"
>>> list3 = [1, mystring, 3]
>>> list3
[1, 'hello', 3]
>>> mystring = "world"
>>> list3
[1, 'hello', 3]

Of course, this raises the question of how you copy without references when references are the default. The answer, for lists, is that you use the [:] slice, which we looked at earlier. This slices from the first element to the last, inclusive, essentially copying it without references. Here is how that looks:

>>> list4 = ["a", "b", "c"]
>>> list5 = list4[:]
>>> list4 = list4 + ["d"]
>>> list5
['a', 'b', 'c']
>>> list4
['a', 'b', 'c', 'd']

Lists have their own collections of built-in methods, such as sort(), append(), and pop(). The latter two add and remove single elements from the end of the list, with pop() also returning the removed element. For example:

>>> list5 = ["nick", "paul", "julian", "graham"]
>>> list5.sort()
>>> list5
['graham', 'julian', 'nick', 'paul']
>>> list5.pop()
'paul'
>>> list5
['graham', 'julian', 'nick']
>>> list5.append("Rebecca")

In addition, one interesting method of strings returns a list: split(). This takes a character to split by and then gives you a list in which each element is a chunk from the string. For example:

>>> string = "This is a test string";
>>> string.split(" ")
['This', 'is', 'a', 'test', 'string']

Lists are used extensively in Python, although this is slowly changing as the language matures.

Unlike lists, dictionaries are collections with no fixed order. Instead, they have a key (the name of the element) and a value (the content of the element), and Python places them wherever it needs to for maximum performance. When defining dictionaries, you need to use braces ({}) and colons (:). You start with an opening brace and then give each element a key and a value, separated by a colon, like this:

>>> mydict = { "perl" : "a language", "php" : "another language" }
>>> mydict
{'php': 'another language', 'perl': 'a language'}

This example has two elements, with keys perl and php. However, when the dictionary is printed, we find that php comes before perl—Python hasn’t respected the order in which we entered them. We can index into a dictionary using the normal code:

>>> mydict["perl"]
'a language'

However, because a dictionary has no fixed sequence, we cannot take a slice, or index by position.

Like lists, dictionaries are mutable and can also be nested; however, unlike lists, you cannot merge two dictionaries by using +. This is because dictionary elements are located using the key. Therefore, having two elements with the same key would cause a clash. Instead, you should use the update() method, which merges two arrays by overwriting clashing keys.

You can also use the keys() method to return a list of all the keys in a dictionary.

So far, we have just been looking at data types, which should show you how powerful Python’s data types are. However, you simply cannot write complex programs without conditional statements and loops.

Python has most of the standard conditional checks, such as > (greater than), <= (less than or equal to), and == (equal), but it also adds some new ones, such as in. For example, we can use in to check whether a string or a list contains a given character/element:

>>> mystring = "J Random Hacker"
>>> "r" in mystring
True
>>> "Hacker" in mystring
True
>>> "hacker" in mystring
False

The last example demonstrates how in is case sensitive. We can use the operator for lists, too:

>>> mylist = ["soldier", "sailor", "tinker", "spy"]
>>> "tailor" in mylist
False

Other comparisons on these complex data types are done item by item:

>>> list1 = ["alpha", "beta", "gamma"]
>>> list2 = ["alpha", "beta", "delta"]
>>> list1 > list2
True

list1’s first element (alpha) is compared against list2’s first element (alpha) and, because they are equal, the next element is checked. That is equal also, so the third element is checked, which is different. The g in gamma comes after the d in delta in the alphabet, so gamma is considered greater than delta and list1 is considered greater than list2.

Loops come in two types, and both are equally flexible. For example, the for loop can iterate through letters in a string or elements in a list:

>>> string = "Hello, Python!"
>>> for s in string: print s,
...
H e l l o ,   P y t h o n !

The for loop takes each letter in string and assigns it to s. The letter is then printed to the screen using the print command, but note the comma at the end; this tells Python not to insert a line break after each letter. The “...” is there because Python allows you to enter more code in the loop; you need to press Enter again here to have the loop execute.

The same construct can be used for lists:

>>> mylist = ["andi", "rasmus", "zeev"]
>>> for p in mylist: print p
...
andi
rasmus
zeev

Without the comma after the print statement, each item is printed on its own line.

The other loop type is the while loop, and it looks similar:

>> while 1: print "This will loop forever!"
...
This will loop forever!
This will loop forever!
This will loop forever!
This will loop forever!
This will loop forever!
(etc)
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
KeyboardInterrupt
>>>

That is an infinite loop (it will carry on printing that text forever), so you have to press Ctrl+C to interrupt it and regain control.

If you want to use multiline loops, you need to get ready to use your Tab key: Python handles loop blocks by recording the level of indent used. Some people find this odious; others admire it for forcing clean coding on users. Most of us, though, just get on with programming!

For example:

>>> i = 0
>>> while i < 3:
...     j = 0
...     while j < 3:
...             print "Pos: " + str(i) + "," + str(j) + ")"
...             j += 1
...     i += 1
...
Pos: (0,0)
Pos: (0,1)
Pos: (0,2)
Pos: (1,0)
Pos: (1,1)
Pos: (1,2)
Pos: (2,0)
Pos: (2,1)
Pos: (2,2)

You can control loops using the break and continue keywords. break exits the loop and continues processing immediately afterward, and continue jumps to the next loop iteration.

Each object has its own set of functions and variables, and you can manipulate those variables independent of objects of the same type. Additionally, some class variables are set to a default value for all classes and can also be manipulated globally.

This script demonstrates two objects of the dog class being created, each with its own name:

class dog(object):
    name = "Lassie"
    def bark(self):
        print self.name + " says 'Woof!'"
    def setName(self, name):
        self.name = name
fluffy = dog()
fluffy.bark()
poppy = dog()
poppy.setName("Poppy")
poppy.bark()

That outputs the following:

Lassie says 'Woof!'
Poppy says 'Woof!'

Each dog starts with the name Lassie, but it gets customized. Keep in mind that Python assigns by reference by default, meaning each object has a reference to the class’s name variable, and as we assign that with the setName() method, that reference is lost. What this means is that any references we do not change can be manipulated globally. Thus, if we change a class’s variable, it also changes in all instances of that class that have not set their own value for that variable. For example:

class dog(object):
    name = "Lassie"
    color = "brown"
fluffy = dog()
poppy = dog()
print fluffy.color
dog.color = "black"
print poppy.color
poppy.color = "yellow"
print fluffy.color
print poppy.color

So, the default color of dogs is brown—both the fluffy and poppy dog objects start off as brown. Then, using dog.color, we set the default color to be black, and because neither of the two objects has set its own color value, they are updated to be black. The third to last line uses poppy.color to set a custom color value for the poppy object—poppy becomes yellow, but fluffy and the dog class in general remain black.

To help you automate the creation and deletion of objects, you can easily override two default methods: __init__ and __del__. These are the methods called by Python when a class is being instantiated and freed, known as the constructor and destructor, respectively.

Having a custom constructor is great when you need to accept a set of parameters for each object being created. For example, you might want each dog to have its own name on creation, and you could implement that with this code:

class dog(object):
    def __init__(self, name):
        self.name = name
fluffy = dog("Fluffy")
print fluffy.name

If you do not provide a name parameter when creating the dog object, Python reports an error and stops. You can, of course, ask for as many constructor parameters as you want, although it is usually better to ask for only the ones you need and have other functions to fill in the rest.

On the other side of things is the destructor method, which allows you to have more control over what happens when an object is destroyed. Using the two, we can show the life cycle of an object by printing messages when it is created and deleted:

class dog(object):
    def __init__(self, name):
        self.name = name
        print self.name + " is alive!"
    def __del__(self):
        print self.name + " is no more!"
fluffy = dog("Fluffy")

The destructor is there to give you the chance to free up resources allocated to the object or perhaps log something to a file.

Python allows you to reuse your code by inheriting one class from one or more others. For example, lions, tigers, and bears are all mammals and so share a number of similar properties. In that scenario, you would not want to have to copy and paste functions between them; it would be smarter (and easier!) to have a mammal class that defines all the shared functionality and then inherit each animal from that.

Consider the following code:

class car(object):
    color = "black"
    speed = 0
    def accelerateTo(self, speed):
        self.speed = speed
    def setColor(self, color):
        self.color = color
mycar = car()
print mycar.color

This creates a car class with a default color and also provides a setColor() function so that people can change their own colors. Now, what do you drive to work? Is it a car? Sure it is, but chances are it is a Ford, or a Dodge, or a Jeep, or some other make—you don’t get cars without a make. On the other hand, you do not want to have to define a class Ford, give it the methods accelerateTo(), setColor(), and however many other methods a basic car has and then do exactly the same thing for Ferrari, Nissan, and so on?

The solution is to use inheritance: Define a car class that contains all the shared functions and variables of all the makes and then inherit from that. In Python, you do this by putting the name of the class from which to inherit inside parentheses in the class declaration, like this:

class car(object):
    color = "black"
    speed = 0
    def accelerateTo(self, speed):
        self.speed = speed
    def setColor(self, color):
        self.color = color

class ford(car): pass
class nissan(car): pass
mycar = ford()
print mycar.color

The pass directive is an empty statement—it means our class contains nothing new. However, because the ford and nissan classes inherit from the car class, they get color, speed, accelerateTo(), and setColor() provided by their parent class. Note that we do not need object after the class names for ford and nissan because they are inherited from an existing class: car.

By default, Python gives you all the methods the parent class has, but you can override that by providing new implementations for the classes that need them. For example:

class modelt(car):
    def setColor(self, color):
        print "Sorry, Model Ts only come in black!"

myford = ford()
ford.setColor("green")
mycar = modelt()
mycar.setColor("green")

The first car is created as a Ford, so setColor() will work fine because it uses the method from the car class. However, the second car is created as a Model T, which has its own setColor() method, so the call will fail.

This provides an interesting scenario: What do you do if you have overridden a method and yet want to call the parent’s method also? If, for example, changing the color of a Model T was allowed but just cost extra, we would want to print a message saying, “You owe $50 more,” but then change the color. To do this, we need to use the class object from which our current class is inherited—car, in this example. Here’s an example:

class modelt(car):
    def setColor(self, color):
        print "You owe $50 more"
        car.setColor(self, color)

mycar = modelt()
mycar.setColor("green")
print mycar.color

That prints the message and then changes the color of the car.

You can inherit as many classes as you need, building up functionality as you go. For example, you could have a class animalia, a subclass chordata, a sub-subclass mammalia, and a sub-sub-subclass homosapiens. Each one is more specific than its parent. However, an interesting addition in Python is the capability to have multiple inheritance—to take functionality from two classes simultaneously.

Again, this is best shown in code:

class car(object):
    def drive(self):
        print "We're driving..."

class timemachine(object):
    def timeTravel(self):
        print "Traveling through time..."

class delorian(car,timemachine): pass
mydelorian = delorian()
mydelorian.drive()
mydelorian.timeTravel()

In that example, we have a class car and a class timemachine. Both work by themselves, so you can have a car and drive around in it or a time machine and travel through time with it. However, we also have a delorian class that inherits from car and timemachine. As you can see, it is able to call both drive() (inherited from car) and timeTravel() (inherited from timemachine).

This introduces another interesting problem: What happens if both car and timemachine have a refuel() function? The answer is that Python picks the correct function to use based on the order in which you listed the parent classes. In the previous code, we use class delorian(car,timemachine), which means “inherit from car and then from timemachine.” As a result, if both classes had a refuel() function, Python would pick car.refuel().

This situation becomes more complex when further inheritance is involved. That is, if car inherits its refuel() method from vehicle, Python still chooses it. What happens behind the scenes is that Python picks the first class you inherited from and searches it and all its parent classes for a matching method call. If it finds none, it goes to the next class and checks it and its parents. This process repeats until it finds a class that has the required method.