Before you start reading this book, you should know the basics of Python programming, including its basic syntax, variable types, data types, tuples, lists, dictionaries, functions, strings, and methods.

We will work with Python version 3.10, available at https://www.python.org/downloads.

The examples and source code for this chapter are available in the GitHub repository at https://github.com/PacktPublishing/Python-for-Security-and-Networking.

Check out the following video to see the Code in Action: https://packt.link/Chapter01.

In this section, we will review different types of data structures, including lists, tuples, and dictionaries. We will see methods and operations for managing these data structures and practical examples where we review the main use cases.

Python lists

Lists in Python are equivalent to structures such as dynamic vectors in programming languages such as C and C++. We can express literals by enclosing their elements between a pair of brackets and separating them with commas. The first element of a list has index 0.

Lists in Python are, used to store sets of related items of the same or different types. Also, a list is a mutable data structure which allows the list content can be modified after it has been created.

To create a list in Python, simply enclose a comma-separated sequence of elements in square brackets []. For example, creating a list with response codes would be done as follows:

>>> responses = [200,400,403,500]

Indexes are used to access an element of a list. An index is an integer that indicates the position of an element in a list. The first element of a list always starts at index 0.

>>> responses[0]
200
>>> responses[1]
400

If an attempt is made to access an index that is outside the range of the list, the interpreter will throw the IndexError exception. Similarly, if an index that is not an integer is used, the TypeError exception will be thrown:

>>> responses[4]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: list index out of range

Consider the following example: a programmer can create a list using the append() method by adding objects, printing the objects, and then sorting them before printing again. We describe a list of protocols in the following example, and use the key methods of a Python list, such as add, index, and remove:

>>> protocolList = []
>>> protocolList.append("FTP")
>>> protocolList.append("SSH")
>>> protocolList.append("SMTP")
>>> protocolList.append("HTTP")
>>> print(protocolList)
['FTP','SSH','SMTP','HTTP']
>>> protocolList.sort()
>>> print(protocolList)
['FTP','HTTP','SMTP','SSH']
>>> type(protocolList)
<type 'list'>
>>> len(protocolList)
4

To access specific positions, we can use the index() method, and to delete an element, we can use the remove() method:

>>> position = protocolList.index('SSH')
>>> print("SSH position"+str(position))
SSH position 3
>>> protocolList.remove("SSH")
>>> print(protocolList)
['FTP','HTTP','SMTP']
>>> count = len(protocolList)
>>> print("Protocol elements "+str(count))
Protocol elements 3

To print out the whole protocol list, use the following instructions. This will loop through all the elements and print them:

>>> for protocol in protocolList:
...     print(protocol)
...
FTP
HTTP
SMTP

Lists also provide methods that help manipulate the values within them and allow us to store more than one variable within them and provide a better way to sort object arrays in Python. These are the techniques commonly used to manage lists:

• .append(value): Appends an element at the end of the list
• .count('x'): Gets the number of 'x' elements in the list
• .index('x'): Returns the index of 'x' in the list
• .insert('y','x'): Inserts 'x' at location 'y'
• .pop(): Returns the last element and removes it from the list
• .remove('x'): Removes the first 'x' from the list
• .reverse(): Reverses the elements in the list
• .sort(): Sorts the list in ascending order

The indexing operator allows access to an element and is expressed syntactically by adding its index in brackets to the list, list [index]. You can change the value of a chosen element in the list using the index between brackets:

protocolList [4] = 'SSH'
print("New list content: ", protocols)

Also, you can copy the value of a specific position to another position in the list:

protocolList [1] = protocolList [4]
print("New list content:", protocols)

The value inside the brackets that selects one element of the list is called an index, while the operation of selecting an element from the list is known as indexing.

Adding elements to a list

Lists are mutable sequences that can be modified, which means items can be added, updated, or removed. To add one or more elements, we can use the extend() method. Also, we can use the insert() method to insert an element in a specific index location. We can add elements to a list by means of the following methods:

• list.append(value): This method allows an element to be inserted at the end of the list. It takes its argument’s value and puts it at the end of the list that owns the method. The list’s length then increases by one.
• list.extend(values): This method allows inserting many elements at the end of the list.
• list.insert(location, value): The insert() method is a bit smarter since it can add a new element at any place in the list, not just at the end. It takes as arguments first the required location of the element to be inserted and then the element to be inserted.

In the following example we are using these methods to add elements to the response code list.

>>> responses.append(503)
>>> responses
[200, 400, 403, 500, 503]
>>> responses.extend([504,505])
>>> responses
[200, 400, 403, 500, 503, 504, 505]
>>> responses.insert(6,300)
>>> responses
[201, 400, 403, 500, 503, 504, 300, 505]

Reversing a list

Another interesting operation that we perform in lists is the one that offers the possibility of getting elements in a reverse way in the list through the reverse() method:

>>> protocolList.reverse()
>>> print(protocolList)
['SMTP','HTTP','FTP']

Another way to do the same operation is to use the -1 index. This quick and easy technique shows how you can access all the elements of a list in reverse order:

>>> protocolList[::-1]
>>> print(protocolList)
['SMTP','HTTP','FTP']

Searching elements in a list

In this example, we can see the code for finding the location of a given element inside a list. We use the range function to get elements inside protocolList and we compare each element with the element to find. When both elements are equal, we break the loop and return the element. To find out if an element is contained in a list, we can use the membership operator in.

>>> 'HTTPS' in protocolList
False
>>> 'HTTP' in protocolList
True

You can find the following code in the search_element_list.py file:

protocolList = ["FTP", "HTTP", "SNMP", "SSH"]
element_to_find = "SSH"
for i in range(len(protocolList)):
    if element_to_find in protocolList[i]:
        print("Element found at index", i)
        break

Now that you know how to add, reverse, and search for elements in a list, let’s move on to learning about tuples in Python.

Python tuples

Like lists, the tuple class in Python is a data structure that can store elements of different types.

Along with the list and range classes, it is one of the sequence types in Python, with the particularity that they are immutable. This means its content cannot be modified after it has been created.

In general, to create a tuple in Python, you simply define a sequence of elements separated by commas. Indices are used to access an element of a tuple. An index is an integer indicating the position of an element in a tuple. The first element of a tuple always starts at index 0.

>>> tuple=("FTP","SSH","HTTP","SNMP")
>>> tuple[0]
'FTP'

If an attempt is made to access an index that is outside the range of the tuple, the interpreter will throw the IndexError exception. Similarly, if an index that is not an integer is used, the TypeError exception will be thrown:

>>> tuple[5]
Traceback (most recent call last):
  File "<input>", line 1, in <module>
IndexError: tuple index out of range

As with lists and all sequential types, it is permissible to use negative indices to access the elements of a tuple. In this case, the index -1 refers to the last element of the sequence, -2 to the penultimate, and so on:

>>> tuple[-1]
'SNMP'
>>> tuple[-2]
'HTTP'

When trying to modify a tuple, we see how we get an error since tuples are immutable objects:

>>> tuple[0]="FTP"
Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment

Now that you know the basic data structures for working with Python, let’s move on to learning about Python dictionaries in order to organize information in the key-value format.

Python dictionaries

The Python dictionary data structure is probably the most important in the entire language and allows us to associate values with keys. Python’s dict class is a map type that maps keys to values. Unlike sequential types (list, tuple, range, or str), which are indexed by a numeric index, dictionaries are indexed by keys. Among the main features of the dictionaries, we can highlight:

• It is a mutable type, that is, its content can be modified after it has been created.
• It is a type that reserves the order in which key-value pairs are inserted.

In Python there are several ways to create a dictionary. The simplest is to enclose a sequence of comma-separated key:value pairs in curly braces {}. In this example we will define the service name as the key and the port number as the value.

>>> services = {"FTP":21, "SSH":22, "SMTP":25, "HTTP":80}

Another way to create a dictionary is using the dict class:

>>> dict(services)
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 80}
>>> type(services)
<class 'dict'>

Accessing an element of a dictionary is one of the main operations for which this type of data exists. Access to a value is done by indexing the key. To do this, simply enclose the key in square brackets. If the key does not exist, the KeyError exception will be thrown.

>>> services['FTP']
21

The dict class also offers the get (key[, default value]) method. This method returns the value corresponding to the key used as the first parameter. If the key does not exist, it does not throw any errors, but returns the second argument by default. If this argument is not supplied, the value None is returned.

>>> services.get('SSH')
22

If the key does not exist, it does not throw any errors, but returns the second argument by default.

>>> services.get('gopher', "service not found")
'service not found'

If this argument is not supplied, the value None is returned.

>>> type(services.get('gopher'))
<class 'NoneType'>

Using the update method, we can combine two distinct dictionaries into one. In addition, the update method will merge existing elements if they conflict:

>>> services = {"FTP":21, "SSH":22, "SMTP":25, "HTTP":80}
>>> services2 = {"FTP":21, "SSH":22, "SMTP":25, "LDAP":389}
>>> services.update(services2)
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 80, 'LDAP': 389}

The first value is the key, and the second the key value. We can use any unchangeable value as a key. We can use numbers, sequences, Booleans, or tuples, but not lists or dictionaries, since they are mutable.

The main difference between dictionaries and lists or tuples is that values contained in a dictionary are accessed by their name and not by their index. You may also use this operator to reassign values, as in the lists and tuples:

>>> services["HTTP"] = 8080
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 8080, 'LDAP': 389}

This means that a dictionary is a set of key-value pairs with the following conditions:

• Each key must be unique: That means it is not possible to have more than one key of the same value.
• A key may be a number or a string.
• A dictionary is not a list: A list contains a set of numbered values, while a dictionary holds pairs of values.
• The len() function: This works for dictionaries and returns the number of key-value elements in the dictionary.

The dict class implements three methods, since they return an iterable data type, known as view objects. These objects provide a view of the keys and values of type dict_values contained in the dictionary, and if the dictionary changes, these objects are instantly updated. The methods are as follows:

• items(): Returns a view of (key, value) pairs from the dictionary.
• keys(): Returns a view of the keys in the dictionary.
• values(): Returns a view of the values in the dictionary.

>>> services.items()
dict_items([('FTP', 21), ('SSH', 22), ('SMTP', 25), ('HTTP', 8080), ('LDAP', 389)])
>>> services.keys()
dict_keys(['FTP', 'SSH', 'SMTP', 'HTTP', 'LDAP'])
>>> services.values()
dict_values([21, 22, 25, 8080, 389])

You might want to iterate over a dictionary and extract and display all the key-value pairs with a for loop:

>>> for key,value in services.items():
...     print(key,value)
... 
FTP 21
SSH 22
SMTP 25
HTTP 8080
LDAP 389

The dict class is mutable, so elements can be added, modified, and/or removed after an object of this type has been created. To add a new item to an existing dictionary, use the assignment operator =. To the left of the operator appears the dictionary object with the new key in square brackets [] and to the right the value associated with said key.

>>> services['HTTPS'] = 443
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 8080, 'LDAP': 389, 'HTTPS': 443}

Now that you know the main data structures for working with Python, let’s move on to learning how to structure our Python code with functions and classes.

Remove an item from a dictionary in Python

In Python there are several ways to remove an element from a dictionary. They are the following:

• pop(key [, default value]): If the key is in the dictionary, it removes the element and return its value; if not, it returns the default value. If the default value is not provided and the key is not in the dictionary, the KeyError exception is raised.
• popitem(): Removes the last key:value pair from the dictionary and returns it. If the dictionary is empty, the KeyError exception is raised.
• del d[key]: Deletes the key:value pair. If the key does not exist, the KeyError exception is thrown.
• clear(): Clears all key:value pairs from the dictionary.

In the following instructions we are removing the elements of the services dictionary using the previous methods:

>>> services = {'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 8080, 'LDAP': 389, 'HTTPS': 443} 
>>> services.pop('HTTPS')
443
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 8080, 'LDAP': 389}
>>> services.popitem()
('LDAP', 389)
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25, 'HTTP': 8080}
>>> del services['HTTP']
>>> services
{'FTP': 21, 'SSH': 22, 'SMTP': 25}
>>> services.clear()
>>> services
{}

In this section, we will review Python functions, classes, and objects in Python scripts. We will review some examples for declaring and using in our script code.

Python functions

A function is a block of code that performs a specific task when the function is invoked. You can use functions to make your code reusable, better organized, and more readable. Functions can have parameters and return values. There are at least four basic types of functions in Python:

• Built-in functions: These are an integral part of Python. You can see a complete list of Python’s built-in functions at https://docs.python.org/3/library/functions.html.
• Functions that come from pre-installed modules.
• User-defined functions: These are written by developers in their own code, and they use them freely in Python.
• The lambda function: This allows us to create anonymous functions that are built using expressions such as product = lambda x,y : x * y, where lambda is a Python keyword and x and y are the function parameters.

In Python, functions include reusable code-ordered blocks. This allows a developer to write a block of code to perform a single action. Although Python offers several built-in features, a developer may build user-defined functionality.

Python functions are defined using the def keyword with the function name, followed by the function parameters. The function’s body is composed of Python statements to be executed. You have the option to return a value to the function caller at the end of the function, or if you do not assign a return value, it will return the None value by default.

For instance, we can define a function that returns True if the item value is found in the dictionary and False otherwise. You can find the following code in the my_function.py file:

def contains(dictionary,item):
    for key,value in dictionary.items():
        if value == item:
            return True
    return False 
dictionary = {1:100,2:200,3:300}
print(contains(dictionary,200))
print(contains(dictionary,300))
print(contains(dictionary,350))

Two important factors make parameters special:

• Parameters only exist within the functions in which they were described, and the only place where the parameter can be specified is in the space between a pair of parentheses in the def state.
• Assigning a value to the parameter is done at the time of the function’s invocation by specifying the corresponding argument.

Python classes

Python is an object-oriented language that allows you to create classes from descriptions and instantiate them. The functions specified inside the class are instance methods, also known as member functions.

Python’s way of constructing objects is via the class keyword. A Python object is an assembly of methods, variables, and properties. Lots of objects can be generated with the same class description. Here is a simple example of a protocol object definition.

You can find the following code in the protocol.py file:

class Protocol(object):
    def __init__(self, name, number,description):
        self.name = name
        self.number = number
        self.description = description
    def getProtocolInfo(self):
        return self.name+ " "+str(self.number)+ " "+self.description

The init method is a special method that acts as a constructor method to perform the necessary initialization operation. The method’s first parameter is a special keyword, and we use the self-identifier for the current object reference. The self keyword is a reference to the object itself and provides a way for its attributes and methods to access it.

An object is a set of requirements and qualities assigned to a specific class. Classes form a hierarchy, which means that an object belonging to a specific class belongs to all the superclasses at the same time.

To build an object, write the class name followed by any parameter needed in parentheses. These are the parameters that will be transferred to the init method, which is the process that is called when the class is instantiated:

>>> https_protocol= Protocol("HTTPS", 443, "Hypertext Transfer Protocol Secure")

Now that we have created our object, we can access its attributes and methods through the object.attribute and object.method() syntax:

>>> protocol_http.getProtocolInfo()
HTTPS 443 Hypertext Transfer Protocol Secure

In summary, object programming is the art of defining and expanding classes. A class is a model of a very specific part of reality, reflecting properties and methods found in the real world. The new class may add new properties and new methods, and therefore may be more useful in specific applications.

Python inheritance

In the previous code, we can see a method with the name __init__, which represents the class constructor. If a class has a constructor, it is invoked automatically and implicitly when the object of the class is instantiated. This method allows us to initialize the internal state of an object when we create an object of a class.

Python inheritance is an important concept in object-oriented programming languages. This feature means creating a new class that inherits all the functionality of the parent class and allows the new class to add additional functionality to the base functionality.

In object-oriented terminology, when class “X” is inherited by class “Y”, “X” is called a Super Class or Base Class and “Y” is called a Subclass or Derived Class. One more fact to keep in mind is that only the fields and methods that are not private are accessible by the Derived Class. Private fields and methods are only accessible by the class itself.

Single inheritance occurs when a child class inherits the attributes and methods of a single parent class. The following is an example of simple inheritance in Python where we have a base class and a child class that inherits from the parent class. Note the presence of the __init__ method in both classes , which allows you to initialize the properties of the class as an object constructor.

You can find the following code in the Inheritance_simple.py file.

class BaseClass:
    def __init__(self, property):
        self.property = property
    def message(self):
        print('Welcome to Base Class')
    def message_base_class(self):
        print('This is a message from Base Class')
 
class ChildClass(BaseClass):
    def __init__(self, property):
        BaseClass.__init__(self, property)
    def message(self):
        print('Welcome to ChildClass')
        print('This is inherited from BaseClass')

In our main program we declare two objects, one of each class, and we call the methods defined in each of the classes. Also, taking advantage of the inheritance features, we call the method of the parent class using an object of the child class.

if __name__ == '__main__':
    base_obj = BaseClass('property')
    base_obj.message()
    child_obj = ChildClass('property')
    child_obj.message()
    child_obj.message_base_class()

Two built-in functions, isinstance() and issubclass(), are used to check inheritances. One of the methods that we can use to check if a class is a subclass of another is through the issubclass() method. This method allows us to check if a subclass is a child of a superclass and returns the Boolean True or False depending on the result.

>>> print(issubclass(ChildClass, BaseClass))
>>> True
>>> print(issubclass(BaseClass, ChildClass))
>>> False

In the same way, the isinstance() method allows you to check if an object is an instance of a class. This method returns True if the object is the instance of the class that is passed as the second parameter. The syntax of this special method is isinstance(Object,Class).

>>> print(isinstance(base_obj, BaseClass))
>>> True
>>> print(isinstance(child_obj, ChildClass))
>>> True
>>> print(isinstance(child_obj, BaseClass))
>>> True

Multiple inheritance occurs when a child class inherits attributes and methods from more than one parent class. We could separate both main classes with a comma when creating the secondary class. In the following example we are implementing multiple inheritance where the child class is inheriting from the MainClass and MainClass2 classes.

You can find the following code in the Inheritance_multiple.py file.

class MainClass:
    def message_main(self):
        print('Welcome to Main Class')
class MainClass2:
    def message_main2(self):
        print('Welcome to Main Class2')
class ChildClass(MainClass,MainClass2):
    def message(self):
        print('Welcome to ChildClass')
        print('This is inherited from MainClass and MainClass2')

Our main program creates an object of the Child class, on which we could access both methods of the parent classes.

if __name__ == '__main__':
    child_obj = ChildClass()
    child_obj.message()
    child_obj.message_main()
    child_obj.message_main2()

Python also supports multilevel inheritance, which allows the child class to have inheritance below it. That means the base class is the parent class of all sub-classes and inheritance goes from parent to child. In this way, child classes can access properties and methods from parent classes, but parent classes cannot access the properties of the child class.

In the following example we are implementing multilevel inheritance where the child class is inheriting from the MainClass and we add another level of inheritance with the ChildDerived class, which is inheriting from the Child class. You can find the following code in the Inheritance_multilevel.py file.

class MainClass:
    def message_main(self):
        print('Welcome to Main Class')
class Child(MainClass):
    def message_child(self):
        print('Welcome to Child Class')
        print('This is inherited from Main')
class ChildDerived(Child):
    def message_derived(self):
        print('Welcome to Derived Class')
        print('This is inherited from Child')

In the previous code we first create a main class and then create a child class that is inherited from Main and create another class derived from the child class. We see how the child_derived_obj object is an instance of each of the classes that are part of the hierarchy. In multilevel inheritance, the features of the base class and the derived class are inherited into the new derived class. In our main program we declare a child-derived object and we call the methods defined in each of the classes.

if __name__ == '__main__':
    child_derived_obj = ChildDerived()
    child_derived_obj.message_main()
    child_derived_obj.message_child()
    child_derived_obj.message_derived()
    print(issubclass(ChildDerived, Child))
    print(issubclass(ChildDerived, MainClass))
    print(issubclass(Child, MainClass))
    print(issubclass(MainClass, ChildDerived))
    print(isinstance(child_derived_obj, Child))
    print(isinstance(child_derived_obj, MainClass))
    print(isinstance(child_derived_obj, ChildDerived))

When executing the previous script, we see how from the ChildDerived class we can call the methods from the Child and Main classes. Also, with the issubclass() and isinstance() methods we can check whether the child_derived_obj object is a subclass and instance of the higher classes within the management hierarchy.

Advantages of Python inheritance

One of the main advantages is code reuse, allowing us to establish a relationship between classes, avoiding the need to re-declare certain methods or attributes.

Classes allow us to build objects on top of a collection of abstractly defined attributes and methods. And the ability to inherit will allow us to create larger and more capable child classes by inheriting multiple attributes and methods from others as well as more specific controlling the same for a single class.

The following are some benefits of using inheritance in Python’s object-oriented programming:

• Python inheritance provides code reusability, readability, and scalability.
• Reduce code repetition. You can define all the methods and attributes in the parent class that are accessible by the child classes.
• By dividing the code into multiple classes, identifying bugs in applications is easier.