Understanding Data Modelling in Python: 4 Critical Aspects

In the Python programming language, each entity is treated as an object. Moreover, unlike other programming languages like C or Java, Python does not work with primitive data or non-primitive data types. Everything whether it be integer, float, string, function, or list in python is represented by objects or relationships between objects. Python Data Model is composed of these objects and it is one of the main reasons make Python a great and widely used programming language.

This article will introduce you to Python programming language and explain Data Modelling in Python. It will further discuss the identity, type and value of objects involved in Data Modelling. Read along to learn the basics of Data Modells and how to work with them in Python.

What is Python Programming Language?

Python is a simple and robust GPP(General-purpose Programming) Language that is preferred by modern-day developers. Its small learning curve and its ability to provide users with complex functionality, give it an edge over its peers such as Java, C++, etc. The Python programming language contains various in-built libraries that can directly deploy both logical and mathematical functions into your program. 

Python’s robust programming model makes it the first choice of developers today. Moreover, its strong performance power and advanced features, make it fit for use in applications related to Software Development, Data Analysis & Visualization, Data Automation and much more.

Explore more about the Python programming language.

What is Data Modelling in Python?

The process of creating Data Models using the syntax and environment of the Python programming language is called Data Modelling in Python. A Data Model is a data abstraction model that organizes different elements of data and standardizes the way they relate to one another and to the properties of real-world entities. In simple words, Data Modelling in Python is the general process by which this programming language organizes everything internally and it treats and processes data.

The Data Model is the building block of Python. Internally Data Model has its design and code blocks for its implementation. It is composed of entities, and entities are none but the objects. It is said that everything is an object in Python. Each entity has its own attributes (properties) and methods(actions) or behaviour associated with it. Each object has three attributes: an identity, a type, and a value. Let’s learn them in detail.

Identity of an object

Every object, either for Data Modelling in Python or any other activity involving Python’s environment, has an identity which can never change once it is created. Think of identity as an object’s address in the memory. 

id() is the function that gives us the identity of the object or we can say it returns the virtual memory address of the object. The memory address will be an integer value.

Python Code:

>>> a='hevodata'
>>> id(a)
1397871458544

>>> b=101
>>> id(b)
1623965024

>>> c='hevodata'
>>> id(c)
1397871458544

From the above code of Data Modelling in Python, we can see variables ‘a’ and ‘b’ have different memory addresses but ‘a’ and ‘c’ have the same memory address as they have the same value.

We can check if two objects are identified using the ‘is’ operator. The ‘is’ operator basically compares the identity of two objects. If the identity of two variables is the same then it returns ‘True’ otherwise ‘False’.

Python Code:

>>> a is b
False

>>> a is c
True

From the above code and output, it is clear that variables ‘a’ and ‘c’ have the same identity while ‘b’ has a different identity than ‘a’ and ‘c’. This is how the identity of an object is decided during Data Modelling in Python.

Type of an Object

During Data Modelling in Python, the type of an object means the name of the class to which the object belongs. Function type() tells the type of the object. By knowing the type of an object, it is easy for user’s to specify two things.

1. The operation allowed on that object
2. The set of values the object can hold.

The type of an object cannot change but under certain controlled conditions, it is possible to change the type of an object. Although it is not a good idea and not advisable as well.

Python Code:

>>> a='hevodata'
>>> x=type(a)
>>> print("Type of variable 'a' is: ", x)
Type of variable 'a' is:  <class 'str'>

>>> b=101
>>> y= type(b)
>>> print("Type of variable 'b' is: ", y)
Type of variable 'b' is:  <class 'int'>

>>> fruits = ('apple', 'banana', 'grapes', 'orange')
>>> t = type(fruits)
>>> print("Type of variable 'fruits' is: ", t)
Type of variable 'fruits' is:  <class 'tuple'>

From the above Python code and its output, you can see types of the different objects as different classes like ‘str’, ‘int’, and ‘tuple’. Explore more classes in Python to get a better understanding.

Value of an Object

An object’s value during Data Modelling in Python is the data that is stored for that object. The value object can hold is decided on the basis of the type of object.

Python Code:

>>> var='article'
>>> print("Value of variable 'var' is: ", var)
Value of variable 'var' is:  article

In the above code, ‘var’ is the variable and ‘article’ is the value of ‘var’.

Object values are changeable and it depends on their type. Python supports the following 2 types of objects based on their values:

• Mutable Objects
• Immutable Objects

There is some type for which the value of an object cannot change those are called immutable objects and whose value can be changed are called mutable objects.

1) Mutable Objects

The mutability of objects is decided on the basis of their type. Lists, Dictionaries are mutable objects. Those objects whose values can be changed are called Mutable objects.

The following Python code is useful for creating a list for Data Modelling in Python:

#Let's create a list
>>> a = [11, 22, 33]
>>> print("List values: ",a)
>>> print("Identity of a: ",id(a))
List values:  [11, 22, 33]
Identity of a:  1397871407048

>>> a[0] = 1 #Change first value of list
>>> print("Changed List values: ",a)
>>> print("Identity of a: ",id(a))
Changed List values:  [1, 22, 33]
Identity of a:  1397871407048

From the above code, you can see the identity of the list remains the same but the value of the list changed. So, the list is mutable.

2) Immutable Objects

During Data Modelling in Python, Immutable Objects are the objects that stored data but their values cannot be modified. Numbers, Strings, Tuples, and Sets are immutable. 

The following Python code is useful for creating a variable with string value during Data Modelling in Python:

#Let's create a varible with string value
s = "Hevo"
print("Variable value: ",s)
print("Identity of s: ",id(s))

Variable value:  Hevo
Identity of s:  1397871732528

s = "Data" #Change value of varibale 's'
print("Variable value: ",s)
print("Identity of s: ",id(s))

Variable value:  Data
Identity of s:  1397836021296

From the above code, if you change the value of a string variable, its identity changes. It means the value of the object at id ‘1397871732528’ does not change but a new variable with the same name but a different value is created at memory address ‘1397836021296’. So, you can conclude strings are immutable.

Special Methods for Data Modelling in Python

Understanding special methods for Data Modelling in Python are very important for you as a data professional. Special methods describe the internal functioning of the basic object operations. The special methods name starts and trails with two underscores. Another name for special methods is the dunder methods or magic methods.

The special method names allow your objects to implement, support, and interact with basic language constructs such as Iteration, object creation, object destruction, collections, attribute access, etc.

Below are some of the examples of special methods for Data Modelling in Python which help to understand how these built-ins work in Python.

• The  __init__() method is for initialization and is called by the python interpreter itself when an instance of an object is created.
• The  len(x) method is for the length calculation of an object, internally the python interpreter calls x.__len()
• Call x[2] to get an item at location 2, internally the python interpreter calls x.__getitem__(2)
• When str(x) is called, internally the python interpreter, calls x.__str__()
• Operators are also magic methods, add operator x + y actually turns to x.__add__(y)

You can also write your own class with your own special methods for Data Modelling in Python. The below example shows a code to get a particular item on a list and find the length of the list and better understanding.

class MyList:
    def __init__(self, *args):
            self._data = list(args)

    def __getitem__(self, index):
        out = self._data[index]
        return (out)

    def __len__(self):
        return len(self._data)


x = MyList(11, 22, 33, 4, 5) #List initialization

# Get length of list
print("Length of list: ",len(x))

# Get an item of list
print("Item of list: ", x[2])

Output:

Length of list:  5
Item of list:  33

In the above example, we have seen the implementation of three special methods, initialization, length, and getting an item. The above code is just an example. Programmers can implement many more functions like these magic methods and excel at Data Modelling in Python.

Conclusion

This article introduced you to Python programming language and explained Data Modelling in Python. These Data Models in Python are so versatile and fun to work with. Implementing special methods in the data model make it even more special.

It opens doors for many new and flexible ways of code implementations. Implementing data modelling with special methods make objects work with Python built-ins and gives a rich experience to programmers.

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