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The self variable in Python explained with Python tips

If you have been working on Python, you might have across the self variable. You can find it in method definitions and in initializing variables. However, before coming to the self variable, let us have an idea about classes and instances in Python.  What are Instance methods and Class methods in Python? You might have heard of instances and classes while working on Python. Class variables are defined within a class and they are shared with all the instances (objects) of the class whereas instance variables are owned by the instances of a class. For different instances, the instance variables are different. Likewise, Python also contains class methods and instance methods. The class methods inform about the status of the class. On the other hand, instance methods are to set or get details about instances or objects.   If you want to define an instance method, the foremost parameter of the method should always have to be self. Let us understand this with the help of example code – class myClass:      def instance_method(self):          return “Instance method is called”, self The method “instance_method” is a regular instance method. The method accepts one single parameter – self. The self variable points to an instance of the class myClass when the method is revoked. Though the method takes only one parameter here, it can also accept more than one parameter. The instance methods can easily access different attributes and other methods on the same object with the help of the self variable. The self variable also has the power to modify the state of an object and using the self.__class__  attribute, instance methods can also access the class. Thus, the instance methods are also able to modify the class state. Now, let us see what happens when we call “instance_method”: >>>obj = myClass()  >>>obj.instance_method()  ('Instance method is called', <myClass instance at 1x09255d103>) This shows that “instance_method” can access the object instance (printed as <myClass instance>) through the self parameter. What happens is that the self parameter is replaced with the instance object obj when the method is called. However, if you pass the instance object manually, you will get the same result as before: >>>myClass.instance_method(obj)  ('Instance method is called', <myClass instance at 1x09255d103>) Note that self is actually not a defined keyword in Python but a convention.What is Self in Python? Unlike this variable in C++, self is not a keyword rather it is more of a coding convention. It represents the instance or objects of a class and binds the attributes of a class with specific arguments. The use of self variable in Python helps to differentiate between the instance attributes (and methods) and local variables. If you do not want the variables of your class to be shared by all instances of the class, you can declare variables within your class without self. Let us understand this with an example: class Car:      def __init__(self, model):  self.model = model      def Car_info(self):  print("Model : ", self.model) Here, we have declared a class Car with one instance variable self.model = model. The value of the instance variable will be unique to the instance objects of the class that might be declared. However, if you want the variables to be shared by all instances of the class, you need to declare the instance variables without self. Otherwise, it would be ambiguous since all cars will have the same model. Need for Self in Python The self variable is used to represent the instance of the class which is often used in object-oriented programming. It works as a reference to the object. Python uses the self parameter to refer to instance attributes and methods of the class.  Unlike other programming languages, Python does not use the “@” syntax to access the instance attributes. This is the sole reason why you need to use the self variable in Python. The language contains methods that allow the instance to be passed automatically but not received automatically. Explicit definition of self The Zen of Python says “Explicit is better than Implicit”. Programmers of other languages often ask why self is passed as an explicit parameter every time to define a method. There are a few reasons for this. Firstly, since Python uses a method or instance attribute instead of a local variable when we read self.name or self.age, it makes it absolutely clear you are using an instance variable or method even if you have no knowledge about the class definition. Secondly, if you explicitly refer or call a method from a particular class in Python, you do not need to use any special syntax for that.  Finally, the third reason is that the explicit definition of self helps Python understand whether to assign to an instance variable or to a local variable. In simpler terms, local variables and instances exist in two separate namespaces and we need to inform Python which namespace should be used. What is a Python class self constructor? The self variable in Python can also be used to access a variable field within the class definition. Let us understand this with the help of example code: class Student:     def __init__(self, Alex):          self.name = Alex    #name created in constructor      def get_student_name(self):          return self.name In the example above, self refers to the variable age of the class Student. The variable age is local to the method. While the method is running, the variable age exists within the class. If there is a variable within a method, the self variable will not work. If you wish to define global fields, you need to define variables outside the class methods.   Is self a keyword in Python? There is a question that always hovers among Python programmers. Is self actually a keyword in Python? Unlike other programming languages like C++, where self is considered to be a keyword, in Python it is a convention that programmers tend to follow. It is basically a parameter in a method definition. However, you can use any other name in place of self like another or me or anything else for the first parameter of a method. Another reason why it is suggested by most people is that it enhances the readability of your code. Let us see an example to understand it: class myClass:   def show(another):   print(“another is used in place of self”)  If you compare this code with the code for the Python class self constructor, you will notice that here we have used the name another in place of self. Now let us create an object of this class and see the output: object = myClass()   object.show()another is used in place of selfYou can see that the program works even if we use some other name in place of the self variable. It works exactly the same way as the self variable does. Why "self" should be used as the first parameter of instance methods in python  This can be understood by the below example. We have a Rectangle class which defines a method area to calculate the area : class Rectangle ():      def __init__(self,x = 0,y = 0):          self.x = x          self.y = y      def area (self):          """Find area of rectangle"""          return (self.x * self.y)  rec1=Rectangle(6,10)  print ("Area is:", rec1.area()) Output:  Area is: 60    In the above example, __init__() defines three parameters but only 2 arguments are passed (6 and 10). Similarly, area () requires one but no arguments are passed.  Rectangle.area and rec1.area in the above example are different and not exactly the same. >>> type(Rectangle.area )  <class 'function'>  >>> type(rec1.area)  <class 'method'> Here, the first one is a function and the second one is a method. A unique feature of Python is that the object itself is passed as the first argument to the corresponding function. In the above example, the method call:  rec1.area()is equivalent to:  Rectangle.area(rec1). Generally, when the method is called with some arguments, the corresponding class function is called by placing the method's object before the first argument.  Therefore: obj.method(args) becomes Class.method(obj, args). This is the reason the first parameter of a function in a class must be the object itself. Writing this parameter as self is merely a convention and not a keyword so it has no special meaning in Python. We could use other names (like this, that) but it is not preferred as it degrades code readability.Should we pass self to a method? Since we can use any other name instead of using the self variable, then what will happen if we just pass self to a method definition. Let us consider the class myClass we have used earlier.  A method named something is defined within the class with a parameter another and two arguments: class myClass:   def something(another, argument1, argument2):   pass Now, let us declare an instance obj of myClass and call the method something with the help of the instance object: obj = myClass()  obj.something(argument1, argument2) Python performs an internal work on the method call and converts it into something like this: myClass.something(obj, argument1, argument2)  This shows that another variable (used in place of self) refers to the instance object of the class. Note that the pass keyword used in the method something does nothing. It is used as a dummy in situations where you do not want any operation to be performed but there is a syntax requirement of a certain programming element. How can we skip self in Python? Consider a situation where the instance method does not need to have access to the instance variables. In such cases, we can consider skipping the self variable in defining methods. Let us have a clear understanding of the fact with example code: class Vehicle:  def Car():  print(“Rolls Royce 1948”)  obj = Vehicle()  print(“Complete”) If you run the following code, the output will be as follows: Complete We have not declared the self variable here but there is still no error in the program and the output comes out fine. However, what will be the case if we call the Car() method: obj = Vehicle()  obj.Car() When we compile the code after calling the Car() method, it shows an error like this: Traceback (most recent call last):   File "<string>", line 11, in <module>  TypeError: Car() takes 0 positional arguments but 1 was given The output shows an error since the method Car() takes 0 positional arguments but we have given 1 positional argument to it. This is because when the instance obj is created, it is automatically passed as the first argument to the method Car() even if we have not declared the self variable. However, if you try to access the instance method Car() with the help of the class reference, there will be no errors and the program will work fine: class Vehicle:  def Car():  print("Rolls Royce 1948")  obj = Vehicle()  Vehicle.Car()  Rolls Royce 1948 Difference between self and __init__  self : self represents the instance of the class. By using the "self" keyword all the attributes and methods of the python class can be accessed. __init__ : "__init__" is a reserved method in python classes. It is known as a constructor in object oriented concepts. This method is called when an object is created from the class and allows the class to initialize class attributes .. Usage of "self" in class to access the methods and attributes: class Rectangle:     def __init__(self, length, breadth, cost_per_unit =0):         self.length = length         self.breadth = breadth         self.cost_per_unit = cost_per_unit     def perimeter(self):         return 2 * (self.length + self.breadth)     def area(self):         return self.length * self.breadth     def calculate_cost(self):         area = self.area()         return area * self.cost_per_unit  # length = 40 cm, breadth = 30 cm and 1 cm^2 = Rs 100  r = Rectangle(40, 30, 100)  print("Area of Rectangle:",r.area())  print("Cost of rectangular field is : Rs ",r.calculate_cost()) Output:  Area of Rectangle: 1200  Cost of rectangular field is : Rs  120000 We have created an object of Rectangle class. While creating the Rectangle object, we passed 3 arguments – 40,30,100; all these arguments are passed to "__init__"method to initialize the object. Here, the keyword "self” represents the instance of the class. It binds the attributes with the given arguments. Self represents the same object or instance of the class. If you see, inside the method "area” , self.length" is used to get the value of the attribute "length".  attribute "length" is bind to the object (instance of the class) at the time of object creation. "self" represents the object inside the class. "self" works just like "r" in the statement “r = Rectangle(40,30, 100)".  If you see the method definition "def area(self): ” , here "self" is used as a parameter in the method because whenever we call the method,  the object (instance of class) is automatically passed as a first argument along with other arguments of the method. If no other arguments are provided only "self" is passed to the method. That's the reason "self" is used to call the method inside the class("self.area()").  We used object (instance of class) to call the method outside of the class definition("r.area()").  "r" is the instance of the class when the method "r.area()” is called; the instance "r" is passed as first argument in the place of self. Miscellaneous Implementations of self Let us now discuss some of the miscellaneous implementations of the self variable. Similar variables for Class Method and Static Method A class method is a method that is bound to the class. Let us understand a class method with an example – class myClass:  @classmethod  def classmethod(cls):  return “Class Method is called”  obj.classmethod() The same behavior of the self variable is present with the Class methods too but the only difference is that for class methods, the convention is to use cls as the variable name instead of self. The class methods take a cls parameter instead of the self parameter. When the method is called, it points to the class. The class method cannot modify the object state but it can modify the class state of all the class instances. On the other hand, static methods are self-sufficient functions and this type of method takes neither a self nor a cls parameter. Let us see an example of a static method – class myClass:  @staticmethod  def staticmethod():  return “Static Method is called”  obj.staticmethod() Since a static method does not accept any parameter, they cannot modify object state or even class state. They are primarily used to namespace different methods and Python restricts them in the data they can access. Note that both the methods here are marked with @classmethod and @staticmethod decorators to flag it as a class method and static method respectively. The self variable is bound to the current instance The self variable allows us to access the properties of the current instance. Let us understand this with an example – class Person:  def __init__(self, n):  self.name = n  def walk(self):  print(f“{self.name} is walking”)  obj1 = Person(“Alex”)  obj2 = Person(“Charles”)  obj1.walk()  obj2.walk()  Alex is walking Charles is walking Here, we have a class Person with two methods __init__ and walk declared with the self parameter. We have created two different instances of the class – obj1 and obj2. When the first instance is revoked, “Alex” is printed with the method walk() whereas when the second instance is revoked, “Charles” gets printed with the properties of the instance method walk(). Tips about the Python self variable Since we have now reached the end of the article, let me give you some tips about when to use and when not to use the self variable in Python. Use self when:you define an instance method, since it is passed automatically as the first parameter when the method is called;you reference a class or an instance attribute from inside an instance method;you want to refer to instance variables and methods from other instance methods. Don’t use self when: you want to call an instance method normally;referencing a class attribute inside the class definition but outside an instance method;you are inside a static method.  Conclusion Let us recap the key points we have covered in this article, namely:Instances and Classes in Python. Self variable and its importance. The explicitness of the self variable. Python class self constructor. Passing self as a method. Skipping self in Python. Variables used for Class methods and Static methods. Bounding of self to the current instance. When to use and when not to use self in Python. With good knowledge about the self variable in Python and its internal working in Python, it is now time for some practice. If you, however, wish to know more about Python self, you can head right on to the official documentation of Python.Happy coding! 
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The self variable in Python explained with Python tips

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The self variable in Python explained with Python tips

If you have been working on Python, you might have across the self variable. You can find it in method definitions and in initializing variables. However, before coming to the self variable, let us have an idea about classes and instances in Python.  

What are Instance methods and Class methods in Python? 

You might have heard of instances and classes while working on Python. Class variables are defined within a class and they are shared with all the instances (objects) of the class whereas instance variables are owned by the instances of a class. For different instances, the instance variables are different. 

Likewise, Python also contains class methods and instance methods. The class methods inform about the status of the class. On the other hand, instance methods are to set or get details about instances or objects.  

 If you want to define an instance method, the foremost parameter of the method should always have to be self. Let us understand this with the help of example code – 

class myClass: 
    def instance_method(self): 
        return “Instance method is called”, self 

The method “instance_method” is a regular instance method. The method accepts one single parameter – self. The self variable points to an instance of the class myClass when the method is revoked. Though the method takes only one parameter here, it can also accept more than one parameter. 

The instance methods can easily access different attributes and other methods on the same object with the help of the self variable. The self variable also has the power to modify the state of an object and using the self.__class__  attribute, instance methods can also access the class. Thus, the instance methods are also able to modify the class state. 

Now, let us see what happens when we call “instance_method”: 

>>>obj = myClass() 
>>>obj.instance_method() 
('Instance method is called', <myClass instance at 1x09255d103>) 

This shows that “instance_method” can access the object instance (printed as <myClass instance>) through the self parameter. What happens is that the self parameter is replaced with the instance object obj when the method is called. However, if you pass the instance object manually, you will get the same result as before: 

>>>myClass.instance_method(obj) 
('Instance method is called', <myClass instance at 1x09255d103>) 

Note that self is actually not a defined keyword in Python but a convention.

What is Self in Python? 

Unlike this variable in C++, self is not a keyword rather it is more of a coding convention. It represents the instance or objects of a class and binds the attributes of a class with specific arguments. The use of self variable in Python helps to differentiate between the instance attributes (and methods) and local variables. 

If you do not want the variables of your class to be shared by all instances of the class, you can declare variables within your class without self. Let us understand this with an example: 

class Car: 
    def __init__(self, model): 
self.model = model 
    def Car_info(self): 
print("Model : ", self.model) 

Here, we have declared a class Car with one instance variable self.model = model. The value of the instance variable will be unique to the instance objects of the class that might be declared. However, if you want the variables to be shared by all instances of the class, you need to declare the instance variables without self. Otherwise, it would be ambiguous since all cars will have the same model. 

Need for Self in Python 

The self variable is used to represent the instance of the class which is often used in object-oriented programming. It works as a reference to the object. Python uses the self parameter to refer to instance attributes and methods of the class.  

Unlike other programming languages, Python does not use the “@” syntax to access the instance attributes. This is the sole reason why you need to use the self variable in Python. The language contains methods that allow the instance to be passed automatically but not received automatically. 

Explicit definition of self 

The Zen of Python says “Explicit is better than Implicit”. Programmers of other languages often ask why self is passed as an explicit parameter every time to define a method. There are a few reasons for this. 

Firstly, since Python uses a method or instance attribute instead of a local variable when we read self.name or self.age, it makes it absolutely clear you are using an instance variable or method even if you have no knowledge about the class definition. 

Secondly, if you explicitly refer or call a method from a particular class in Python, you do not need to use any special syntax for that.  

Finally, the third reason is that the explicit definition of self helps Python understand whether to assign to an instance variable or to a local variable. In simpler terms, local variables and instances exist in two separate namespaces and we need to inform Python which namespace should be used. 

What is a Python class self constructor? 

The self variable in Python can also be used to access a variable field within the class definition. Let us understand this with the help of example code: 

class Student: 

    def __init__(self, Alex): 
        self.name = Alex    #name created in constructor 
    def get_student_name(self): 
        return self.name 

In the example above, self refers to the variable age of the class Student. The variable age is local to the method. While the method is running, the variable age exists within the class. 

If there is a variable within a method, the self variable will not work. If you wish to define global fields, you need to define variables outside the class methods.   

Is self a keyword in Python? 

There is a question that always hovers among Python programmers. Is self actually a keyword in Python? 

Unlike other programming languages like C++, where self is considered to be a keyword, in Python it is a convention that programmers tend to follow. It is basically a parameter in a method definition. However, you can use any other name in place of self like another or me or anything else for the first parameter of a method. Another reason why it is suggested by most people is that it enhances the readability of your code. 

Let us see an example to understand it: 

class myClass:  
def show(another):  
print(“another is used in place of self”)  

If you compare this code with the code for the Python class self constructor, you will notice that here we have used the name another in place of self. Now let us create an object of this class and see the output: 

object = myClass()  
object.show()

another is used in place of self

You can see that the program works even if we use some other name in place of the self variable. It works exactly the same way as the self variable does. 

Why "self" should be used as the first parameter of instance methods in python  

This can be understood by the below example. We have a Rectangle class which defines a method area to calculate the area : 

class Rectangle (): 
    def __init__(self,x = 0,y = 0): 
        self.x = x 
        self.y = y 
    def area (self): 
        """Find area of rectangle""" 
        return (self.x * self.y) 
rec1=Rectangle(6,10) 
print ("Area is:", rec1.area()) 

Output:  

Area is: 60    

In the above example, __init__() defines three parameters but only 2 arguments are passed (6 and 10). Similarly, area () requires one but no arguments are passed.  

Rectangle.area and rec1.area in the above example are different and not exactly the same. 

>>> type(Rectangle.area ) 
<class 'function'> 
>>> type(rec1.area) 
<class 'method'> 

Here, the first one is a function and the second one is a method. A unique feature of Python is that the object itself is passed as the first argument to the corresponding function. 

In the above example, the method call:  rec1.area()is equivalent to:  Rectangle.area(rec1). 

Generally, when the method is called with some arguments, the corresponding class function is called by placing the method's object before the first argument.  

Therefore: obj.method(args) becomes Class.method(obj, args)

This is the reason the first parameter of a function in a class must be the object itself. Writing this parameter as self is merely a convention and not a keyword so it has no special meaning in Python. We could use other names (like this, that) but it is not preferred as it degrades code readability.

Should we pass self to a method? 

Since we can use any other name instead of using the self variable, then what will happen if we just pass self to a method definition. Let us consider the class myClass we have used earlier.  

A method named something is defined within the class with a parameter another and two arguments: 

class myClass:  
def something(another, argument1, argument2):  
pass 

Now, let us declare an instance obj of myClass and call the method something with the help of the instance object: 

obj = myClass() 
obj.something(argument1, argument2) 

Python performs an internal work on the method call and converts it into something like this: 

myClass.something(obj, argument1, argument2)  

This shows that another variable (used in place of self) refers to the instance object of the class. 

Note that the pass keyword used in the method something does nothing. It is used as a dummy in situations where you do not want any operation to be performed but there is a syntax requirement of a certain programming element. 

How can we skip self in Python? 

Consider a situation where the instance method does not need to have access to the instance variables. In such cases, we can consider skipping the self variable in defining methods. Let us have a clear understanding of the fact with example code: 

class Vehicle: 
def Car(): 
print(“Rolls Royce 1948”) 
obj = Vehicle() 
print(“Complete”) 

If you run the following code, the output will be as follows: 

Complete 

We have not declared the self variable here but there is still no error in the program and the output comes out fine. However, what will be the case if we call the Car() method: 

obj = Vehicle() 
obj.Car() 

When we compile the code after calling the Car() method, it shows an error like this: 

Traceback (most recent call last): 
  File "<string>", line 11, in <module> 
TypeError: Car() takes 0 positional arguments but 1 was given 

The output shows an error since the method Car() takes 0 positional arguments but we have given 1 positional argument to it. This is because when the instance obj is created, it is automatically passed as the first argument to the method Car() even if we have not declared the self variable. 

However, if you try to access the instance method Car() with the help of the class reference, there will be no errors and the program will work fine: 

class Vehicle: 
def Car(): 
print("Rolls Royce 1948") 
obj = Vehicle() 
Vehicle.Car() 
Rolls Royce 1948 

Difference between self and __init__  

self : self represents the instance of the class. By using the "self" keyword all the attributes and methods of the python class can be accessed. 

__init__ : "__init__" is a reserved method in python classes. It is known as a constructor in object oriented concepts. This method is called when an object is created from the class and allows the class to initialize class attributes .. 

Usage of "self" in class to access the methods and attributes: 

class Rectangle: 
   def __init__(self, length, breadth, cost_per_unit =0): 
       self.length = length 
       self.breadth = breadth 
       self.cost_per_unit = cost_per_unit 
   def perimeter(self): 
       return 2 * (self.length + self.breadth) 
   def area(self): 
       return self.length * self.breadth 
   def calculate_cost(self): 
       area = self.area() 
       return area * self.cost_per_unit 
# length = 40 cm, breadth = 30 cm and 1 cm^2 = Rs 100 
r = Rectangle(40, 30, 100) 
print("Area of Rectangle:",r.area()) 
print("Cost of rectangular field is : Rs ",r.calculate_cost()) 

Output:  

Area of Rectangle: 1200 
Cost of rectangular field is : Rs  120000 

We have created an object of Rectangle class. While creating the Rectangle object, we passed 3 arguments – 40,30,100; all these arguments are passed to "__init__"method to initialize the object. 

Here, the keyword "self” represents the instance of the class. It binds the attributes with the given arguments. 

Self represents the same object or instance of the class. If you see, inside the method "area” , self.length" is used to get the value of the attribute "length".  attribute "length" is bind to the object (instance of the class) at the time of object creation. "self" represents the object inside the class. "self" works just like "r" in the statement “r = Rectangle(40,30, 100)".  If you see the method definition "def area(self): ” , here "self" is used as a parameter in the method because whenever we call the method,  the object (instance of class) is automatically passed as a first argument along with other arguments of the method. If no other arguments are provided only "self" is passed to the method. That's the reason "self" is used to call the method inside the class("self.area()").  We used object (instance of class) to call the method outside of the class definition("r.area()").  

"r" is the instance of the class when the method "r.area()” is called; the instance "r" is passed as first argument in the place of self. 

Miscellaneous Implementations of self 

Let us now discuss some of the miscellaneous implementations of the self variable. 

Similar variables for Class Method and Static Method 

A class method is a method that is bound to the class. Let us understand a class method with an example – 

class myClass: 
@classmethod 
def classmethod(cls): 
return “Class Method is called” 
obj.classmethod() 

The same behavior of the self variable is present with the Class methods too but the only difference is that for class methods, the convention is to use cls as the variable name instead of self. 

The class methods take a cls parameter instead of the self parameter. When the method is called, it points to the class. The class method cannot modify the object state but it can modify the class state of all the class instances. 

On the other hand, static methods are self-sufficient functions and this type of method takes neither a self nor a cls parameter. Let us see an example of a static method – 

class myClass: 
@staticmethod 
def staticmethod(): 
return “Static Method is called” 
obj.staticmethod() 

Since a static method does not accept any parameter, they cannot modify object state or even class state. They are primarily used to namespace different methods and Python restricts them in the data they can access. 

Note that both the methods here are marked with @classmethod and @staticmethod decorators to flag it as a class method and static method respectively. 

The self variable is bound to the current instance 

The self variable allows us to access the properties of the current instance. Let us understand this with an example – 

class Person: 
def __init__(self, n): 
self.name = n 
def walk(self): 
print(f“{self.name} is walking”) 
obj1 = Person(“Alex”) 
obj2 = Person(“Charles”) 
obj1.walk() 
obj2.walk() 
Alex is walking 
Charles is walking 

Here, we have a class Person with two methods __init__ and walk declared with the self parameter. We have created two different instances of the class – obj1 and obj2. When the first instance is revoked, “Alex” is printed with the method walk() whereas when the second instance is revoked, “Charles” gets printed with the properties of the instance method walk(). 

Tips about the Python self variable 

Since we have now reached the end of the article, let me give you some tips about when to use and when not to use the self variable in Python. 

Use self when:

  • you define an instance method, since it is passed automatically as the first parameter when the method is called;
  • you reference a class or an instance attribute from inside an instance method;
  • you want to refer to instance variables and methods from other instance methods. 

Don’t use self when: 

  • you want to call an instance method normally;
  • referencing a class attribute inside the class definition but outside an instance method;
  • you are inside a static method.  

Conclusion 

Let us recap the key points we have covered in this article, namely:

  • Instances and Classes in Python. 
  • Self variable and its importance. 
  • The explicitness of the self variable. 
  • Python class self constructor. 
  • Passing self as a method. 
  • Skipping self in Python. 
  • Variables used for Class methods and Static methods. 
  • Bounding of self to the current instance. 
  • When to use and when not to use self in Python. 

With good knowledge about the self variable in Python and its internal working in Python, it is now time for some practice. If you, however, wish to know more about Python self, you can head right on to the official documentation of Python.

Happy coding! 

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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How to Round Numbers in Python

While you are dealing with data, sometimes you may come across a biased dataset. In statistics, bias is whereby the expected value of the results differs from the true underlying quantitative parameter being estimated. Working with such data can be dangerous and can lead you to incorrect conclusions. To learn more about various other concepts of Python, go through our Python Tutorials or enroll to our Python Certification course online.There are many types of biases such as selection bias, reporting bias, sampling bias and so on. Similarly, rounding bias is related to numeric data. In this article we will see:Why is it important to know the ways to round numbersHow to use various strategies to round numbersHow data is affected by rounding itHow to use NumPy arrays and Pandas DataFrames to round numbersLet us first learn about Python’s built-in rounding process.About Python’s Built-in round() FunctionPython Programming offers a built-in round() function which rounds off a number to the given number of digits and makes rounding of numbers easier. The function round() accepts two numeric arguments, n and n digits and then returns the number n after rounding it to ndigits. If the number of digits are not provided for round off, the function rounds off the number n to the nearest integer.Suppose, you want to round off a number, say 4.5. It will be rounded to the nearest whole number which is 5. However, the number 4.74 will be rounded to one decimal place to give 4.7.It is important to quickly and readily round numbers while you are working with floats which have many decimal places. The inbuilt Python function round() makes it simple and easy.Syntaxround(number, number of digits)The parameters in the round() function are:number - number to be roundednumber of digits (Optional) - number of digits up to which the given number is to be rounded.The second parameter is optional. In case, if it is missing then round() function returns:For an integer, 12, it rounds off to 12For a decimal number, if the last digit after the decimal point is >=5 it will round off to the next whole number, and if =5 print(round(5.476, 2))     # when the (ndigit+1)th digit is  1 print(round("x", 2)) TypeError: type str doesn't define __round__ methodAnother example,print(round(1.5)) print(round(2)) print(round(2.5))The output will be:2 2 2The function round() rounds 1.5 up to 2, and 2.5 down to 2. This is not a bug, the round() function behaves this way. In this article you will learn a few other ways to round a number. Let us look at the variety of methods to round a number.Diverse Methods for RoundingThere are many ways to round a number with its own advantages and disadvantages. Here we will learn some of the techniques to rounding a number.TruncationTruncation, as the name means to shorten things. It is one of the simplest methods to round a number which involves truncating a number to a given number of digits. In this method, each digit after a given position is replaced with 0. Let us look into some examples.ValueTruncated ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34The truncate() function can be used for positive as well as negative numbers:>>> truncate(19.5) 19.0 >>> truncate(-2.852, 1) -2.8 >>> truncate(2.825, 2) 2.82The truncate() function can also be used to truncate digits towards the left of the decimal point by passing a negative number.>>> truncate(235.7, -1) 230.0 >>> truncate(-1936.37, -3) -1000.0When a positive number is truncated, we are basically rounding it down. Similarly, when we truncate a negative number, the number is rounded up. Let us look at the various rounding methods.Rounding UpThere is another strategy called “rounding up” where a number is rounded up to a specified number of digits. For example:ValueRound Up ToResult12.345Tens place2018.345Ones place1918.345Tenths place18.418.345Hundredths place18.35The term ceiling is used in mathematics to explain the nearest integer which is greater than or equal to a particular given number. In Python, for “rounding up” we use two functions namely,ceil() function, andmath() functionA non-integer number lies between two consecutive integers. For example, considering a number 5.2, this will lie between 4 and 5. Here, ceiling is the higher endpoint of the interval, whereas floor is the lower one. Therefore, ceiling of 5.2 is 5, and floor of 5.2 is 4. However, the ceiling of 5 is 5.In Python, the function to implement the ceiling function is the math.ceil() function. It always returns the closest integer which is greater than or equal to its input.>>> import math >>> math.ceil(5.2) 6 >>> math.ceil(5) 5 >>> math.ceil(-0.5) 0If you notice you will see that the ceiling of -0.5 is 0, and not -1.Let us look into a short code to implement the “rounding up” strategy using round_up() function:def round_up(n, decimals=0):     multiplier = 10 ** decimals     return math.ceil(n * multiplier) / multiplierLet’s look at how round_up() function works with various inputs:>>> round_up(3.1) 4.0 >>> round_up(3.23, 1) 3.3 >>> round_up(3.543, 2) 3.55You can pass negative values  to decimals, just like we did in truncation.>>> round_up(32.45, -1) 40.0 >>> round_up(3352, -2) 3400You can follow the diagram below to understand round up and round down. Round up to the right and down to the left.Rounding up always rounds a number to the right on the number line, and rounding down always rounds a number to the left on the number line.Rounding DownSimilar to rounding up we have another strategy called rounding down whereValueRounded Down ToResult19.345Tens place1019.345Ones place1919.345Tenths place19.319.345Hundredths place19.34In Python, rounding down can be implemented using a similar algorithm as we truncate or round up. Firstly you will have to shift the decimal point and then round an integer. Lastly shift the decimal point back.math.ceil() is used to round up to the ceiling of the number once the decimal point is shifted. For “rounding down” we first need to round the floor of the number once the decimal point is shifted.>>> math.floor(1.2) 1 >>> math.floor(-0.5) -1Here’s the definition of round_down():def round_down(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n * multiplier) / multiplierThis is quite similar to round_up() function. Here we are using math.floor() instead of math.ceil().>>> round_down(1.5) 1 >>> round_down(1.48, 1) 1.4 >>> round_down(-0.5) -1Rounding a number up or down has extreme effects in a large dataset. After rounding up or down, you can actually remove a lot of precision as well as alter computations.Rounding Half UpThe “rounding half up” strategy rounds every number to the nearest number with the specified precision, and breaks ties by rounding up. Here are some examples:ValueRound Half Up ToResult19.825Tens place1019.825Ones place2019.825Tenths place19.819.825Hundredths place19.83In Python, rounding half up strategy can be implemented by shifting the decimal point to the right by the desired number of places. In this case you will have to determine whether the digit after the shifted decimal point is less than or greater than equal to 5.You can add 0.5 to the value which is shifted and then round it down with the math.floor() function.def round_half_up(n, decimals=0):     multiplier = 10 ** decimals return math.floor(n*multiplier + 0.5) / multiplierIf you notice you might see that round_half_up() looks similar to round_down. The only difference is to add 0.5 after shifting the decimal point so that the result of rounding down matches with the expected value.>>> round_half_up(19.23, 1) 19.2 >>> round_half_up(19.28, 1) 19.3 >>> round_half_up(19.25, 1) 19.3Rounding Half DownIn this method of rounding, it rounds to the nearest number similarly like “rounding half up” method, the difference is that it breaks ties by rounding to the lesser of the two numbers. Here are some examples:ValueRound Half Down ToResult16.825Tens place1716.825Ones place1716.825Tenths place16.816.825Hundredths place16.82In Python, “rounding half down” strategy can be implemented by replacing math.floor() in the round_half_up() function with math.ceil() and then by subtracting 0.5 instead of adding:def round_half_down(n, decimals=0):     multiplier = 10 ** decimals return math.ceil(n*multiplier - 0.5) / multiplierLet us look into some test cases.>>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0 >>> round_half_down(2.25, 1) 2.2In general there are no bias for both round_half_up() and round_half_down(). However, rounding of data with more number of ties results in bias. Let us consider an example to understand better.>>> data = [-2.15, 1.45, 4.35, -12.75]Let us compute the mean of these numbers:>>> statistics.mean(data) -2.275Now let us compute the mean on the data after rounding to one decimal place with round_half_up() and round_half_down():>>> rhu_data = [round_half_up(n, 1) for n in data] >>> statistics.mean(rhu_data) -2.2249999999999996 >>> rhd_data = [round_half_down(n, 1) for n in data] >>> statistics.mean(rhd_data) -2.325The round_half_up() function results in a round towards positive infinity bias, and round_half_down() results in a round towards negative infinity bias.Rounding Half Away From ZeroIf you have noticed carefully while going through round_half_up() and round_half_down(), neither of the two is symmetric around zero:>>> round_half_up(1.5) 2.0 >>> round_half_up(-1.5) -1.0 >>> round_half_down(1.5) 1.0 >>> round_half_down(-1.5) -2.0In order to introduce symmetry, you can always round a tie away from zero. The table mentioned below illustrates it clearly:ValueRound Half Away From Zero ToResult16.25Tens place2016.25Ones place1616.25Tenths place16.3-16.25Tens place-20-16.25Ones place-16-16.25Tenths place-16.3The implementation of “rounding half away from zero” strategy on a number n is very simple. All you need to do is start as usual by shifting the decimal point to the right a given number of places and then notice the digit d immediately to the right of the decimal place in this new number. Here, there are four cases to consider:If n is positive and d >= 5, round upIf n is positive and d < 5, round downIf n is negative and d >= 5, round downIf n is negative and d < 5, round upAfter rounding as per the rules mentioned above, you can shift the decimal place back to the left.There is a question which might come to your mind - How do you handle situations where the number of positive and negative ties are drastically different? The answer to this question brings us full circle to the function that deceived us at the beginning of this article: Python’s built-in  round() function.Rounding Half To EvenThere is a way to mitigate rounding bias while you are rounding values in a dataset. You can simply round ties to the nearest even number at the desired precision. Let us look at some examples:ValueRound Half To Even ToResult16.255Tens place2016.255Ones place1616.255Tenths place16.216.255Hundredths place16.26To prove that round() really does round to even, let us try on a few different values:>>> round(4.5) 4 >>> round(3.5) 4 >>> round(1.75, 1) 1.8 >>> round(1.65, 1) 1.6The Decimal ClassThe  decimal module in Python is one of those features of the language which you might not be aware of if you have just started learning Python. Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – except from the decimal arithmetic specification. Some of the benefits of the decimal module are mentioned below -Exact decimal representation: 0.1 is actually 0.1, and 0.1 + 0.1 + 0.1 - 0.3 returns 0, as expected.Preservation of significant digits: When you add 1.50 and 2.30, the result is 3.80 with the trailing zero maintained to indicate significance.User-alterable precision: The default precision of the decimal module is twenty-eight digits, but this value can be altered by the user to match the problem at hand.Let us see how rounding works in the decimal module.>>> import decimal >>> decimal.getcontext() Context(     prec=28,     rounding=ROUND_HALF_EVEN,     Emin=-999999,     Emax=999999,     capitals=1,     clamp=0,     flags=[],     traps=[         InvalidOperation,         DivisionByZero,         Overflow     ] )The function decimal.getcontext() returns a context object which represents the default context of the decimal module. It also includes the default precision and the default rounding strategy.In the above example, you will see that the default rounding strategy for the decimal module is ROUND_HALF_EVEN. It allows to align with the built-in round() functionLet us create a new Decimal instance by passing a string containing the desired value and declare a number using the decimal module’s Decimal class.>>> from decimal import Decimal >>> Decimal("0.1") Decimal('0.1')You may create a Decimal instance from a floating-point number but in that case, a floating-point representation error will be introduced. For example, this is what happens when you create a Decimal instance from the floating-point number 0.1>>> Decimal(0.1) Decimal('0.1000000000000000055511151231257827021181583404541015625')You may create Decimal instances from strings containing the decimal numbers you need in order to maintain exact precision.Rounding a Decimal using the .quantize() method:>>> Decimal("1.85").quantize(Decimal("1.0")) Decimal('1.8')The Decimal("1.0") argument in .quantize() allows to determine the number of decimal places in order to round the number. As 1.0 has one decimal place, the number 1.85 rounds to a single decimal place. Rounding half to even is the default strategy, hence the result is 1.8.Decimal class:>>> Decimal("2.775").quantize(Decimal("1.00")) Decimal('2.78')Decimal module provides another benefit. After performing arithmetic the rounding is taken care of automatically and also the significant digits are preserved.>>> decimal.getcontext().prec = 2 >>> Decimal("2.23") + Decimal("1.12") Decimal('3.4')To change the default rounding strategy, you can set the decimal.getcontect().rounding property to any one of several  flags. The following table summarizes these flags and which rounding strategy they implement:FlagRounding Strategydecimal.ROUND_CEILINGRounding updecimal.ROUND_FLOORRounding downdecimal.ROUND_DOWNTruncationdecimal.ROUND_UPRounding away from zerodecimal.ROUND_HALF_UPRounding half away from zerodecimal.ROUND_HALF_DOWNRounding half towards zerodecimal.ROUND_HALF_EVENRounding half to evendecimal.ROUND_05UPRounding up and rounding towards zeroRounding NumPy ArraysIn Data Science and scientific computation, most of the times we store data as a  NumPy array. One of the most powerful features of NumPy is the use of  vectorization and broadcasting to apply operations to an entire array at once instead of one element at a time.Let’s generate some data by creating a 3×4 NumPy array of pseudo-random numbers:>>> import numpy as np >>> np.random.seed(444) >>> data = np.random.randn(3, 4) >>> data array([[ 0.35743992,  0.3775384 ,  1.38233789,  1.17554883],        [-0.9392757 , -1.14315015, -0.54243951, -0.54870808], [ 0.20851975, 0.21268956, 1.26802054, -0.80730293]])Here, first we seed the np.random module to reproduce the output easily. Then a 3×4 NumPy array of floating-point numbers is created with np.random.randn().Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.To round all of the values in the data array, pass data as the argument to the  np.around() function. The desired number of decimal places is set with the decimals keyword argument. In this case, round half to even strategy is used similar to Python’s built-in round() function.To round the data in your array to integers, NumPy offers several options which are mentioned below:numpy.ceil()numpy.floor()numpy.trunc()numpy.rint()The np.ceil() function rounds every value in the array to the nearest integer greater than or equal to the original value:>>> np.ceil(data) array([[ 1.,  1.,  2.,  2.],        [-0., -1., -0., -0.], [ 1., 1., 2., -0.]])Look at the code carefully, we have a new number! Negative zero! Let us now take a look at Pandas library, widely used in Data Science with Python.Rounding Pandas Series and DataFramePandas has been a game-changer for data analytics and data science. The two main data structures in Pandas are Dataframe and Series. Dataframe works like an Excel spreadsheet whereas you can consider Series to be columns in a spreadsheet. Series.round() and DataFrame.round() methods. Let us look at an example.Do not forget to install pip3 before executing the code mentioned above. If you are using  Anaconda you are good to go.>>> import pandas as pd >>> # Re-seed np.random if you closed your REPL since the last example >>> np.random.seed(444) >>> series = pd.Series(np.random.randn(4)) >>> series 0    0.357440 1    0.377538 2    1.382338 3    1.175549 dtype: float64 >>> series.round(2) 0    0.36 1    0.38 2    1.38 3    1.18 dtype: float64 >>> df = pd.DataFrame(np.random.randn(3, 3), columns=["A", "B", "C"]) >>> df           A         B         C 0 -0.939276 -1.143150 -0.542440 1 -0.548708  0.208520  0.212690 2  1.268021 -0.807303 -3.303072 >>> df.round(3)        A      B      C 0 -0.939 -1.143 -0.542 1 -0.549  0.209  0.213 2  1.268 -0.807 -3.303 The DataFrame.round() method can also accept a dictionary or a Series, to specify a different precision for each column. For instance, the following examples show how to round the first column of df to one decimal place, the second to two, and the third to three decimal places: >>> # Specify column-by-column precision with a dictionary >>> df.round({"A": 1, "B": 2, "C": 3})      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 >>> # Specify column-by-column precision with a Series >>> decimals = pd.Series([1, 2, 3], index=["A", "B", "C"]) >>> df.round(decimals)      A     B      C 0 -0.9 -1.14 -0.542 1 -0.5  0.21  0.213 2  1.3 -0.81 -3.303 If you need more rounding flexibility, you can apply NumPy's floor(), ceil(), and print() functions to Pandas Series and DataFrame objects: >>> np.floor(df)      A    B    C 0 -1.0 -2.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -4.0 >>> np.ceil(df)      A    B    C 0 -0.0 -1.0 -0.0 1 -0.0  1.0  1.0 2  2.0 -0.0 -3.0 >>> np.rint(df)      A    B    C 0 -1.0 -1.0 -1.0 1 -1.0  0.0  0.0 2  1.0 -1.0 -3.0 The modified round_half_up() function from the previous section will also work here: >>> round_half_up(df, decimals=2)       A     B     C 0 -0.94 -1.14 -0.54 1 -0.55  0.21  0.21 2 1.27 -0.81 -3.30Best Practices and ApplicationsNow that you have come across most of the rounding techniques, let us learn some of the best practices to make sure we round numbers in the correct way.Generate More Data and Round LaterSuppose you are dealing with a large set of data, storage can be a problem at times. For example, in an industrial oven you would want to measure the temperature every ten seconds accurate to eight decimal places, using a temperature sensor. These readings will help to avoid large fluctuations which may lead to failure of any heating element or components. We can write a Python script to compare the readings and check for large fluctuations.There will be a large number of readings as they are being recorded each and everyday. You may consider to maintain three decimal places of precision. But again, removing too much precision may result in a change in the calculation. However, if you have enough space, you can easily store the entire data at full precision. With less storage, it is always better to store at least two or three decimal places of precision which are required for calculation.In the end, once you are done computing the daily average of the temperature, you may calculate it to the maximum precision available and finally round the result.Currency Exchange and RegulationsWhenever we purchase an item from a particular place, the tax amount paid against the amount of the item depends largely on geographical factors. An item which costs you $2 may cost you less (say $1.8)  if you buy the same item from a different state. It is due to regulations set forth by the local government.In another case, when the minimum unit of currency at the accounting level in a country is smaller than the lowest unit of physical currency, Swedish rounding is done. You can find a list of such rounding methods used by various countries if you look up on the internet.If you want to design any such software for calculating currencies, keep in mind to check the local laws and regulations applicable in your present location.Reduce errorAs you are rounding numbers in a large datasets used in complex computations, your primary concern should be to limit the growth of the error due to rounding.SummaryIn this article we have seen a few methods to round numbers, out of those “rounding half to even” strategy minimizes rounding bias the best. We are lucky to have Python, NumPy, and Pandas already have built-in rounding functions to use this strategy. Here, we have learned about -Several rounding strategies, and how to implement in pure Python.Every rounding strategy inherently introduces a rounding bias, and the “rounding half to even” strategy mitigates this bias well, most of the time.You can round NumPy arrays and Pandas Series and DataFrame objects.If you enjoyed reading this article and found it to be interesting, leave a comment. To learn more about rounding numbers and other features of Python, join our Python certification course.
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How to Round Numbers in Python

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What are Python KeyError Exceptions and How to Handle Them

There are times when you have written your code but while you execute, it might not run. These types of situations occur when the input is inappropriate or you try to open a file with a wrong path or try to divide a number by zero. Due to some errors or incorrect command the output will not be displayed. This is because of errors and exceptions which are a part of the Python programming language. Learn about such concepts and gain further knowledge by joining Python Programming Course.What is Exception Handling?Python raises exceptions when it encounters errors during execution. A Python Exception is basically a construct that signals any important event, such as a run-time error.Exception Handling is the process of responding to executions during computations, which often interrupts the usual flow of executing a program. It can be performed both at the software level as part of the program and also at hardware level using built-in CPU mechanisms.Why is Exception Handling Important?Although exceptions might be irritating when they occur, they play an essential role in high level languages by acting as a friend to the user.An error at the time of execution might lead to two things— either your program will die or will display a blue screen of death. On the other hand, exceptions act as communication tools. It allows the program to answer the questions — what, why and how something goes wrong and then terminates the program in a delicate manner.In simple words, exception handling protects against uncontrollable program failures and increases the potency and efficiency of your code. If you want to master yourself in programming, the knowledge of exceptions and how to handle them is very crucial, especially in Python.What are the Errors and Exceptions in Python?Python doesn’t like errors and exceptions and displays its dissatisfaction by terminating the program abruptly.There are basically two types of errors in the Python language-Syntax Error.Errors occuring at run-time or Exceptions.Syntax ErrorsSyntax Errors, also known as parsing errors, occur when the parser identifies an incorrect statement. In simple words, syntax error occurs when the proper structure or syntax of the programming language is not followed.An example of a syntax error:>>> print( 1 / 0 )) File "", line 1 print( 1 / 0 ))   ^SyntaxError: invalid syntaxExceptionsExceptions occur during run-time. Python raises an exception when your code has a correct syntax but it encounters a run-time issue which it is not able to handle.There are a number of defined built-in exceptions in Python which are used in specific situations. Some of the built-in exceptions are:ExceptionCause Of ErrorArithmeticErrorRaised when numerical computation fails.FloatingPointErrorRaised when floating point calculation fails.AssertionErrorRaised in case of failure of the Assert statement.ZeroDivisionErrorRaised when division or modulo by zero takes place for all numerical values.OverflowErrorRaised when result of an arithmetic operation is very large to be represented.IndexErrorRaised when an index is not found in a sequence.ImportErrorRaised when the imported module is not found.IndentationErrorRaised when indentation is not specified properly.KeyboardInterruptRaised when the user hits interrupt key.RuntimeErrorRaised when a generated error does not fall into any category.SyntaxErrorRaised when there is an error in Python syntax.IOErrorRaised when Python cannot access a file correctly on disk.KeyErrorRaised when a key is not found in a dictionary.ValueErrorRaised when an argument to a function is the right type but not in the right domain.NameErrorRaised when an identifier is not found in the local or global namespace.TypeErrorRaised when an argument to a function is not in the right type.There are another type of built-in exceptions called warnings. They are usually issued in situations where the user is alerted of some conditions. The condition does not raise an exception; rather it  terminates the program.What is a Python KeyError?Before getting into KeyError, you must know the meaning of dictionary and mapping in Python. Dictionary (dict) is an unordered collection of objects which deals with data type key. They are Python’s implementation of data structures and are also known as associative arrays. They comprise key-value pairs, in which each pair maps the key to its associated value.Dictionary is basically a data structure that maps one set of values into another and is the most common mapping in Python.Exception hierarchy of KeyError:->BaseException              ->Exception                         ->LookupError                                       ->KeyErrorA Python KeyError is raised when you try to access an invalid key in a dictionary. In simple terms, when you see a KeyError, it denotes that the key you were looking for could not be found.An example of KeyError:>>> prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} >>> prices['Eraser'] Traceback (most recent call last): File "", line 1, in prices['Eraser'] KeyError: 'Eraser'Here, dictionary prices is declared with the prices of three items. The KeyError is raised when the item ‘Eraser’ is being accessed which is not present in prices.Whenever an exception is raised in Python, it is done using traceback, as you can see in the example code above. It tells why an exception is raised and what caused it.Let’s execute the same Python code from a file. This time, you will be asked to give the name of the item whose price you want to know:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') print(f'The price of {item} is {prices[item]}')You will get a traceback again but you’ll also get the information about the line from which the KeyError is raised:Get price of: Eraser Traceback (most recent call last): File "prices.py", line 5, in print(f'The price of {item} is {prices[item]}') KeyError: 'Eraser'The traceback in the example above provides the following information:A KeyError was raised.The key ‘Eraser’ was not found.The line number which raised the exception along with that line.Where else will you find a Python KeyError?Although most of the time, a KeyError is raised because of an invalid key in a Python dictionary or a dictionary subclass, you may also find it in other places in the Python Standard Library, such as in a zipfile. However, it denotes the same semantic meaning of the Python KeyError, which is not finding the requested key.An example of such:>>> from zipfile import ZipFile >>> my_zip_file = ZipFile('Avengers.zip') >>> my_zip_file.getinfo('Batman')Traceback (most recent call last): File "", line 1, in File "myzip.py", line 1119, in getinfo 'There is no item named %r in the archive' % name) KeyError: "There is no item named 'Batman' in the archive"In this example, the zipfile.ZipFile class is used to derive information about a ZIP archive ‘Batman’ using the getinfo() function. Here, the traceback indicates that the problem is not in your code but in the zipfile code, by showing the line which caused the problem. The exception raised here is not because of a LookUpError but rather due to the zipfile.ZipFile.getinfo()function call.When do you need to raise a Python KeyError?In Python Programming, it might be sensible at times to forcefully raise exceptions in your own code. You can usually raise an exception using the raise keyword and by calling the KeyError exception:>>> raise KeyError('Batman')Here, ‘Batman’ acts as the missing key. However, in most cases, you should provide more information about the missing key so that your next developer has a clear understanding of the problem.Conditions to raise a Python KeyError in your code:It should match the generic meaning behind the exception.A message should be displayed about the missing key along with the missing key which needs to be accessed.How to Handle a Python KeyError?The main motive of handling a Python KeyError is to stop unexpected KeyError exceptions to be raised. There are a number of number of ways of handling a KeyError exception.Using get()The get()is useful in cases where the exception is raised due to a failed dictionary LookupError. It returns either the specified key value or a default value.# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') price = prices.get(item) if price:   print(f'The price of {item} is {prices[item]}')   else:   print(f'The price of {item} is not known')This time, you’ll not get a KeyError because the get() uses a better and safer method to retrieve the price and if not found, the default value is displayed:Get price of: EraserThe price of Eraser is not knownIn this example, the variable price will either have the price of the item in the dictionary or the default value ( which is None by default ).In the example above, when the key ‘Eraser’ is not found in the dictionary, the get() returns  None by default rather than raising a KeyError. You can also give another default value as a second argument by calling get():price = prices.get(item,0)If the key is not found, it will return 0 instead of None.Checking for KeysIn some situations, the get() might not provide the correct information. If it returns None, it will mean that the key was not found or the value of the key in Python Dictionary is actually None, which might not be true in some cases. In such situations, you need to determine the existence of a key in the dictionary. You can use the if and in operator to handle such cases. It checks whether a key is present in the mapping or not by returning a boolean (True or False) value:dict = dictionary() for i in range(50):   key = i % 10     if key in dict: dict[key] += 1 else: dict[key] = 1In this case, we do not check what the value of the missing key is but rather we check whether the key is in the dictionary or not. This is a special way of handling an exception which is used rarely.This technique of handling exceptions is known as Look Before You Leap(LBYL).Using try-exceptThe try-except block is one of the best possible ways to handle the KeyError exceptions. It is also useful where the get() and the if and in operators are not supported.Let’s apply the try-except block on our earlier retrieval of prices code:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known')Here, in this example there are two cases— normal case and a backup case. try block corresponds to the normal case and except block to the backup case. If the normal case doesn’t print the name of the item and the price and raises a KeyError, the backup case prints a different statement or a message.Using try-except-elseThis is another way of handling exceptions. The try-except-else  has three blocks— try block, except block and else block.The else condition in a try-except statement is useful when the try condition doesn’t raise an exception. However, it must follow all the except conditions.Let us take our previous price retrieval code to illustrate try-except-else:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of:') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known') else: print(f'There is no error in the statement')First, we access an existing key in the try-except block. If the Keyerror is not raised, there are no errors. Then the else condition is executed and the statement is displayed on the screen.Using finallyThe try statement in Python can have an optional finally condition. It is used to define clean-up actions and is always executed irrespective of anything. It is generally used to release external sources.An example to show finally:# prices.py prices = { 'Pen' : 10, 'Pencil' : 5, 'Notebook' : 25} item = input('Get price of: ') try: print(f'The price of {item} is {prices[item]}') except KeyError: print(f'The price of {item} is not known') finally: print(f'The finally statement is executed')Remember, the finally statement will always be executed whether an exception has occurred or not.How to raise Custom Exceptions in Python?Python comprises of a number of built-in exceptions which you can use in your program. However, when you’re developing your own packages, you might need to create your own custom exceptions to increase the flexibility of your program.You can create a custom Python exception using the pre-defined class Exception:def square(x): if x
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How to Work With a PDF in Python

Whether it is an ebook, digitally signed agreements, password protected documents, or scanned documents such as passports, the most preferred file format is PDF or Portable Document Format. It was originally developed by Adobe and is a file format used to present and transfer documents easily and reliably. It uses the file extension .pdf. In fact, PDF being the most widely used digital media, is now considered as an open standard which is maintained by the International Standards Organization (ISO). Python has relatively easy syntax which makes it even easier for the ones who are in their initial stage of learning the language. The popular Python libraries are well suited and integrated which allows to easily extract documents from a PDF, rotate pages if required, split pdf to make separate documents, or add watermarks in them.Now an important question rises, why do we need Python to process PDFs? Well, processing a PDF falls under the category of text analytics. There are several libraries and frameworks available which are designed in Python exclusively for text analytics. This makes it easier to play with a PDF in Python. You can also extract information from PDF and use into Natural Language Processing or any other Machine Learning models. Get certified and learn more about Python Programming and apply those skills and knowledge in the real world.History of  pyPDF, PyPDF2, pyPDF4The first PyPDF package was released in 2005 and the last official release in 2010. After a year or so, a  company named Phasit sponsored a branch of the PyPDF called PyPDF2 which was consistent with the original package and worked pretty well for several years.A series of packages were released later on with the name of PyPDF3 and later renamed as PyPDF4. The biggest difference between PyPDF and the other versions was that the later versions supported Python3. PyPDF2 has been discarded recently. But since PyPDF4 is not fully backward compatible with the PyPDf2, it is suggested to use PyPDF2. You can also use a substitute package - pdfrw. Pdfrw was created by Patrick Maupin and allows you to perform all functions which PyPDF2 is capable of except a few such as encryption, decryption, and types of decompression.Some common libraries in PythonLet us look into some of the libraries Python offers to handle PDFs:PdfMiner It is a tool used to extract information from PDF documents. PDFMiner allows the user to analyze text data and obtain the definite location of a text. It provides information such as fonts and lines. We can also use it as a PDF transformer and a PDF parser.PyPDF2PyPDF2 is purely a Python library which allows users to split, merge, crop, encrypt, and transform PDFs. You can also add customized data, view options, and passwords to the documents. Tabula-pyIt is a Python wrapper of tabula-java which can read tables from PDF files and convert into Pandas Dataframe or into CSV/TSV/JSON file formats.SlateIt is a Python package which facilitates the extraction of information and is dependent on the PdfMiner package.PDFQueryA light Python wrapper which uses minimum code to extract data from PDFs.xPDFIt is an open source viewer of PDF which also includes an extractor, converter and other utilities. Out of all the libraries mentioned above, PyPDF2 is the most used to perform operations like extraction, merging, splitting and so on.Installing PyPDF2If you're using Anaconda, you can install PyPDF2 using pip or conda. To install PyPDF2 using pip, run the following command in the command line:pip install PyPDF2The module is case-sensitive. So you need to make sure that proper syntax is followed. The installation is really quick since PyPDF2 is free of dependencies.Extracting Document Information from a PDF in PythonPyPDF2 can be used to extract metadata and all sorts of texts from PDF when you are performing operations on preexisting PDF files. The types of data you can extract are:AuthorCreatorProducerSubjectTitleNumber of PagesTo understand it better, let us use an existing PDF in your system or you can go to Leanpub and download a book sample.The code for extracting the document information from the PDF—# get_doc_info.py from PyPDF2 import PdfFileReader def getinfo(path):     with open(path, 'rb') as f:         PDF = PdfFileReader(f)         information = PDF.getDocumentInfo()         numberofpages = PDF.getNumPages()     print(information)     author = information.author     creator = information.creator     producer =information .producer     subject = information.subject     title = information.title if __name__ == '__main__':     path = 'reportlab-sample.pdf'     getinfo(path)The output of the program above will look like—Here, we have firstly imported PdfFileReader from the PyPDF2 package. The class PdfFileReader is used to interact with PDF files like reading and extracting information using accessor methods. Then, we have created our own function getinfo with a PDF file as an argument and then called the getdocumentinfo(). This returned an instance of DocumentInformation. And finally we got extract information like the author, creator, subject or title, etc.getNumPages() is used to count the number of pages in the document. PdfMiner can be used when you want to extract text from a PDF file. It is potent and particularly designed for extracting text from PDF.We have learned to extract information from PDF. Now let’s learn how to rotate a PDF. Rotating pages in PDFA lot of times we receive PDFs which contain pages in landscape orientation instead of portrait. You may also find certain documents to be upside down, which happens while scanning a document or mailing. However, we can rotate the pages clockwise or counterclockwise according to our choice using Python with PyPDF2.The code for rotating the article is as follows—# rotate_pages.py from PyPDF2 import PdfFileReader, PdfFileWriter def rotate(pdf_path):     pdf_write = PdfFileWriter()     pdf_read = PdfFileReader(path)     # Rotate page 90 degrees to the right     page1 = pdf_read.getPage(0).rotateClockwise(90)     pdf_write.addPage(page1)     # Rotate page 90 degrees to the left     page2 = pdf_read.getPage(1).rotateCounterClockwise(90)     pdf_write.addPage(page2)     # Add a page in normal orientation     pdf_write.addPage(pdf_read.getPage(2))     with open('rotate_pages.pdf', 'wb') as fh:         pdf_write.write(fh) if __name__ == '__main__':     path = 'mldocument.pdf'     rotate(path)The output of the code will be as follows—Here firstly we imported the PdfFileReader and the PdfFileWriter so that we can write out a new PDF file. Then we declared a function rotate with a path to the PDF that is to be modified. Within the function, we created a read object pdf_read and write object pdf_write.Then, we used the getPage() to grab the pages. Two pages page1 and page2 are taken and rotated to 90 degrees clockwise and 90 degrees counterclockwise respectively using rotateClockwise() and rotateCounterClockwise().We used addPage() function after each rotation method calls. This adds the rotated page to the write object. The last page we add is page3 without any rotation.Lastly, we have used write() with a file-like parameter to write out the new PDF. The final PDF contains three pages, the first two will be in the landscape mode and rotated in reversed direction and the third page will be in normal orientation.Now we will learn to merge different PDFs into one.Merging PDFsIn many cases, we need to merge two PDFs into a single one. For example, suppose you are working on a project report and you need to print it and bind it into a book. It contains a cover page followed by the project report. So you have two different PDFs and you want to merge them into one PDF. You can simply use Python to do so. Let us see how can we merge PDFs into one.The code for merging two PDF documents using PyPDF in mentioned below:# pdf_merging.py from PyPDF2 import PdfFileReader, PdfFileWriter def pdfmerger(paths, output):     pdfwrite = PdfFileWriter()     for path in paths:         pdfread = PdfFileReader(path)         for page in range(pdfread.getNumPages()):             # Add each page to the writer object             pdfwrite.addPage(pdfread.getPage(page))     # Write out the merged PDF     with open(output, 'wb') as out:         pdfwrite.write(out) if __name__ == '__main__':     paths = ['document-1.pdf', 'document-2.pdf']     pdfmerger(paths, output='merged.pdf')Here we have created a function pdfmerger() which takes a number of inputs and a single output. Then we created a PdfFileReader() object for each PDF path and looped over the pages, added each page to the write object. Finally, using the write() function the object’s contents are written to the disk.PyPDF2 makes the process of merging simpler by creating the PdfFileMerger class.Code for merging two documents using PyPDF2—# pdf_merger2.py import glob from PyPDF2 import PdfFileMerger def merger(output_path, input_paths):     pdfmerge = PdfFileMerger()     file_handles = []     for path in input_paths:         pdfmerge.append(path)     with open(output_path, 'wb') as fileobj:         pdfmerge.write(fileobj) if __name__ == '__main__':     paths = glob.glob('d-1.pdf')     paths.sort()     merger('d-2.pdf', paths)The PyPDF2 makes it simpler in the way that we don’t need to loop the pages of each document ourselves.  Here, we created the object pdfmerge and looped through the PDF paths. The PyPDF2 automatically appends the whole document. Finally, we write it out.Let’s perform the opposite of merging now!Splitting PDFsThe PyPDF2 package has the ability to split up a single PDF into multiple PDFs. It allows us to split pages into different PDFs. Suppose we have a set of scanned documents in a single PDF and we need to separate the pages into different PDFs as per requirement, we can simply use Python to select pages we want to split and get the work done.Code for splitting a single PDF into multiple PDFs—# pdf_splitter.py import os from PyPDF2 import PdfFileReader, PdfFileWriter def splitpdf(path):     fname = os.path.splitext(os.path.basename(path))[0]     pdf = PdfFileReader(path)     for page in range(pdf.getNumPages()):         pdfwrite = PdfFileWriter()         pdfwrite.addPage(pdf.getPage(page))         outputfilename = '{}_page_{}.pdf'.format(             fname, page+1)         with open(outputfilename, 'wb') as out:             pdfwrite.write(out)         print('Created: {}'.format(outputfilename)) if __name__ == '__main__':     path = 'document-1.pdf'     splitpdf(path)Here we have imported the PdfFileReader and PdfFileWriter from PyPDF2. Then we created a function called splitpdf() which accepts the path of PDF we want to split. The first line of the function takes the name of the input file. Then we open the PDF and create a read object. Using the read object’s getNumPages(), we loop over all the pages.In the next step, we created an instance of PdfFileWriter inside the for loop. Then, we created a PDF write instance and added each page to it for each of the pages in the PDF input. We also created a unique filename using the original filename + the word ‘page’ + the page number + 1.Once we are done with running the script, we will have each of the pages of the input PDF split into multiple PDFs. Now let us learn how to add a watermark to a PDF and keep it secured.Adding Overlays/WatermarksAn image or superimposed text on selected pages in a PDF document is referred to as a Watermark. The Watermark adds security features and protects our rational property like images and PDFs. Watermarks are also called overlays.The PyPDF2 allows us to watermark documents. We just need to have a PDF which will consist of our watermark text, image or signature.Code for adding a watermark in a PDF—# watermarker.py from PyPDF2 import PdfFileWriter, PdfFileReader def watermark(inputpdf, outputpdf, watermarkpdf):     watermark = PdfFileReader(watermarkpdf)     watermarkpage = watermark.getPage(0)     pdf = PdfFileReader(inputpdf)     pdfwrite = PdfFileWriter()     for page in range(pdf.getNumPages()):         pdfpage = pdf.getPage(page)         pdfpage.mergePage(watermarkpage)         pdfwrite.addPage(pdfpage)     with open(outputpdf, 'wb') as fh:         pdfwrite.write(fh) if __name__ == '__main__':     watermark(inputpdf='document-1.pdf',               outputpdf='watermarked_w9.pdf',               watermarkpdf='watermark.pdf')The output of the code will look like— There are three arguments of the function watermark(): inputpdf: The path of the PDF that is to be watermarked. outputpdf: The path where the watermarked PDF will be saved. watermarkpdf: The PDF which contains the watermark.Firstly, we extract the PDF page which contains the watermark image or text and then open that PDF page where we want to give the desired watermark.Using the inputpdf, we create a read object and using the pdfwrite, we create a write object to write out the watermarked PDF and then iterate over the pages.Next, we call the page object’s mergePage and apply the watermark and add that to the write object pdfwrite.When the loop terminates, the watermarked PDF is written out to the disk and it’s done!Encrypting a PDFIn the PDF world, the PyPDF2 package allows an owner password which gives the user the advantage to work as an administrator. The package also provides the user password which allows us to open the document upon entering the password.The PyPDF2 basically doesn’t permit any allowances on any PDF file yet it allows the user to set the owner password and user password.Code to add a password and add encryption to a PDF—# pdf_encrypt.py from PyPDF2 import PdfFileWriter, PdfFileReader def encryption(inputpdf, outputpdf, password):     pdfwrite = PdfFileWriter()     pdfread = PdfFileReader(inputpdf)     for page in range(pdfread.getNumPages()):         pdfwrite.addPage(pdfread.getPage(page))     pdfwrite.encrypt(user_pwd=password, owner_pwd=None,                       use_128bit=True)     with open(outputpdf, 'wb') as fh:         pdfwrite.write(fh) if __name__ == '__main__':     encryption(inputpdf='document-1.pdf',                   outputpdf='document-1-encrypted.pdf',                   password='twofish')We declare a  function named encryption() with three arguments—the input PDF path, the output PDF path and the password that we want to keep. Then we create one read object pdfread and one write object pdfwrite. Now we loop over all the pages and add them to the write object since we need to encrypt the entire document.Finally, we call the encrypt() function which accepts three parameters—the user password, the owner password and the whether or not to use 128-bit encryption. The PDF  will be encrypted to 40-bit encryption if the argument use128bit is set to false. Also if the owner password is set to none, then it will be set to user password automatically.Reading the Table data from PDFSuppose you want to work with the Table data in Pdf, you can use tabula-py to read tables in a PDF. To install tabula-py, run:pip install tabula-pyCode to extract simple Text from pdf using PyPDF2:import tabula # readinf the PDF file that contain Table Data # you can find the pdf file with complete code in below # read_pdf will save the pdf table into Pandas Dataframe df = tabula.read_pdf("document.pdf") # in order to print first 5 lines of Table df.head()If you PDF file contains Multiple Tabledf = tabula.read_pdf("document.pdf",multiple_tables=True)If you want to extract Information from the specific part of any specific page of PDFtabula.read_pdf("document.pdf", area=(126,149,212,462), pages=1)If you want the output into JSON Formattabula.read_pdf("offense.pdf", output_format="json")Exporting PDF into ExcelSuppose you want to export a PDF into Excel, you can do so by writing the following code and convert the PDF Data into Excel or CSV.tabula.convert_into("document.pdf", "document_testing.xlsx", output_format="xlsx")Let us sum up what we have learned in the article:Extraction of data from a PDFRotate pages in a PDFMerge PDFs into one PDFSplit a PDF into many PDFsAdd watermarks or overlays in a PDFAdd password or encryption to a PDFReading table from PDFExporting PDF into Excel or CSVAs you have seen, PyPDF2 is one of the most useful tools available in Python. The features of PyPDF2 makes life easier whether you are working on a large project or even when you quickly want to make some changes to your PDF documents. Learn more about such libraries and frameworks as KnowledgeHut offers Python Certification Course for Programmers, Developers, Jr./Sr Software Engineers/Developers and anybody who wants to learn Python.
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How to Work With a PDF in Python

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