Skip to main content

Access Modifiers in Python


 


Python: Types of Access Modifiers

There are 3 types of access modifiers for a class in Python. 

These access modifiers define how the members of the class can be accessed. Of course, any member of a class is accessible inside any member function of that same class. 


Moving ahead to the type of access modifiers, they are:

Access Modifier: Public

The members declared as Public are accessible from outside the Class through an object of the class.

Access Modifier: Protected

The members declared as Protected are accessible from outside the class but only in a class derived from it that is in the child or subclass.

Access Modifier: Private

These members are only accessible from within the class. No outside Access is allowed.







EXAMPLE:





class Demo:
  x = 5
  _y = 10
  __z = 20

  def met1(self):
    print(Demo.x)
    print(Demo._y)
    print(Demo.__z)


s = Demo()

s.met1()

5 10 20

print(Demo.x) #public variable
or print(s.x)

5

print(Demo._y) #protected variable
or print(s._y)

10

print(Demo.__z) #private variable
or print(s.__z)
---------------------------------------------------------------------------
AttributeError    
                        Traceback (most recent call last)
<ipython-input-131-bd3c01a1063d> in <module>()
----> 1 print(Demo.__z)

AttributeError:
 
type object 'Demo' has no attribute '__z'




To Access a Private Variable:


s._Demo__z #(objreference._classname__privatevariablename)

20













Comments

Popular posts from this blog

Exception Handling in Python

  Introduction   An error is an abnormal condition that results in unexpected behavior of a program. Common kinds of errors are syntax errors and logical errors. Syntax errors arise due to poor understanding of the language. Logical errors arise due to poor understanding of the problem and its solution.   Anomalies that occur at runtime are known as exceptions. Exceptions are of two types: synchronous exceptions and asynchronous exceptions. Synchronous exceptions are caused due to mistakes in the logic of the program and can be controlled. Asynchronous exceptions are caused due to hardware failure or operating system level failures and cannot be controlled.   Examples of synchronous exceptions are: divide by zero, array index out of bounds, etc.) . Examples of asynchronous exceptions are: out of memory error, memory overflow, memory underflow, disk failure, etc. Overview of errors and exceptions in Python is as follows:     Handling Exceptions   Flowch...

JENKINS BASICS

  Jenkins  – an open source automation server which enables developers around the world to reliably build, test, and deploy their software. Dashboard: " Dashboard " is the default view shown when you open Jenkins and shows an overview of all  projects configured on a  Jenkins  instance. Creating a New Project: Step 2:  In the next screen, enter the Item name, Choose the ‘Freestyle project option’ Step 3  − The following screen will come up in which you can specify the details of the job. Note  − If you repository if hosted on Github, you can also enter the url of that repository here. In addition to this, you would need to click on the Add button for the credentials to add a user name and password to the github repository so that the code can be picked up from the remote repository. Step 4 − Now go to the Build section and click on Add build step → Execute Windows batch command Before Executing and building a file. Let me describe the task which w...

NumPY In python

 NumPy Stands for numerical python How do I install NumPy? To install Python NumPy, go to your command prompt and type “pip install numpy”.  Once the installation is completed, go to your IDE (For example: PyCharm) and simply import it by typing:  “import numpy as np” Here, I have different elements that are stored in their respective memory locations. It is said to be two dimensional because it has rows as well as columns. In the above image, we have 3 columns and 4 rows available. How do I start NumPy? Single-dimensional Numpy Array: 1 2 3 import numpy as np a = np.array([ 1 , 2 , 3 ]) print (a) Output – [1 2 3] Multi-dimensional Array: 1 2 a = np.array([( 1 , 2 , 3 ),( 4 , 5 , 6 )]) print (a) O/P – [[ 1 2 3] [4 5 6]] Python NumPy Array v/s List Why NumPy is used in Python? We use python NumPy array instead of a list because of the below three reasons: Less Memory Fast Convenient The very first reason to choose python NumPy array is that it occupies less memory as co...