IWT UNIT 4 NOTES
-------------------------------------Python introduction ----------------------------------------------------------
Introduction to Python for University Level Exam
Python is a versatile and powerful programming language widely used in various fields such as web development, data analysis, artificial intelligence, and scientific computing. If you are preparing for a university-level exam on Python, understanding the basics of the language and its key features is essential. In this introduction, I will provide you with an overview of Python, its syntax, data types, control structures, and some important concepts you should know.
1. Syntax and Variables:
Python has a clear and readable syntax that emphasizes code readability and simplicity. It uses indentation (whitespace at the beginning of a line) to define blocks of code. Variables are dynamically typed, meaning you don't need to declare their type explicitly. You can assign values to variables using the assignment operator "=".
Example:
```python
x = 5
name = "John"
```
2. Data Types:
Python supports various data types, including:
- Numeric types: int (integer), float (floating-point), and complex (complex numbers).
- Sequence types: list (mutable), tuple (immutable), and string (immutable).
- Mapping type: dictionary (key-value pairs).
- Boolean type: bool (True or False).
- Other types: None (null-like object).
Example:
```python
numbers = [1, 2, 3, 4, 5]
person = {"name": "John", "age": 25}
is_valid = True
```
3. Control Structures:
Python provides control structures to control the flow of execution in a program. The main control structures are:
- Conditional statements: if, elif (else if), and else.
- Loops: for and while.
Example:
```python
if x > 5:
print("x is greater than 5")
else:
print("x is not greater than 5")
for i in range(1, 5):
print(i)
while x > 0:
print(x)
x -= 1
```
4. Functions:
Functions are reusable blocks of code that perform a specific task. They help in organizing and modularizing code. Python allows you to define your own functions using the "def" keyword.
Example:
```python
def greet(name):
print("Hello, " + name + "!")
greet("John")
```
5. Input and Output:
Python provides functions for standard input and output. You can use the "input()" function to get input from the user and the "print()" function to display output on the console.
Example:
```python
name = input("Enter your name: ")
print("Hello, " + name + "!")
```
These are some fundamental concepts of Python that are likely to be covered in a university-level exam. It's important to practice coding examples and exercises to reinforce your understanding. Additionally, consider studying topics such as file handling, error handling, object-oriented programming, and modules/libraries commonly used in Python, such as NumPy, Pandas, and Matplotlib, depending on the focus of your course.
------------------------------------- Data types and expression -------------------------------------
Certainly! Here's a more detailed explanation of data types and expressions in Python:
1. Data Types:
Python provides several built-in data types, each serving a specific purpose. Understanding these data types is crucial for working with variables and performing operations.
- Numeric Types:
- int: Represents integers, such as 3, -5, 0.
- float: Represents floating-point numbers with decimal places, such as 3.14, -2.5.
- complex: Represents complex numbers in the form a + bj, where a and b are floats or integers, and j represents the square root of -1.
- Sequence Types:
- list: An ordered collection of items, enclosed in square brackets [], separated by commas. Lists can contain elements of different types and can be modified (mutable).
- tuple: Similar to lists but enclosed in parentheses (). Tuples are immutable, meaning their values cannot be changed once assigned.
- string: Represents a sequence of characters, enclosed in single (' ') or double (" ") quotes. Strings are immutable.
- Mapping Type:
- dictionary: Represents a collection of key-value pairs, enclosed in curly braces {}. Keys are unique and used to access corresponding values.
- Boolean Type:
- bool: Represents either True or False. Used in logical expressions and control flow.
- Other Type:
- None: Represents the absence of a value or null-like object. Used when a variable doesn't have a value assigned.
2. Expressions:
Expressions in Python are combinations of values, variables, operators, and function calls that evaluate to a single value.
- Arithmetic Operators: Perform mathematical operations.
- Addition (+), Subtraction (-), Multiplication (*), Division (/), Floor Division (//), Modulo (%), Exponentiation (**).
- Comparison Operators: Compare values and return a Boolean result.
- Equal to (==), Not equal to (!=), Greater than (>), Less than (<), Greater than or equal to (>=), Less than or equal to (<=).
- Logical Operators: Combine Boolean expressions.
- and: Returns True if both operands are True.
- or: Returns True if either operand is True.
- not: Returns the opposite Boolean value of the operand.
- Assignment Operators: Assign values to variables.
- =: Assigns a value to a variable.
- +=: Adds a value to the variable and assigns the result.
- -=: Subtracts a value from the variable and assigns the result.
- *=: Multiplies the variable by a value and assigns the result.
- /=: Divides the variable by a value and assigns the result.
- String Operations: Perform operations specific to strings.
- Concatenation (+): Combines two or more strings.
- Repetition (*): Repeats a string a specified number of times.
- Indexing ([]): Accesses individual characters in a string based on their position.
- Slicing ([]): Extracts a portion of a string based on a specified range.
These are just some of the key data types and expressions in Python. It's important to familiarize yourself with these concepts and practice using them to solve problems and manipulate data effectively.
Remember to refer to the Python documentation and engage in hands-on coding exercises to deepen your understanding.
--------------------------------------controlled statement ------------------------
In Python, Loops are used to iterate repeatedly over a block of code. In order to change the way a loop is executed from its usual behavior, control statements are used. Control statements are used to control the flow of the execution of the loop based on a condition. There are many types of control statements in Python and in this tutorial, we will discuss all of them.
Control Statements in Python
Break statement
Continue statement
Pass statement
Break statement
The break statement in Python is used to terminate or abandon the loop containing the statement and brings the control out of the loop. It is used with both the while and the for loops, especially with nested loops (loop within a loop) to quit the loop. It terminates the inner loop and control shifts to the statement in the outer loop.
Input:
age = “\n Please enter your age: ”
while True:
age = input
if age >= 18:
break
else:
print (“You’re not eligible to vote”)
Output:
Please enter your age: 17 You’re not eligible to vote
Please enter your age: 18
In the above example, if the age entered is equal to or more than 18, it breaks out of the loop.
Continue statement
When a program encounters a continue statement in Python, it skips the execution of the current iteration when the condition is met and lets the loop continue to move to the next iteration. It is used to continue running the program even after the program encounters a break during execution.
Input:
for letter in 'Flexi ple':
if letter == ' ':
continue
print ('Letters: ', letter)
Output:
Letters: F
Letters: l
Letters: e
Letters: x
Letters: i
Letters: p
Letters: l
Letters: e
In this example, the program will skip the space ‘ ‘ in the word and continue with the rest of the iteration.
Pass statement
The pass statement is a null operator and is used when the programmer wants to do nothing when the condition is satisfied. This control statement in Python does not terminate or skip the execution, it simply passes to the next iteration.
A loop cannot be left empty otherwise the interpreter will throw an error and to avoid this, a programmer can use the pass statement.
Input:
for letter in 'Flexiple':
if letter == 'x':
pass
print ('Letters: ', letter)
Output:
Letters: F
Letters: l
Letters: e
Letters: x
Letters: i
Letters: p
Letters: l
Letters: e
As you can see in the above example, even though the condition was met, the pass statement didn’t do anything and execution moved to the next iteration.
-----------------------------------string and concept of file-------------------------------------------------
Strings and text files are closely related in Python as strings are often used to read, write, and manipulate text files. In Python, you can open a text file using the built-in `open()` function, which returns a file object. Once you have a file object, you can perform various operations on it, such as reading its contents, writing to it, or modifying its content.
Here's how you can use strings to read the contents of a text file:
```python
# Open the file in read mode
file = open('myfile.txt', 'r')
# Read the entire content of the file
content = file.read()
# Close the file
file.close()
# Print the content
print(content)
```
In the example above, the `open()` function is used to open the file named `'myfile.txt'` in read mode (`'r'`). Then, the `read()` method is called on the file object to read the entire content of the file into a string variable called `content`. Finally, the file is closed using the `close()` method.
Similarly, you can write strings to a text file using the `write()` method:
```python
# Open the file in write mode
file = open('myfile.txt', 'w')
# Write a string to the file
file.write('Hello, World!')
# Close the file
file.close()
```
In this case, the `open()` function is used with the file mode `'w'` to open the file for writing. The `write()` method is then called on the file object to write the string `'Hello, World!'` to the file.
Apart from reading and writing, you can also perform various string manipulation operations on the content of a text file. For example, you can use string methods like `split()`, `replace()`, or `find()` to extract specific information, replace text, or search for patterns within the file's content.
By combining the power of strings and file handling, you can effectively work with text files in Python, whether it's reading data from files, writing data to files, or manipulating the content within files.
Certainly! Here are some additional concepts related to working with text files in Python:
1. File Modes:
When opening a file, you can specify different modes to control how the file is accessed. The most commonly used modes are:
- `'r'`: Read mode (default). Opens the file for reading.
- `'w'`: Write mode. Opens the file for writing. If the file already exists, it gets overwritten. If it doesn't exist, a new file is created.
- `'a'`: Append mode. Opens the file for appending. The content is added to the end of the file. If the file doesn't exist, a new file is created.
- `'x'`: Exclusive creation mode. Creates a new file but raises an error if the file already exists.
- `'b'`: Binary mode. Opens the file in binary mode for reading or writing binary data.
- `'t'`: Text mode (default). Opens the file in text mode for reading or writing text data.
You can combine modes by specifying them together, such as `'rb'` for reading a file in binary mode.
2. Reading Line by Line:
Instead of reading the entire content of a file at once, you can read it line by line using the `readline()` or `readlines()` methods. `readline()` reads one line at a time, while `readlines()` reads all lines and returns them as a list.
```python
file = open('myfile.txt', 'r')
line1 = file.readline()
line2 = file.readline()
file.close()
```
3. Iterating over File Object:
You can iterate over a file object directly using a `for` loop to read the file line by line without manually calling `readline()`.
```python
file = open('myfile.txt', 'r')
for line in file:
print(line)
file.close()
```
4. Writing Multiple Lines:
To write multiple lines to a file, you can pass a list of strings to the `writelines()` method.
```python
lines = ['Line 1\n', 'Line 2\n', 'Line 3\n']
file = open('myfile.txt', 'w')
file.writelines(lines)
file.close()
```
5. Handling Exceptions:
It's a good practice to handle exceptions when working with files. You can use `try...except` blocks to catch and handle any potential errors that may occur during file operations.
```python
try:
file = open('myfile.txt', 'r')
# Perform file operations
except IOError:
print("An error occurred while accessing the file.")
finally:
file.close()
```
6. Context Managers:
Python provides a convenient way to handle file operations using context managers. The `with` statement ensures that the file is automatically closed after the block of code executes, even if an exception occurs.
```python
with open('myfile.txt', 'r') as file:
# Perform file operations
```
In the above example, the file is automatically closed when the `with` block is exited, eliminating the need for an explicit `close()` call.
These are some additional concepts that can enhance your understanding of working with text files in Python. Remember to handle errors appropriately, close the file when you're done, and leverage context managers to ensure clean and efficient file handling.
-------------------------------------------- List and dictionary -------------------------------------------
In Python, both lists and dictionaries are built-in data structures used to store collections of values. They have different characteristics and serve different purposes.
Lists:
Lists are ordered collections of items enclosed in square brackets [].
They can contain elements of different types, such as integers, strings, or even other lists.
Elements in a list are accessed using their index, starting from 0.
Lists are mutable, meaning you can modify, add, or remove elements after creation.
Example of a list:
python
Copy code
fruits = ['apple', 'banana', 'orange', 'grape']
Dictionaries:
Dictionaries are unordered collections of key-value pairs enclosed in curly braces {}.
Each element in a dictionary is stored as a key-value pair, where the key is unique and associated with a value.
Values can be of any type, but keys must be immutable types like strings or numbers.
Elements in a dictionary are accessed using their keys.
Dictionaries are mutable, allowing you to modify, add, or remove key-value pairs.
Example of a dictionary:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
Certainly! Here are some commonly used built-in functions for dictionaries in Python:
keys(): Returns a view object that contains all the keys in the dictionary.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
keys = person.keys()
print(keys) # Output: dict_keys(['name', 'age', 'city'])
values(): Returns a view object that contains all the values in the dictionary.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
values = person.values()
print(values) # Output: dict_values(['John', 30, 'New York'])
items(): Returns a view object that contains all the key-value pairs in the dictionary as tuples.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
items = person.items()
print(items) # Output: dict_items([('name', 'John'), ('age', 30), ('city', 'New York')])
get(): Returns the value associated with a specified key. If the key is not found, it returns a default value.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
name = person.get('name')
print(name) # Output: 'John'
occupation = person.get('occupation', 'Unknown')
print(occupation) # Output: 'Unknown' (default value)
pop(): Removes and returns the value associated with a specified key. If the key is not found, it raises a KeyError or returns a default value if provided.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
age = person.pop('age')
print(age) # Output: 30
print(person) # Output: {'name': 'John', 'city': 'New York'}
occupation = person.pop('occupation', 'Unknown')
print(occupation) # Output: 'Unknown' (default value)
update(): Updates the dictionary with key-value pairs from another dictionary or iterable.
Example:
python
Copy code
person = {'name': 'John', 'age': 30}
extra_info = {'city': 'New York', 'occupation': 'Engineer'}
person.update(extra_info)
print(person) # Output: {'name': 'John', 'age': 30, 'city': 'New York', 'occupation': 'Engineer'}
clear(): Removes all the key-value pairs from the dictionary, making it empty.
Example:
python
Copy code
person = {'name': 'John', 'age': 30, 'city': 'New York'}
person.clear()
print(person) # Output: {}
--------------------------------function in python --------------------------------------------------------
Certainly! Functions in Python are a fundamental concept that allows you to organize and reuse blocks of code. They are defined using the `def` keyword and can have parameters and return values. Here's a comprehensive explanation of various concepts related to functions in Python:
1. Function Definition:
A function is defined using the `def` keyword followed by the function name and parentheses. The function body is indented under the function definition. For example:
```python
def greet():
print("Hello, World!")
```
2. Function Invocation:
Once a function is defined, you can invoke or call it by using its name followed by parentheses. For example:
```python
greet() # Output: Hello, World!
```
3. Parameters:
Functions can accept input parameters, which are values passed to the function when it is called. Parameters are defined within the parentheses in the function definition. For example:
```python
def greet(name):
print("Hello,", name)
```
In this example, the `greet()` function accepts a parameter called `name`, which is then used within the function body.
4. Arguments:
Arguments are the actual values passed to a function when it is called. They can be provided as positional arguments or keyword arguments. Positional arguments are passed in the same order as the parameters, while keyword arguments are explicitly assigned to specific parameters using their names. For example:
```python
greet("Alice") # Output: Hello, Alice
greet(name="Bob") # Output: Hello, Bob
```
5. Return Values:
Functions can return values using the `return` statement. The return statement is followed by the value or expression to be returned. For example:
```python
def add(a, b):
return a + b
result = add(2, 3)
print(result) # Output: 5
```
In this example, the `add()` function returns the sum of two numbers, which is then assigned to the `result` variable.
6. Default Parameters:
Function parameters can have default values assigned to them. If a parameter has a default value, it becomes optional when calling the function. If no value is provided for that parameter, the default value is used. For example:
```python
def greet(name="World"):
print("Hello,", name)
greet() # Output: Hello, World
greet("Alice") # Output: Hello, Alice
```
In this example, the `greet()` function has a default parameter `name` set to `"World"`. If no argument is passed, it will use the default value.
7. Variable Scope:
Variables defined within a function have a local scope, meaning they are only accessible within that function. However, variables defined outside of any function, known as global variables, have a global scope and can be accessed from anywhere in the program. If a local variable has the same name as a global variable, the local variable takes precedence within the function. For example:
```python
global_var = 10
def my_function():
local_var = 5
print(local_var) # Output: 5
print(global_var) # Output: 10
my_function()
print(global_var) # Output: 10
```
8. Docstrings:
Docstrings are used to provide documentation for functions. They are enclosed in triple quotes (`'''...'''` or `"""..."""`) and are placed immediately after the function definition. Docstrings provide information about the purpose of the function, its parameters, return values
, and any other relevant details. For example:
```python
def greet(name):
"""Prints a greeting message with the given name."""
print("Hello,", name)
```
Docstrings are not only helpful for understanding the function's functionality but can also be accessed using the built-in `help()` function.
9. Recursive Functions:
Recursive functions are functions that call themselves within their own function body. They are useful for solving problems that can be divided into smaller subproblems. Recursive functions must have a base case that terminates the recursion. For example, here's a recursive function to calculate the factorial of a number:
```python
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n - 1)
result = factorial(5)
print(result) # Output: 120
```
In this example, the `factorial()` function calls itself to calculate the factorial of `n` by multiplying it with the factorial of `n - 1` until it reaches the base case (`n == 0`).
These are the key concepts related to functions in Python. Functions help make your code modular, reusable, and easier to understand. By defining functions, you can break down complex tasks into smaller, manageable pieces of code.
Certainly! Here are some commonly used built-in functions and methods related to functions in Python:
1. Built-in Functions:
- `len(iterable)`: Returns the length (number of items) of an iterable object, such as a string, list, or tuple.
- `type(object)`: Returns the type of an object.
- `print(*objects, sep=' ', end='\n', file=sys.stdout, flush=False)`: Prints the given objects to the standard output.
- `input(prompt='')`: Reads a line of input from the user via the standard input and returns it as a string.
- `range(start, stop, step)`: Returns an iterable sequence of numbers from `start` to `stop` (exclusive) with a specified `step` size.
- `open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)`: Opens a file and returns a file object.
- `help([object])`: Provides interactive help and documentation for objects, modules, functions, etc.
2. Function-specific Methods:
- `function_name.__doc__`: Returns the docstring (documentation) of a function.
- `function_name.__name__`: Returns the name of a function.
- `function_name.__module__`: Returns the name of the module in which the function is defined.
Here's an example that demonstrates the usage of some of these functions and methods:
```python
def greet(name):
"""Prints a greeting message."""
print("Hello,", name)
print(len("Hello")) # Output: 5
print(type(greet)) # Output: <class 'function'>
print(greet.__doc__) # Output: Prints a greeting message.
print(greet.__name__) # Output: greet
print(greet.__module__) # Output: __main__
help(print)
user_input = input("Enter your name: ")
greet(user_input)
```
In this example, we use the `len()` function to get the length of a string, the `type()` function to determine the type of the `greet` function, and the `help()` function to get information about the `print` function. We also access the docstring, name, and module of the `greet` function using the corresponding methods.
These functions and methods can be useful for retrieving information about functions, manipulating function objects, and performing common tasks in Python programming.
Designing with functions in Python involves organizing your code into modular and reusable functions that perform specific tasks. This approach, known as functional programming, promotes code readability, maintainability, and reusability. Here's a comprehensive explanation of various concepts related to designing with functions in Python:
1. Modularity:
Modularity is the practice of breaking down a program into smaller, self-contained modules or functions. Each function should have a specific purpose and perform a well-defined task. This helps in separating concerns and making the code easier to understand and maintain.
2. Single Responsibility Principle (SRP):
The Single Responsibility Principle states that a function should have only one reason to change. Each function should be responsible for performing a single task or operation. This principle helps in keeping functions focused and prevents them from becoming overly complex.
3. Function Composition:
Function composition is the practice of combining small, reusable functions to build more complex functionality. By chaining together functions, you can achieve complex operations by combining simpler, well-defined functions. This approach promotes code reuse and readability.
4. Pure Functions:
Pure functions are functions that have no side effects and produce the same output for the same input every time they are called. They do not modify any external state or variables and do not rely on external resources. Pure functions are predictable, easier to test, and promote code stability.
5. Avoiding Global State:
Global state refers to variables or data that are accessible from anywhere in the code. It is generally recommended to minimize the use of global state as it can make code harder to reason about and maintain. Instead, functions should take inputs as parameters and return outputs as results, avoiding reliance on external state.
6. Function Parameters:
Function parameters allow you to pass data into a function for processing. There are two types of function parameters: positional parameters and keyword parameters. Positional parameters are passed in the same order as defined in the function signature, while keyword parameters are explicitly assigned values using their names. Parameters provide flexibility and allow functions to be reused with different inputs.
7. Return Values:
Functions can return values using the `return` statement. The return statement allows a function to produce an output or result that can be used by other parts of the program. Functions can return single values, multiple values (as a tuple), or even other functions (higher-order functions).
8. Function Documentation:
Documentation is essential for understanding the purpose, usage, and behavior of functions. It is recommended to provide descriptive docstrings (documentation strings) for each function. Docstrings should describe what the function does, list the parameters it accepts, and explain the return values or side effects, if any.
9. Error Handling:
Functions can handle errors and exceptions using the `try...except` statements. By encapsulating potentially error-prone code within functions and handling exceptions appropriately, you can improve code robustness and maintainability.
10. Function Naming:
Choosing meaningful and descriptive names for functions is crucial for code readability and understandability. Function names should accurately reflect the purpose or action performed by the function. Use verbs or verb phrases to indicate what the function does.
11. Testing and Debugging:
Functions should be thoroughly tested to ensure their correctness and reliability. Unit tests can be written to validate the behavior of functions and detect any potential issues. Additionally, functions can be debugged using appropriate debugging techniques and tools to identify and fix problems.
By incorporating these concepts into your design approach, you can create well-structured, reusable, and maintainable code using functions in Python.
--------------------------------------class in python ------------------------------------------------------------------
Certainly! Classes in Python are used to define objects that have properties (attributes) and behaviors (methods). They provide a way to structure and organize code in an object-oriented manner. Here's a comprehensive explanation of various concepts related to classes in Python:
1. Class Definition:
A class is defined using the `class` keyword followed by the class name. The class body is indented under the class definition. For example:
```python
class MyClass:
pass
```
In this example, we define a simple class called `MyClass` using the `class` keyword.
2. Object Instantiation:
Once a class is defined, you can create objects (instances) of that class. This is known as object instantiation. To instantiate an object, you call the class as if it were a function. For example:
```python
obj = MyClass()
```
In this example, we create an object `obj` of the `MyClass` class.
3. Attributes:
Attributes are variables associated with a class or its objects. They represent the state or characteristics of the objects. Attributes can be defined within the class body or dynamically assigned to objects. For example:
```python
class Person:
def __init__(self, name):
self.name = name
person = Person("Alice")
print(person.name) # Output: Alice
```
In this example, the `Person` class has an attribute `name` that is assigned during object initialization using the special method `__init__()`.
4. Methods:
Methods are functions defined within a class. They define the behavior of the objects of the class. Methods can access and modify the attributes of the class. For example:
```python
class Person:
def __init__(self, name):
self.name = name
def greet(self):
print("Hello, my name is", self.name)
person = Person("Alice")
person.greet() # Output: Hello, my name is Alice
```
In this example, the `Person` class has a method `greet()` that prints a greeting message using the `name` attribute of the object.
5. Constructor (`__init__()`):
The `__init__()` method is a special method used to initialize the attributes of an object. It is called automatically when an object is instantiated from a class. For example:
```python
class Person:
def __init__(self, name):
self.name = name
person = Person("Alice")
```
In this example, the `__init__()` method initializes the `name` attribute of the `Person` class with the value passed during object instantiation.
6. Inheritance:
Inheritance is a mechanism in which a class inherits attributes and methods from another class. The class from which a class inherits is called the superclass or parent class, and the class that inherits is called the subclass or child class. Subclasses can extend or override the attributes and methods of the superclass. For example:
```python
class Animal:
def sound(self):
print("Animal makes a sound")
class Dog(Animal):
def sound(self):
print("Dog barks")
dog = Dog()
dog.sound() # Output: Dog barks
```
In this example, the `Dog` class inherits the `sound()` method from the `Animal` class and overrides it with its own implementation.
7. Encapsulation:
Encapsulation is the principle of bundling data and the methods that operate on that data within a class. It helps in organizing and protecting data by preventing direct access from outside
the class. By default, all attributes and methods in a class are accessible from outside. However, you can use naming conventions to indicate that an attribute or method should be treated as private or internal. For example:
```python
class MyClass:
def __init__(self):
self._private_attr = 10
self.__private_attr = 20
def _private_method(self):
print("This is a private method")
def __private_method(self):
print("This is a private method")
obj = MyClass()
print(obj._private_attr) # Output: 10
print(obj.__private_attr) # Error: AttributeError
obj._private_method() # Output: This is a private method
obj.__private_method() # Error: AttributeError
```
In this example, the attributes and methods prefixed with a single underscore `_` are considered conventionally private, while those prefixed with double underscore `__` undergo name mangling, making them more difficult to access from outside the class.
8. Class and Instance Variables:
Class variables are shared by all instances of a class, while instance variables are specific to each instance of a class. Class variables are defined within the class body but outside any method, whereas instance variables are defined within methods using the `self` keyword. For example:
```python
class Circle:
pi = 3.14
def __init__(self, radius):
self.radius = radius
def area(self):
return self.pi * self.radius * self.radius
circle1 = Circle(5)
circle2 = Circle(7)
print(circle1.area()) # Output: 78.5
print(circle2.area()) # Output: 153.86
```
In this example, `pi` is a class variable, shared by all instances of the `Circle` class. `radius` is an instance variable that varies for each instance.
These are the key concepts related to classes in Python. Classes provide a way to create objects with attributes and methods, allowing you to model real-world entities and implement complex behaviors. Inheritance and encapsulation contribute to code reusability and organization, making classes a powerful tool in object-oriented programming.
---------------------------------------encapulation vs abstraction ---------------------------------------------
Encapsulation and abstraction are two fundamental concepts in object-oriented programming, including Python. While they are related and often used together, they serve different purposes:
Encapsulation:
Encapsulation is the practice of bundling data (attributes) and the methods (functions) that operate on that data within a single unit called a class. It hides the internal details and implementation of an object from the outside world and provides a well-defined interface for interacting with the object. Encapsulation allows for information hiding and protects the internal state of an object from being accessed or modified directly by external code.
In Python, encapsulation is achieved by using access modifiers to control the visibility and accessibility of attributes and methods. The access modifiers are:
- Public (`public`): Attributes and methods are accessible from anywhere, both within and outside the class.
- Protected (`protected`): Attributes and methods are intended to be accessed within the class itself and its subclasses.
- Private (`private`): Attributes and methods are intended to be accessed only within the class itself.
Python uses naming conventions to indicate the visibility of attributes and methods. By convention, attributes and methods that are intended to be private are prefixed with a single underscore (`_`), indicating that they should not be accessed directly from outside the class. However, it is still possible to access and modify them, as Python does not enforce strict access control.
Abstraction:
Abstraction is the process of simplifying complex systems by representing essential features while hiding unnecessary details. It focuses on defining a clear and concise interface for interacting with objects without exposing the internal implementation. Abstraction allows you to work with objects at a higher level of understanding, without needing to know the intricate details of how they are implemented.
In Python, abstraction is achieved by defining abstract classes and interfaces. An abstract class is a class that cannot be instantiated directly but serves as a blueprint for its subclasses. It may contain one or more abstract methods, which are declared but do not have an implementation in the abstract class. Subclasses of the abstract class must provide an implementation for these abstract methods.
Python provides the `abc` module to support abstraction through abstract base classes. By inheriting from abstract base classes and implementing their abstract methods, you ensure that objects of your classes adhere to a specific contract or interface, allowing for polymorphism and code reuse.
The main difference between encapsulation and abstraction is that encapsulation focuses on bundling data and methods within a class and controlling their visibility, while abstraction focuses on providing a simplified interface for interacting with objects and hiding unnecessary implementation details.
In summary, encapsulation is about data hiding and controlling access, while abstraction is about simplifying complex systems and providing a clear interface. Encapsulation is achieved through access modifiers, while abstraction is achieved through abstract classes and interfaces.
------------------------------------------------access modifier---------------------------------
In Python, access modifiers are used to control the visibility and accessibility of attributes and methods within a class. They help enforce encapsulation by defining the level of access that external code has to the internal components of a class. Although Python does not enforce strict access control, naming conventions are used to indicate the intended visibility of attributes and methods. Here are the three access modifiers commonly used in Python:
1. Public Access Modifier:
- Syntax: No special syntax is required. Attributes and methods without any prefix are considered public by convention.
- Description: Public attributes and methods are accessible from anywhere, both within and outside the class.
- Example:
```python
class MyClass:
def public_method(self):
return "This is a public method"
def public_attribute(self):
return "This is a public attribute"
obj = MyClass()
print(obj.public_method()) # Output: This is a public method
print(obj.public_attribute()) # Output: This is a public attribute
```
In this example, the `public_method()` and `public_attribute()` methods are accessible from outside the class.
2. Protected Access Modifier:
- Syntax: Attributes and methods are prefixed with a single underscore `_`.
- Description: Protected attributes and methods are intended to be accessed within the class itself and its subclasses.
- Example:
```python
class MyClass:
def _protected_method(self):
return "This is a protected method"
def _protected_attribute(self):
return "This is a protected attribute"
class SubClass(MyClass):
def access_protected(self):
print(self._protected_method()) # Output: This is a protected method
print(self._protected_attribute()) # Output: This is a protected attribute
obj = SubClass()
obj.access_protected()
```
In this example, the `_protected_method()` and `_protected_attribute()` methods are intended for internal use within the class and its subclasses. They can be accessed from the `SubClass` subclass.
3. Private Access Modifier:
- Syntax: Attributes and methods are prefixed with a double underscore `__`.
- Description: Private attributes and methods are intended to be accessed only within the class itself.
- Example:
```python
class MyClass:
def __private_method(self):
return "This is a private method"
def __private_attribute(self):
return "This is a private attribute"
def access_private(self):
print(self.__private_method()) # Output: This is a private method
print(self.__private_attribute()) # Output: This is a private attribute
obj = MyClass()
obj.access_private()
```
In this example, the `__private_method()` and `__private_attribute()` methods are intended to be used only within the `MyClass` class. They cannot be accessed directly from outside the class.
It's important to note that access modifiers in Python are primarily based on naming conventions and are not strictly enforced. They act as a form of documentation and help communicate the intended visibility of attributes and methods to other developers. However, it is still possible to access and modify attributes and methods with any access modifier from outside the class, though it is considered a best practice to respect the conventions and treat them as intended.
Comments
Post a Comment