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"Python Programming Essentials:
Comprehensive Notes for Beginners and
Beyond"
Topics Covered:-
1.Variables and Data Types
In Python, variables are used to store data values. They act as placeholders that can be assigned values of di erent data types. Each variable has a name, which is used to access and manipulate the stored value. Python is dynamically typed, which means you don't need to declare the data type of a variable explicitly. Instead, the data type is inferred automatically based on the value assigned to it. Data types in Python represent the kinds of values that variables can hold. Python has several built-in data types, including:
- Variables and Data Types
- Operators and Expressions
- Control Flow (if-else statements, loops)
- Data Structures (lists, tuples, dictionaries, sets)
- Functions and Modules
- File Handling (reading and writing files)
- Exception Handling
- Object-Oriented Programming (classes, objects, inheritance, polymorphism)
- Regular Expressions
- Working with Strings
- Working with Dates and Times
- Built-in Functions and Libraries
- List Comprehensions
- Generators and Iterators
- Decorators
- Context Managers
- Multithreading and Multiprocessing
- Numeric Types: Integers (int): Whole numbers, such as 1, 2, -5, etc. Floating-Point Numbers (float): Real numbers with decimal points, such as 3.14, 2.0, -1.5, etc. Complex Numbers (complex): Numbers with real and imaginary parts, represented as a + bj, where a and b are floats or integers.
- Text Type:
Variables can be assigned values using the assignment operator (=). For example: x = 10 # assigning an integer value to x name = 'John' # assigning a string value to name numbers = [1, 2, 3, 4] # assigning a list to numbers Python allows variables to be reassigned to di erent values, even with di erent data types. The data type of a variable can change dynamically during the execution of a program. x = 10 print(x) # Output: 10 x = 'Hello' print(x) # Output: Hello Understanding variables and data types is fundamental to Python programming, as they form the basis for storing and manipulating information within a program. Strings (str): Sequences of characters enclosed in single quotes ('') or double quotes (""). For example, "Hello, World!" or 'Python'.
- Sequence Types: Lists (list): Ordered, mutable collections of elements enclosed in square brackets ([]). Elements can be of di erent data types. For example, [1, 2, 3, 'four']. Tuples (tuple): Ordered, immutable collections of elements enclosed in parentheses (()). Elements can be of di erent data types. For example, (1, 2, 'three'). Range (range): Represents a sequence of numbers generated by the range() function. It is commonly used in loops.
- Mapping Type: Dictionaries (dict): Unordered collections of key-value pairs enclosed in curly braces ({}). Keys are unique and used to access corresponding values. For example, {'name': 'John', 'age': 25}.
- Set Types: Sets (set): Unordered collections of unique elements enclosed in curly braces ({}). Duplicates are not allowed. For example, {1, 2, 3}. Frozen Sets (frozenset): Immutable sets, similar to sets, but their elements cannot be changed after creation.
- Boolean Type: Booleans (bool): Represents either True or False. These are often the result of comparisons and logical operations.
- None Type: None (None): Represents the absence of a value or a null value.
- Exponentiation Assignment (
**=): Raises the left operand to the power of the right operand and assigns the result to the left operand. - Floor Division Assignment (
//=): Performs floor division on the left operand with the right operand and assigns the result to the left operand.
- Comparison Operators:
- Equal to (
==): Returns True if the operands are equal; otherwise, returns False. - Not equal to (
!=): Returns True if the operands are not equal; otherwise, returns False. - Greater than (
>): Returns True if the left operand is greater than the right operand; otherwise, returns False. - Less than (
<): Returns True if the left operand is less than the right operand; otherwise, returns False. - Greater than or equal to (
>=): Returns True if the left operand is greater than or equal to the right operand; otherwise, returns False. - Less than or equal to (
<=): Returns True if the left operand is less than or equal to the right operand; otherwise, returns False.
- Logical Operators:
- Logical AND (
and): Returns True if both operands are True; otherwise, returns False. - Logical OR (
or): Returns True if at least one of the operands is True; otherwise, returns False. - Logical NOT (
not): Returns the opposite boolean value of the operand.
- Membership Operators:
in: Returns True if a value is found in the specified sequence; otherwise, returns False.not in: Returns True if a value is not found in the specified sequence; otherwise, returns False.
- Identity Operators:
is: Returns True if both operands refer to the same object; otherwise, returns False.
is not: Returns True if both operands do not refer to the same
3.Control Flow (if-else statements, loops)
Control flow is a fundamental concept in Python that allows you to control the execution of your code based on certain conditions or perform repetitive tasks using loops. The two main components of control flow in Python are if-else statements and loops.
- If-else Statements:
- If statement: It allows you to execute a block of code only if a specified condition is true. The syntax is as follows:
if condition: # code block to execute if condition is true
- If-else statement: It allows you to execute one block of code if a condition is true and another block of code if the condition is false. The syntax is as follows:
if condition: # code block to execute if condition is true else: # code block to execute if condition is false
- If-elif-else statement: It allows you to check multiple conditions and execute di erent code blocks accordingly. The
elif (short for "else if") keyword is used to specify additional conditions. The syntax is as follows:
numbers = [1, 2, 3, 4, 5] for number in numbers: print(number)
Output:
1 2 3 4 5
- Range function: It is often used with for loops to generate a sequence of numbers. The `range()` function generates a sequence of numbers from a starting value to an ending value (exclusive) with a specified step size. The syntax is as follows: ```python for i in range(start, stop, step): # code block to execute
For example:
for i in range(1, 6): print(i)
Output:
1 2 3 4 5
- Loop Control Statements:
break: It terminates the loop and moves the execution to the next statement after the loop.
continue: It skips the rest of the code block for the current iteration and moves the execution to the next iteration. Control flow statements like if-else statements and loops allow you to make decisions and repeat tasks, giving you more control over the flow of your program. They are essential for creating dynamic and e cient code.
4.Data Structures (lists, tuples, dictionaries, sets)
Data structures are fundamental concepts in programming that allow you to organize and store collections of data. In Python, there are four commonly used built-in data structures: lists, tuples, dictionaries, and sets. Each has its own characteristics and use cases.
- Lists:
- Lists are ordered, mutable collections of elements enclosed in square brackets ([]). Elements can be of any data type, and they can be accessed and modified using indices. Lists allow duplicate values and maintain the order of elements.
- Example:
```
Common operations on these data structures include:
- Accessing Elements: Elements in lists, tuples, and dictionaries can be accessed using indexing or keys.
fruits = ['apple', 'banana', 'cherry'] print(fruits[0]) # Output: 'apple' person = {'name': 'John', 'age': 25} print(person['age']) # Output: 25
- Modifying Elements: Lists and dictionaries allow you to modify elements.
fruits = ['apple', 'banana', 'cherry'] fruits[0] = 'orange' print(fruits) # Output: ['orange', 'banana', 'cherry'] person = {'name': 'John', 'age': 25} person['age'] = 30 print(person) # Output: {'name': 'John', 'age': 30}
- Adding Elements: Lists and dictionaries allow you to add elements.
fruits = ['apple', 'banana', 'cherry'] fruits.append('orange') print(fruits) # Output: ['apple', 'banana', 'cherry', 'orange'] person = {'name': 'John', 'age': 25} person['city'] = 'New York' print(person) # Output: {'name': 'John', 'age': 25, 'city': 'New York'}
- Removing Elements: Lists, tuples, and dictionaries provide methods to remove elements. ```python fruits = ['apple', 'banana', 'cherry'] fruits.remove('banana') print(fruits) # Output: ['apple', 'cherry'] person = {'name': 'John', 'age': 25} del person['age'] print(person) # Output: {'name': 'John'} ``` These data structures are versatile and can be combined to represent complex data structures and solve various programming problems. ## 5.Functions and Modules Functions and modules are essential components of modular programming in Python. They help organize code into reusable and manageable units, promoting code reusability, readability, and maintainability. - Return Statement: Functions can optionally return a value using the `return` statement. The return value can be assigned to a variable or used directly in the code. Example of a function with a return statement: ```python def add(a, b): return a + b result = add(3, 5) print(result) # Output: 8
Modules: A module is a file containing Python definitions and statements. It serves as a way to organize related code into separate files, making it easier to manage and reuse code. Modules can contain functions, classes, variables, and other Python objects. To use a module, it needs to be imported into your code. Example of importing a module and using its function:
# Importing the math module import math # Using the math module's function result = math.sqrt(16) print(result) # Output: 4. #### ``` - Importing Modules: Modules can be imported using the `import` statement. There are di erent ways to import modules: - `import module_name`: Imports the entire module and makes its functions and objects accessible using the module name as a prefix. - `import module_name as alias`: Imports the module and assigns an alias to it, allowing you to use a shorter name when referencing the module. - `from module_name import function_name`: Imports only a specific function or object from the module, allowing you to use it directly without the module name prefix. - `from module_name import *`: Imports all functions and objects from the module, allowing you to use them directly without the module name prefix. Example of importing specific functions from a module: ```python from math import sqrt, pi result = sqrt(25) print(result) # Output: 5. circumference = 2 * pi * 5 print(circumference) # Output: 31.
Functions and modules are powerful tools in Python that enable code organization, reusability, and modularity. They allow you to break down complex tasks into smaller, manageable functions and utilize external code libraries e ciently.
file = open("file.txt", "r") content = file.read() print(content) file.close()
- Writing to a File:
- Appending to a File:
- Closing a File:
To simplify file handling and ensure proper handling of resources, it is recommended to use the with statement. The with statement automatically closes the file once you are done with it, even if an exception occurs within the block. Example:
with open("file.txt", "r") as file: content = file.read() print(content)
File handling in Python allows you to work with files, read data from them, write data to
them, and perform various file-related operations. Proper handling and closing of files are important to ensure e cient and safe file operations.
7.Exception Handling
Exception handling in Python allows you to handle and manage errors or exceptional situations that may occur during the execution of a program. By using exception handling,
finally:
Code to execute regardless of exceptions
3. Handling Multiple Exceptions: - You can handle multiple exceptions by including multiple `except` blocks. The `except` blocks are checked in the order they are written, and the first matching `except` block is executed. - Example: ```python try: # Code that might raise exceptions except ValueError: # Code to handle ValueError except FileNotFoundError: # Code to handle FileNotFoundError ``` 4. Raising Exceptions: - You can raise exceptions explicitly using the `raise` statement. This is useful when you want to raise an exception based on certain conditions in your code. - Example: ```python x = 10 if x > 5: raise ValueError("x should not exceed 5") ``` 5. Handling Exceptions with Context Managers: - Python provides the `with` statement, also known as a context manager, for managing resources that need to be cleaned up automatically. Context managers are commonly used with files, database connections, and other resources. - Example using file handling: ```python with open("file.txt", "r") as file: # Code to read from the file ``` Exception handling allows you to handle errors gracefully and provides better control over the flow of your program. By handling exceptions, you can prevent unexpected crashes and ensure that your program handles errors in a controlled manner. ### 8.Object-Oriented Programming ### (classes,objects,inheritance,polymorphism) Object-Oriented Programming (OOP) is a programming paradigm that organizes code into objects, which are instances of classes. OOP focuses on modeling real-world entities as objects and provides concepts like encapsulation, inheritance, and polymorphism. Here's an explanation of the key concepts in OOP: 1. Classes: - A class is a blueprint or a template that defines the properties (attributes) and behaviors (methods) of objects. It encapsulates data and functions into a single unit. Objects are instances of classes. - Example of a class definition: ```python class Car: def __init__(self, make, model):
