Python
Python Basics
- Introduction to Python and Its History
- Python Syntax and Indentation
- Python Variables and Data Types
- Dynamic and Strong Typing
- Comments and Docstrings
- Taking User Input (input())
- Printing Output (print())
- Python Operators (Arithmetic, Logical, Comparison)
- Type Conversion and Casting
- Escape Characters and Raw Strings
Data Structures in Python
- Lists
- Dictionaries
- Dictionary Comprehensions
- Strings and String Manipulation
- Tuples
- Python Sets: Unordered Collections
- List Comprehensions and Generator Expressions
- Set Comprehensions
- String Formatting
- Indexing and Slicing
Control Flow and Loops
- Conditional Statements: if, elif, and else
- Loops and Iteration
- While Loops
- Nested Loops
- Loop Control Statements
- Iterators and Iterables
- List, Dictionary, and Set Iterations
Functions and Scope
- Defining and Calling Functions (`def`)
- Function Arguments (`*args`, `**kwargs`)
- Default Arguments and Keyword Arguments
- Lambda Functions
- Global and Local Scope
- Function Return Values
- Recursion in Python
Object-Oriented Programming (OOP)
- Object-Oriented Programming
- Classes and Objects
- the `__init__()` Constructor
- Instance Variables and Methods
- Class Variables and `@classmethod`
- Encapsulation and Data Hiding
- Inheritance and Subclasses
- Method Overriding and super()
- Polymorphism
- Magic Methods and Operator Overloading
- Static Methods
- Abstract Classes and Interfaces
Python Programs
- Array : Find median in an integer array
- Array : Find middle element in an integer array
- Array : Find out the duplicate in an array
- Array : Find print all subsets in an integer array
- Program : Array : Finding missing number between from 1 to n
- Array : Gap and Island problem
- Python Program stock max profit
- Reverse words in Python
- Python array duplicate program
- Coin change problem in python
- Python Write fibonacci series program
- Array : find all the pairs whose sum is equal to a given number
- Find smallest and largest number in array
- Iterate collections
- List comprehensions
- Program: Calculate Pi in Python
- String Formatting in Python
List comprehensions in Python
In Python, list comprehensions provide a concise and efficient way to create new lists based on existing lists or other iterable objects. List comprehensions follow a specific syntax that allows you to combine looping and conditional expressions in a single line of code.
Here’s the general syntax of a list comprehension:
new_list = [expression for item in iterable if condition]
expression is the expression or transformation you want to apply to each item.
item is the variable representing each element in the iterable.
iterable is the existing list, tuple, string, or any other iterable object.
condition is an optional condition that filters elements based on a specific criterion.
Here are a few examples to illustrate the usage of list comprehensions:
h_letters = [ letter for letter in 'human' ]print( h_letters)#list comprehensions are usually more human readable than lambda functionsletters = list(map(lambda x: x+"new", 'human'))print(letters)
number_list = [ x for x in range(20) if x % 2 == 0]print(number_list)
#nested if
num_list = [y for y in range(100) if y % 2 == 0 if y % 5 == 0]print(num_list)
obj = ["Even" if i%2==0 else "Odd" for i in range(10)]print(obj)
List comprehensions offer a concise and readable way to manipulate lists and perform operations on their elements. They are widely used in Python programming to simplify code and make it more expressive.