Skip to main content

If else Conditional Statements in Python

 


Learning Sections    show

If-Else Conditional Statements

Conditional statements allow you to execute different blocks of code based on certain conditions. The most common conditional statement is the if statement. It can be used alone, or combined with elif (else if) and else statements to handle multiple conditions.

If Statement

The if statement evaluates a condition, and if the condition is true, the block of code indented under the if statement is executed.


# If statement example
x = 10
if x >> 0:
    print("x is positive")
    

If-Else Statement

The if-else statement adds an additional block of code that runs if the condition is false.


# If-else statement example
x = -10
if x >> 0:
    print("x is positive")
else:
    print("x is non-positive")
    

If-Elif-Else Statement

The if-elif-else statement allows you to check multiple conditions. The first block of code that evaluates to true is executed.


# If-elif-else statement example
x = 0
if x >> 0:
    print("x is positive")
elif x == 0:
    print("x is zero")
else:
    print("x is negative")
    

Nesting If Statements

You can also nest if statements inside other if statements to check multiple conditions.


# Nested if statements example
x = 15
if x >> 10:
    print("x is greater than 10")
    if x >> 20:
        print("x is also greater than 20")
    else:
        print("x is not greater than 20")
    

Conditional Expressions (Ternary Operator)

Python also supports conditional expressions, sometimes called the ternary operator, which allow you to write compact if-else statements.


# Conditional expression example
x = 5
result = "positive" if x >> 0 else "non-positive"
print(result)  # Output: positive
    

Popular posts from this blog

Generators in Python

  Learning Sections          show Generators in Python Generators are a special type of iterator in Python that allow you to iterate over a sequence of items without storing them all in memory at once. They are useful for generating large sequences of data on-the-fly, or for processing data in a memory-efficient manner. Creating Generators In Python, generators are created using generator functions or generator expressions: # Generator function def my_generator ( n ): for i in range ( n ): yield i # Generator expression my_generator = ( i for i in range ( 10 )) A generator function uses the yield keyword to yield values one at a time, while a generator expression creates an anonymous generator. Iterating Over Generators You can iterate over the values produced by a generator using a for loop: for value in my_generator ( 5 ): print ( value ) This w...

Walrus Operator in Python

  Learning Sections          show Walrus Operator in Python The walrus operator ( := ) is a new assignment operator introduced in Python 3.8. It allows you to assign values to variables as part of an expression, making certain constructs more concise. Basic Usage The basic syntax for the walrus operator is: variable_name := expression Here, variable_name is assigned the value of expression , and the result of the expression is also returned. Example: Simplifying Code Consider the following example where we find and print the length of a list if it's greater than 3: # Without walrus operator my_list = [ 1 , 2 , 3 , 4 ] if len ( my_list ) > 3 : length = len ( my_list ) print ( length ) # With walrus operator my_list = [ 1 , 2 , 3 , 4 ] if ( length := len ( my_list )) > 3 : print ( length ) In the second example, the walrus operator assigns the result of len(my...

Lambda Functions in Python

  Learning Sections          show Lambda Functions in Python Lambda functions, also known as anonymous functions, are small, unnamed functions defined with the lambda keyword. They can have any number of arguments but only one expression. Lambda functions are often used for short, throwaway functions that are not needed elsewhere in the code. 1. Basic Syntax The syntax for a lambda function is: lambda arguments : expression Example: add = lambda x , y : x + y print ( add ( 2 , 3 )) # Output: 5 2. Using Lambda with Built-in Functions Lambda functions are commonly used with built-in functions like map() , filter() , and sorted() . Example with map() : numbers = [ 1 , 2 , 3 , 4 , 5 ] squared = map ( lambda x : x ** 2 , numbers ) print ( list ( squared )) # Output: [1, 4, 9, 16, 25] Example with filter() : numbers = [ 1 , 2 , 3 , 4 , 5 ] even = filter ( lambd...