1.4 Gen AI: Python Refresher

Sun, 29 Mar 26

Python Functions Overview

• Functions are collections of related statements for specific actions

• Execute only when called

• Support both parameterized and non-parameterized functions

• Key advantages:

• Modularity - avoid monolithic code chunks

• Enhanced comprehensibility and programming quality

• Promote reusability across application

• Improve scalability and ease of use

Function Syntax & Structure

• Basic syntax: def function_name(arguments):

• Function name can be any valid Python variable name

• Arguments are optional (zero to multiple allowed)

• Function calling requires matching the original structure

• Example: def multiply_numbers(n1, n2): return n1 * n2

Function Arguments Types

• Positional arguments: must match exact order from function definition

• Keyword arguments: referenced by name, can be in any order

• Can mix both positional and keyword arguments

• Default values can be defined for arguments

• Arbitrary arguments using *args (non-keyword) and **kwargs (keyword)

• *args captures variable number of arguments into tuple

• **kwargs allows named variables, works like dictionary mapping

Return vs Yield Statements

• Return statement: passes control back to caller, destroys local variables

• Yield statement: temporary transfer of control, preserves function state

• Functions with yield statements are called generators

• Generators return iterator objects with sequence of values

• Can be used with next() function or for loops

Lambda Functions

• Anonymous functions defined with lambda keyword

• Syntax: lambda argument: expression

• May have any number of arguments but only one expression

• Reduce code length compared to regular function definitions

• Example: add_10 = lambda x: x + 10

Built-in Functional Programming Methods

• Map function: applies function to each element of iterable

• Syntax: map(function, iterable)

• Example: converting Celsius to Fahrenheit for temperature list

• Filter function: filters data based on conditional function

• Returns boolean values to determine inclusion

• Example: filtering odd numbers using modulo operation

• Reduce function: processes iterable to produce single cumulative value

• Must import from functools: from functools import reduce

Variable Scope

• Global scope: variables/functions defined outside all classes and functions

• Accessible anywhere within the program

• Local scope: variables/functions defined inside function blocks

• Only accessible within that specific function

• Analogy: global scope like company database (universal access), local scope like personal Excel file