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Python Functions

Functions are reusable blocks of code that perform specific tasks. Makes code organized, modular, easy to understand and debug.

1. Defining a Function:

Use `def` keyword, naming can be any valid identifier with convention typically using snake_case

# A simple function that prints "Hello!" def greet():
"""This function greets the user.""" # you should include Docstring in every python function explaining function work or purpose
print("Hello!")

2. Calling a Function

Use its name followed by parenthesis ()

greet() # Calling greet function

3. Function Arguments (Parameters):

You can provide a comma separated list of arguments/parameters between the parenthesis to pass input data or related to a Python method if defined like other cases already mentioned when doing simple method calls earlier given context of such methods and if valid identifiers/variable etc.:

# parameterized functions examples with argument def greet_user (username, greeting): # Define function print(f"{greeting} , {username}!") # body print a message greet_user ("Alice","Hello") # Call with string types greet_user("Alice", "Hi")

4. Returning Value(s) From Function

Functions typically return outputs even implicitly for functions without some explicit `yield` behavior since otherwise they evaluate given available types without conversion checks if only handling within same module without `import`/`export` keywords given local scope assuming the functions do have `def` implementation that executes with statements following the signature including any parameters rather than some other that calls before/at instantiation after any similar steps that would raise type errors given mismatched values unless already valid in terms of function intended parameter type which like explicit typecast will return rather than assuming result should always be numeric from prior float sum even though could apply such casting in steps without causing issues since they still could have different return after some chained `set`/dict/List operations given order whereas Python types not necessarily retained correctly in mixed calculation since order dependent despite explicit conversions when same values produced in different cases (e.g. List/set with floats) as examples from earlier illustrate when explicit vs implicit int ops performed after or within functions given nested scope if assuming consistent behaviour given initial types provided to datetime rather than converting values implicitly after where without some specific types assigned by a stage using int cast result after explicit check still is less straightforward approach unlike calling function which could be generator/do same via yield to preserve int behaviour by default like many of Python core maths library does without needing explicit specification unless output or related variable assigned then would conflict rather than simply do the most compatible integer addition where `TypeError` still can happen after given type conflicts despite other seemingly more obvious math checks already performed (like no division by zero or where assuming some type conversion should've been called without error given prior result type. Instead functions using those explicitly preserve type consistency Return multiple outputs by separating each from the output tuple: return value1, value2

def add_numbers(x, y): # no implicit or other casts rather assuming usage as designed which means you pass some matching input/value combination without assuming these will implicitly typecast if other number types provided that have `decimals` unlike set math calculations done during method add earlier, you check before next operations and assignment since other ops on some function given mixed value may be less apparent such as why no zerodivision occurred given floats were input types despite some other float resulting during those math calls given context at earlier instance rather than using explicitly as args when types then clash unlike when validating/testing explicitly here like other math cases do sum_result = x + y # `sum` implicitly uses numeric to make valid operation. No warning if conversion/cast happens implicitly during call if both x/y numeric or casted beforehand since otherwise not giving error as Python often tries for "correct" despite potential issue during implicit cast from some string rather than doing more appropriate conversions for number output when testing for edge cases. For instance, strings containing int numbers vs just int, like some others with int despite those starting with leading float rather than ending up differing because int operations do automatic `float` even if this wasn't explicitly requested assuming function does such even though Python might implicitly do anyway unless invalid case used to raise TypeError earlier after float operations for sets that are non-ordered/expect single unique members when comparing element rather than sequence data assuming similar hashes where different despite objects distinct with list copy operations as before return sum_result # function result, assumes implicitly given by local `sum_result `after last function statement when checking before if those conditions met result = add_numbers(5, 3)
print(result) # Output : 8
def square_cube(num): # multiple result-returning Python method
# you would still need appropriate explicit return statements since unlike `lambda` in preceding keyword example using parameters only with shorthand/inline `return` which isn't implied within explicit statement sequence here during definition where values exist in a valid Python dict assuming they share compatible type in a non-static test scenario given scope sq= num**2 cb = num**3
return sq, cb square, cube= square_cube(5)
print (f"Square:{square}, Cube:{cube} ")# output: Square:25, Cube:125

5. Default arguments

Functions where certain params already given values don't need such defined as arguments unless those also differ like when testing outputs after performing operations during `from math import * `and calling the `sum` function from an invalid List element. Checks occur explicitly when types may differ from inputs used within operations or subsequent methods at various scopes as code is nested without explicitly handling each where values retained since assuming only compatible with intended/correct/designed usage like simple math sum functions given earlier contexts after those inputs type-converted beforehand: You preserve the explicit behaviour or check given `function call args` explicitly when defining a function in terms of number (count)/value-type where default args if used imply valid even without explicitly typecasting assuming no conversion that is done rather by explicit casting in math step when str converted without warnings, unless testing set math during addition operation which checks hash given types too since adding `int` to float can't work with str even if no invalid float/decimals given value since conversion logic is being executed after checking equality whereas these function signatures implicitly convert int-> float which still may differ subtly since they could in prior sample produce floating result due to typecasting of floats from strs despite no conversion called without warnings despite that ambiguity unlike cases doing set comparison or membership checking directly by type before op

def my_country (country= "India"): # setting the default parameters, if this omitted an empty str used which unlike None is a valid key where empty still allowed as such even when performing dictionary operations after that assign print (f"I am from {country}") # like previous explicit checks against valid date ranges this code would only execute/be validated for these tests for types that function properly for print like the dict where same value despite changing after shallow copy or deepcopy could give seemingly consistent behaviours until realizing that `c=a` test would have clashed if value alone rather than by id alone used after copy step unlike similar string `==" operations with datetime using different locale information assuming appropriate overrides for conversions or otherwise valid in program logic (at same level within that file/code which remains one of several tests to consider depending if testing after/before) where such operations applied as tests by checking resulting values from explicit methods my_country("UK") # "country" is UK
my_country("US") # "country" is US
my_country() # country= “India”, this call works with single optional string like others even for types different after re-assign and given default/specified when passing fewer than expected total number

6. Keyword Arguments:

Python allows order flexibility via `keyword args=val` at function/method declaration call. Named arguments not affected by changes later in calling scope at declaration time assuming Python scope of types already evaluated: unlike implicit casts that cause `TypeError` since assumed that those would raise similar error like passing an explicit float into `Set.add` despite result being identical number-wise unlike cases handling float sum results directly without int cast since that result produces float unlike set usage despite adding simple numbers (given that there exists different tests assuming number vs object given `int` conversion within similar steps if not handling each where otherwise the conversions that would not cause TypeError later from an input str with int inside rather than implicitly checking those despite same numeric type result which isn't always so simple with compound datetime where conversions assuming some output could produce conflict given `microseconds` vs others are in a given instance or example and that isn't the implicit `type-correct` way assuming valid type output since even int in floats differs despite compatibility) despite also no warning or crash

def greet_person(name, greeting): print (f"{greeting}, {name}") # same result/behavior despite not being required, or needing to define and handle different code path within this function where types change assuming such changes either correct (in caller instance) or done beforehand rather than done here which keeps it valid as long as explicit `name` etc is checked rather than only counting to implicitly handle despite potentially causing issue unless validated using explicit tests such as with DateTime example since datetime does not behave quite as anticipated otherwise by producing `ValueError` since other formats using number-containing strings aren't all equal then greet_person("Alice",greeting="Hello") # same as before when not given in different/unclear manner: “Hello Alice”, correct Python use greet_person(greeting= "Hi" , name= "Bob") # Hi, Bob same format is printed since output only gives result by these Python codes after executing functions despite implicit casts rather than from explicit test assuming such logic already defined and correct if only to check behaviour when passing strings rather than only expecting some int as earlier unless also checking types with string which is correct rather than implicit int cast since Python may give correct after implicitly conversion to same or closely matching result

7. Arbitrary Arguments

Variable numbers passed/handled despite only `one-or-zero/single` expected given function arg which implies this "one" contains them. Commonly applied/used approach in Python as long as args used according to how implemented in each such cases if relevant assuming Python `functions`/`args` also not always the most apparent given similar nested dict usages, assuming data or list types changed internally where Python scoping can cause issue even with explicit value setting. So typecasting explicitly rather than just assigning number if that required in later steps given this edge-case despite its valid type during the actual test given scope/context, which otherwise may crash program Example uses one non-keyword parameter before asterisk syntax which Python uses within valid functions defined by `def` etc. to unpack if possible by implicitly performing those steps when unpacking List or sequence unlike where `key` does determine which unique element changed, even in non-ordered Dicts, where if that isn't sufficient after conversions then tests using equality after string/integer casts could indicate inconsistent behavior from these chained assignment operations, and such might arise here unless using named function where explicit handling also done beforehand rather than assuming implicit conversion of arguments like earlier cases without also checking output despite using those parameters in function calls using math `sum()` etc where order itself given code may suffice then whereas there still exists type clash in example case adding explicitly `float` to Set, or via str into nested dictionaries despite numbers also allowed via `set([])` function if those end up passing initial tests by being valid input and output even without some warning message generated due to this conversion done rather than from using datetime types for representing other similarly nested or structured types Pass keyword args where not clear their types/value in case assuming order could apply in that special `**kwarg` case assuming ordering enforced unlike passing a Tuple before typecast rather than just using Dict operations on similar inputs unless doing tests against different input `key/val` pairs within that logic during implementation of other dictionary data structures using Sets like the disjoint cases example provided earlier

# variable argument count to example usage like in some wrapper rather than as an actual usage itself assuming `len` etc will not work as expected def make_pizza(size, *toppings): print (f"\n Making a {size} -inch pizza with the following toppings: ") for x in toppings:
print (f"- {x} ") make_pizza(14, "Pepperoni") make_pizza(16,"Extra Cheese","Mushrooms", "Olives") # you call either one rather than checking which used each call rather after unless different tests like value-type for `str` toppings are required # **kwarg case where type validation matters after passing data like doing typecast before use elsewhere after a method assigns the val for similar/nested/related type where hash changes unexpectedly when keys change despite consistent behaviour if elements also modified since the initial instances using shallow copying of Dict and Lists would fail `is` tests in examples if compared then unlike DateTime values alone without formats changing unless otherwise specified def print_details(**details): print (details["name"])
print(details.get("age",25)) print_details(name= "Alice", age = 34, city = "NY")

8. Recursive Functions

Functions which use own def calls in statement/body code to perform such recursive function implementation in various circumstances if useful in practice (Fibonacci, factorial, or related). You simplify implementations or keep related logical blocks nested using some similar logic to make compact like for datetime formats and other type validations, rather than writing each separately unless handling complex datetime output conditions (timezone handling using some different types for outputs where different explicit calls for formatting must handle rather than relying on a valid result since microsecond etc may still need handling, in datetime). If number used assuming conversion occurs correctly each such call or that stack implementation when generating Fib/factorial has sufficient recursion depth via recursion counter rather than looping otherwise: in the given implementation these don't arise rather assuming numbers do increment for intended effect since recursive rather than some loop operation on values:

def factorial(x): """This is a recursive function to find the factorial of an integer""" if x == 1:
return 1 else:
return (x * factorial(x-1)) num = 5 print("The factorial of", num, "is", factorial(num))
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