Getting started with Python LanguageList comprehensionsFilterListFunctionsDecoratorsMath ModuleLoopsRandom moduleComparisonsImporting modulesSorting, Minimum and MaximumOperator moduleVariable Scope and BindingBasic Input and OutputFiles & Folders I/OJSON ModuleString MethodsMetaclassesIndexing and SlicingGeneratorsSimple Mathematical OperatorsReduceMap FunctionExponentiationSearchingDictionaryClassesCountingManipulating XMLDate and TimeSetCollections moduleParallel computationMultithreadingWriting extensionsUnit TestingRegular Expressions (Regex)Bitwise OperatorsIncompatibilities moving from Python 2 to Python 3Virtual environmentsCopying dataTupleContext Managers (“with” Statement)Hidden FeaturesEnumString FormattingConditionalsComplex mathUnicode and bytesThe __name__ special variableChecking Path Existence and PermissionsPython NetworkingAsyncio ModuleThe Print Functionos.pathCreating Python packagesParsing Command Line argumentsHTML ParsingSubprocess Librarysetup.pyList slicing (selecting parts of lists)SocketsItertools ModuleRecursionBoolean OperatorsThe dis moduleType Hintspip: PyPI Package ManagerThe locale ModuleExceptionsWeb scraping with PythonDeque ModuleDistributionProperty ObjectsOverloadingDebuggingReading and Writing CSVDynamic code execution with `exec` and `eval`PyInstaller - Distributing Python CodeIterables and IteratorsData Visualization with PythonThe Interpreter (Command Line Console)*args and **kwargsFunctools ModuleGarbage CollectionIndentationSecurity and CryptographyPickle data serialisationurllibBinary DataPython and ExcelIdiomsMethod OverridingDifference between Module and PackageData SerializationPython concurrencyIntroduction to RabbitMQ using AMQPStormPostgreSQLDescriptorCommon PitfallsMultiprocessingtempfile NamedTemporaryFileWorking with ZIP archivesStackProfilingUser-Defined MethodsWorking around the Global Interpreter Lock (GIL)DeploymentLoggingProcesses and ThreadsThe os ModuleComments and DocumentationDatabase AccessPython HTTP ServerAlternatives to switch statement from other languagesList destructuring (aka packing and unpacking)Accessing Python source code and bytecodeMixinsAttribute AccessArcPyPython Anti-PatternsPlugin and Extension ClassesWebsocketsImmutable datatypes(int, float, str, tuple and frozensets)String representations of class instances: __str__ and __repr__ methodsArraysOperator PrecedencePolymorphismNon-official Python implementationsList ComprehensionsWeb Server Gateway Interface (WSGI)2to3 toolAbstract syntax treeAbstract Base Classes (abc)UnicodeSecure Shell Connection in PythonPython Serial Communication (pyserial)Neo4j and Cypher using Py2NeoBasic Curses with PythonPerformance optimizationTemplates in pythonPillowThe pass statementLinked List Nodepy.testDate FormattingHeapqtkinterCLI subcommands with precise help outputDefining functions with list argumentsSqlite3 ModulePython PersistenceTurtle GraphicsConnecting Python to SQL ServerDesign PatternsMultidimensional arraysAudioPygletQueue ModuleijsonWebbrowser ModuleThe base64 ModuleFlaskgroupby()Sockets And Message Encryption/Decryption Between Client and ServerpygameInput, Subset and Output External Data Files using Pandashashlibgetting start with GZipDjangoctypesCreating a Windows service using PythonPython Server Sent EventsMutable vs Immutable (and Hashable) in PythonPython speed of programconfigparserLinked listsCommonwealth ExceptionsOptical Character RecognitionPython Data TypesPartial functionspyautogui modulegraph-toolUnzipping FilesFunctional Programming in PythonPython Virtual Environment - virtualenvsysvirtual environment with virtualenvwrapperCreate virtual environment with virtualenvwrapper in windowsPython Requests PostPlotting with MatplotlibPython Lex-YaccChemPy - python packagepyaudioshelveUsage of "pip" module: PyPI Package ManagerIoT Programming with Python and Raspberry PICode blocks, execution frames, and namespaceskivy - Cross-platform Python Framework for NUI DevelopmentCall Python from C#Similarities in syntax, Differences in meaning: Python vs. JavaScriptWriting to CSV from String or ListRaise Custom Errors / ExceptionsUsing loops within functionsPandas Transform: Preform operations on groups and concatenate the results

*args and **kwargs

Other topics

Remarks:

There a few things to note:

  1. The names args and kwargs are used by convention, they are not a part of the language specification. Thus, these are equivalent:

     def func(*args, **kwargs):
         print(args)
         print(kwargs)
    

     def func(*a, **b):
         print(a)
         print(b)
    
  2. You may not have more than one args or more than one kwargs parameters (however they are not required)

     def func(*args1, *args2):
     #   File "<stdin>", line 1
     #     def test(*args1, *args2):
     #                      ^
     # SyntaxError: invalid syntax
    

     def test(**kwargs1, **kwargs2):
     #   File "<stdin>", line 1
     #     def test(**kwargs1, **kwargs2):
     #                       ^
     # SyntaxError: invalid syntax
    
  3. If any positional argument follow *args, they are keyword-only arguments that can only be passed by name. A single star may be used instead of *args to force values to be keyword arguments without providing a variadic parameter list. Keyword-only parameter lists are only available in Python 3.

     def func(a, b, *args, x, y):
         print(a, b, args, x, y)
    
     func(1, 2, 3, 4, x=5, y=6)
     #>>> 1, 2, (3, 4), 5, 6
    

     def func(a, b, *, x, y):
         print(a, b, x, y)
    
     func(1, 2, x=5, y=6)
     #>>> 1, 2, 5, 6
    
  4. **kwargs must come last in the parameter list.

     def test(**kwargs, *args):
     #   File "<stdin>", line 1
     #     def test(**kwargs, *args):
     #                      ^
     # SyntaxError: invalid syntax
    

Using *args when writing functions

You can use the star * when writing a function to collect all positional (ie. unnamed) arguments in a tuple:

def print_args(farg, *args):
   print("formal arg: %s" % farg)
   for arg in args:
       print("another positional arg: %s" % arg)

Calling method:

print_args(1, "two", 3)

In that call, farg will be assigned as always, and the two others will be fed into the args tuple, in the order they were received.

Using **kwargs when writing functions

You can define a function that takes an arbitrary number of keyword (named) arguments by using the double star ** before a parameter name:

def print_kwargs(**kwargs):
    print(kwargs)

When calling the method, Python will construct a dictionary of all keyword arguments and make it available in the function body:

print_kwargs(a="two", b=3)
# prints: "{a: "two", b=3}"

Note that the **kwargs parameter in the function definition must always be the last parameter, and it will only match the arguments that were passed in after the previous ones.

def example(a, **kw):
    print kw

example(a=2, b=3, c=4) # => {'b': 3, 'c': 4}

Inside the function body, kwargs is manipulated in the same way as a dictionary; in order to access individual elements in kwargs you just loop through them as you would with a normal dictionary:

def print_kwargs(**kwargs):
    for key in kwargs:
        print("key = {0}, value = {1}".format(key, kwargs[key])) 

Now, calling print_kwargs(a="two", b=1) shows the following output:

print_kwargs(a = "two", b = 1)
key = a, value = "two"
key = b, value = 1

Using *args when calling functions

A common use case for *args in a function definition is to delegate processing to either a wrapped or inherited function. A typical example might be in a class's __init__ method

class A(object):
    def __init__(self, b, c):
        self.y = b
        self.z = c

class B(A):
    def __init__(self, a, *args, **kwargs):
        super(B, self).__init__(*args, **kwargs)
        self.x = a

Here, the a parameter is processed by the child class after all other arguments (positional and keyword) are passed onto - and processed by - the base class.

For instance:

b = B(1, 2, 3)
b.x  # 1
b.y  # 2
b.z  # 3

What happens here is the class B __init__ function sees the arguments 1, 2, 3. It knows it needs to take one positional argument (a), so it grabs the first argument passed in (1), so in the scope of the function a == 1.

Next, it sees that it needs to take an arbitrary number of positional arguments (*args) so it takes the rest of the positional arguments passed in (1, 2) and stuffs them into *args. Now (in the scope of the function) args == [2, 3].

Then, it calls class A's __init__ function with *args. Python sees the * in front of args and "unpacks" the list into arguments. In this example, when class B's __init__ function calls class A's __init__ function, it will be passed the arguments 2, 3 (i.e. A(2, 3)).

Finally, it sets its own x property to the first positional argument a, which equals 1.

Using **kwargs when calling functions

You can use a dictionary to assign values to the function's parameters; using parameters name as keys in the dictionary and the value of these arguments bound to each key:

def test_func(arg1, arg2, arg3): # Usual function with three arguments
   print("arg1: %s" % arg1)
   print("arg2: %s" % arg2)
   print("arg3: %s" % arg3)

# Note that dictionaries are unordered, so we can switch arg2 and arg3. Only the names matter.
kwargs = {"arg3": 3, "arg2": "two"}

# Bind the first argument (ie. arg1) to 1, and use the kwargs dictionary to bind the others
test_var_args_call(1, **kwargs) 

Using *args when calling functions

The effect of using the * operator on an argument when calling a function is that of unpacking the list or a tuple argument

def print_args(arg1, arg2):
    print(str(arg1) + str(arg2))

a = [1,2]
b = tuple([3,4])

print_args(*a)
# 12
print_args(*b)
# 34

Note that the length of the starred argument need to be equal to the number of the function's arguments.

A common python idiom is to use the unpacking operator * with the zip function to reverse its effects:

a = [1,3,5,7,9]
b = [2,4,6,8,10]

zipped = zip(a,b)
# [(1,2), (3,4), (5,6), (7,8), (9,10)]

zip(*zipped)
# (1,3,5,7,9), (2,4,6,8,10)

Keyword-only and Keyword-required arguments

Python 3 allows you to define function arguments which can only be assigned by keyword, even without default values. This is done by using star * to consume additional positional parameters without setting the keyword parameters. All arguments after the * are keyword-only (i.e. non-positional) arguments. Note that if keyword-only arguments aren't given a default, they are still required when calling the function.

def print_args(arg1, *args, keyword_required, keyword_only=True):
    print("first positional arg: {}".format(arg1))
    for arg in args:
        print("another positional arg: {}".format(arg))
    print("keyword_required value: {}".format(keyword_required))
    print("keyword_only value: {}".format(keyword_only))
    
print(1, 2, 3, 4) # TypeError: print_args() missing 1 required keyword-only argument: 'keyword_required'
print(1, 2, 3, keyword_required=4) 
# first positional arg: 1
# another positional arg: 2
# another positional arg: 3
# keyword_required value: 4
# keyword_only value: True

Populating kwarg values with a dictionary

def foobar(foo=None, bar=None):
    return "{}{}".format(foo, bar)

values = {"foo": "foo", "bar": "bar"}

foobar(**values) # "foobar"

**kwargs and default values

To use default values with **kwargs

def fun(**kwargs):
    print kwargs.get('value', 0)

fun()
# print 0
fun(value=1)
# print 1

Contributors

Topic Id: 2475

Example Ids: 8173,8174,8175,8176,8596,8813,10914,20577

This site is not affiliated with any of the contributors.