A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing.
As you already know, Python gives you many built-in functions like print(), etc. but you can also create your own functions. These functions are called user-defined functions.
You can define functions to provide the required functionality. Here are simple rules to define a function in Python.
Function blocks begin with the keyword def followed by the function name and parentheses ( ).
Any input parameters or arguments should be placed within these parentheses. You can also define parameters inside these parentheses.
The first statement of a function can be an optional statement - the documentation string of the function or docstring.
The code block within every function starts with a colon (:) and is indented.
The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
Syntax
def functionname( parameters ):
"function_docstring"
function_suite
return [expression]
By default, parameters have a positional behavior and you need to inform them in the same order that they were defined.
Example The following function takes a string as input parameter and prints it on standard screen.
def printme( str ):
"This prints a passed string into this function"
print (str)
return
Defining a function gives it a name, specifies the parameters that are to be included in the function and structures the blocks of code.
Once the basic structure of a function is finalized, you can execute it by calling it from another function or directly from the Python prompt. Following is an example to call the printme() function −
#!/usr/bin/python3
# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print (str)
return
# Now you can call printme function
printme("This is first call to the user defined function!")
printme("Again second call to the same function")
When the above code is executed, it produces the following result −
This is first call to the user defined function!
Again second call to the same function
All parameters (arguments) in the Python language are passed by reference. It means if you change what a parameter refers to within a function, the change also reflects back in the calling function. For example −
#!/usr/bin/python3
# Function definition is here
def changeme( mylist ):
"This changes a passed list into this function"
print ("Values inside the function before change: ", mylist)
mylist[2]=50
print ("Values inside the function after change: ", mylist)
return
# Now you can call changeme function
mylist = [10,20,30]
changeme( mylist )
print ("Values outside the function: ", mylist)
Here, we are maintaining reference of the passed object and appending values in the same object. Therefore, this would produce the following result −
Values inside the function before change: [10, 20, 30]
Values inside the function after change: [10, 20, 50]
Values outside the function: [10, 20, 50]
There is one more example where argument is being passed by reference and the reference is being overwritten inside the called function.
#!/usr/bin/python3
# Function definition is here
def changeme( mylist ):
"This changes a passed list into this function"
mylist = [1,2,3,4] # This would assi new reference in mylist
print ("Values inside the function: ", mylist)
return
# Now you can call changeme function
mylist = [10,20,30]
changeme( mylist )
print ("Values outside the function: ", mylist)
The parameter mylist is local to the function changeme. Changing mylist within the function does not affect mylist. The function accomplishes nothing and finally this would produce the following result −
Values inside the function: [1, 2, 3, 4]
Values outside the function: [10, 20, 30]
You can call a function by using the following types of formal arguments −
Required arguments are the arguments passed to a function in correct positional order. Here, the number of arguments in the function call should match exactly with the function definition.
To call the function printme(), you definitely need to pass one argument, otherwise it gives a syntax error as follows −
#!/usr/bin/python3
# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print (str)
return
# Now you can call printme function
printme()
When the above code is executed, it produces the following result −
Traceback (most recent call last):
File "test.py", line 11, in <module>
printme();
TypeError: printme() takes exactly 1 argument (0 given)
Keyword arguments are related to the function calls. When you use keyword arguments in a function call, the caller identifies the arguments by the parameter name.
This allows you to skip arguments or place them out of order because the Python interpreter is able to use the keywords provided to match the values with parameters. You can also make keyword calls to the printme() function in the following ways −
#!/usr/bin/python3
# Function definition is here
def printme( str ):
"This prints a passed string into this function"
print (str)
return
# Now you can call printme function
printme( str = "My string")
When the above code is executed, it produces the following result −
The following example gives a clearer picture. Note that the order of parameters does not matter.
#!/usr/bin/python3
# Function definition is here
def printinfo( name, age ):
"This prints a passed info into this function"
print ("Name: ", name)
print ("Age ", age)
return
# Now you can call printinfo function
printinfo( age = 50, name = "miki" )
When the above code is executed, it produces the following result −
Name: miki
Age 50
A default argument is an argument that assumes a default value if a value is not provided in the function call for that argument. The following example gives an idea on default arguments, it prints default age if it is not passed −
#!/usr/bin/python3
# Function definition is here
def printinfo( name, age = 35 ):
"This prints a passed info into this function"
print ("Name: ", name)
print ("Age ", age)
return
# Now you can call printinfo function
printinfo( age = 50, name = "miki" )
printinfo( name = "miki" )
When the above code is executed, it produces the following result −
Name: miki
Age 50
Name: miki
Age 35
You may need to process a function for more arguments than you specified while defining the function. These arguments are called variable-length arguments and are not named in the function definition, unlike required and default arguments.
Syntax for a function with non-keyword variable arguments is given below −
def functionname([formal_args,] *var_args_tuple ):
"function_docstring"
function_suite
return [expression]
An asterisk * is placed before the variable name that holds the values of all nonkeyword variable arguments. This tuple remains empty if no additional arguments are specified during the function call. Following is a simple example −
#!/usr/bin/python3
# Function definition is here
def printinfo( arg1, *vartuple ):
"This prints a variable passed arguments"
print ("Output is: ")
print (arg1)
for var in vartuple:
print (var)
return
# Now you can call printinfo function
printinfo( 10 )
printinfo( 70, 60, 50 )
When the above code is executed, it produces the following result −
Output is:
10
Output is:
70
60
50
*args and **kwargs in PythonIn Python, we can pass a variable number of arguments to a function using special symbols. There are two special symbols:
Special Symbols Used for passing arguments:-
1.)*args (Non-Keyword Arguments)
2.)**kwargs (Keyword Arguments)
Note: “We use the “wildcard” or “*” notation like this – *args OR **kwargs – as our function’s argument when we have doubts about the number of arguments we should pass in a function.”
*argsThe special syntax *args in function definitions in python is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list.
The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
What *args allows you to do is take in more arguments than the number of formal arguments that you previously defined. With *args, any number of extra arguments can be tacked on to your current formal parameters (including zero extra arguments).
For example : we want to make a multiply function that takes any number of arguments and able to multiply them all together. It can be done using *args. Using the *, the variable that we associate with the * becomes an iterable meaning you can do things like iterate over it, run some higher-order functions such as map and filter, etc.
# Python program to illustrate
# *args for variable number of arguments
def myFun(*argv):
for arg in argv:
print (arg)
myFun('Hello', 'Welcome', 'to', 'GeeksforGeeks')
Output:
Hello
Welcome
to
GeeksforGeeks
# Python program to illustrate
# *args with first extra argument
def myFun(arg1, *argv):
print ("First argument :", arg1)
for arg in argv:
print("Next argument through *argv :", arg)
myFun('Hello', 'Welcome', 'to', 'GeeksforGeeks')
Output:
First argument : Hello
Next argument through *argv : Welcome
Next argument through *argv : to
Next argument through *argv : GeeksforGeeks
**kwargsThe special syntax **kwargs in function definitions in python is used to pass a keyworded, variable-length argument list. We use the name kwargs with the double star. The reason is because the double star allows us to pass through keyword arguments (and any number of them).
A keyword argument is where you provide a name to the variable as you pass it into the function. One can think of the kwargs as being a dictionary that maps each keyword to the value that we pass alongside it. That is why when we iterate over the kwargs there doesn’t seem to be any order in which they were printed out.
**kwargs:# Python program to illustrate
# *kargs for variable number of keyword arguments
def myFun(**kwargs):
for key, value in kwargs.items():
print ("%s == %s" %(key, value))
# Driver code
myFun(first ='Geeks', mid ='for', last='Geeks')
Output:
last == Geeks
mid == for
first == Geeks
# Python program to illustrate **kargs for
# variable number of keyword arguments with
# one extra argument.
def myFun(arg1, **kwargs):
for key, value in kwargs.items():
print ("%s == %s" %(key, value))
# Driver code
myFun("Hi", first ='Geeks', mid ='for', last='Geeks')
Output:
last == Geeks
mid == for
first == Geeks
*args and **kwargs to call a functionExample:
def myFun(arg1, arg2, arg3):
print("arg1:", arg1)
print("arg2:", arg2)
print("arg3:", arg3)
# Now we can use *args or **kwargs to
# pass arguments to this function :
args = ("Geeks", "for", "Geeks")
myFun(*args)
kwargs = {"arg1" : "Geeks", "arg2" : "for", "arg3" : "Geeks"}
myFun(**kwargs)
Output:
arg1: Geeks
arg2: for
arg3: Geeks
arg1: Geeks
arg2: for
arg3: Geeks
*args and **kwargs in same line to call a functionExample:
def myFun(*args,**kwargs):
print("args: ", args)
print("kwargs: ", kwargs)
# Now we can use both *args ,**kwargs
# to pass arguments to this function :
myFun('geeks','for','geeks',first="Geeks",mid="for",last="Geeks")
Output:
args: ('geeks', 'for', 'geeks')
kwargs {'first': 'Geeks', 'mid': 'for', 'last': 'Geeks'}
The first string after the function is called the Document string or Docstring in short. This is used to describe the functionality of the function. The use of docstring in functions is optional but it is considered a good practice.
The below syntax can be used to print out the docstring of a function:
Syntax:
print(function_name.__doc__)
Example: Adding Docstring to the function
# A simple Python function to check
# whether x is even or odd
def evenOdd(x):
"""Function to check if the number is even or odd"""
if (x % 2 == 0):
print("even")
else:
print("odd")
# Driver code to call the function
print(evenOdd.__doc__)
Output
Function to check if the number is even or odd
These functions are called anonymous because they are not declared in the standard manner by using the def keyword. You can use the
lambdakeyword to create small anonymous functions.
Lambda forms can take any number of arguments but return just one value in the form of an expression. They cannot contain commands or multiple expressions.
An anonymous function cannot be a direct call to print because lambda requires an expression.
Lambda functions have their own local namespace and cannot access variables other than those in their parameter list and those in the global namespace.
Although it appears that lambdas are a one-line version of a function, they are not equivalent to inline statements in C or C++, whose purpose is to stack allocation by passing function, during invocation for performance reasons.
Syntax The syntax of lambda functions contains only a single statement, which is as follows −
lambda [arg1 [,arg2,.....argn]]:expression
Following is an example to show how lambda form of function works −
#!/usr/bin/python3
# Function definition is here
sum = lambda arg1, arg2: arg1 + arg2
# Now you can call sum as a function
print ("Value of total : ", sum( 10, 20 ))
print ("Value of total : ", sum( 20, 20 ))
When the above code is executed, it produces the following result −
Value of total : 30
Value of total : 40
The statement return [expression] exits a function, optionally passing back an expression to the caller. A return statement with no arguments is the same as return None.
All the examples given below are not returning any value. You can return a value from a function as follows −
#!/usr/bin/python3
# Function definition is here
def sum( arg1, arg2 ):
# Add both the parameters and return them."
total = arg1 + arg2
print ("Inside the function : ", total)
return total
# Now you can call sum function
total = sum( 10, 20 )
print ("Outside the function : ", total )
When the above code is executed, it produces the following result −
Inside the function : 30
Outside the function : 30
yield instead of returnThe yield statement suspends function’s execution and sends a value back to the caller, but retains enough state to enable function to resume where it is left off. When resumed, the function continues execution immediately after the last yield run. This allows its code to produce a series of values over time, rather than computing them at once and sending them back like a list.
# A Simple Python program to demonstrate working
# of yield
# A generator function that yields 1 for the first time,
# 2 second time and 3 third time
def simpleGeneratorFun():
yield 1
yield 2
yield 3
# Driver code to check above generator function
for value in simpleGeneratorFun():
print(value)
Output:
1
2
3
Return sends a specified value back to its caller whereas Yield can produce a sequence of values. We should use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory.
Yield are used in Python generators. A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.
# A Python program to generate squares from 1
# to 100 using yield and therefore generator
# An infinite generator function that prints
# next square number. It starts with 1
def nextSquare():
i = 1
# An Infinite loop to generate squares
while True:
yield i*i
i += 1 # Next execution resumes
# from this point
# Driver code to test above generator
# function
for num in nextSquare():
if num > 100:
break
print(num)
Output:
1
4
9
16
25
36
49
64
81
100
A function that is defined inside another function is known as the inner function or nested function. Nested functions are able to access variables of the enclosing scope. Inner functions are used so that they can be protected from everything happening outside the function.
# Python program to
# demonstrate accessing of
# variables of nested functions
def f1():
s = 'I love GeeksforGeeks'
def f2():
print(s)
f2()
# Driver's code
f1()
Output
I love GeeksforGeeks
All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable.
The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python −
Variables that are defined inside a function body have a local scope, and those defined outside have a global scope.
This means that local variables can be accessed only inside the function in which they are declared, whereas global variables can be accessed throughout the program body by all functions. When you call a function, the variables declared inside it are brought into scope. Following is a simple example −
#!/usr/bin/python3
total = 0 # This is global variable.
# Function definition is here
def sum( arg1, arg2 ):
# Add both the parameters and return them."
total = arg1 + arg2; # Here total is local variable.
print ("Inside the function local total : ", total)
return total
# Now you can call sum function
sum( 10, 20 )
print ("Outside the function global total : ", total )
When the above code is executed, it produces the following result −
Inside the function local total : 30
Outside the function global total : 0