- A function is a group of related statements that perform a specific task.
- Functions help break programs into smaller and modular chunks. As our program grows larger and larger, functions make it more organized and manageable.
- Furthermore, it avoids repetition and makes code reusable.
Types of Functions
Basically, we can divide functions into the three types:
- Built-in function: Functions that are built into Python.
- User-defined function: Functions defined by the users themselves.
- Lambda function: Function that is defined without a name.
Anonymous/Lambda Function
- In Python, an anonymous function is a function that is defined without a name.
- While normal functions are defined using the def keyword, in Python, anonymous functions are defined using the lambda keyword.
- Hence, anonymous functions are also called lambda functions.
Syntax:
lambda arguments: expression
Notes :
- Lambda functions can have any number of arguments but only one expression.
- The expression is evaluated and returned.
- Lambda functions can be used wherever function objects are required.
Example 1 : Double the Number
double = lambda x: x * 2 print(double(5)) [out: ] 10
Example 2 : Addition of two numbers
sum = lambda var1, var2 : var1 + var2 print (sum(5,6)) # call function from here [out: ] 11
sum = (lambda var1, var2 : var1 + var2)(5,6) # can call function from outside the bracket print (sum) [out: ] 11
filter() function
- The filter() function takes a function and sets, lists, tuples, or containers of any iterators as arguments.
- The function is called with all the items in the list and a new list is returned which contains items for which the function evaluates to True.
Syntax: filter(function, iterable)
Example 1: Using User-Defined Function
marks = [10,20,30,55,80,90]
def fun1(arg1):
if arg1>50:
print (arg1)
pass_students = filter(fun1,marks)
for i in pass_students:
print (i)
[out: ]
55
80
90
Example 2 : Using Lambda Function
my_list = [1,5,4,68,11,3,12] new_list = list (filter(lambda x:(x%2==0),my_list)) print (new_list) [out: ] [4, 68, 12]
Example 3 : Supplanting function with ‘None’
With filter function is None, the function defaults to Identity function, and each element in random_list is checked if it’s true or not.
marks = ['False',True,0,10,False] pass_students = list(filter(None,marks)) pass_students [out: ] ['False', True, 10]
map() function
- The map() function takes a function and multiple iterable such as list, tuple, etc. as the argument.
- The function is called with all the items in the list and a new list is returned which contains items returned by that function for each item.
Syntax: map(function, iterable1, iterable2, ...)
Example 1 : Iterating all elements and returns results
marks = [10,20,30,55,80,90]
def fun1(arg1):
if arg1>50:
return (arg1)
pass_students = list(map(fun1,marks))
print (pass_students)
[out: ]
[None, None, None, 55, 80, 90]
Example 2: Iterating through all elements
my_list = [1,5,4,68,11,3,12] new_list = list (map(lambda x:(x%2==0),my_list)) print (new_list) [out: ] [False, False, True, True, False, False, True]
Example 3 : Iterating all elements
numbers = (1, 2, 3, 4,0) result = map(lambda x,y: x*y, numbers,numbers) print(list(result)) [out: ] [1, 4, 9, 16, 0]
Example 4 : Using multiple iterables
a = (5,6,7)
b = (5,6,7)
new_lst = set(map(lambda a,b : a + b,a,b))
print (new_lst)
[out: ]
{10, 12, 14}
Example 5 : Listify the list of strings individually
l = ['parsis', 'pratik', 'sakshi'] # bool or integer value is not supported
test = list(map(tuple,l))
print(test)
[out: ]
[('p', 'a', 'r', 's', 'i', 's'), ('p', 'r', 'a', 't', 'i', 'k'), ('s', 'a', 'k', 's', 'h', 'i')]
Difference between filter() and map()
| filter() | map() |
| can use only single iterable | can use multiple iterables |
| applied to only those objects of iterable who goes True on the condition specified in expression | applied to all objects of iterables irrespecitve result on the condition specified in expression |
| Take None as function | Does not takes None as function |
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