# Topic covered
* Functions
* Built-in Functions
    * Anonymous/lambda Functions
    * map() function
    * reduce() function
    * filter() function
    * eval() function
* User Defined Functions
* Return Statement
* Parameters & Arguments
    * Positional arguments
    * Keyword arguments
    * Default arguments
    * Variable length arguments
* Generators
* Iterable and Iterator in Python
    * Create an Iterator Class

9. Functions

If a group of statements is repeatedly required then it is not recommended to write these statements everytime separately.

We have to define these statements as a single unit, and we can call that unit any number of times based on our requirement without rewriting. This unit is nothing but function.

• The main advantage of functions is code Reusability.
• Python supports 2 types of functions
  1. Built-in Functions
  2. User Defined Functions

9.1 Built-in Functions

The functions which are coming along with Python software automatically, are called built-in functions or pre-defined functions

Eg:
id() , type(), input(),
map(), reduce(), filter(), lambda(), eval()

9.2 User Defined Functions

The functions which are developed by programmer explicitly according to business requirements, are called user defined functions.

# Syntax
def function_name(parameters):
    """ doc string"""
    ----
    -----
    return value

9.3 Return Statement

Function can take input values as parameters and executes business logic, and returns output to the caller with return statement.

def add(x,y):
    return x+y

result=add(10,20)
print("The sum is",result)

# Returning multiple values
def sum_sub(a,b):
    sum=a+b
    sub=a-b
    return sum,sub
x,y=sum_sub(100,50)
print(x,y)

9.4 Parameters

Parameters are inputs to the function. If a function contains parameters, then at the time of calling, compulsory we should provide values otherwise, otherwise we will get error.

A parameter is the variable defined within the parentheses during function definition.

# Here a,b are the parameters
def sum(a,b):
    print(a+b)
sum(1,2)

9.5 Arguments

An argument is a value that is passed to a function when it is called. It might be a variable, value or object passed to a function or method as input. They are written when we are calling the function

def f1(a,b):
    ------
    ------
    ------
f1(10,20)

There are 4 types are actual arguments are allowed in Python.

1. Positional arguments
2. Keyword arguments
3. Default arguments
4. Variable length arguments

Positional arguments

The number of arguments and position of arguments must be matched. If we change the order then result may be changed.

def sub(a,b):
    print(a-b)
sub(100,200)  # -100
sub(200,100)  # 100

Keyword arguments

Here the order of arguments is not important but number of arguments must be matched.

def wish(name, msg):
    print("Hello", name, msg)

wish(name="Amrit", msg="Good Morning")
# Hello Amrit Good Morning

wish(msg="Good Morning", name="Amrit")
# Hello Amrit Good Morning

We can use both positional and keyword arguments simultaneously. But first we have to take positional arguments and then keyword arguments, otherwise we will get syntax-error.

def wish(name, msg):
    print("Hello", name, msg)

wish("Amrit", "GM")  # valid
wish("Amrit", msg="GM") # valid

wish(name="Amrit", "GM") # invalid
# SyntaxError: positional argument follows keyword argument

Default Arguments

Sometimes we can provide default values for our positional arguments.

def wish(name="Guest"):
    print("Hello", name, "GoodMorning")

wish("Amrit")
# Hello Amrit Good Morning

wish()
# Hello Amrit Good Morning

• SyntaxError: non-default argument follows default argument

def wish(name="Guest", msg="Good Morning"): # valid
def wish(name, msg="Good Morning"):      # valid

def wish(name="Guest", msg):  # Invalid

Variable length arguments

Sometimes we can pass variable number of arguments to our function, such type of arguments are called variable length arguments.

We can declare a variable length argument with * symbol as follows

def sum(*n):
    total=0
    for i in n:
        total=total+i
    print(total)

sum()  # 0
sum(10)  # 10
sum(10, 20)  # 30
sum(10, 20, 30, 40)  # 100

We can mix variable length arguments with positional arguments.

def f1(n1,*s):
    print(n1, s)

f1(10)      # 10 ()
f1(10,20)   # 10 (20,)
f1(10, "A", 30) # 10 ('A', 30)

We can declare key word variable length arguments also. For this we have to use **.

def display(**kwargs):
    print(kwargs)

display(n1=10)
# {'n1': 10}

display(rno=100,name="Amrit")
# {'rno': 100, 'name': 'Amrit'}

• Using both *args and **kwargs in Python to call a function

def test7(*args , **kwargs) : 
    return args , kwargs

test7(2,3,4,5,a= 34, b = 98)
# ((2, 3, 4, 5), {'a': 34, 'b': 98})

9.6 Anonymous/lambda Functions

Sometimes we can declare a function without any name, such type of nameless functions are called anonymous/lambda functions

The main purpose of anonymous function is just for instant use(i.e. for one time usage)

AKA lambda functions or shorthand function

# Syntax
lambda argument_list : expression

print((lambda x: x + 1)(2)) # 3

s = lambda n:n*n
print(s(4)) # 64

s = lambda a,b:a+b
print(s(2, 3)) # 5

s = lambda a,b:a if a>b else b
print(s(10,20)) # 20
print(s(100,200)) # 200

print((lambda x, y, z=3: x + y + z)(1, 2))

Note

Lambda Function internally returns expression value and we are not required to write return statement explicitly

Sometimes we can pass function as argument to another function. In such cases lambda functions are the best choice.

We can use lambda functions very commonly with filter(), map() and reduce() functions, because these functions expect function as argument.

9.7 map() function

map() function returns a map object(which is an iterator) of the results after applying the given function to each item of a given iterable (list, tuple etc.)

# str to int
num = ['1', '2', '3']
res = list(map(int, num))
print(res)

# Square 
def square(number):
	return number ** 2

numbers = [1, 2, 3, 4, 5]
print(list(map(square, numbers)))

# Square using map and lambda
print(list(map(lambda x:x*x, numbers)))


first_it = [1, 2, 3]
second_it = [4, 5, 6, 7]
print(list(map(pow, first_it, second_it)))

# x- y
print(list(map(lambda x, y: x - y, [2, 4, 6], [1, 3, 5])))
# [1, 1, 1]

print(list(map(lambda x, y, z: x + y + z, [2, 4], [1, 3], [7, 8])))
# [10, 15]


# Eg 5
string_it = ["processing", "strings", "with", "map"]
print(list(map(str.upper, string_it)))
# ['PROCESSING', 'STRINGS', 'WITH', 'MAP']


with_spaces = ["processing ", "  strings", "with   ", " map   "]
print(list(map(str.strip, with_spaces)))
# ['processing', 'strings', 'with', 'map']

with_dots = ["processing..", "...strings", "with....", "..map.."]
print(list(map(lambda s: s.strip("."), with_dots)))
# ['processing', 'strings', 'with', 'map']


# Eg 6
import re
def remove_punctuation(word):
	return re.sub(r'[!?.:;,"()-]', "", word)

text = """Some people, when ,,confronted... with a problem, think"""
words = text.split()
print(list(map(remove_punctuation, words)))

9.8 reduce() function

reduce() function reduces sequence of elements into a single element by applying the specified function. reduce() function present in functools module and hence we should write import statement.

reduce(function, sequence)

from functools import *
l = [10,20,30,40,50]
result=reduce(lambda x,y:x+y,l)
print(result) # 150

l = [1,2,3,4,5,4]

# Does not work on 3 variable
reduce(lambda x , y , z : x+y+z , l) # TypeError

reduce(lambda x , y : x+y , [1])
# 1

# Biggest emement in list
reduce(lambda x,y : x if x> y else y , l)
# 5

9.9 filter() function

We can use filter() function to filter values from the given sequence based on some condition.

filter(function, sequence) - where function argument is responsible to perform conditional check sequence can be list or tuple or string.

#  Working of filter()
def check_even(number):
    if number % 2 == 0:
          return True  
    return False

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
list(filter(check_even, numbers))
# Output: [2, 4, 6, 8, 10]

# Using Lambda Function Inside filter()

list(filter(lambda x: (x%2 == 0), numbers))
list(filter(lambda x: True if x%2==0 else False, numbers))
# [2, 4, 6, 8, 10]


l2 = ["sudh" , "pwskills" , "kumar" , "bengalore" , "krish"]
print(list(filter(lambda x : len(x) < 6 , l2)))

9.10 eval() function

• The eval function evaluates the String like a python expression and returns the result as an integer.
• eval(expression, globals=None, locals=None)
  • The globals must be represented as a dictionary and the locals as a mapped object.

x = 1
print(eval('x + 1'))
print(eval("1024 + 1024"))

code = compile("5 + 4", "<string>", "eval")
print(eval(code))
print()

x = 100
print(eval("x + 10", {"x": x}))

print(eval("x + y", {"x": x, "y": x}))

9.10 Generators

Generator is a function which is responsible to generate a sequence of values.

We can write generator functions just like ordinary functions, but it uses yield keyword to return values.

def simpleGeneratorFun(): 
  yield 1         
  yield 2         
  yield 3         

for value in simpleGeneratorFun(): 
  print(value, end=' ')
# 1 2 3

x = simpleGeneratorFun() 
print(list(x))
# [1, 2, 3]

Advantages of Generator Functions

1. When compared with class level iterators, generators are very easy to use
2. Improves memory utilization and performance.
3. Generators are the best suitable for reading data from large number of large files
4. Generators work great for web scraping and crawling.

We will get MemoryError in this case because all these values are required to store in the memory.

# Normal Collection --> []
l=[x*x for x in range(10000000000000000)]
print(l[0])

We won't get any MemoryError because the values won’t be stored at the beginning

# Generators --> () 
g=(x*x for x in range(10000000000000000))
print(next(g))
# 0

9.11 Iterable and Iterator in Python

• Iteration means to access each item of something one after another generally using a loop
• list, tuples, dictionaries, etc. –> all are iterables
• One important property it has
  • an __iter__() method or iter() method
  • which allows any iterable to return an iterator object.
• iter() and next()

# list of cars
cars = ['Audi', 'BMW', 'Jaguar', 'Kia', 'MG', 'Skoda']

# get iterator object using the iter() / __iter__() method
cars_iter = iter(cars) 
cars_iter = cars.__iter__()   

# use the next / __next__() method to iterate through the list
print(next(cars_iter))      
# OP: Audi

print(cars_iter.__next__()) 
# OP: BMW

Create an Iterator Class

• To create an object/class as an iterator you have to implement the methods __iter__() and __next__() to your object.
• To prevent the iteration to go on forever, we can use the StopIteration statement.

class MyNumbers:
  def __iter__(self):
    self.a = 1
    return self

  def __next__(self):
      if self.a <= 3:
          x = self.a
          self.a += 1
          return x
      else:
          raise StopIteration

my_class = MyNumbers()
my_iter = iter(my_class)

print(next(my_iter))
print(next(my_iter))
print(next(my_iter))
print(next(my_iter))
print(next(my_iter))