map in python

def percentage_error(actual, predicted):
    res = np.empty(actual.shape)
    for j in range(actual.shape[0]):
        if actual[j] != 0:
            res[j] = (actual[j] - predicted[j]) / actual[j]
        else:
            res[j] = predicted[j] / np.mean(actual)
    return res

def mean_absolute_percentage_error(y_true, y_pred): 
    return np.mean(np.abs(percentage_error(np.asarray(y_true), np.asarray(y_pred)))) * 100

'''try this instead of defining a function'''

a = [1,2,3,4]
a = list(map(lambda n: n+n, a))

print(a)

map(function_to_apply, list_of_inputs)

# Python program to demonstrate working 
# of map. 
  
# Return double of n 
def addition(n): 
    return n + n 
  
# We double all numbers using map() 
numbers = (1, 2, 3, 4) 
result = map(addition, numbers) 
print(list(result)) 


# Output : [2, 4, 6, 8]

#Syntax
map(functions, iterables)

# Python program to demonstrate working 
# of map. 
  
# Return double of n 
def addition(n): 
    return n + n 
  
# We double all numbers using map() 
numbers = (1, 2, 3, 4) 
result = map(addition, numbers) 
print(list(result))

# Python program to demonstrate working 
# of map. 
  
# Return double of n 
def addition(n): 
    return n + n 
  
# We double all numbers using map() 
numbers = (1, 2, 3, 4) 
result = map(addition, numbers) 
print(list(result)) 

map(function_to_apply, list_of_inputs)

def calculateSquare(n):
    return n*n


numbers = (1, 2, 3, 4)
result = map(calculateSquare, numbers)
print(result)

# converting map object to set
numbersSquare = list(result)
print(numbersSquare)

# Add two lists using map and lambda 
numbers1 = [1, 2, 3] 
numbers2 = [4, 5, 6] 
  
result = map(lambda x, y: x + y, numbers1, numbers2) 
print(list(result)) 

Output:
[5, 7, 9]