Using Python Lambda, Reduce, Map and Filter
Lambda, map, filter, reduce¶
Typically I use list, dictionary and set comprehensions over using lambda and map. But you may prefer this.
Lambda¶
The syntax is lambda variables: function.
In [1]:
add = lambda x, y: x + y
add(2, 3)
Out[1]:
In [3]:
multiply = lambda x, y: x * y
multiply(2, 3)
Out[3]:
Map¶
The syntax is map(function, sequence).
In [3]:
lst = [i for i in range(11)]
In [4]:
lst
Out[4]:
This works in Python 2.
In [4]:
square_element = map(lambda x: x**2, lst)
print(square_element)
For Python 3, you have to explicitly return a list.
In [5]:
square_element = list(map(lambda x: x**2, lst))
print(square_element)
If you would like to apply map to multiple lists, map would apply the function to the first index.
In [1]:
lst_1 = [1, 2, 3, 4]
lst_2 = [2, 4, 6, 8]
lst_3 = [3, 6, 9, 12]
In [3]:
add_elements = map(lambda x, y: x + y, lst_1, lst_2)
add_elements
Out[3]:
In [8]:
add_elements = map(lambda x, y ,z : x + y + z, lst_1, lst_2, lst_3)
add_elements
Out[8]:
Filter¶
The syntax is filter(function, list).
In [13]:
lst = range(11)
lst
Out[13]:
In [15]:
multiples_of_three = filter(lambda x: x % 3 == 0, lst)
multiples_of_three
Out[15]:
Reduce¶
The syntax is reduce(function, sequence). The function is applied to the elements in the list in a sequential manner. Meaning if lst = [1, 2, 3, 4] and you have a sum function, you would arrive with ((1+2) + 3) + 4.
In [16]:
lst = range(11)
lst
Out[16]:
In [18]:
sum_all = reduce(lambda x, y: x + y, lst)
sum_all
Out[18]: