This post serves as a refresher and introduces some basic functional programming in Python, as well as an addition to my previous post on the data pipeline. The post material is divided into four parts:
Functional programming principles
The operator
lambda
, functionmap
,filter
,reduce
and others
Inclusion in the sequence
Closure
Functional programming principles
KEY PROVISIONS:
Functional programming is a software writing technique that focuses on functions. Functions can be assigned to variables, they can be passed to other functions and spawned new functions. Python has a rich and powerful arsenal of tools that make it easy to develop function-oriented programs.
In recent years, almost all known procedural and object-oriented programming languages have begun to support functional programming (FP) tools. And Python is no exception.
When talking about FP, they primarily mean the following:
Functions are first-class objects, that is, anything you can do with "data" can also be done with functions (like passing a function to another function as an argument).
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return x + y
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>>> (lambda x, y: x+y)(5, 7)
12
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>>> lambda_function = lambda x, y: x + y
>>> lambda_function(5,7)
12
>>> func = lambda_function
>>> func(3,4)
7
>>> dic = {'1': lambda_function}
>>> dic['1'](7,8)
15
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>>> seq = (1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> seq2 = (5, 6, 7, 8, 9, 0, 3, 2, 1)
>>> result = map(lambda_function, seq, seq2)
>>> result
<map object at 0x000002897F7C5B38>
>>> list(result)
[6, 8, 10, 12, 14, 6, 10, 10, 10]
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is_even = lambda x: x % 2 == 0
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>>> seq = (1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> filtered = filter(is_even, seq)
>>> list(filtered)
[2, 4, 6, 8]
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>>> filtered = iter(filter(lambda x: x % 2 == 0, seq))
>>> list(filtered)
[2, 4, 6, 8]
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>>> next(filtered)
2
>>> next(filtered)
4
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return
reduce
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def reduce(fn, seq, initializer=None):
it = iter(seq)
value = next(it) if initializer is None else initializer
for element in it:
value = fn(value, element)
return value
reduce
:
reduce(, , )
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>>> seq = (1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> get_sum = lambda a, b: a + b
>>> summed_numbers = reduce(get_sum, seq)
>>> summed_numbers
45
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>>> sentences = [".",
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>>> wsum = lambda a, sentence: a + len(sentence.split())
>>> number_of_words = reduce(wsum, sentences, 0)
>>> number_of_words
13
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>>> x = ''
>>> y = ''
>>> zipped = zip(x, y)
>>> list(zipped)
[('', ''), ('', ''), ('', '')]
*
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>>> x2, y2 = zip(*zip(x, y))
>>> x2
('', '', '')
>>> y2
('', '', '')
>>> x == ''.join(x2) and y == ''.join(y2)
True
enumerate
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>>> lazy = enumerate(['','',''])
>>> list(lazy)
[(0, ''), (1, ''), (2, '')]
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>>> convert = lambda tup: tup[1].upper() + str(tup[0])
>>> lazy = map(convert, enumerate(['','','']))
>>> list(lazy)
['0', '1', '2']
convert
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squared_numbers = [x*x for x in numbers]
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>>> numbers = [1, 2, 3, 4, 5]
>>> squared_numbers = [x*x for x in numbers]
>>> squared_numbers
[1, 4, 9, 16, 25]
:
>>> squared_numbers = []
>>> for x in numbers:
>>> squared_numbers.append(x*x)
>>> squared_numbers
[1, 4, 9, 16, 25]
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>>> seq = (1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> seq2 = (5, 6, 7, 8, 9, 0, 3, 2, 1)
>>> result = [x + y for x, y in zip(seq, seq2)]
>>> result
[6, 8, 10, 12, 14, 6, 10, 10, 10]
– , . , zip
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filter
:
>>> result = [x for x in seq if is_even(x)]
>>> result
[2, 4, 6, 8]
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def adder(n, m):
return n + m
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def adder(n):
def fn(m):
return n + m
return fn
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adder = lambda n: lambda m: n + m
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>>> sum_three = adder(3)
>>> sum_three
<function __main__.<lambda>.<locals>.<lambda>>
>>> sum_three(1)
4
adder(3)
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def power_generator(base):
return lambda x: pow(x, base)
power_generator
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>>> square = power_generator(2) #
>>> square(2)
4
>>> cube = power_generator(3) #
>>> cube(2)
8
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COUNT = 0
def count_add(x):
global COUNT
COUNT += x
return COUNT
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def make_adder():
n = 0
def fn(x):
nonlocal n
n += x
return n
return fn
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>>> my_adder = make_adder()
>>> print(my_adder(5)) # 5
>>> print(my_adder(2)) # 7 (5 + 2)
>>> print(my_adder(3)) # 10 (5 + 2 + 3)
5
7
10
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