functools.reduce() / functools.partial()
functools.reduce() applies a two-argument function cumulatively to the elements of an iterable from left to right, reducing it to a single value. functools.partial() creates a new function with some arguments of an existing function pre-filled (a partially applied function). Both are useful for writing code in a functional programming style.
Syntax
from functools import reduce, partial # Reduction (apply function from left to right) reduce(function, iterable, initializer=None) # Partial application (create a new function with fixed arguments) partial(func, *args, **kwargs)
Functions
| Function | Description |
|---|---|
| reduce(func, iterable) | Applies func cumulatively to the elements of the iterable from left to right and returns a single value. |
| reduce(func, iterable, init) | Starts the reduction with init as the initial value. Does not raise an error if the iterable is empty. |
| partial(func, *args, **kwargs) | Returns a new function object with some arguments of func pre-filled. |
Sample Code
from functools import reduce, partial
import operator
# reduce: sum all elements in a list (same result as sum())
nums = [1, 2, 3, 4, 5]
total = reduce(lambda a, b: a + b, nums)
print(total) # 15
# reduce: combine with the operator module
product = reduce(operator.mul, nums)
print(product) # 120 (1×2×3×4×5)
# reduce: specify an initial value
total2 = reduce(lambda a, b: a + b, nums, 100)
print(total2) # 115 (100 + 15)
# reduce: find the maximum value (same result as max())
max_val = reduce(lambda a, b: a if a > b else b, [3, 1, 4, 1, 5, 9, 2, 6])
print(max_val) # 9
# reduce: flatten a nested list
nested = [[1, 2], [3, 4], [5, 6]]
flat = reduce(lambda a, b: a + b, nested)
print(flat) # [1, 2, 3, 4, 5, 6]
# partial: pre-fill some arguments of a function
def power(base, exp):
return base ** exp
square = partial(power, exp=2) # fix exp to 2
cube = partial(power, exp=3) # fix exp to 3
print(square(5)) # 25
print(cube(3)) # 27
# partial: fix the separator for print
print_tsv = partial(print, sep='\t')
print_tsv('Name', 'Age', 'Department') # Name\tAge\tDepartment
# partial: fix the first argument
add_10 = partial(lambda a, b: a + b, 10)
print(add_10(5)) # 15
print(add_10(20)) # 30
Notes
reduce() was a built-in function in Python 2, but was moved to the functools module in Python 3. In many cases it can be replaced with built-in functions such as sum(), max(), min(), any(), and all(), but reduce() is handy when you want to express more complex aggregation logic in a single line. If the iterable is empty and no initializer is provided, a TypeError is raised.
partial() is useful when you need to pass a function as a callback with certain arguments already set, or when you want to reuse the same function with slightly different configurations. You can achieve the same result with a lambda expression, but using partial() makes the intent clearer.
If you find any errors or copyright issues, please contact us.
シェアする