函數(shù)式編程是一種通過組合純函數(shù)、避免共享狀態(tài)和可變數(shù)據(jù)來構(gòu)建軟件的編程範式。 1. map() 用於將函數(shù)應(yīng)用於可迭代對象的每個元素，生成新結(jié)果；2. filter() 用於篩選滿足條件的元素；3. reduce()（需導(dǎo)入functools 模塊）用於累積處理元素，返回單一值。這些函數(shù)支持聲明式編程風格，使代碼更簡潔清晰，適用於數(shù)據(jù)轉(zhuǎn)換、選擇和聚合場景，但應(yīng)權(quán)衡可讀性，必要時可用列表推導(dǎo)式替代。

Python's built-in functions like map() , filter() , and reduce() are tools that let you work with sequences (like lists) in a functional programming style. They help apply operations to elements in a collection without writing explicit loops, making your code more concise and expressive.

What is functional programming, and why does it matter?

Functional programming is a programming paradigm where you build software by composing pure functions, avoiding shared state and mutable data. In Python, even though it's not purely functional like Haskell, it supports several functional programming concepts — and map() , filter() , and reduce() are part of that support.

These functions allow you to process data declaratively: instead of telling the computer how to do something step-by-step, you say what should be done.

How map() transforms data

The map() function applies a given function to each item of an iterable (like a list or tuple) and returns an iterator of the results.

Basic usage:

 numbers = [1, 2, 3]
squared = list(map(lambda x: x ** 2, numbers))

Here, we square every number in the list. The lambda function defines what we want to do to each element.

• It works with multiple iterables too:

   list(map(lambda x, y: xy, [1, 2], [3, 4])) # Result: [4, 6]

Use case: Great when you want to transform all elements in a sequence uniformly.

How filter() selects data

As the name suggests, filter() picks out elements from an iterable for which a given function returns True .

Example:

 numbers = [1, 2, 3, 4, 5]
evens = list(filter(lambda x: x % 2 == 0, numbers))

This filters out only the even numbers from the list.

• If the function is None , it filters out falsy values:

   list(filter(None, [0, 1, False, True, '', 'hello'])) # Result: [1, True, 'hello']

Use case: Useful when you need to conditionally select items from a collection.

How reduce() accumulates values

reduce() is a bit different because it lives in the functools module, so you have to import it first.

It applies a binary function (a function that takes two arguments) cumulatively to the items of an iterable, going from left to right, effectively reducing the iterable to a single value.

Example:

 from functools import reduce

numbers = [1, 2, 3, 4]
total = reduce(lambda x, y: xy, numbers)

This adds up all the numbers in the list.

• You can also provide an initial value:

   reduce(lambda x, y: xy, numbers, 10) # Starts sum at 10

Use case: Perfect for aggregating data into a single result, like summing, multiplying, or concatenating.

Why these functions fit into functional programming

All three encourage writing code that avoids side effects:

• map() and filter() don't change the original data — they return new iterators.
• reduce() helps accumulate values without using temporary variables or loops.
• Using lambdas keeps transformations short and inline, making intent clearer.

They're especially useful when chaining operations together:

 result = list(
    filter(lambda x: x > 10,
           map(lambda x: x * 2,
               [1, 2, 3, 4, 5]))
)

This multiplies each number by 2, then keeps only those greater than 10.

When not to use them

While these functions are powerful, readability matters. If using map() or filter() makes your logic harder to follow, a regular loop or list comprehension might be better.

For example, this:

 [x ** 2 for x in numbers if x % 2 == 0]

is often more readable than combining map() and filter() .

So while Python gives you options, knowing when to reach for map() , filter() , or reduce() comes down to clarity and style. Used wisely, they make your code cleaner and more expressive.基本上就這些。

以上是Python的內(nèi)置功能是什麼，例如Map（），F(xiàn)ilter（）和redain（），以及它們與功能編程有何關(guān)係？的詳細內(nèi)容。更多資訊請關(guān)注PHP中文網(wǎng)其他相關(guān)文章！