10 Python One-Liners That Will Make Your Code Cleaner and Faster

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KDnuggets2026年10月1日

10 Python One-Liners That Will Make Your Code Cleaner and Faster

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10 个让 Python 代码更简洁高效的单行技巧 - KDnuggets

发布日期：2026年10月1日

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10 个让 Python 代码更简洁高效的单行技巧

高效完成工作，从此不再需要为完美敲出的第一行代码撰写第二行、第三行——甚至令人震惊的第四行后续代码，省下的时间将重新回到你的生活中。

作者：

Matthew Mayo

，KDnuggets 主编，2026年10月1日发布于

Python

厌倦了反复输入？键盘敲击让你疲惫？想要高效完成任务的同时减少冗长的代码？你来对地方了。就在你此刻阅读的下方，有10个实用且版本兼容的 Python 单行代码技巧，每个都比传统的多行替代方案更优。高效完成工作，从此不再需要为完美敲出的第一行代码撰写第二行、第三行——甚至令人震惊的第四行后续代码，省下的时间将重新回到你的生活中。

请注意，尽管每个示例都只用一行代码实现功能，但需要做一些前提假设。首先，示例 2、6、9 和 10 假设代码中已提前导入了 import itertools、import functools 和 from collections import Counter。其次，注意每个单行代码中提到的最低 Python 版本要求，因为使用早期版本的 Python 在示例 3、5、6 和 10 中将不可避免地出现错误。

现在，让我们看看这些单行代码技巧。

1. 去除重复项同时保留顺序

这行代码用字典替代了手动循环，不仅消除了重复项，还保留了原始顺序，而 list(set(items)) 无法做到这一点。

code

unique = list(dict.fromkeys(items))

该单行代码通过将项目作为键构建字典，从而在保留首次出现顺序的同时自动去除重复项（Python 3.7 及以上版本保证该特性）。然后将字典的键转换回列表。

2. 扁平化嵌套列表

与嵌套循环或常见的 sum(nested, []) 技巧相比，这行代码更快且更易读，后者的时间复杂度为平方级。

code

flat = list(itertools.chain.from_iterable(nested))

该代码以惰性方式依次遍历每个子列表，并将所有元素收集到一个扁平列表中。

3. 合并字典

这行代码用一个含义明确的运算符（Python 3.9+）替代了 .copy() 后接 .update() 或较难理解的 {a, b} 解包方式。

code

merged = defaults | overrides

该代码会创建一个新字典，包含两个字典的所有键，当键冲突时，右侧字典的值会覆盖左侧字典的值。

4. 使用短路求值检查集合中的条件

这行代码会在找到第一个匹配项时立即停止扫描，因此比设置标志的循环更简洁高效。

code

has_negative = any(x < 0 for x in values)

只要有一个元素满足条件，该代码就会立即返回 True；如果没有任何元素满足条件，则返回 False（all() 是反向检查）。

5. 一次性计算，过滤并保留

This line uses the walrus operator to avoid calling an expensive function twice — once to filter and once to keep the value. This exposes, and circumvents, a common hidden inefficiency in comprehensions (Python 3.8+).

code

results = [y for x in data if (y := transform(x)) is not None]

The one-liner applies transform to each element; it then assigns the result to y inline, and keeps only the results that aren't None.

6. Add Instant Memoization to a Function

This single decorator line can turn exponential-time recursive functions or repeated expensive computations into near-instant lookups (Python 3.9+; use @lru_cache(maxsize=None) on older versions).

code

@functools.cache

This code stores the return value for each unique set of arguments. Then it returns the stored result on repeat calls instead of recomputing it, for a real time savings.

7. Transpose a Matrix

This single line swaps rows and columns without index arithmetic or NumPy. This makes it an ideal tool for quick tabular reshaping.

code

transposed = list(zip(*matrix))

Each row is unpacked as a separate argument to zip. zip then groups the first elements of every row together, then the second elements, and so on, producing tuples of columns.

8. Find the Key with the Highest Value in a Dictionary

This line can replace a loop that tracks a running maximum and its key.

code

best = max(scores, key=scores.get)

The code iterates over the dictionary's keys, returning the key whose associated value is largest.

9. Get the Most Frequent Items

This replaces manual dictionary counting and sorting with an optimized, purpose-built standard library tool.

code

top3 = Counter(words).most_common(3)

The line counts occurrences of each element, returning a list of the three most common (item, count) pairs in descending order.

10. Split an Iterable into Fixed-Size Chunks

This single line removes the error-prone slicing arithmetic usually written for batching API calls or model inputs (Python 3.12+).

code

batches = list(itertools.batched(records, 100))

The code groups the iterable into tuples of up to 100 elements each. The final tuple holds whatever remains.

Wrapping Up

Note the simplicity of each of these entries. There isn't any magic happening; it's just in-the-trenches work, and the value is the expressiveness of each statement. I told you all the time you'd be saving right off the bat, but you didn't believe me, did you? You're welcome.

Don't forget to add this newly-acquired coding-optimization skill to your resume. You're welcome again.

Matthew Mayo ( @mattmayo13 ) holds a master's degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

10 Python One-Liners That Will Make Your Code Cleaner and Faster

TL;DR · AI 摘要

核心要点

10 个让 Python 代码更简洁高效的单行技巧 - KDnuggets

10 个让 Python 代码更简洁高效的单行技巧

1. 去除重复项同时保留顺序

2. 扁平化嵌套列表

3. 合并字典

4. 使用短路求值检查集合中的条件

5. 一次性计算，过滤并保留

6. Add Instant Memoization to a Function

7. Transpose a Matrix

8. Find the Key with the Highest Value in a Dictionary

9. Get the Most Frequent Items

10. Split an Iterable into Fixed-Size Chunks

Wrapping Up

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