10 Python One-Liners That Replace 50 Lines of Code separate beginners from experienced developers.
Not because they are complex. Because they reveal language features most developers never discover.
Here are ten one-liners that replace verbose code with elegant solutions. Each saves lines. Each improves readability. Each demonstrates Python’s expressive power.
10 Python One-Liners That Replace 50 Lines of Code
1. Flatten Nested Lists
Verbose approach:
nested_list = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
flattened = []
for sublist in nested_list:
for item in sublist:
flattened.append(item)
# Result: [1, 2, 3, 4, 5, 6, 7, 8, 9]
One-liner:
flattened = [item for sublist in nested_list for item in sublist]
List comprehensions with nested iteration flatten lists without explicit loops. The syntax reads left to right matching the logical flow. This pattern works for any depth of nesting with minor modifications.
When to use: Processing data from APIs that return nested structures, combining multiple result sets, or preparing data for analysis.
2. Remove Duplicates Preserving Order
Verbose approach:
original = [1, 5, 2, 1, 9, 1, 5, 10]
seen = set()
result = []
for item in original:
if item not in seen:
seen.add(item)
result.append(item)
# Result: [1, 5, 2, 9, 10]
One-liner:
result = list(dict.fromkeys(original))
Dictionary keys maintain insertion order since Python 3.7. Converting to dict removes duplicates while preserving the first occurrence of each element. Converting back to list provides the desired output.
When to use: Cleaning user input, processing database results with duplicates, or maintaining unique values in processing pipelines.
3. Read File Into List
Verbose approach:
file_handle = open('data.txt', 'r')
lines = []
for line in file_handle:
lines.append(line.strip())
file_handle.close()
One-liner:
lines = [line.strip() for line in open('data.txt')]
File objects are iterators. List comprehensions consume iterators directly without explicit file handling. The file closes automatically when the comprehension completes due to reference counting.
When to use: Quick scripts reading configuration files, processing log files, or loading test data. For production code requiring explicit resource management, use context managers instead.
4. Swap Variable Values
Verbose approach:
a = 5
b = 10
temp = a
a = b
b = temp
# a is now 10, b is now 5
One-liner:
a, b = b, a
Python evaluates the right side completely before assignment. The tuple unpacking syntax swaps values without temporary variables. This works for any number of variables simultaneously.
When to use: Algorithm implementations requiring value swaps, sorting operations, or any situation needing variable exchange.
5. Create Dictionary From Two Lists
Verbose approach:
keys = ['name', 'age', 'city']
values = ['Alice', 30, 'Boston']
result = {}
for i in range(len(keys)):
result[keys[i]] = values[i]
# Result: {'name': 'Alice', 'age': 30, 'city': 'Boston'}
One-liner:
result = dict(zip(keys, values))
The zip function pairs elements from multiple iterables. Dict consumes these pairs to create key-value mappings. This pattern handles lists of any length automatically.
When to use: Processing CSV data, combining separate data sources, or creating configuration dictionaries from parallel arrays.
6. Filter List By Condition
Verbose approach:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
evens = []
for num in numbers:
if num % 2 == 0:
evens.append(num)
# Result: [2, 4, 6, 8, 10]
One-liner:
evens = [num for num in numbers if num % 2 == 0]
List comprehensions accept conditional expressions. Elements passing the condition enter the result list. Elements failing the condition are skipped. This combines filtering and transformation in one expression.
When to use: Data cleaning, extracting subsets matching criteria, or preprocessing before analysis.
7. Count Element Occurrences
Verbose approach:
items = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
counts = {}
for item in items:
if item in counts:
counts[item] += 1
else:
counts[item] = 1
# Result: {'apple': 3, 'banana': 2, 'orange': 1}
One-liner:
from collections import Counter
counts = Counter(items)
Counter is a dictionary subclass specifically designed for counting hashable objects. It handles initialization, increment logic, and provides additional methods for common counting operations.
When to use: Analyzing frequency distributions, processing survey responses, or identifying most common elements in datasets.
8. Reverse String or List
Verbose approach:
original = "Hello World"
reversed_str = ""
for i in range(len(original) - 1, -1, -1):
reversed_str += original[i]
# Result: "dlroW olleH"
One-liner:
reversed_str = original[::-1]
Slice notation with negative step reverses sequences. The syntax [start:stop:step] uses -1 as step to traverse backwards. This works identically for strings, lists, and tuples.
When to use: String manipulation, palindrome checking, or reversing order of processing results.
9. Get Multiple Dictionary Values
Verbose approach:
data = {'name': 'Bob', 'age': 25, 'city': 'NYC', 'country': 'USA'}
name = data.get('name')
age = data.get('age')
city = data.get('city')
One-liner:
name, age, city = [data.get(k) for k in ['name', 'age', 'city']]
List comprehension extracts multiple dictionary values in one operation. Unpacking assigns values to individual variables. The get method prevents KeyError exceptions for missing keys.
When to use: Extracting API response fields, processing configuration objects, or working with structured data dictionaries.
10. Find Most Common Element
Verbose approach:
items = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
max_count = 0
most_common = None
for item in set(items):
count = items.count(item)
if count > max_count:
max_count = count
most_common = item
# Result: 4
One-liner:
most_common = max(set(items), key=items.count)
The max function accepts a key parameter for comparison. Using count as the key compares elements by frequency rather than value. The set conversion ensures each unique element is checked only once.
When to use: Analyzing mode in statistical data, finding most frequent errors in logs, or identifying popular choices in user data.
Practical Application
These one-liners are not about showing off. They demonstrate Python’s design philosophy: there should be one obvious way to do things.
Each pattern solves a common problem more clearly than verbose alternatives. The code reads closer to English. The intent becomes immediately apparent. Maintenance becomes easier because less code means fewer places for bugs to hide.
Start incorporating these patterns gradually. Replace verbose loops with comprehensions when filtering or transforming data. Use built-in functions like zip and enumerate instead of manual index management. Leverage standard library tools like Counter instead of reinventing counting logic.
The goal is not memorizing tricks. The goal is recognizing when Python provides a better way. These patterns become second nature with practice. Your code becomes more Pythonic and more maintainable.
When to Avoid One-Liners
One-liners have limits. Readability matters more than brevity. Complex logic spread across multiple lines with clear variable names beats a cryptic one-liner requiring deep analysis to understand.
Use one-liners when they improve clarity. Avoid them when they sacrifice readability for character count. The test is simple: if you need to reread the line multiple times to understand it, break it into multiple statements.
Python values explicit over implicit. Flat over nested. Readable over clever. These principles apply to one-liners as much as any other code.
Master these ten patterns. Recognize situations where they apply. Write clearer, more maintainable Python code.
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