10 Python One-Liners That Will Change Your Coding Game

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Python one-liners are, as the post title suggests, game-changing solutions to make your code more compact and efficient, typically by simplifying a process that often requires multiple lines of code into a single one. This article lists 10 efficient examples of one-liners that, despite their simplicity, can significantly enhance your coding tasks by simplifying and streamlining common operations and repetitive tasks needed frequently.

Let’s get right into it.

1. Lambda Functions

 
Arguably one of the most well-known one-liners, lambda functions is a very compact approach to defining anonymous functions by simply specifying input arguments on the left-hand side of a “:” sign, and what you want to do to them on the right side. This code defines a function to calculate a discounted price by reducing an original product’s price by 10%.

price_after_discount = lambda price: price*0.9

2. Map Operations on Lists

 
Map operations are extremely useful for applying the same transformation to all elements in a collection like lists. They can be also used in combination with custom reusable lambda functions. For instance, suppose you have a list of original product prices in a tourist souvenir store subject to tax-free policy, and you want another list with the final price after tax deduction (10%) of the total price. By using the previously defined lambda function, can try something like:

discounted_prices = list(map(price_after_discount, prices))

3. Unpacking Lists

 
Suppose you have a price list like product_prices = [19.99, 5.49, 12.99], and you want to print all these prices one by one. Instead of doing this with a loop structure, why not use the ‘*’ operator to unpack the list and print its elements separated by white spaces in a single line?

4. List Comprehension with a Condition

 
You have a list of product names in your shop, like products = ["Keychain", "T-Shirt", "Mug", "Magnet", "Snow Globe"], and you want to obtain a new list containing the indices of products whose name starts with ‘M’. You can do this through list comprehension, that is, building a new list based on analyzing a condition in the values of an existing list.

[index for index in range(len(products)) if products[index][0] == 'M']

5. Checking Conditions Efficiently with any and all

 
A useful pair of functions to quickly check a condition in all elements in a collection, are any and all. Both of them return a True/False value, indicating whether at least one element holds the condition (any), or whether all elements in the collection accomplish it (all).

If you have a list of inventory levels for your products, like inventory = [4, 0, 7, 10, 0], you can try:

any_out_of_stock = any(stock == 0 for stock in inventory)
all_in_stock = all(stock > 0 for stock in inventory)

6. Walrus Operator for Faster Condition Checking

 
The Walrus operator ‘:=’ combines assignment and use of a variable within the same expression, thereby simplifying the approach to perform conditional statements where we need a single-use variable. For example, assuming we are analyzing a customer text review before being submitted, an efficient way to check that the review has at least 30 characters is:

if (n := len(customer_review)) 

7. Sorting Dictionary Entries by Values

 
Let’s get into dictionaries now! Assume we have a dictionary containing the sales number for each of our products.

sales_data = 
    'Keychain': 1200,
    'T-shirt': 800,
    'Mug': 500,
    'Magnet': 1500

This single line of code does the job of sorting products in descending sales order:

sorted_sales = dict(sorted(sales_data.items(), key=lambda item: item[1], reverse=True))

8. Filter Entries with filter

 
You can also filter entries in a Python dictionary by using together the filter and lambda functions as shown below to filter best-selling products (those where 1000 units or more were sold):

best_selling_products = list(filter(lambda item: item[1] > 1000, sales_data.items()))

9. Use reduce to Perform Aggregations

 
Performing aggregations over elements in a list cannot be simpler thanks to the reduce function, which in combination with lambda functions helps “reduce” the elements in a collection (such as sales per product) into a single representative value, for instance the total number of sales across all products:

total_sales = reduce(lambda x, y: x + y, sales_data.values())

10. Generate List Permutations

 
We wrap up with an interesting operator that defines a set of lists given by all possible permutations of a list passed as an argument.

from itertools import permutations
list(permutations(['Alicia', 'Bob', 'Cristina', 'David']))

Need to sit a committee of four in a linear table for an event, and are unsure of which/how many ways to set them one next to another? The permutations will give you all possible solutions.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.