15 Concise Python Tips for More Efficient Coding

This guide introduces fifteen compact Python tricks aimed at improving code simplicity and performance.

Multi‑key sorting: By leveraging the stability of list.sort() and operator.itemgetter , you can sort a list of dictionaries first by age and then by name, ensuring that entries with the same name remain grouped by age.

Data classes: Introduced in Python 3.7, @dataclass reduces boilerplate, provides automatic __eq__ , type hints, and a helpful __repr__ , making it ideal for lightweight data containers.

List comprehensions: Replace explicit loops with concise expressions for generating lists, performing arithmetic, calling functions, or filtering elements (e.g., keeping only even numbers).

Memory usage inspection: Use sys.getsizeof() to examine the memory footprint of objects; note that range objects are memory‑efficient compared to materialized lists.

Finding the most frequent value: Combine max() with list.count or use a set to identify the element that appears most often in a list or string.

Merging dictionaries: For Python 3.5+ use unpacking ( {**d1, **d2} ) and for Python 3.9+ the | operator to combine dictionaries, with later keys overriding earlier ones.

Returning multiple values: Functions can return tuples of values; for more than three results, consider wrapping them in a data class for clarity.

Filtering with filter() : Provide a predicate function and an iterable; the result can be converted to a list, or achieve the same effect with a list comprehension.

Mapping with map() : Apply a function to each element of an iterable, such as squaring numbers, either via map() or a list comprehension.

Combining lists with zip() : Pair elements from multiple lists positionally to create tuples of corresponding items.

Reversing a list: Use slice notation my_list[::-1] for a quick in‑memory reversal.

Checking element existence: The in operator determines whether a value is present in a list, useful for membership tests.

Flattening nested lists: A double list comprehension can flatten two‑level nested lists; for deeper nesting, third‑party libraries like dm‑tree can handle arbitrary depth.

Uniqueness verification: Convert a list to a set and compare lengths to confirm all elements are unique.