Produces a list of squared numbers using a list comprehension.

Expresses a concise intention to compute squared values for a small range.

Need a quick list of squared numbers for testing or demonstration. Teaching list comprehensions in Python.

Interactive Python shells Jupyter notebooks Teaching material Simple data‑initialization scripts

[i*i for i in range(start, stop+1)]

[expression for variable in iterable]

{"expression":"i*i","variable":"i","iterable":"range(1,6)"}

• list comprehension range square squared integers

• [i**2 for i in range(1
• 6)] [x*x for x in range(1
• 6)] [i*i for i in range(1
• n+1)]

Omitting the closing bracket, resulting in a generator expression. Using range(1,5) which excludes 5. Using i+1 instead of i*i. Confusing with map(lambda x: x*x, range(1,6))

{"similar":["[i for i in range(1,6)]","[i*i for i in range(1,6) if i%2==0]"],"contrasting":["[i+i for i in range(1,6)]","list(map(lambda x: x*x, range(1,6)))]"],"interference_warnings":["Confusing list comprehension with generator expression (i*i for i in range(1,6)) which yields a generator, not a list.","Misplacing the expression before the for clause."],"nuance":"The comprehension eagerly evaluates and builds a new list; it is not lazy like a generator expression.","pragmatic_effect":"Signals a concise, Pythonic way to produce a small collection of computed values.","note":"Common idiom for generating a sequence of squares; often used in teaching list comprehensions.","recall_cue":"Think of squaring the numbers one through five.","spacing_tag":"medium","upgrade_path":"Replace with a generator expression (i*i for i in range(1,6)) for lazy evaluation when memory efficiency is needed.","chunk_family":"list comprehensions","example_1":"[i*i for i in range(1,6)]","example_2":"[i**2 for i in range(1,6)]","example_3":"[x*x for x in range(1,6)]","register":"neutral","type_label":"idiom","semantic_transparency":"transparent","discourse_function":"referential"}

Coming from JavaScript: using Array.map with side effects → prefer list comprehension for concise, readable transformations in Python.

• list comprehensions
• generator expressions
• map
• filter

Do not use for very large ranges because it builds the entire list in memory; for large data sets prefer a generator expression. List comprehensions evaluate eagerly, which can impact performance. Remember that range’s stop value is exclusive, so add +1 to include the upper bound.

Enables rapid creation of computed lists without explicit loops, reducing code size and potential errors.

A list comprehension is like a kitchen blender that mixes an expression with each item from an iterable to produce a fresh list.

Using ** for exponentiation (e.g., i**2) is more idiomatic than i*i for readability.

Replace with a generator expression (i*i for i in range(1,6)) for lazy evaluation when memory efficiency is needed.