Small Executable Python Examples

A runnable example is evidence, not decoration

An executable example is code that a reader can place in a Python 3 interpreter or a file and run. Its value comes from a narrow question and an observable result. For example, print(7 + 3) addresses one question: what value does this expression produce? It is stronger than a prose claim because another reader can repeat the observation. The Python tutorial presents the interactive interpreter as a place to enter expressions and see their values, while a script can make output explicit with print(). A small example should therefore identify its input or starting state, its operation, and what the reader should observe.

Small does not mean trivial or context-free. It means that irrelevant setup has been removed. If the point is comparison, use named values and one comparison; do not also introduce files, classes, user input, and a loop. This makes a surprising result easier to locate. It also makes the example portable: a reader can run it without needing data downloads, credentials, or a particular project. Version and environment still matter, so label the language as Python 3 and avoid claiming that one run proves behavior in every implementation or library version.

Read, predict, run, and compare

A disciplined reading pass starts with state. In x = 7; y = 3, the names x and y are bound to values. The language reference describes assignment as binding or rebinding names. Next, evaluate expressions using those values. x + y produces 10, and x > y produces True. A print(...) call then displays a representation of its argument followed by a newline. Predicting these results before execution is useful because it exposes what you think the code means; running the example tests that prediction.

Comparison matters as much as execution. If the program prints a different value, first record the exact code and the exact output rather than rewriting the evidence from memory. Check the Python version, indentation, spelling, and whether a prior line changed a name. This lesson is about a controlled observation, not a general debugging workflow: one compact program makes it feasible to account for every line. An example can teach a rule, reveal a mistaken assumption, or document a behavior, but it should say which of those jobs it is doing.

A useful record separates facts from interpretation. The code and output are the observation; the explanation is the reason you think they are connected. Include only details a reader needs to repeat the observation: the code, its Python version when relevant, and any input values. That separation lets another reader challenge or confirm the explanation without guessing what was actually run.

Assertions state a condition that must hold

Output is useful for a person; an assertion is a check built into code. Python's assert condition statement evaluates the condition. If it is true, execution continues. If it is false, Python raises AssertionError; an optional second expression supplies an error message. Thus assert x + y == 10 is a concise executable claim about the current values of x and y. It does not print confirmation by itself, so a following print("checked") can make successful completion visible.

Assertions are appropriate when an example has a condition that should be true. They are not a substitute for validating untrusted external input or for a full testing strategy; Python documentation notes that assertions may be disabled with optimization. In a learning example, that limitation is worth naming because it prevents a reader from treating assert as a universal safety mechanism. Good examples pair a specific expected behavior with a modest conclusion: this code, run in this environment with these inputs, produced this result. They invite a reader to alter one value, rerun, and observe how the claim changes.