Python 3.12+ · Updated November 2026 · Every program explained line by line

Python Comments & Docstrings
Write code that explains itself

A complete, runnable reference for Python's two documentation mechanisms: # comments (ignored by the interpreter) and """docstrings""" (stored in __doc__ and retrievable at runtime). Every code block includes a numbered line-by-line breakdown, plus PEP 257 conventions, three docstring styles, and practice exercises.

"""Sales report utilities.

Usage:
    from reports import summarize
    print(summarize([100, 250, 75]))
"""

A module-level constant — the comment explains why, not what
DEFAULT_CURRENCY = "USD"  fallback when no country is set

def summarize(amounts: list[float]) -> dict:
    """Return a summary of the given amounts.

    Args:
        amounts: A non-empty list of numeric values.

    Returns:
        A dict with keys ``count``, ``total``, and ``average``.
    """
    total = sum(amounts)
    return {
        "count": len(amounts),
        "total": total,
        "average": total / len(amounts),
    }

print(summarize([100, 250, 75]))
Written & reviewed by Glenn Junsay Pansensoy Last reviewed: November 2026 Python 3.12+ ~20 min read

🎯 Why Comments and Docstrings Matter

Code is read far more often than it is written — comments and docstrings are how you make that reading cheap

Comments and docstrings solve two different problems. A comment explains why a particular piece of code exists — the decision, workaround, or constraint that isn't obvious from the code itself. A docstring explains what a module, class, or function does — its public contract, retrievable by IDEs, Sphinx, and help(). Mixing these up is one of the most common documentation mistakes in real codebases.

The split, in one line each

Comment: "Why is this retry set to 3? Because the payment API times out roughly 1 in 10 requests."
Docstring: "Retry the given callable up to N times with exponential backoff."

💡 A reasonable rule of thumb: if you're tempted to write a comment that starts with "This function…", it should almost certainly be a docstring instead. Comments that describe what code does tend to drift out of sync; docstrings, at least, are attached to the object they describe.

💬 Python Comment Types

Single-line, inline, and multi-line — plus what Python doesn't have

1️⃣ Single-Line and Inline Comments

Any text after a # on a line is a comment — from that character to the end of the line. It is discarded by the interpreter and never stored anywhere.

A single-line comment: the whole line is ignored.
x = 10  An inline comment: only text after the # is ignored.

print(x)  prints 10

A URL inside a comment doesn't start a second comment:
https://example.com/docs
url = "https://example.com"  a string, not a comment
Line-by-line explanation
  1. 1Everything from # to the end of the line is a comment. The line has no effect on the program — it exists only for human readers.
  2. 2x = 10 runs. The # An inline comment… part after it is ignored. Inline comments must be separated from code by at least two spaces (PEP 8).
  3. 4Prints 10. The comment doesn't appear in output.
  4. 6–7Both are comments. A # inside a comment doesn't nest — the whole line is already a comment.
  5. 8"https://example.com" is a string literal, not a comment — the # would be part of the string. The trailing comment is ignored.
💡 PEP 8 recommends at least two spaces between code and an inline comment: x = 10 # comment, not x = 10 #comment. Small detail, but it keeps code readable when you're scanning thousands of lines.

2️⃣ Multi-Line Comments — The Truth

Python has no dedicated multi-line comment syntax. Two common patterns exist, but only one is idiomatic.

Idiomatic multi-line comment:
each line is a separate # comment.
Readable, greppable, and unambiguous.

The triple-quoted string below looks like a comment
but is actually an unassigned string literal:
"""This is NOT a comment.

It is a string expression whose result is discarded.
If this is the FIRST statement in a module/class/function,
Python stores it in __doc__ as a docstring instead.
"""

y = 42
print(y)
Line-by-line explanation
  1. 1–3Each line starts with #. This is the correct way to write a multi-line comment in Python.
  2. 5–6More explanatory comments — still using #.
  3. 7–11A triple-quoted string, not at the top of a module or function. It is a string literal expression — Python evaluates it (cheaply, since it's a constant) and discards the result. It does not appear in __doc__.
  4. 13–14Ordinary statements that run normally.
💡 Why the triple-quoted pattern is discouraged: tools like grep and linters treat the string as code (because it is code), not as documentation. It also creates a small runtime cost. Use # for real multi-line comments. Reserve triple-quoted strings for docstrings, which belong at the top of a module, class, or function.

📖 Docstrings — Strings That Document Code

A docstring is a string literal that becomes the first statement of a module, class, or function

🔍 How a Docstring Works

When a triple-quoted string is the very first statement after a def, class, or at the top of a file, Python stores it in the object's __doc__ attribute. That makes it retrievable at runtime — the key difference from a comment.

def add(a: int, b: int) -> int:
    """Return the sum of two integers."""
    return a + b

print(add.__doc__)
print(add(2, 3))

def add_no_doc(a, b):
    This is a comment, not a docstring.
    return a + b

print(add_no_doc.__doc__)
Line-by-line explanation
  1. 1Function definition with type hints. The body follows on subsequent indented lines.
  2. 2The """…""" on the first line of the body becomes the function's docstring. Python stores it in add.__doc__.
  3. 3The function returns the sum of the two arguments.
  4. 5Prints the docstring — the string is retrievable at runtime. Output: Return the sum of two integers.
  5. 6Calls the function — output 5.
  6. 8–10A function with a # comment instead of a docstring. The comment is ignored entirely; the function has no docstring.
  7. 12Prints None — add_no_doc.__doc__ is None because no docstring exists.
💡 This is the single most important difference between a comment and a docstring: the docstring survives at runtime; the comment does not. That's why docstrings power help(), IDE tooltips, Sphinx-generated docs, and doctest.

📄 Function, Class, and Module Docstrings

Docstrings can appear in three places. A module docstring goes at the very top of a .py file; a class docstring goes right after the class line; a function docstring goes right after the def line.

"""geometry.py — small 2D helpers.

This module-level docstring describes the file.
"""

class Circle:
    """A circle with a given radius."""

    def __init__(self, radius: float) -> None:
        """Initialise a circle.

        Args:
            radius: Must be non-negative.
        """
        if radius < 0:
            raise ValueError("radius must be non-negative")
        self.radius = radius

    def area(self) -> float:
        """Return the area of the circle (πr²)."""
        import math
        return math.pi * self.radius ** 2

c = Circle(2)
print(f"area ≈ {c.area():.2f}")
Line-by-line explanation
  1. 1–4The module docstring. Because it's the first statement in the file, Python stores it in __doc__ at module level — geometry.__doc__ returns this text.
  2. 6Class definition — no docstring yet, but a docstring will go on the next line.
  3. 7Class docstring. It describes the class's purpose. Stored in Circle.__doc__.
  4. 9The __init__ method takes a radius argument. Its own docstring follows on the next line.
  5. 10–15__init__'s docstring — a short summary plus a parameter description. The blank line and indentation follow PEP 257 conventions.
  6. 16–18Validation logic. The docstring talks about why the argument must be non-negative — the exception message confirms it, but the docstring makes it discoverable without reading the code.
  7. 20–23The area method with its own docstring. Notice the formula πr² is documented in plain text — that's a helpful hint, not code repetition.
  8. 25–26Creates a Circle(2) and prints its area to two decimal places. Expected output: area ≈ 12.57.

♻️ One-Line vs Multi-Line Docstrings

For trivially simple functions, a one-line docstring — ending with a period — is enough. For anything more complex, use the multi-line form with a summary line, a blank line, then details.

def double(n: int) -> int:
    """Return n multiplied by 2."""
    return n * 2

def clamp(value: float, lo: float, hi: float) -> float:
    """Restrict value to the closed interval [lo, hi].

    If value is below lo, returns lo.
    If value is above hi, returns hi.
    Otherwise returns value unchanged.

    Args:
        value: The number to clamp.
        lo: The lower bound (inclusive).
        hi: The upper bound (inclusive).

    Returns:
        The clamped value.
    """
    return max(lo, min(value, hi))

print(double(7))
print(clamp(15, 0, 10))
print(clamp(-3, 0, 10))
print(clamp(5, 0, 10))
Line-by-line explanation
  1. 1–3double is simple enough that its docstring fits on one line, ending with a period. The function body is the return expression.
  2. 5–6clamp uses a multi-line docstring. The first line is a summary — one sentence, ends with a period, appears in generated docs tables.
  3. 7A blank line separates the summary from the rest. PEP 257 requires this for multi-line docstrings.
  4. 8–10The details section — behaviour described in prose, one condition per line. No bullet markers required, though they're common.
  5. 12–17An Args: section listing parameters. This convention (from Google style, discussed later) is what Sphinx and IDE tooltips parse.
  6. 19–20A Returns: section describing the return value.
  7. 22Implementation — max(lo, min(value, hi)) clamps in a single expression.
  8. 24–27Four test calls confirming the clamp behaves as documented. Expected output: 14, 10, 0, 5.
💡 The summary line matters more than you might expect: when Sphinx builds a module's index, only the first line appears in the table. A vague first line makes the whole module index useless. A precise one — "Restrict value to the closed interval [lo, hi]" — is immediately informative.

📜 PEP 257 — The Docstring Convention

The Python community's official style guide for docstrings, adopted in 2001 and still current

📐 The Rules, Condensed

PEP 257 is short — you can read the whole thing in about ten minutes. Here are the rules that matter most in practice:

  • ✓Use triple double quotes for all docstrings: """Like this."""
  • ✓End one-line docstrings with a period, right before the closing quotes
  • ✓For multi-line docstrings, the summary on the first line ends with a period, and the closing """ goes on its own line
  • ✓Leave a blank line before and after the docstring in a class (but not before the docstring of a function)
  • ✓Document the public contract — not implementation details or internal state
  • ✓Start with an imperative verb: "Return…", "Compute…", "Raise…", not "Returns…" or "This function computes…"
def mean(values: list[float]) -> float:
    """Return the arithmetic mean of the given values.

    Args:
        values: A non-empty sequence of numeric values.

    Returns:
        The arithmetic mean as a float.

    Raises:
        ValueError: If ``values`` is empty.
    """
    if not values:
        raise ValueError("values must not be empty")
    return sum(values) / len(values)

print(mean([2, 4, 6, 8]))
Line-by-line explanation
  1. 1Function signature — the type hints already declare the public contract.
  2. 2The summary line: imperative verb ("Return"), full sentence, ends with a period. This is what appears in the module index.
  3. 3Blank line separating summary from details.
  4. 4–5Args: section describing the input parameter. The colon after values is convention — some style guides use a dash instead.
  5. 7–8Returns: section describing the return value and its type.
  6. 10–11Raises: section documenting which exception is raised and under what condition. The ValueError is part of the public contract — callers need to know it can happen.
  7. 12–14Implementation. Note that the docstring says what happens (a ValueError for empty input), and the code makes it happen. The two are kept in sync.
  8. 16Test call — expected output 5.0.
💡 The imperative-mood rule catches many people out. Compare: "Return the sum" (correct, imperative) vs. "Returns the sum" (descriptive, technically a sentence fragment) vs. "This function returns the sum" (redundant, and awkward in generated docs). Stick to imperative — it reads well as a short description under the function name.

🎨 Three Docstring Styles in the Wild

Different ecosystems have converged on different formats — pick one per project and be consistent

🅰️ Google Style

The most readable for humans, widely used at Google and in many open-source projects. Uses indented sections with a colon after the name.

def fetch(url: str, timeout: int = 10) -> bytes:
    """Fetch the given URL and return the response body.

    Args:
        url: A fully-qualified HTTP or HTTPS URL.
        timeout: Seconds to wait before raising.

    Returns:
        The response body as raw bytes.

    Raises:
        TimeoutError: If the request exceeds ``timeout``.
    """
    import urllib.request
    with urllib.request.urlopen(url, timeout=timeout) as r:
        return r.read()

print(len(fetch("https://example.com")))
Line-by-line explanation
  1. 1Signature with default parameter value — timeout=10 is documented as an optional override.
  2. 2Google-style summary — imperative, one line, ends with a period.
  3. 3Blank line separating summary from sections.
  4. 4–6Args: section with parameter names and descriptions, indented two spaces. Google style uses a colon after each parameter, not a dash.
  5. 8–9Returns: section describing the return value type and meaning.
  6. 11–12Raises: section listing exceptions that callers may encounter.
  7. 14–16Implementation using urllib.request. The with block ensures the response is closed after reading.
  8. 18Test call — prints the response size in bytes.

🅱️ NumPy / SciPy Style

Widely used in scientific Python (NumPy, SciPy, pandas, scikit-learn). More structured than Google style, with parameter names on their own lines followed by a type and a description.

def correlate(x: list[float], y: list[float]) -> float:
    """Compute the Pearson correlation coefficient.

    Parameters
    ----------
    x : list of float
        First sequence of numeric values. Must have the same length as ``y``.
    y : list of float
        Second sequence of numeric values.

    Returns
    -------
    float
        The Pearson correlation coefficient in [-1.0, 1.0].

    Raises
    ------
    ValueError
        If ``x`` and ``y`` have different lengths.
    """
    if len(x) != len(y):
        raise ValueError("x and y must have equal length")
    n = len(x)
    mx = sum(x) / n
    my = sum(y) / n
    num = sum((a - mx) * (b - my) for a, b in zip(x, y))
    den = (sum((a - mx) ** 2 for a in x) * sum((b - my) ** 2 for b in y)) ** 0.5
    return num / den

print(correlate([1, 2, 3], [2, 4, 6]))
Line-by-line explanation
  1. 1Signature — both parameters are list[float].
  2. 2Summary line, imperative, ends with period.
  3. 4–5Parameters section header with a dashed underline (this exact length of dashes matters — Sphinx and IDEs parse it).
  4. 6–9Each parameter on two lines: name : type first, then an indented description. Long descriptions can wrap.
  5. 11–12Returns section — the return type on its own line, then an indented description.
  6. 14–16Raises section — exception name on its own line, description below.
  7. 18–24Implementation. The Pearson formula is broken into named intermediates for readability rather than one enormous expression.
  8. 26Test call — expected output 1.0 (the two sequences are perfectly correlated).

🅲 reStructuredText / Sphinx Style

The oldest and most "machine-first" style, used with Sphinx. Uses :param name: and :returns: field markers.

def slugify(text: str) -> str:
    """Convert text to a URL-safe slug.

    Lowercases the input, replaces whitespace runs with single hyphens,
    and strips leading/trailing hyphens.

    :param text: The input string to slugify.
    :type text: str
    :returns: The slugified string.
    :rtype: str
    """
    import re
    s = text.strip().lower()
    s = re.sub(r"\s+", "-", s)
    return s.strip("-")

print(slugify("  Hello, Python World!  "))
Line-by-line explanation
  1. 1Signature — takes a str, returns a str.
  2. 2Summary line — imperative, ends with period.
  3. 4–6Detailed description as plain prose. Sphinx renders this as the body of the docstring.
  4. 8–9:param text: and :type text: — the Sphinx field syntax. The :type: line is redundant if type hints are present, but traditional Sphinx projects still include it.
  5. 10–11:returns: and :rtype: — the return value and its type.
  6. 13–16Implementation. re.sub(r"\s+", "-", s) replaces runs of whitespace with one hyphen. .strip("-") removes hyphens from the ends.
  7. 18Test call — expected output "hello,-python-world!" because punctuation isn't stripped by this simple version. A real slugify would handle punctuation too.

⚖️ Which Style Should You Choose?

Style Readability Machine-parseable Common in
Google★★★★★★★★★Google, many OSS projects
NumPy★★★★★★★★★NumPy, SciPy, pandas, scikit-learn
Sphinx / rST★★★★★★★Older Python projects, Read the Docs
💡 For new projects, Google style hits the sweet spot: readable as plain text, well-supported by Sphinx (via napoleon) and by most IDEs, and compact. If you're contributing to the scientific Python ecosystem, match NumPy style instead.

👁️ Viewing Docstrings at Runtime

Docstrings aren't static — they're accessible from live objects

🔎 help(), __doc__, and pydoc

Three ways to read a docstring, each useful in a different context.

def greet(name: str) -> str:
    """Return a friendly greeting.

    Args:
        name: The person to greet.

    Returns:
        A greeting string of the form "Hello, !".
    """
    return f"Hello, {name}!"

print(greet.__doc__)
print("---")
print(greet.__doc__.splitlines()[0])
Line-by-line explanation
  1. 1Function definition — a simple, well-documented greet.
  2. 2–9The full docstring. Google style, with Args: and Returns: sections.
  3. 10Returns a formatted greeting — never called in this snippet, only inspected.
  4. 12Prints the full docstring — exactly as typed, including newlines and indentation.
  5. 13A separator line for readability.
  6. 14Accesses the first line only — the summary. splitlines()[0] is a common pattern when you want just the short description for an index or table of contents.
💡 For an interactive help page in a terminal, use help(greet) — it renders the docstring with the function signature above it, plus any Args:/Returns: sections. For a module, python -m pydoc mymodule generates a formatted HTML page.

🧪 Doctest — Tests Embedded in Docstrings

Write examples that double as tests, so your documentation never drifts from reality

🧪 Docstring Examples That Are Also Tests

The doctest module scans docstrings for lines starting with >>>, executes them, and compares the output to the lines that follow. If they don't match, the test fails.

def add(a: int, b: int) -> int:
    """Return the sum of a and b.

    >>> add(2, 3)
    5
    >>> add(-1, 1)
    0
    >>> add(0, 0)
    0
    """
    return a + b

import doctest
result = doctest.testmod()
print(result)
Line-by-line explanation
  1. 1Function definition — add takes two integers.
  2. 2Summary line — imperative, ends with a period.
  3. 3–9Docstring containing three interactive examples. Each >>> line is Python code that doctest will run; the line immediately after must be the exact expected output.
  4. 10The actual implementation. Notice the docstring examples are now a contract — if the implementation changed to return a - b, doctest would fail immediately.
  5. 12Imports doctest.
  6. 13doctest.testmod() scans the current module's docstrings, runs each >>> block, and reports results.
  7. 14Prints the result — a TestResults object showing how many examples were run and how many failed. Expected output: TestResults(failed=0, attempted=3).
💡 Doctests are not a replacement for proper unit tests — they don't handle edge cases well and can make docstrings verbose. Use them for short illustrative examples that happen to be verifiable. For real test coverage, use unittest or pytest.

⚠️ Doctest Gotcha — Output Must Match Exactly

Whitespace, quotes, and float precision all matter. A common error is writing an expected output that looks correct but doesn't match the actual string byte-for-byte.

def divide(a: float, b: float) -> float:
    """Divide a by b.

    >>> divide(10, 3)
    3.3333333333333335
    >>> round(divide(10, 3), 2)
    3.33
    """
    return a / b
Line-by-line explanation
  1. 1Signature — both parameters are floats.
  2. 3–4The expected output 3.3333333333333335 is the exact float Python computes for 10/3. If you write 3.3333, the doctest fails.
  3. 5–6Wrap the call in round() to make the output predictable. This is the recommended pattern for float-returning functions.
  4. 8The implementation.
💡 Rule of thumb: if you can't predict the exact printed representation, wrap the call in round() or repr(), or write a comparison like abs(divide(10, 3) - 3.333) < 0.001. Floating-point doctests are a common source of false failures.

✅ Comments & Docstrings — Best Practices

The habits that keep code readable six months after you wrote it

🎯 Do

  • ✓Use comments to explain why, not what. "We retry here because the payment API is flaky" is useful; "this line retries" is not.
  • ✓Use docstrings on every public function and class — these are what IDEs and help() display.
  • ✓Follow PEP 257 — triple double quotes, summary on the first line, blank line before details.
  • ✓Pick one style per project (Google, NumPy, or Sphinx) and stick to it.
  • ✓Document public contracts: parameters, return values, exceptions raised, side effects.
  • ✓Update comments and docstrings when you change the code — a stale comment is worse than no comment.

🚫 Don't

  • ✗Don't comment obvious code. x = x + 1 # increment x adds noise without adding value.
  • ✗Don't use triple-quoted strings as comments — they're executed string literals, not real comments.
  • ✗Don't duplicate the function name in the docstring. def get_user(): """Gets the user.""" tells the reader nothing.
  • ✗Don't leave commented-out code in production. Version control preserves history — deleted code doesn't need to live in comments.
  • ✗Don't let docstrings drift. A docstring that no longer matches the code actively misleads.
  • ✗Don't put secrets in comments. They end up in commits, logs, and dumps.

🏋️ Practice — Check Your Understanding

Three exercises with solutions. Try each before expanding the answer.

Exercise 1 — Comment or Docstring?

For each scenario, decide whether a comment or a docstring is the better fit.

  1. Explaining why a retry count is set to 5 instead of 1.
  2. Describing the parameters a public fetch() function accepts.
  3. Noting that a particular line works around a known bug in a third-party library.
  4. Explaining what a module utils.py is for.
Show solution

1. Comment — this is the "why," and it belongs near the retry logic, not in a public docstring.
2. Docstring — parameters are part of the function's public contract.
3. Comment — workaround notes belong next to the code they protect, not in the public API docs.
4. Module docstring — a one-paragraph summary at the top of the file.

Exercise 2 — Fix the Docstring

This function has a docstring that violates PEP 257. Rewrite it.

def area_of_rectangle(w, h):
    '''Computes the area.'''
    return w * h
Show solution
def area_of_rectangle(w: float, h: float) -> float:
    """Return the area of a rectangle.

    Args:
        w: The rectangle's width.
        h: The rectangle's height.

    Returns:
        The area, computed as w × h.
    """
    return w * h

Three fixes applied: (1) triple double quotes instead of triple single quotes, (2) imperative verb ("Return" not "Computes") with a period, (3) the function name isn't duplicated — the docstring adds information instead of repeating area_of_rectangle.

Exercise 3 — Add a Doctest

Add a doctest to this function that verifies it returns the correct greeting.

def greet(name: str) -> str:
    """Return a friendly greeting."""
    return f"Hello, {name}!"
Show solution
def greet(name: str) -> str:
    """Return a friendly greeting.

    >>> greet("Ana")
    'Hello, Ana!'
    >>> greet("Bob")
    'Hello, Bob!'
    """
    return f"Hello, {name}!"

The expected output 'Hello, Ana!' must include the quotes — repr() of a string includes them, and doctest compares against repr() by default. Writing Hello, Ana! without quotes would fail.

❓ Frequently Asked Questions

Common questions about Python comments and docstrings, answered directly

Python does not have a true multi-line comment syntax. The idiomatic approach is to prefix each line with #. Some developers use triple-quoted strings as multi-line comments, but those are actually unassigned string literals — they are still evaluated at runtime (cheaply, since they're constants) and discarded. If the string is the first statement in a module, class, or function, Python stores it in __doc__ as a docstring instead. Use # for real comments; reserve triple-quoted strings for docstrings.

A comment (#) is ignored by the interpreter and never stored anywhere. It exists only for human readers. A docstring is a string literal that Python stores as the __doc__ attribute of a module, class, or function, and it is retrievable at runtime via help() or __doc__. Comments explain why code exists; docstrings explain what a public interface does. This is why docstrings power IDE tooltips, Sphinx-generated docs, and doctest — comments power none of these.

PEP 257 recommends triple double quotes ("""...""") for all docstrings. Triple single quotes ('''...''') work but are not the convention. Using one consistent style across a codebase makes it easy for tooling (Sphinx, pydoc, IDE introspection) to parse docstrings reliably — and it avoids strange interactions when the docstring itself contains an apostrophe.

Three ways: (1) use the help() built-in — help(my_function) renders the docstring with the function signature above it; (2) access the __doc__ attribute directly — print(my_function.__doc__) returns the raw string; (3) run python -m pydoc mymodule on the command line to generate a formatted HTML or text page for an entire module. All three read the same underlying attribute.

📋 Quick-Reference Table

Every comment and docstring pattern at a glance — bookmark this page for quick reference

Construct Syntax When to use
Single-line comment# textExplain why a line or block exists
Inline commentx = 10 # noteShort clarifications next to specific code
Multi-line comment# line 1
# line 2
Longer explanations, TODOs, workaround notes
Function docstring"""Return the sum."""Document what a public function does
Class docstring"""A circle with a radius."""Describe a class's purpose and invariants
Module docstringTriple-quoted string at top of fileDescribe an entire module's contents
Multi-line docstringSummary line + blank line + detailsNon-trivial functions, classes, modules
Google styleArgs: / Returns: / Raises:Readable docs, general projects
NumPy styleParameters\n----------Scientific Python (NumPy, SciPy, pandas)
Sphinx / rST style:param name: / :returns:Sphinx projects, older codebases
Doctest>>> f(2, 3)\n5Short illustrative examples that double as tests
View docstringhelp(func) / func.__doc__Interactive inspection, debugging, learning
Review Python data types

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