Programming Fundamentals Questions

Direct answer
A pure function's output depends only on its inputs, and calling it produces no observable effect outside its own return value, no mutation of external state, no I/O, no reliance on anything that could change between calls. A function with side effects does at least one of those things: it might mutate a variable outside its own scope, write to a file, or return a different result for the same input depending on some external state.

Structured elaboration

• Same input, same output, always: this is the defining property. math.sqrt(4) is pure, it returns 2.0 every single time. A function that reads the current time, a global counter, or a mutable default argument that accumulates across calls is not pure, its output can differ across calls even with identical arguments.
• No observable effects outside the return value: a pure function doesn't print, doesn't write to a database, doesn't mutate an object passed into it, doesn't increment a counter defined outside itself. If you could delete every call to it (assuming nothing used the return value) and the rest of the program's behavior would be unchanged, that's a strong sign of purity; if deleting a call changes behavior beyond 'the return value is no longer available', something impure happened inside it.
• Why purity matters for testing: a pure function needs no setup beyond its arguments and no teardown, you call it, you assert on the return value, done. An impure function's test has to also arrange whatever external state it reads, and verify whatever external state it mutates, which multiplies both the setup complexity and the number of ways the test can be wrong or flaky.
• Why purity matters for parallel execution: if a function only reads its inputs and produces a return value, calling it concurrently from multiple threads for different inputs is automatically safe, there's no shared mutable state for two calls to race on. An impure function that mutates shared state needs explicit synchronization (locks) to be safe under concurrency, or it will produce wrong results or crashes under load in a way that's notoriously hard to reproduce.

Worked example

def add_tax_pure(price, rate):
    return price * (1 + rate)          # no side effects

total_calls = {"count": 0}
def add_tax_impure(price, rate):
    total_calls["count"] += 1          # side effect: mutates external state
    return price * (1 + rate)

Verified: add_tax_pure(100, 0.08) returns 108.0 every time it's called, and calling it twice does not change anything else observable in the program. add_tax_impure(100, 0.08) returns the same 108.0, but after two calls total_calls["count"] has become 2, a change visible to any OTHER code that also reads total_calls, which is exactly the kind of hidden coupling purity avoids: two unrelated pieces of code that both happen to call add_tax_impure now silently affect each other's view of total_calls.

Trade-offs & pitfalls
Purity isn't free, and most real programs need SOME side effects (writing output, updating a database) somewhere; the useful discipline is not 'eliminate all side effects everywhere' but 'push side effects to the edges of the system and keep the core computation (the actual business logic, the actual data transformation) pure', so the large majority of the code gets the testing and concurrency benefits, and the necessarily-impure parts are small, isolated, and easy to reason about individually.

Direct answer
A bare except: (or except Exception: with a silent return None) doesn't just handle the error you intended, it catches every error that happens to occur in that block and treats all of them identically, so a genuine bug (wrong type, a typo'd attribute, a logic error) gets misclassified as 'expected failure' and hidden from anyone who could act on it. The fix is to catch only the SPECIFIC exception you actually expect, and make failure visible (log it, re-raise it, or return a value the caller is forced to check) instead of silently returning a value indistinguishable from a normal result.

Structured elaboration

• Why this is dangerous, not just untidy: None is frequently also a valid, meaningful return value elsewhere in the codebase. A caller receiving None from this function cannot tell 'there was no data' from 'something crashed while getting the data', those are very different situations that need very different handling, and the swallowed exception has erased the distinction.
• Why 'catch everything' is worse than it looks: a bare except catches ValueError (probably intended) but ALSO TypeError, AttributeError, even KeyboardInterrupt in some forms, errors that indicate a real bug in the calling code, not a data-quality issue the function was designed to tolerate. Narrowing to the specific expected exception type is what lets a genuine bug surface loudly instead of being absorbed by the same catch-all.
• What replaces it: catch only the exception type(s) you actually expect and know how to handle; log enough context to diagnose it (what input caused it); then either re-raise (if the caller has no way to proceed without this data) or return an explicit, unambiguous sentinel that cannot be confused with a valid result (not bare None if None is otherwise meaningful).
• The review conversation: once the technical fix is clear, raising it with the author is a normal, low-friction code-review comment focused on the concrete failure mode ('this will hide a real TypeError as if it were expected'), not a judgment about the person, that's what keeps the fix landing quickly.

Worked example

def bad_parse(raw):
    try:
        return int(raw)
    except Exception:
        return None            # catches ValueError AND TypeError identically

def good_parse(raw, logger):
    try:
        return int(raw)
    except ValueError:
        logger.warning("could not parse %r as int", raw)
        raise                  # or: return a sentinel the caller is forced to check

Verified: bad_parse(None) returns None silently, giving no signal that int(None) actually raised a TypeError (passing None where a string/number was expected, a real bug at the call site, not a data-quality issue). good_parse(None, logger) instead lets that TypeError propagate uncaught (confirmed: it raises TypeError, not swallowed), because the function only catches ValueError. good_parse("not-a-number", logger) correctly logs a warning and re-raises ValueError (confirmed by execution), a case the function WAS designed to handle, with a visible trail.

Trade-offs & pitfalls
The judgment call is choosing between re-raising and returning a sentinel: re-raise when the caller genuinely cannot proceed without valid data (most cases); return an explicit sentinel only when the caller has a real, intentional fallback path for 'this record was unparseable' and the sentinel can't be confused with a legitimate value. What never belongs in either path is catching a broader exception type than you can actually reason about, that's the pattern that turns a narrow, expected failure mode into a general-purpose bug hiding place.

Direct answer
A closure is a function bundled together with references to the variables from its enclosing scope that it uses, captured by reference (not by value), so it keeps seeing the CURRENT value of those variables even after the enclosing function has returned. That captured reference can create a reference cycle, which is why a closure or callback can keep an object alive longer than you expect.

Structured elaboration

• What gets captured: a closure captures the variable itself (technically, the enclosing scope's cell), not a snapshot of its value at creation time. Two closures created from the same enclosing call share independent state; two closures created from the SAME variable in a loop share the same captured cell, which is the classic 'all my callbacks report the same, final loop value' bug.
• Why closures can leak memory: a closure keeps a live reference to everything it captures for as long as the closure itself is reachable. If you then store that closure back onto an object it captured (a callback registered on the very object it was built from), you've created object -> closure -> object, a reference cycle.
• Why reference counting alone can't free a cycle: CPython's primary memory management is reference counting, an object is freed the instant its reference count hits zero. In a cycle, each object holds a reference to the other, so neither one's count ever reaches zero on its own, even after nothing OUTSIDE the cycle references either of them. This is precisely why CPython also runs a separate cyclic garbage collector (gc module) that periodically looks for groups of objects that reference each other but are unreachable from anywhere else, and frees them as a group.
• Mitigation strategies: avoid storing a closure back onto the object it captures when you can restructure to avoid it; use weakref for a back-reference that shouldn't keep the target alive (a common pattern for observer/callback registries); or simply trust the cyclic collector for genuinely short-lived cycles and only investigate further if profiling shows real, growing retention in a long-running process.

Worked example

class Node:
    def __init__(self, name):
        self.name = name
        self.on_event = None

def wire(node):
    def handler():                 # closure: captures `node`
        return f"{node.name} handled"
    node.on_event = handler        # node -> handler -> node : a cycle
    return handler

Verified by running it with gc.disable() and a weakref to the node: after del n (dropping the only external reference), the node is STILL alive (ref() is not None is True) because the cycle keeps both objects' reference counts above zero. Re-enabling the collector and calling gc.collect() reclaims it (ref() is None becomes True immediately after), confirming the cyclic collector, not reference counting, is what actually frees this pattern.

Trade-offs & pitfalls
In a long-running service, this usually shows up as slow, steady memory growth rather than an obvious crash, because the cyclic collector DOES eventually run and free most cycles; the real danger is cycles involving objects with a __del__ method (historically these were UNCOLLECTABLE by the cyclic GC before Python 3.4, and even post-3.4 they add real collection overhead) or large cycles that make each collection pass more expensive as the live object graph grows. The fix is rarely 'stop using closures', it's to be deliberate about back-references specifically, using weakref where a callback registry would otherwise hold the only thing keeping a large object graph alive.

Direct answer
Plain assignment doesn't copy anything, it just gives a second name to the same object. A shallow copy creates a new outer container but reuses references to the same nested objects inside it, so mutating a nested element through either the original or the shallow copy is visible in both. A deep copy recursively copies every nested object too, giving you a fully independent structure.

Structured elaboration

• Assignment (b = a): a and b are now two names for the exact same object; b is a is True. There is no 'original' versus 'copy', they're the same thing.
• Shallow copy (copy.copy(a), or list(a), or a[:] for a list): creates a genuinely new outer object (b is a is now False), but for every element that is itself a mutable object (a nested list, a dict, a custom object), the copy holds a reference to the SAME nested object, not a copy of it (b[0] is a[0] is True).
• Deep copy (copy.deepcopy(a)): recursively walks the structure and makes a new copy of every nested mutable object too, so nothing is shared (b[0] is a[0] is False).
• The bug shape this causes: code that shallow-copies a nested structure believing it now has an independent snapshot, then mutates the original, and the 'snapshot' silently changes too, because the shallow copy's nested elements were never actually copied.

Worked example

import copy
original = [[1, 2, 3], [4, 5, 6]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)

original[0].append(999)   # mutate a NESTED element of the original

Verified results after that mutation: original == [[1, 2, 3, 999], [4, 5, 6]], shallow == [[1, 2, 3, 999], [4, 5, 6]] (the nested list was shared, so the shallow copy sees the change too), deep == [[1, 2, 3], [4, 5, 6]] (fully independent, unaffected).

A realistic version of this bug: code takes a shallow copy of a dataset as a 'before' snapshot, then runs an in-place normalization pass over the dataset:

def normalize_inplace(rows):
    for row in rows:
        total = sum(row)
        for i in range(len(row)):
            row[i] = row[i] / total if total else 0

After running normalize_inplace on the dataset, the shallow-copied 'snapshot' taken beforehand is bitwise identical to the now-normalized dataset (verified by running it: snapshot == dataset evaluates True after normalization), because normalize_inplace mutates each row list in place, and the shallow copy's rows are the SAME row objects as the original's. The 'backup' was never a backup.

Trade-offs & pitfalls
The fix depends on what you actually need: if you truly need an independent snapshot, use copy.deepcopy (accepting its cost, see the mutability discussion) or rebuild the structure by copying each nested piece explicitly. If deep-copying every row of a large dataset is too expensive, the more scalable fix is usually to stop mutating in place at all, have normalize_inplace return a new structure instead of mutating its argument, which sidesteps the shallow/deep copy question entirely by removing the shared-mutable-state pattern that created the risk.

Direct answer
Recursion trades stack space and a per-call overhead for code that mirrors the problem's natural self-similar structure; iteration trades that clarity for constant stack usage and typically better raw performance. The concrete risk with recursion is a stack overflow on deep input, and the usual mitigating technique, tail-call optimization, is not guaranteed across mainstream languages (notably CPython does not do it).

Structured elaboration

• Readability: recursion often reads closer to the mathematical or structural definition of the problem (a tree, a fractal-like decomposition, n! = n * (n-1)!). Iteration usually needs an explicit accumulator or work-list and can obscure that structure, especially for tree/graph problems.
• Stack usage: each recursive call pushes a new stack frame (return address, local variables). A recursive call chain of depth n uses O(n) stack space, while a well-written iterative loop uses O(1) auxiliary stack space (the loop variables live in one frame).
• Stack overflow risk: if depth exceeds the runtime's limit, you get a hard failure (Python's RecursionError, a native segfault-style crash in some languages). This is a real production risk whenever recursion depth is driven by input size rather than a small fixed bound.
• Tail-call optimization (TCO): in a 'tail-recursive' function, the recursive call is the very last operation, nothing happens after it returns. A compiler or runtime that supports TCO can reuse the current stack frame for that call instead of pushing a new one, turning the recursion into a loop under the hood with O(1) stack usage. Languages like Scheme and (in the target-relevant case) Java's Scala guarantee this for self-tail-calls; CPython deliberately does NOT implement it (a language design choice, not a limitation of the trick) partly because it would make stack traces less informative for debugging.

Worked example

def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)      # NOT tail-recursive: multiply happens after the call returns

def factorial_tail_style(n, acc=1):
    if n <= 1:
        return acc
    return factorial_tail_style(n - 1, acc * n)  # tail-recursive IN FORM, but Python still doesn't optimize it

def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

All three agree on small input (verified: factorial_recursive(10) == factorial_iterative(10) == factorial_tail_style(10) == 3628800). The difference shows up at depth: with CPython's default recursion limit of 1000, factorial_recursive(5000) raises RecursionError: maximum recursion depth exceeded (confirmed by running it), while factorial_iterative(5000) completes normally regardless of the tail-style rewrite, because CPython never collapses the recursive call chain into a loop. The same real-world shape shows up walking a deep hierarchical structure (a category tree, a nested comment thread): a recursive walker is elegant until the tree gets deep enough that the recursion limit, not the actual computation, is what fails.

Trade-offs & pitfalls
A correct senior answer does not claim 'just write tail-recursive code and it'll be fine' in a language like Python, that is a common and wrong mental shortcut. The real decision is: if depth is bounded and small (most tree structures in practice), recursion's readability usually wins; if depth scales with untrusted or unbounded input, convert to an explicit iterative version with your own stack (see the tree-traversal conversion question for a worked version of exactly that conversion) rather than relying on the language to save you.