44. Performance: profiling and complexity awareness

Before optimizing, measure. `time.perf_counter()` gives simple timing; the `timeit` module benchmarks small snippets more rigorously by running them many times.

python
import timeit

timeit.timeit("sum(range(1000))", number=10000)

# common trap: repeated string concatenation in a loop is O(n^2)
result = ""
for chunk in chunks:
    result += chunk   # slow for many chunks

# better: O(n)
result = "".join(chunks)

Checking membership with `in` is O(1) average on a `set`/`dict` but O(n) on a `list`; for repeated lookups, converting a list to a set upfront can turn an O(n²) algorithm into O(n).

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