Profiling

What is the core idea behind profiling before optimising?

• Always optimise the longest function first
• Rewrite everything in C
• Measure what's slow instead of guessing
• Add more print statements

Answer: Measure what's slow instead of guessing. You cannot optimise what you do not measure — profiling replaces guessing with data.

Which standard-library tool profiles function call counts and time?

• cProfile
• tracemalloc
• asyncio
• logging

Answer: cProfile. cProfile records every call, how long each takes, and how many times it runs.

In cProfile output, what does 'tottime' measure?

• Total time including all sub-calls
• Number of times the function was called
• Total program runtime
• Time in the function itself, excluding sub-calls

Answer: Time in the function itself, excluding sub-calls. tottime is time spent in the function body alone; cumtime includes time in sub-calls.

Which sort key best reveals the actually-slow functions?

• "ncalls"
• "tottime"
• "cumtime"
• "name"

Answer: "tottime". Sort by tottime to find where time is really spent; then use cumtime to trace callers.

What does functools.lru_cache do to a recursive fib function?

• Caches results so repeated calls are instant
• Slows it down by caching
• Runs it on multiple cores
• Converts it to a loop

Answer: Caches results so repeated calls are instant. lru_cache memoizes results, turning exponential recursion into near-instant lookups.

What does timeit.timeit('expr', number=1000) return?

• The result of the expression
• A profile report object
• The total time (a float) to run it that many times
• The number of calls

Answer: The total time (a float) to run it that many times. timeit returns the total elapsed time as a float for the given number of executions.

Why use a generator expression (x*x for x in nums) over a list for large data?

• It is always faster to build
• It saves memory by yielding items lazily
• It sorts the data
• It runs in parallel

Answer: It saves memory by yielding items lazily. Generators produce items one at a time instead of building the whole list in memory.

What is the recommended fix for slow string concatenation in a loop?

• Use s = s + x each time
• Use a global string
• Use print() to build it
• Use ''.join(list_of_strings)

Answer: Use ''.join(list_of_strings). ''.join builds the result in one pass; repeated + creates a new string object every iteration.

What does the 80/20 rule say about performance?

• 80% of code runs in 20% of the time
• Roughly 20% of code accounts for ~80% of runtime
• Optimise 80% of functions
• 20% of bugs cause 80% of crashes

Answer: Roughly 20% of code accounts for ~80% of runtime. A small fraction of code dominates runtime, so optimising the wrong 80% gives no improvement.

Which tool tracks memory usage line-by-line?

• cProfile
• timeit
• memory_profiler
• pstats

Answer: memory_profiler. memory_profiler shows memory growth per line; cProfile and timeit measure time, not memory.