- 01Measure Time Of A Small LoopFree→
- 02Vectorize A Computation With NumPy (Fixed Seed)Free→
- 03List Comprehension Vs. For LoopFree→
- 04Use CProfile On A Small Summation FunctionLocked→
- 05Identify Bottlenecks And Print Optimized ResultLocked→
- 06Demonstrate Built-In Sum() Vs. Manual LoopLocked→
- 07Preallocate A List And Print Final SumLocked→
- 08Compare Dictionary Vs. List Lookup, Then Print ResultsLocked→
- 09Use Map() On 1..50, Doubling Each Number. Print 'MAPSUM:' Plus The Sum Of Mapped Results.Locked→
- 10Document Code & Show Performance Gains, Print Final ResultLocked→
- 11Set Vs. List Membership Test With 0..9999, Print Times In A Single StringLocked→
- 12Itertools For Summation, Print 'ISLICE_SUM:' Plus SumLocked→
- 13Vectorize String Uppercase With Pandas, Print 'STRING_VEC:' Plus The First Item Of The Transformed SeriesLocked→
- 14Compare Function Calls In A Loop, Print 'FCALL_SUM:' Plus Final SumLocked→
- 15Generator Expression Summation, Print 'GEN_SUM:' Plus ResultLocked→
- 16Measure A Snippet With Timeit, Then Print 'TIMEIT_DONE'Locked→
- 17Memoized Fibonacci(10), Then Print 'FIB:' Plus ResultLocked→
- 18Avoid Repeated Computations By Caching A Sum, Then Print 'CACHED:' Plus Final IntegerLocked→
- 19Compare Slicing Vs. Entire List, Print 'SLICE_SUM:' Plus Sum( Arr[:50] )Locked→
- 20Demonstrate NumPy Array Performance And Print 'NUMPY_SUM:' Plus A Stable SumLocked→
