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Python: Writing Efficient Code (Time Complexity, Vectorization)

20 exercises3 free
  1. 01Measure Time Of A Small Loop
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  2. 02Vectorize A Computation With NumPy (Fixed Seed)
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  3. 03List Comprehension Vs. For Loop
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  4. 04Use CProfile On A Small Summation Function
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  5. 05Identify Bottlenecks And Print Optimized Result
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  6. 06Demonstrate Built-In Sum() Vs. Manual Loop
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  7. 07Preallocate A List And Print Final Sum
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  8. 08Compare Dictionary Vs. List Lookup, Then Print Results
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  9. 09Use Map() On 1..50, Doubling Each Number. Print 'MAPSUM:' Plus The Sum Of Mapped Results.
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  10. 10Document Code & Show Performance Gains, Print Final Result
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  11. 11Set Vs. List Membership Test With 0..9999, Print Times In A Single String
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  12. 12Itertools For Summation, Print 'ISLICE_SUM:' Plus Sum
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  13. 13Vectorize String Uppercase With Pandas, Print 'STRING_VEC:' Plus The First Item Of The Transformed Series
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  14. 14Compare Function Calls In A Loop, Print 'FCALL_SUM:' Plus Final Sum
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  15. 15Generator Expression Summation, Print 'GEN_SUM:' Plus Result
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  16. 16Measure A Snippet With Timeit, Then Print 'TIMEIT_DONE'
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  17. 17Memoized Fibonacci(10), Then Print 'FIB:' Plus Result
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  18. 18Avoid Repeated Computations By Caching A Sum, Then Print 'CACHED:' Plus Final Integer
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  19. 19Compare Slicing Vs. Entire List, Print 'SLICE_SUM:' Plus Sum( Arr[:50] )
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  20. 20Demonstrate NumPy Array Performance And Print 'NUMPY_SUM:' Plus A Stable Sum
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