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