How to improve the running speed of python
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First, analyze the code running time
Formula 1, measure code runtime
trivial method
Shortcuts (jupyter environment)
Formula 2: Calculate the average running time of code for many times
trivial method
Shortcuts (jupyter environment)
Formula 3: Analyze code runtime by calling function
trivial method
Shortcuts (jupyter environment)
Formula 4, Analyze code runtime by line
trivial method
Shortcuts (jupyter environment)
Second, speed up your search.
5. Use a set instead of a list.
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Formula 6: Use dict instead of two lists for matching.
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Three, speed up your cycle.
7. Use the for loop rather than the while loop.
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Equation 8: Avoid double counting in the loop body
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Four, speed up your function.
Equation 9: Replace recursive function with loop mechanism
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Equation 10: Speed up recursive functions with caching mechanisms
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Formula 11, using numba to speed up Python functions
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Fifth, use standard library functions for acceleration
Formula 12, use collections.Counter to accelerate counting
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Formula 13, use collections.ChainMap to accelerate dictionary merging
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VI. Use higher-order functions for acceleration
Equation 14: Use map instead of derivation to accelerate
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Equation 15: Use filter instead of derivation to accelerate
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Seven, use numpy vectorization for acceleration
16, use np.array instead of list
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In equation 17, use np.ufunc instead of math.func
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18, use np.where instead of if
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Eight, speed up your Pandas
Formula 19, use csv file to read and write instead of excel file to read and write
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Formula 20, using the pandas multiprocess tool pandartle
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9. Use Dask to accelerate
Equation 21: Use dask to accelerate dataframe
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Equation 22: Use dask.delayed to accelerate
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Ten, apply multi-threaded multi-process acceleration
Formula 23, apply multithreading to accelerate IO-intensive tasks
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Formula 24: Apply multi-process acceleration to CPU-intensive tasks
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