Why we should use generators? [Python]

Utpal Kumar     1 minute read

  • Generators don’t hold the entire result in memory. It yields one result at a time.
  • Ways of creating generators:
    1. Using a function
       def squares_gen(num):
               for i in num:
                       yield i**2
       def squares(num):
               for i in num:
               return results
      • Elapsed time for list: 7.360722 Seconds

      • Elapsed time for generators: 5.999999999950489e-06 Seconds

      • Difference in time taken for the list and generators: 7.360716 Seconds for num = np.arange(1,10000000)

    2. Like a list comprehension
       resl = [i**2 for i in num]
       resg = (i**2 for i in num)
      • Elapsed time for list: 7.663468000000001 Seconds

      • Elapsed time for generators: 9.999999999621423e-06 Seconds

      • Difference in time taken: 7.663458000000001 Seconds for num = np.arange(1,10000000)

  • Getting the results from the generator function:
    1. Using next
       resg = squares_gen(num)
       print('res of generators: ',next(resg))
       print('res of generators: ',next(resg))
       print('res of generators: ',next(resg))
    2. Using loop:
       for n in resg:

Advantages of using generators:

  1. The generator codes are more readable.
  2. Generators are much faster and uses little memory.


  1. Using function is a faster way of creating values in Python than using loop or list comprehension for both lists and generators.
  2. The difference between using list or generators is more pronounced when using a comprehension (though generators are still much faster.)
  3. When we need the result of whole array at a time then the amount of time (or memory) taken to create a list or list(generators) are almost same.
How to used Generator Memory usage

Overall, generators gives a performance boost not only in execution time but with the memory as well.


How I calculated the time taken by the process

  • Calculate sum of the system and user CPU time of the current process.
    • time.process_time provides the system and user CPU time of the current process in seconds.
    • Use time.process_time_ns to get the result in nanoseconds

NOTE: The “time taken” shown in this study is subjective to different computers and varies each time depending on the state of the CPU. But each and everytime, the using generators are much faster.

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