list filter remove momory profile and execute time profile

list_filter.py

from memory_profiler import profile
from itertools import filterfalse

@profile(precision=1)
def test():
    a = []
    for i in range(100000):
        a.append((10.23 + i, {"data": i}))

    b = filterfalse(lambda x: float(x[0]) >= 1000.26, a)
    
@profile(precision=1)
def test2():
  a = []
    for i in range(100000):
        a.append((10.23 + i, {"data": i}))
  for b in a[:]:
        if float(b[0]) >= 1000.23:
            a.remove(b)

# memory profile

# pip install memory-profiler

# Line #    Mem usage    Increment   Line Contents
# ================================================
#      4     35.3 MiB      0.0 MiB   @profile(precision=1)
#      5                             def test():
#      6     35.3 MiB      0.0 MiB       a = []
#      7     69.5 MiB     34.2 MiB       for i in range(100000):
#      8     69.5 MiB      0.0 MiB           a.append((10.23 + i, {"data": i}))
#      9                             
#     10     70.2 MiB      0.7 MiB       b = filterfalse(lambda x: float(x[0]) >= 10.26, a)
#     11     70.2 MiB      0.0 MiB       print(list(b))

# memory profile
# Line #    Mem usage    Increment   Line Contents
# ================================================
#      4     35.3 MiB      0.0 MiB   @profile(precision=1)
#      5                             # @profile()
#      6                             # @profile
#      7                             def test():
#      8     35.3 MiB      0.0 MiB       a = []
#      9     69.7 MiB     34.4 MiB       for i in range(100000):
#     10     69.7 MiB      0.0 MiB           a.append((10.23 + i, {"data": i}))
#     11                             
#     12                                 # b = filterfalse(lambda x: float(x[0]) >= 1000.26, a)
#     13                             
#     14     71.2 MiB      1.5 MiB       for b in a[:]:
#     15     71.2 MiB      0.0 MiB           if float(b[0]) >= 1000.23:
#     16     71.2 MiB      0.0 MiB               a.remove(b)

# time 

# pip install line_profiler

# for liner-profile need to just add decorator @profile, no need to import from other library 
# @profile
# def slow_function(a, b, c):
#    ... 

# >>> kernprof -l list_filter.py 
# >>> python3 -m line_profiler list_filter.py.lprof

# Timer unit: 1e-06 s

# Total time: 0.286401 s
# File: aa.py
# Function: test at line 6

# Line #      Hits         Time  Per Hit   % Time  Line Contents
# ==============================================================
#      6                                           @profile
#      7                                           def test():
#      8         1            1      1.0      0.0      a = []
#      9    100001       106407      1.1     37.2      for i in range(100000):
#     10    100000       119515      1.2     41.7          a.append((10.23 + i, {"data": i}))
#     11                                           
#     12         1            3      3.0      0.0      b = filterfalse(lambda x: float(x[0]) >= 10.26, a)
#     13         1        60475  60475.0     21.1      print(list(b))


# Timer unit: 1e-06 s

# Total time: 5.62963 s
# File: aa.py
# Function: test at line 6

# Line #      Hits         Time  Per Hit   % Time  Line Contents
# ==============================================================
#      6                                           @profile
#      7                                           def test():
#      8         1            1      1.0      0.0      a = []
#      9    100001        98297      1.0      1.7      for i in range(100000):
#     10    100000        98972      1.0      1.8          a.append((10.23 + i, {"data": i}))
#     11                                           
#     12                                               # b = filterfalse(lambda x: float(x[0]) >= 10.26, a)
#     13                                               # print(list(b))
#     14                                           
#     15    100001        89726      0.9      1.6      for b in a[:]:
#     16    100000        95212      1.0      1.7          if float(b[0]) >= 1000.23:
#     17     99010      5247421     53.0     93.2              a.remove(b)



if __name__ == "__main__":
    test()