如何加快 Python 中 for 循环的执行速度？

Question

我编写了一个for 循环，在每次迭代时都会对列表变量进行条件语句和更新，这可能会使过程变得非常缓慢。有没有办法加快这个过程并完成与这段代码 sn-p 执行的相同的结果？

fault_array =[] 
for i in x_range_original:
  for j in range(0,16):
    lower_threshold = min(df_records[:,j+1])
    upper_threshold = max(df_records[:,j+1])

    if((df_log[i,j] < lower_threshold) or (df_log[i,j] > upper_threshold)):
      print("Fault detected at timestep: ",df_records['Time'][i])
      fault_array.append(1)
    else:
      print("Normal operation at timestep: ",df_records['Time'][i])

      fault_array.append(0)

Answer 1

迷你代码审查：

fault_array =[] 
for i in x_range_original:
  for j in range(0,16):
    # recomputed on every i; perhaps you wanted j to be an outer loop
    # use vectorized versions of min and max
    lower_threshold = min(df_log[:,j])
    upper_threshold = max(df_log[:,j])

    # this condition is never true:
    # df_log[i,j] cannot be less than min(df_log[:,j])
    # same about upper threshold
    if((df_log[i,j] < lower_threshold) or (df_log[i,j] > upper_threshold)):
      print("Fault detected at timestep: ",df_records['Time'][i])
      fault_array.append(1)
    else:
      # perhaps you need to use a vectorized operation here instead of for loop:
      # fault_array = df.apply(lambda row: ...)
      print("Normal operation at timestep: ",df_records['Time'][i])
      fault_array.append(0)

除了总是消极的条件，我想你正在寻找类似的东西：

columns = list(range(16))
# I guess the thresholds logic should be different
upper_thresholds = df[columns].max(axis=0)
lower_thresholds = df[columns].min(axis=0)
# faults is a series of bools
faults = df[columns].apply(lambda row: any(row < lower_thresholds) or any(row > upper_thresholds), axis=1)
normal_timesteps = df_records.loc[faults, 'Time']