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总结：

1. 从字节码上来看，两者应该是等效的。但是从实际测试中来看，while True可能比while 1稍微高效一些。
2. 为什么会出现这种情况呢？在python2中True不是关键字，True会转化成1之后在进行对比，字节码会比1多，运行效率会慢。但是在python3中，True是关键字，两者的字节码是一样的，但是关键字经过优化，会比整数1效率高一些。

import disdef run_1():while True:print("run_1")breakdef run_2():while 1:print("run_2")breakdis.dis(run_1)print("-------------------------------------")dis.dis(run_2)

效率对比代码:

import time
# import sys
# import numpy as np
# import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Bardef test_func_time(n_rows, n_times=100):a = time.perf_counter()for j in range(n_times):for _i in range(n_rows):passb = time.perf_counter()for_loop_time = (b - a) / n_times# 测试while_truea = time.perf_counter()for j in range(n_times):for _i in range(n_rows):while True:breakb = time.perf_counter()while_true_time = (b - a) / n_times - for_loop_time# 测试while_1a = time.perf_counter()for j in range(n_times):for _i in range(n_rows):while 1:breakb = time.perf_counter()while_1_time = (b - a) / n_times - for_loop_timevalue = round(while_true_time / while_1_time, 4)return [value, 1]if __name__ == '__main__':index_list = ["一千行", "一万行", "10万行", "100万行"]result = []for i in [1000, 10000, 100000, 1000000]:r1 = test_func_time(n_rows=i, n_times=1000)result.append(r1)c = (Bar().add_xaxis(index_list).add_yaxis("while True占用时间", [i[0] for i in result]).add_yaxis("while 1占用时间", [i[1] for i in result]).reversal_axis().set_series_opts(label_opts=opts.LabelOpts(position="right")).set_global_opts(title_opts=opts.TitleOpts(title="以while_1为基准，while_True效率"))# .render("d:/result/夏普率耗费时间对比.html").render("./while_True和while_1的效率对比.html"))