%time与%timeit

%time %timeit

要在ipython下才可以使用。（所以说Jupyter Notebook当然是可以用的，pycharm里的python环境也是jupyter Notebook的）
%time可以测量一行代码执行的时间
%timeit可以测量一行代码多次执行的时间
网上有说法说，%timeit是测量一行代码100000次循环内，3次最快速度的平均值，就像下面这样：

# 一个非常大的字符串数组
strings = ['foo', 'foobar', 'baz', 'qux', 'python', 'Guido Van Rossum'] * 100000

%time method1 = [x for x in strings if x.startswith('foo')]
CPU times: user 0.19 s, sys: 0.00 s, total: 0.19 s
Wall time: 0.19 s

%time method2 = [x for x in strings if x[:3] == 'foo']
CPU times: user 0.09 s, sys: 0.00 s, total: 0.09 s
Wall time: 0.09 s
    
%timeit [x for x in strings if x.startswith('foo')]
10 loops, best of 3: 159 ms per loop
　　
%timeit [x for x in strings if x[:3] == 'foo']
10 loops, best of 3: 59.3 ms per loop

但是现在去跑时，已经不是这个效果了：

strings = ['foo', 'foobar', 'baz', 'qux', 'python', 'Guido Van Rossum'] * 100000

%time method1 = [x for x in strings if x.startswith('foo')]
Wall time: 96.7 ms

 %time method2 = [x for x in strings if x[:3] == 'foo']
Wall time: 66.8 ms

%timeit [x for x in strings if x.startswith('foo')]
115 ms ± 15.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit [x for x in strings if x[:3] == 'foo']
69.1 ms ± 4.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

应该是产生了变化，现在的效果应该是测了7轮，每轮10次，取平均。