为什么numpy.array()有时很慢？

Question

我正在使用numpy.array()函数从列表中创建numpy.float64 ndarrays.

我注意到当列表包含None或列表列表时,这非常慢.

以下是一些有时间的例子.有明显的解决方法,但为什么这么慢？

无列表的示例:

### Very slow to call array() with list of None
In [3]: %timeit numpy.array([None]*100000, dtype=numpy.float64)
1 loops, best of 3: 240 ms per loop

### Problem doesn't exist with array of zeroes
In [4]: %timeit numpy.array([0.0]*100000, dtype=numpy.float64)
100 loops, best of 3: 9.94 ms per loop

### Also fast if we use dtype=object and convert to float64
In [5]: %timeit numpy.array([None]*100000, dtype=numpy.object).astype(numpy.float64)
100 loops, best of 3: 4.92 ms per loop

### Also fast if we use fromiter() insead of array()
In [6]: %timeit numpy.fromiter([None]*100000, dtype=numpy.float64)
100 loops, best of 3: 3.29 ms per loop

列表列表的示例:

### Very slow to create column matrix
In [7]: %timeit numpy.array([[0.0]]*100000, dtype=numpy.float64)
1 loops, best of 3: 353 ms per loop

### No problem to create column vector and reshape
In [8]: %timeit numpy.array([0.0]*100000, dtype=numpy.float64).reshape((-1,1))
100 loops, best of 3: 10 ms per loop

### Can use itertools to flatten input lists
In [9]: %timeit numpy.fromiter(itertools.chain.from_iterable([[0.0]]*100000),dtype=numpy.float64).reshape((-1,1))
100 loops, best of 3: 9.65 ms per loop

Answer 1

我的猜测是，转换列表的代码只是调用float所有内容。如果参数定义了__float__，我们就调用它，否则我们将其视为字符串（在 None 上抛出异常，我们捕获它并放入np.nan）。异常处理应该相对较慢。

时间似乎验证了这个假设：

import numpy as np
%timeit [None] * 100000
> 1000 loops, best of 3: 1.04 ms per loop

%timeit np.array([0.0] * 100000)
> 10 loops, best of 3: 21.3 ms per loop
%timeit [i.__float__() for i in [0.0] * 100000]
> 10 loops, best of 3: 32 ms per loop


def flt(d):
    try:
        return float(d)
    except:
        return np.nan

%timeit np.array([None] * 100000, dtype=np.float64)
> 1 loops, best of 3: 477 ms per loop    
%timeit [flt(d) for d in [None] * 100000]
> 1 loops, best of 3: 328 ms per loop

添加另一个案例只是为了清楚地表明我的处理方向。如果对 None 进行显式检查，则上面的速度不会这么慢：

def flt2(d):                              
    if d is None:
        return np.nan
    try:
        return float(d)
    except:
        return np.nan

%timeit [flt2(d) for d in [None] * 100000]
> 10 loops, best of 3: 45 ms per loop