我使用numpy.array()函数从列表中创建numpy.float64 ndarrays.

我注意到,当列表包含None或提供列表列表时,这是非常慢的.

以下是一些时代的例子.有明显的解决方法,但为什么这么慢？

无列表示例：

### 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