numpy.isfinite()中的意外异常

Question

由于我不明白的原因,我得到了这个例外.这是非常复杂的,我的np.array v来自,但这是异常发生时的代码:

print v, type(v)

for val in v:
    print val, type(val)

print "use isfinte() with astype(float64): "
np.isfinite(v.astype("float64"))

print "use isfinite() as usual: "
try:
    np.isfinite(v)
except Exception,e:
    print e

这给出了以下输出:

[6.4441947744288255 7.2246449651781788 4.1028442021807656
 4.8832943929301189] <type 'numpy.ndarray'> 

6.44419477443 <type 'numpy.float64'>
7.22464496518 <type 'numpy.float64'>
4.10284420218 <type 'numpy.float64'>
4.88329439293 <type 'numpy.float64'>

np.isfinte() with astype(float64): 
[ True  True  True  True]

np.isfinte() as usual: 
ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

我不明白TypeError.所有元素都是np.float64,应该没问题.也许是个bug？此错误有时只会发生,但我找不到数组之间的差异.总是有相同的类型.

提前致谢.

编辑:工作示例:

数据结构与上面显示的一样小.

import pandas as pd
import numpy as np


def forward_estim(H,end):

    old_idx = H.index
    new_idx = pd.period_range(old_idx[-1],end,freq=old_idx.freq)

    H_estim = pd.DataFrame(columns=["A","B","C","D"],index=new_idx)

    H_chg = H.values[1:]-H.values[:-1]
    mean_ = H_chg.mean()
    std_  = H_chg.std()

    H_estim.ix[0] = H.ix[-1]

    for i in range(1,len(H_estim)):
        H_estim.A[i] = H_estim.A[i-1] + mean_ + std_/2
        H_estim.B[i] = H_estim.B[i-1] + mean_ + std_
        H_estim.C[i] = H_estim.C[i-1] + mean_ - std_
        H_estim.D[i] = H_estim.D[i-1] + mean_ - std_/2

    return H_estim.ix[1:]


H_idx = pd.period_range("2010-01-01","2012-01-01",freq="A")
print H_idx

H = pd.Series(np.array([2.3,3.0,2.9]),index=H_idx)
print H

H_estim = forward_estim(H,"2014-01-01")
print H_estim

np.isfinite(H_estim.values.astype("float64"))
print "This works!"

np.isfinite(H_estim.values)
print "This does not work!"

这是在这里使用:

MacOsX Mavericks,Python 2.7.6,numpy 1.8.1,pandas 0.13.1

Answer 1

H_estim.values是一个数据类型的numpy数组object(看看H_estim.values.dtype):

In [62]: H_estim.values
Out[62]: 
array([[3.4000000000000004, 3.6000000000000005, 2.7999999999999998, 3.0],
       [3.9000000000000004, 4.3000000000000007, 2.6999999999999993,
        3.0999999999999996]], dtype=object)

In [63]: H_estim.values.dtype
Out[63]: dtype('O')

在object数组中,存储在数组内存中的数据是指向python对象的指针,而不是对象本身.在这种情况下,对象是np.float64实例:

In [65]: H_estim.values[0,0]
Out[65]: 3.4000000000000004

In [66]: type(H_estim.values[0,0])
Out[66]: numpy.float64

因此,在许多方面,这个数组看起来像一个np.float64值数组,但它不一样.特别是,numpy ufuncs(包括np.isfinite)不处理对象数组.

H_estim.values.astype(np.float64)将数组转换为具有数据类型np.float64的数组(即数组元素是实际浮点值的数组,而不是指向对象的指针).将以下内容与上面显示的输出进行比较H_estim.values.

In [70]: a = H_estim.values.astype(np.float64)

In [71]: a
Out[71]: 
array([[ 3.4,  3.6,  2.8,  3. ],
       [ 3.9,  4.3,  2.7,  3.1]])

In [72]: a.dtype
Out[72]: dtype('float64')