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，但我无法找到数组之间的差异。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