ValueError:缓冲区dtype不匹配，应为'float64_t'，但为'float' [英] ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'

问题描述

有一个DataFrame'modtso':

There is a DataFrame 'modtso':

In [4]: modtso
Out[4]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 74006 entries, 2002-07-27 15:00:00 to 2010-12-31 22:58:08
Data columns:
0    74006  non-null values
dtypes: float32(1)

In [5]: modtso[1:10]
Out[5]: 
                         0
2002-07-27 16:01:53   9.336845
2002-07-27 16:58:08   9.337487
2002-07-27 18:00:00   9.343308
2002-07-27 19:01:53   9.364368
2002-07-27 19:58:08   9.389445
...

现在，我想按以下方式对其进行重新采样:

Now I want to resample it as below:

a=modtso.resample('D',how='std')

它将引发异常:

ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'

出什么问题了?我该如何解决? 谢谢

what's the problem? how can I fix it? thanks

推荐答案

0.11-dev完全支持 在0.10中，我认为它可以工作，但是您的float32对于几乎所有操作都将变为float64

this is fully supported on 0.11-dev in 0.10 I think it will work, but your float32 will become float64 for almost any operation

和FYI可以显式转换类型

and FYI to convert types explicitly

df.astype('float64')

在此处查看示例 http://pandas.pydata.org /pandas-docs/dev/whatsnew.html#v0-11-0-march-2013

这篇关于ValueError:缓冲区dtype不匹配，应为'float64_t'，但为'float'的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！