填充日期时间索引时出现值错误

Question

我正试图用DateTimeIndex填充数据帧上的日期。以下是获取初始数据帧的设置：

days =  (date(2018,8,5),date(2018,8,6),date(2018,8,9))
colors = ('red','red','blue')
tuples = list(zip(days,colors))
index = pd.MultiIndex.from_tuples(tuples,names=['day','color'])
df = pd.DataFrame(np.random.randn(3,2) 
,index=index,columns=['first','second'])

它产生这个数据帧：

first      second
day     color        
2018-08-05  red   0.044029   1.135556
2018-08-06  red   0.212579  -0.157853
2018-08-09  blue -0.502317  -0.019823

现在要重新索引以填写缺失的日期：

start = df.index.get_level_values('day').min()
end = df.index.get_level_values('day').max()
reindexer = pd.date_range(start,end)
df2 = df.groupby('color').apply(lambda x: x.reindex(reindexer))

这就产生了这个错误：

ValueError: cannot include dtype 'M' in a buffer

网上有几篇帖子将此消息描述为由于datetime64数组不支持缓冲，以及一些关于如何解决的技巧的提示。我做错什么了吗？或者这是一个bug？建议的解决方法是什么？

Answer 1

我相信您需要reset_index作为DatetimeIndex的第二级，然后使用您的解决方案：

df2 = (df.reset_index(level=1)
.groupby('color')['first','second']
.apply(lambda x: x.reindex(reindexer)))
print (df2)
first    second
color                               
blue  2018-08-05       NaN       NaN
2018-08-06       NaN       NaN
2018-08-07       NaN       NaN
2018-08-08       NaN       NaN
2018-08-09 -0.917287 -1.115499
red   2018-08-05  0.182462  0.205502
2018-08-06  0.541304 -1.525548
2018-08-07       NaN       NaN
2018-08-08       NaN       NaN
2018-08-09       NaN       NaN

或者：

start = df.index.get_level_values('day').min()
end = df.index.get_level_values('day').max()
colors = df.index.get_level_values('color').unique()
dates = pd.date_range(start,end)
mux = pd.MultiIndex.from_product([dates, colors], names=['day','color'])
df = df.reindex(mux)
print (df)
first    second
day        color                    
2018-08-05 red   -0.181284  0.162945
blue        NaN       NaN
2018-08-06 red    0.920916  0.691335
blue        NaN       NaN
2018-08-07 red         NaN       NaN
blue        NaN       NaN
2018-08-08 red         NaN       NaN
blue        NaN       NaN
2018-08-09 red         NaN       NaN
blue   1.286144 -2.356252