pandas.to_dict返回None与nan混合

问题描述

我偶然发现了一个熊猫的小问题，它是to_dict的方法.我有一个表，可以肯定每行中有相同数量的相同列，让我们说它看起来像这样:

I've stumbled upon a minor problem with pandas and it's method to_dict.I have a table that I'm certain have equal number of identical columns in each row, let's say it looks like that:

+----|----|----+
|COL1|COL2|COL3|
+----|----|----+
|VAL1|    |VAL3|
|    |VAL2|VAL3|
|VAL1|VAL2|    |
+----|----|----+

当我执行df.to_dict(orient='records')时，我得到:

[{
     "COL1":"VAL1"
     ,"COL2":nan
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":None
     ,"COL2":"VAL2"
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":"VAL1"
     ,"COL2":"VAL2"
     ,"COL3":nan
}]

在某些列中注意nan，在其他列中注意None(总是一样，同一列中似乎没有nan和None)

Notice nan's in some columns and None's in other (always the same, there appears to be no nan and None in same column)

当我执行json.loads(df.to_json(orient='records'))时，我只会得到None而没有nan(需要输出).

And when I do json.loads(df.to_json(orient='records')) i get only None and no nan's (which is desired output).

赞:

[{
     "COL1":"VAL1"
     ,"COL2":None
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":None
     ,"COL2":"VAL2"
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":"VAL1"
     ,"COL2":"VAL2"
     ,"COL3":None
}]

我希望您能解释为什么会发生这种情况以及是否可以通过某种方式对其进行控制.

I would appreciate some explanation as to why it happens and if it can be controlled in some way.

== EDIT ==

==EDIT==

根据评论，最好先用None替换那些nan，但是那些nan不是np.nan:

According to comments it would be better to first replace those nan's with None's, but those nan's are not np.nan:

>>> a = df.head().ix[0,60]
>>> a
nan
>>> type(a)
<class 'numpy.float64'>
>>> a is np.nan
False
>>> a == np.nan
False

推荐答案

我认为您只能 replace ，则无法在 to_dict :

I think you can only replace, it is not possible control in to_dict:

L = [{
     "COL1":"VAL1"
     ,"COL2":np.nan
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":None
     ,"COL2":"VAL2"
     ,"COL3":"VAL3"
 }
 ,{
     "COL1":"VAL1"
     ,"COL2":"VAL2"
     ,"COL3":np.nan
}]

df = pd.DataFrame(L).replace({np.nan:None})
print (df)
   COL1  COL2  COL3
0  VAL1  None  VAL3
1  None  VAL2  VAL3
2  VAL1  VAL2  None

print (df.to_dict(orient='records'))
[{'COL3': 'VAL3', 'COL2': None, 'COL1': 'VAL1'},
 {'COL3': 'VAL3', 'COL2': 'VAL2', 'COL1': None},
 {'COL3': None, 'COL2': 'VAL2', 'COL1': 'VAL1'}]

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