Pandas分组长度与NaNs不匹配

Question

我正在尝试对pandas中的groupby对象应用转换。在

代码如下：

df = pd.DataFrame({
    'id':['012', '013', '014', '014', '015', '015', '016', '016', '017', '017'],
    'date': pd.to_datetime(
        ['2008-11-05', 'NaT', 'NaT', '2008-11-05', 'NaT', '2008-11-05',
         'NaT', '2008-11-05', 'NaT', '2008-11-05']),
    'grade': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
              np.nan, np.nan],
    'length': [1, 2, 3, 4, 5, 6, 7, 8, np.nan, 10]})

df['uuid'] = np.nan

df

Out[7]: 
    id       date  grade  length  uuid
0  012 2008-11-05    NaN     1.0   NaN
1  013        NaT    NaN     2.0   NaN
2  014        NaT    NaN     3.0   NaN
3  014 2008-11-05    NaN     4.0   NaN
4  015        NaT    NaN     5.0   NaN
5  015 2008-11-05    NaN     6.0   NaN
6  016        NaT    NaN     7.0   NaN
7  016 2008-11-05    NaN     8.0   NaN
8  017        NaT    NaN     NaN   NaN
9  017 2008-11-05    NaN    10.0   NaN

In[8]:
df.groupby(['id', 'date']).uuid.transform(lambda g: uuid.uuid4())

Out[9]:
...
...
ValueError: Length mismatch: Expected axis has 5 elements, new values have 10 elements

与this问题类似，我假设问题出在日期列中的NaT，所以我尝试了df.fillna('nan')。不幸的是，这抛出了相同的错误-这是因为date列将字符串'nan'识别为np.nan？在

我试着用一个字符串'nullv'填充，结果得到了'ValueError: could not convert string to Timestamp'。在

所以，我目前的解决方案是：

^{pr2}$

当然还有别的方法，而不是转换成字符串再转换回来？在

Answer 1

似乎这是groupby()的一个公开问题，我上面介绍的方法确实是目前的解决方法，请参见here。在