提出了许多类似的类似问题，这对我帮助很大，我遵循了以下帮助： Fill in missing dates of groupby 和 Pandas- adding missing dates to DataFrame while keeping column/index values?
但是它仍然没有解决问题。
我制作了一个玩具数据集来演示我面临的问题：
data = pd.DataFrame({'Date': ['2012-01-01', '2012-01-01','2012-01-01','2012-01-02','2012-01-02','2012-01-02','2012-01-03'], 'Id': ['21','21','22','21','22','23','21'], 'Quantity': ['5','1','4','4','2','1','4'], 'NetAmount': ['66','45','76','35','76','73','45']})
data['Quantity'] = data['Quantity'].astype('int')
data['NetAmount'] = data['NetAmount'].astype('float')
我按照下面的代码对数据集进行了分组：
data['Date'] =pd.to_datetime(data.Date) - pd.to_timedelta(7,unit = 'd')
data =data.groupby(['Id',pd.Grouper(key='Date', freq='W-MON')])['Quantity', 'NetAmount'].sum().reset_index().sort_values('Date')
data.reset_index()
data1 = data.groupby(['Id','Date']).agg({'Quantity': sum, 'NetAmount': sum}).reset_index()
然后我填写缺少的日期：
data2 = data1.set_index(['Date', 'Id','NetAmount']).Quantity.unstack(-3).\
reindex(columns=pd.date_range(data1['Date'].min(), data1['Date'].max(),freq='W-MON'),fill_value=0).\
stack(dropna=False).unstack().stack(dropna=False).\
unstack('NetAmount').stack(dropna=False).fillna(0).reset_index()
给出结果数据框：
Id level_1 NetAmount 0
0 21 2011-12-26 45.0 0.0
1 21 2011-12-26 73.0 0.0
2 21 2011-12-26 146.0 10.0
3 21 2011-12-26 152.0 0.0
4 21 2012-01-02 45.0 4.0
5 21 2012-01-02 73.0 0.0
6 21 2012-01-02 146.0 0.0
7 21 2012-01-02 152.0 0.0
8 22 2011-12-26 45.0 0.0
9 22 2011-12-26 73.0 0.0
10 22 2011-12-26 146.0 0.0
11 22 2011-12-26 152.0 6.0
12 22 2012-01-02 45.0 0.0
13 22 2012-01-02 73.0 0.0
14 22 2012-01-02 146.0 0.0
15 22 2012-01-02 152.0 0.0
16 23 2011-12-26 45.0 0.0
17 23 2011-12-26 73.0 1.0
18 23 2011-12-26 146.0 0.0
19 23 2011-12-26 152.0 0.0
20 23 2012-01-02 45.0 0.0
21 23 2012-01-02 73.0 0.0
22 23 2012-01-02 146.0 0.0
23 23 2012-01-02 152.0 0.0
但实际上我希望得到：
0 21 2011-12-26 66.0 5.0
1 21 2011-12-26 45.0 1.0
2 21 2011-12-26 35.0 4.0
3 21 2012-02-02 45.0 4.0
4 22 2011-12-26 76.0 4.0
5 22 2012-02-02 76.0 2.0
6 23 2011-12-26 0.0 0.0
7 23 2012-02-02 73.0 1.0
填充有效，但是，我不了解结果数据框中实际发生的情况，例如netAmount列中的实例，结果不可用我是unstack / stack函数的新手，我是否在过程中缺少某些东西？谢谢您的帮助！
更新：添加“ 0”值后，我尝试按ID和数据重新分组：
data2 = pd.DataFrame(data2)
data3 = data2.groupby(['Id','Date']).agg({'Quantity': sum, 'NetAmount': sum}).reset_index()
但是我得到这个错误
Traceback (most recent call last):
File "", line 48, in <module>
data3 = data2.groupby(['Id','Date']).agg({'Quantity': sum, 'NetAmount': sum}).reset_index()
File "", line 7632, in groupby
observed=observed, **kwargs)
File "", line 2110, in groupby
return klass(obj, by, **kwds)
File "", line 360, in __init__
mutated=self.mutated)
File "", line 578, in _get_grouper
raise KeyError(gpr)
KeyError: 'Date'
答案 0 :(得分：1)
您需要将列Quantity和NetAmount转换为数字
data['Quantity'] = data['Quantity'].astype('int')
data['NetAmount'] = data['NetAmount'].astype('float')
当列为字符串时，sum函数将所有字符串按组连接。
现在重新运行您的代码，它应该可以按预期运行
# Id level_1 NetAmount 0
#0 21 2011-12-26 45.0 0.0
#1 21 2011-12-26 73.0 0.0
#2 21 2011-12-26 146.0 10.0
#3 21 2011-12-26 152.0 0.0
#4 21 2012-01-02 45.0 4.0
#5 21 2012-01-02 73.0 0.0
#6 21 2012-01-02 146.0 0.0
#7 21 2012-01-02 152.0 0.0
#8 22 2011-12-26 45.0 0.0
#9 22 2011-12-26 73.0 0.0
#10 22 2011-12-26 146.0 0.0
#11 22 2011-12-26 152.0 6.0
#12 22 2012-01-02 45.0 0.0
#13 22 2012-01-02 73.0 0.0
#14 22 2012-01-02 146.0 0.0
#15 22 2012-01-02 152.0 0.0
#16 23 2011-12-26 45.0 0.0
#17 23 2011-12-26 73.0 1.0
#18 23 2011-12-26 146.0 0.0
#19 23 2011-12-26 152.0 0.0
#20 23 2012-01-02 45.0 0.0
#21 23 2012-01-02 73.0 0.0
#22 23 2012-01-02 146.0 0.0
#23 23 2012-01-02 152.0 0.0