Pandas：按月汇总每个子组
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Pandas: Aggregate by month for every subgroup
提问by FooBar
I have the following pandastable
我有下pandas表
TUFNWGTP TELFS t070101 t070102 t070103 t070104 \
TUDIARYDATE status
2003-01-03 emp 8155462.672158 2 0 0 0 0
2003-01-04 emp 1735322.527819 1 0 0 0 0
emp 3830527.482672 2 60 0 0 0
2003-01-02 unemp 6622022.995205 4 0 0 0 0
2003-01-09 emp 3068387.344956 1 0 0 0 0
and I want to aggregate the daily data to monthly data, for every subgroup.
我想将每个子组的每日数据汇总为每月数据。
That is, if there was no statussubindex, I would do
也就是说，如果没有子status索引，我会做
df.resample('M', how='sum')
How can I do the monthly aggregation for every subgroup?
如何对每个子组进行月度汇总？
回答by Andy Hayden
I think you need to have a DatetimeIndex (rather than a MultiIndex):
我认为您需要有一个 DatetimeIndex（而不是 MultiIndex）：
In [11]: df1 = df.reset_index('status')
In [12]: df1
Out[12]:
status TUFNWGTP TELFS t070101 t070102 t070103 t070104
TUDIARYDATE
2003-01-03 emp 8155462.672158 2 0 0 0 0
2003-01-04 emp 1735322.527819 1 0 0 0 0
2003-01-04 emp 3830527.482672 2 60 0 0 0
2003-01-02 unemp 6622022.995205 4 0 0 0 0
2003-01-09 emp 3068387.344956 1 0 0 0 0
then do a groupby with a monthly TimeGrouper andthe status column:
然后使用每月的 TimeGrouper和状态列进行分组：
In [13]: df1.groupby([pd.TimeGrouper('M'), 'status']).sum()
Out[13]:
TUFNWGTP TELFS t070101 t070102 t070103 t070104
TUDIARYDATE status
2003-01-31 emp 16789700.027605 6 60 0 0 0
unemp 6622022.995205 4 0 0 0 0
