python - 在python中按特定年份分组数据

Question

我想创建一个按地区和日期分组的数据框，显示特定年份某个地区的平均年龄。所以我的列看起来像

region, year, average age

到目前为止我有:

#specify aggregation functions to column'age'    
ageAverage = {'age':{'average age':'mean'}} 

#groupby and apply functions    
ageDataFrame = data.groupby(['Region', data.Date.dt.year]).agg(ageAverage)

这很好用，但我怎样才能做到只对特定年份的数据进行分组？例如 2010 年到 2015 年之间？

Answer 1

您需要先通过 between 进行过滤:

ageDataFrame = (data[data.Date.dt.year.between(2010, 2015)]
                  .groupby(['Region', data.Date.dt.year])
                  .agg(ageAverage))

也在 last version of pandas 0.22.0 中得到:

SpecificationError: cannot perform renaming for age with a nested dictionary

正确的解决方案是在 groupby 之后指定列表中的列并按 tuple 聚合 - 第一个值是新的列名和第二个聚合函数:

np.random.seed(123)

rng = pd.date_range('2009-04-03', periods=10, freq='13M')
data = pd.DataFrame({'Date': rng,
                     'Region':['reg1'] * 3 + ['reg2'] * 7,
                     'average age': np.random.randint(20, size=10)})  
print (data)
        Date Region  average age
0 2009-04-30   reg1           13
1 2010-05-31   reg1            2
2 2011-06-30   reg1            2
3 2012-07-31   reg2            6
4 2013-08-31   reg2           17
5 2014-09-30   reg2           19
6 2015-10-31   reg2           10
7 2016-11-30   reg2            1
8 2017-12-31   reg2            0
9 2019-01-31   reg2           17

ageAverage = {('age','mean')}

#groupby and apply functions    
ageDataFrame = (data[data.Date.dt.year.between(2010, 2015)]
                 .groupby(['Region', data.Date.dt.year])['average age']
                 .agg(ageAverage))
print (ageDataFrame)
             age
Region Date     
reg1   2010    2
       2011    2
reg2   2012    6
       2013   17
       2014   19
       2015   10