python - Pandas 中提到的按时间帧对数据帧进行分组

Question

我有一个类似于下面的数据框:

   detaildate   detailquantity
0   5/6/2014    8550
1   5/8/2014    0
2   3/3/2015    -3250
3   4/14/2015   -3250
4   5/19/2015   3250
5   5/20/2015   -1200
6   2/22/2016   40000
7   4/23/2016   -4500
8   5/23/2016   -2500
9   5/30/2016   -5000
10  4/3/2017    -4750
11  6/5/2017    -2000

现在我想按某个时间范围对这些数据进行分组。例如，如果我每年对其进行分组，我想要以下结果:

   detaildate   detailquantity
0   5/6/2014    8550
1   5/8/2014    0
   detaildate   detailquantity
0   3/3/2015    -3250
1   4/14/2015   -3250
2   5/19/2015   3250
3   5/20/2015   -1200
   detaildate   detailquantity
0   2/22/2016   40000
1   4/23/2016   -4500
2   5/23/2016   -2500
3   5/30/2016   -5000
   detaildate   detailquantity
0   4/3/2017    -4750
1   6/5/2017    -2000

我为其编写了以下代码:

S = pd.to_datetime(df.detaildate)
for i, g in df.groupby([(S - S[0]).astype('timedelta64[Y]')]):
    print (g.reset_index(drop=True))

但是，它不是按日历年分组，而是按从开始日期起的 1 年分组。我得到的结果是:

   detaildate   detailquantity
0   5/6/2014    8550
1   5/8/2014    0
2   3/3/2015    -3250
3   4/14/2015   -3250
   detaildate   detailquantity
0   5/19/2015   3250
1   5/20/2015   -1200
2   2/22/2016   40000
3   4/23/2016   -4500
   detaildate   detailquantity
0   5/23/2016   -2500
1   5/30/2016   -5000
2   4/3/2017    -4750
   detaildate   detailquantity
0   6/5/2017    -2000

如何解决这个问题？

另外，我想在方法中编写上述代码并保留时间范围(M，Y，W，D)作为参数。如下所示:

def groupData(df,timeFrame):
    S = pd.to_datetime(df.detaildate)
    #pass timeFrame as parameter below instead of hardcoded Y
    for i, g in df.groupby([(S - S[0]).astype('timedelta64[Y]')]):
        print (g.reset_index(drop=True))

如何用我的方法的参数 timeFrame 替换上面的硬编码 Y？

Answer 1

使用series.dt.year()在groupby下:

#df.detaildate=pd.to_datetime(df.detaildate)
for i,g in df.groupby(df.detaildate.dt.year):
    print(g.reset_index(drop=True))

<小时/>

   detaildate  detailquantity
0 2014-05-06            8550
1 2014-05-08               0
  detaildate  detailquantity
0 2015-03-03           -3250
1 2015-04-14           -3250
2 2015-05-19            3250
3 2015-05-20           -1200
  detaildate  detailquantity
0 2016-02-22           40000
1 2016-04-23           -4500
2 2016-05-23           -2500
3 2016-05-30           -5000
  detaildate  detailquantity
0 2017-04-03           -4750
1 2017-06-05           -2000