我有以下数据框：
Year Month Booked
0 2016 Aug 55999.0
6 2017 Aug 60862.0
1 2016 Jul 54062.0
7 2017 Jul 58417.0
2 2016 Jun 42044.0
8 2017 Jun 48767.0
3 2016 May 39676.0
9 2017 May 40986.0
4 2016 Oct 39593.0
10 2017 Oct 41439.0
5 2016 Sep 49677.0
11 2017 Sep 53969.0
我想获得相对于去年同月的百分比变化。我尝试了以下代码：
df['pct_ch'] = df.groupby(['Month','Year'])['Booked'].pct_change()
但是我得到以下信息，这根本不是我想要的：
Year Month Booked pct_ch
0 2016 Aug 55999.0 NaN
6 2017 Aug 60862.0 0.086841
1 2016 Jul 54062.0 -0.111728
7 2017 Jul 58417.0 0.080556
2 2016 Jun 42044.0 -0.280278
8 2017 Jun 48767.0 0.159904
3 2016 May 39676.0 -0.186417
9 2017 May 40986.0 0.033017
4 2016 Oct 39593.0 -0.033987
10 2017 Oct 41439.0 0.046624
5 2016 Sep 49677.0 0.198798
11 2017 Sep 53969.0 0.086398
答案 0 :(得分：0)
请勿groupby 年份，否则您将不会一起获得Aug 2017和Aug 2016。另外，使用transform将结果广播回原始索引
尝试：
df['pct_ch'] = df.groupby(['Month'])['Booked'].transform(lambda s: s.pct_change())
Year Month Booked pct_ch
0 2016 Aug 55999.0 NaN
6 2017 Aug 60862.0 0.086841
1 2016 Jul 54062.0 NaN
7 2017 Jul 58417.0 0.080556
2 2016 Jun 42044.0 NaN
8 2017 Jun 48767.0 0.159904
3 2016 May 39676.0 NaN
9 2017 May 40986.0 0.033017
4 2016 Oct 39593.0 NaN
10 2017 Oct 41439.0 0.046624
5 2016 Sep 49677.0 NaN
11 2017 Sep 53969.0 0.086398