我有以下数据框：Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition2400188 February-2018 4597566 1 02400188 March-2018 4597566 1 02400188 April-2018 4597566 1 02400188 May-2018 4597566 1 02400188 June-2018 4597566 1 02400188 July-2018 4597566 1 02400188 August-2018 4597566 1 02400188 September-2018 4597566 0 12400188 October-2018 4597566 0 02400188 November-2018 4597566 0 02400188 December-2018 4597566 0 02400188 January-2019 4597566 0 02400188 February-2019 4597566 0 02400188 March-2019 4597566 0 02400188 April-2019 4597566 0 02400188 May-2019 4597566 0 02400614 May-2015 2297544 0 02400614 June-2015 2297544 0 02400614 July-2015 2297544 0 02400614 August-2015 2297544 0 02400614 September-2015 2297544 0 02400614 October-2015 2297544 0 02400614 November-2015 2297544 0 02400614 December-2015 2297544 0 02400614 January-2016 2297544 1 12400614 February-2016 2297544 1 02400614 March-2016 2297544 1 0当Chef_is_Masterchef列中发生从0 到 1或1 到 0 的转换时，此转换在Transition列中指示为1。实际上，我想创建另一列（名为“ Var ”），其中的值将按照下面提到的原始数据框填充，
1 回答
杨__羊羊
TA贡献1943条经验 获得超7个赞
pandas.DataFrame.groupby.transformIIUC，与numpy.arange和一起使用numpy.argmax：
df["Var"] = df.groupby("Chef_Id")["Transition"].transform(lambda x: np.arange(x.size) - np.argmax(x))
print(df)
输出：
Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition Var
0 2400188 February-2018 4597566 1 0 -7
1 2400188 March-2018 4597566 1 0 -6
2 2400188 April-2018 4597566 1 0 -5
3 2400188 May-2018 4597566 1 0 -4
4 2400188 June-2018 4597566 1 0 -3
5 2400188 July-2018 4597566 1 0 -2
6 2400188 August-2018 4597566 1 0 -1
7 2400188 September-2018 4597566 0 1 0
8 2400188 October-2018 4597566 0 0 1
9 2400188 November-2018 4597566 0 0 2
10 2400188 December-2018 4597566 0 0 3
11 2400188 January-2019 4597566 0 0 4
12 2400188 February-2019 4597566 0 0 5
13 2400188 March-2019 4597566 0 0 6
14 2400188 April-2019 4597566 0 0 7
15 2400188 May-2019 4597566 0 0 8
16 2400614 May-2015 2297544 0 0 -8
17 2400614 June-2015 2297544 0 0 -7
18 2400614 July-2015 2297544 0 0 -6
19 2400614 August-2015 2297544 0 0 -5
20 2400614 September-2015 2297544 0 0 -4
21 2400614 October-2015 2297544 0 0 -3
22 2400614 November-2015 2297544 0 0 -2
23 2400614 December-2015 2297544 0 0 -1
24 2400614 January-2016 2297544 1 1 0
25 2400614 February-2016 2297544 1 0 1
26 2400614 March-2016 2297544 1 0 2
27 3400624 May-2016 2597531 0 0 -3
28 3400624 June-2016 2597531 0 0 -2
29 3400624 July-2016 2597531 0 0 -1
30 3400624 August-2016 2597531 1 1 0
31 2400133 February-2016 4597531 0 0 -6
32 2400133 March-2016 4597531 0 0 -5
33 2400133 April-2016 4597531 0 0 -4
34 2400133 May-2016 4597531 0 0 -3
35 2400133 June-2016 4597531 0 0 -2
36 2400133 July-2016 4597531 0 0 -1
37 2400133 August-2016 4597531 1 1 0
38 2400133 September-2016 4597531 1 0 1
39 2400133 October-2016 4597531 1 0 2
40 2400133 November-2016 4597531 1 0 3
41 2400133 December-2016 4597531 1 0 4
42 2400133 January-2017 4597531 1 0 5
43 2400133 February-2017 4597531 1 0 6
44 2400133 March-2017 4597531 1 0 7
45 2400133 April-2017 4597531 1 0 8
46 2400133 May-2017 4597531 1 0 9
添加回答
举报
0/150
提交
取消