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，
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ITMISS
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用于GroupBy.cumcount每组计数器，然后0通过比较0和来减去值的数量GroupBy.transform：
s = df['Chef_is_masterchef'].eq(0).groupby(df['Chef_Id']).transform('sum')
df['var'] = df.groupby('Chef_Id').cumcount().sub(s)
print (df)
Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition var
0 2400614 May-2015 2297544 0 0 -8
1 2400614 June-2015 2297544 0 0 -7
2 2400614 July-2015 2297544 0 0 -6
3 2400614 August-2015 2297544 0 0 -5
4 2400614 September-2015 2297544 0 0 -4
5 2400614 October-2015 2297544 0 0 -3
6 2400614 November-2015 2297544 0 0 -2
7 2400614 December-2015 2297544 0 0 -1
8 2400614 January-2016 2297544 1 1 0
9 2400614 February-2016 2297544 1 0 1
10 2400614 March-2016 2297544 1 0 2
11 3400624 May-2016 2597531 0 0 -3
12 3400624 June-2016 2597531 0 0 -2
13 3400624 July-2016 2597531 0 0 -1
14 3400624 August-2016 2597531 1 1 0
15 2400133 February-2016 4597531 0 0 -6
16 2400133 March-2016 4597531 0 0 -5
17 2400133 April-2016 4597531 0 0 -4
18 2400133 May-2016 4597531 0 0 -3
19 2400133 June-2016 4597531 0 0 -2
20 2400133 July-2016 4597531 0 0 -1
21 2400133 August-2016 4597531 1 1 0
22 2400133 September-2016 4597531 1 0 1
23 2400133 October-2016 4597531 1 0 2
24 2400133 November-2016 4597531 1 0 3
25 2400133 December-2016 4597531 1 0 4
26 2400133 January-2017 4597531 1 0 5
27 2400133 February-2017 4597531 1 0 6
28 2400133 March-2017 4597531 1 0 7
29 2400133 April-2017 4597531 1 0 8
30 2400133 May-2017 4597531 1 0 9
最后过滤依据Series.between：
df1 = df[df['var'].between(-3, 2)]
print (df1)
df2 = df[df['var'].between(-6, 5)]
print (df2)
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