如何根据每个id的条件选择行 [英] How to select rows based on conditions for each id
本文介绍了如何根据每个id的条件选择行的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有以下数据框:
Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition
2400188 February-2018 4597566 1 0
2400188 March-2018 4597566 1 0
2400188 April-2018 4597566 1 0
2400188 May-2018 4597566 1 0
2400188 June-2018 4597566 1 0
2400188 July-2018 4597566 1 0
2400188 August-2018 4597566 1 0
2400188 September-2018 4597566 0 1
2400188 October-2018 4597566 0 0
2400188 November-2018 4597566 0 0
2400188 December-2018 4597566 0 0
2400188 January-2019 4597566 0 0
2400188 February-2019 4597566 0 0
2400188 March-2019 4597566 0 0
2400188 April-2019 4597566 0 0
2400188 May-2019 4597566 0 0
2400614 May-2015 2297544 0 0
2400614 June-2015 2297544 0 0
2400614 July-2015 2297544 0 0
2400614 August-2015 2297544 0 0
2400614 September-2015 2297544 0 0
2400614 October-2015 2297544 0 0
2400614 November-2015 2297544 0 0
2400614 December-2015 2297544 0 0
2400614 January-2016 2297544 1 1
2400614 February-2016 2297544 1 0
2400614 March-2016 2297544 1 0
3400624 May-2016 2597531 0 0
3400624 June-2016 2597531 0 0
3400624 July-2016 2597531 0 0
3400624 August-2016 2597531 1 1
2400133 February-2016 4597531 0 0
2400133 March-2016 4597531 0 0
2400133 April-2016 4597531 0 0
2400133 May-2016 4597531 0 0
2400133 June-2016 4597531 0 0
2400133 July-2016 4597531 0 0
2400133 August-2016 4597531 1 1
2400133 September-2016 4597531 1 0
2400133 October-2016 4597531 1 0
2400133 November-2016 4597531 1 0
2400133 December-2016 4597531 1 0
2400133 January-2017 4597531 1 0
2400133 February-2017 4597531 1 0
2400133 March-2017 4597531 1 0
2400133 April-2017 4597531 1 0
2400133 May-2017 4597531 1 0
当 Chef_is_Masterchef 列中从 0 到 1 或 1 到 0 的转换发生时，此转换会在 >Transition 列作为 1.
When the transition takes place from 0 to 1 or 1 to 0 in the Chef_is_Masterchef column, this transition is indicated in the Transition column as 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，
Actually, I thought of creating another column (named as "Var") where the values will be filled as mentioned below for the original data frame,
预期数据框:
Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition Var
2400188 February-2018 4597566 1 0 -7
2400188 March-2018 4597566 1 0 -6
2400188 April-2018 4597566 1 0 -5
2400188 May-2018 4597566 1 0 -4
2400188 June-2018 4597566 1 0 -3
2400188 July-2018 4597566 1 0 -2
2400188 August-2018 4597566 1 0 -1
2400188 September-2018 4597566 0 1 0
2400188 October-2018 4597566 0 0 1
2400188 November-2018 4597566 0 0 2
2400188 December-2018 4597566 0 0 3
2400188 January-2019 4597566 0 0 4
2400188 February-2019 4597566 0 0 5
2400188 March-2019 4597566 0 0 6
2400188 April-2019 4597566 0 0 7
2400188 May-2019 4597566 0 0 8
2400614 May-2015 2297544 0 0 -8
2400614 June-2015 2297544 0 0 -7
2400614 July-2015 2297544 0 0 -6
2400614 August-2015 2297544 0 0 -5
2400614 September-2015 2297544 0 0 -4
2400614 October-2015 2297544 0 0 -3
2400614 November-2015 2297544 0 0 -2
2400614 December-2015 2297544 0 0 -1
2400614 January-2016 2297544 1 1 0
2400614 February-2016 2297544 1 0 1
2400614 March-2016 2297544 1 0 2
3400624 May-2016 2597531 0 0 -3
3400624 June-2016 2597531 0 0 -2
3400624 July-2016 2597531 0 0 -1
3400624 August-2016 2597531 1 1 0
2400133 February-2016 4597531 0 0 -6
2400133 March-2016 4597531 0 0 -5
2400133 April-2016 4597531 0 0 -4
2400133 May-2016 4597531 0 0 -3
2400133 June-2016 4597531 0 0 -2
2400133 July-2016 4597531 0 0 -1
2400133 August-2016 4597531 1 1 0
2400133 September-2016 4597531 1 0 1
2400133 October-2016 4597531 1 0 2
2400133 November-2016 4597531 1 0 3
2400133 December-2016 4597531 1 0 4
2400133 January-2017 4597531 1 0 5
2400133 February-2017 4597531 1 0 6
2400133 March-2017 4597531 1 0 7
2400133 April-2017 4597531 1 0 8
2400133 May-2017 4597531 1 0 9
如果观察到，在 Var 列中的转换点，我将值设为零，并且对于我维护相应整数值之前和之后的行.
If observed, at the point of transition in the Var column I am giving the value as zero and for the rows before and after I am maintaining the corresponding integer values.
但是在使用以下代码后，我在 Var 列中遇到了问题，
s = df['Chef_is_masterchef'].eq(0).groupby(df['Chef_Id']).transform('sum')
df['var'] = df.groupby('Chef_Id').cumcount().sub(s)
以上代码的输出:
Hotel_id Month_Year Chef_Id Chef_is_masterchef Transition Var
2400188 February-2018 4597566 1 0 -9
2400188 March-2018 4597566 1 0 -8
2400188 April-2018 4597566 1 0 -7
2400188 May-2018 4597566 1 0 -6
2400188 June-2018 4597566 1 0 -5
2400188 July-2018 4597566 1 0 -4
2400188 August-2018 4597566 1 0 -3
2400188 September-2018 4597566 0 1 -2
2400188 October-2018 4597566 0 0 -1
2400188 November-2018 4597566 0 0 0
2400188 December-2018 4597566 0 0 1
2400188 January-2019 4597566 0 0 2
2400188 February-2019 4597566 0 0 3
2400188 March-2019 4597566 0 0 4
2400188 April-2019 4597566 0 0 5
2400188 May-2019 4597566 0 0 6
2400614 May-2015 2297544 0 0 -8
2400614 June-2015 2297544 0 0 -7
2400614 July-2015 2297544 0 0 -6
2400614 August-2015 2297544 0 0 -5
2400614 September-2015 2297544 0 0 -4
2400614 October-2015 2297544 0 0 -3
2400614 November-2015 2297544 0 0 -2
2400614 December-2015 2297544 0 0 -1
2400614 January-2016 2297544 1 1 0
2400614 February-2016 2297544 1 0 1
2400614 March-2016 2297544 1 0 2
3400624 May-2016 2597531 0 0 -3
3400624 June-2016 2597531 0 0 -2
3400624 July-2016 2597531 0 0 -1
3400624 August-2016 2597531 1 1 0
2400133 February-2016 4597531 0 0 -6
2400133 March-2016 4597531 0 0 -5
2400133 April-2016 4597531 0 0 -4
2400133 May-2016 4597531 0 0 -3
2400133 June-2016 4597531 0 0 -2
2400133 July-2016 4597531 0 0 -1
2400133 August-2016 4597531 1 1 0
2400133 September-2016 4597531 1 0 1
2400133 October-2016 4597531 1 0 2
2400133 November-2016 4597531 1 0 3
2400133 December-2016 4597531 1 0 4
2400133 January-2017 4597531 1 0 5
2400133 February-2017 4597531 1 0 6
2400133 March-2017 4597531 1 0 7
2400133 April-2017 4597531 1 0 8
2400133 May-2017 4597531 1 0 9
如果观察到，对于 Chef_Id = 4597566，您可以在转换点看到 Var 列中的值不同而不是零.
If Observed, for the Chef_Id = 4597566 you can see at the point of transition the value is different instead of zero in the Var column.
这会产生一个问题，因为在转换点，我必须为每个 ID 选择最多 3 个月前和 2 个月后的行.同样在转换点，我必须使用以下代码为每个 id 选择最多 6 个月之前和 5 个月之后的行:
This creates a problem because, at the point of transition, I have to select rows including up to 3 months before and 2 months after for each id. Also at the point of transition, I have to select rows including up to 6 months before and 5 months after for each id using the below code:
df1 = df[df['var'].between(-3, 2)]
print (df1)
df2 = df[df['var'].between(-6, 5)]
print (df2)
所以请告诉我解决方案.
So please let me know the solution.
提前致谢！
推荐答案
使用 GroupBy.cumcount 用于每组的计数器，然后通过 比较减去 和 0 值的数量0GroupBy.transform:
Use GroupBy.cumcount for counter per groups and then subtract number of 0 values by compare by 0 and 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
最后过滤器 系列.之间:
Last filter by Series.between:
df1 = df[df['var'].between(-3, 2)]
print (df1)
df2 = df[df['var'].between(-6, 5)]
print (df2)
这篇关于如何根据每个id的条件选择行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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