我希望比较多个模型运行的输出，计算这些值：
我已经尝试过多指数，并怀疑答案在于那个方向带有一些创造性的转变（）。但是，我担心通过随意应用各种枢轴/熔化/组合实验来解决问题。也许你可以帮我弄清楚如何解决这个问题：
import pandas as pd
ids = [1,2,3] * 5
year = ['2013', '2013', '2013', '2014', '2014', '2014', '2014', '2014', '2014', '2015', '2015', '2015', '2015', '2015', '2015']
run = ['actual','actual','actual','forecast','forecast','forecast','actual','actual','actual','forecast','forecast','forecast','actual','actual','actual']
revenue = [10,20,20,30,50,90,10,40,50,120,210,150,130,100,190]
change_from_previous_year = ['NA','NA','NA',20,30,70,0,20,30,90,160,60,120,60,140]
change_from_forecast = ['NA','NA','NA','NA','NA','NA',-20,-10,-40,'NA','NA','NA',30,-110,40]
d = {'ids':ids, 'year':year, 'run':run, 'revenue':revenue}
df = pd.DataFrame(data=d, columns=['ids','year','run','revenue'])
print df
ids year run revenue
0 1 2013 actual 10
1 2 2013 actual 20
2 3 2013 actual 20
3 1 2014 forecast 30
4 2 2014 forecast 50
5 3 2014 forecast 90
6 1 2014 actual 10
7 2 2014 actual 40
8 3 2014 actual 50
9 1 2015 forecast 120
10 2 2015 forecast 210
11 3 2015 forecast 150
12 1 2015 actual 130
13 2 2015 actual 100
14 3 2015 actual 190
....进入这个：
ids year run revenue chg_from_prev_year chg_from_forecast
0 1 2013 actual 10 NA NA
1 2 2013 actual 20 NA NA
2 3 2013 actual 20 NA NA
3 1 2014 forecast 30 20 NA
4 2 2014 forecast 50 30 NA
5 3 2014 forecast 90 70 NA
6 1 2014 actual 10 0 -20
7 2 2014 actual 40 20 -10
8 3 2014 actual 50 30 -40
9 1 2015 forecast 120 90 NA
10 2 2015 forecast 210 160 NA
11 3 2015 forecast 150 60 NA
12 1 2015 actual 130 120 30
13 2 2015 actual 100 60 -110
14 3 2015 actual 190 140 40
编辑 - 我对此非常接近：
df['prev_year'] = df.groupby(['ids','run']).shift(1)['revenue']
df['chg_from_prev_year'] = df['revenue'] - df['prev_year']
df['curr_forecast'] = df.groupby(['ids','year']).shift(1)['revenue']
df['chg_from_forecast'] = df['revenue'] - df['curr_forecast']
唯一遗漏的（正如预期的那样）是2014年预测与预测之间的比较。 2013年实际。我可以在数据集中复制2013年运行，计算2014年预测的chg_from_prev_year，并隐藏/删除最终数据帧中不需要的数据。
答案 0 :(得分：1)
首先要获得上一年的变化，对每个群体进行转变：
In [11]: g = df.groupby(['ids', 'run'])
In [12]: df['chg_from_prev_year'] = g['revenue'].apply(lambda x: x - x.shift())
下一部分更复杂，我认为你需要为下一部分做pivot_table：
In [13]: df1 = df.pivot_table('revenue', ['ids', 'year'], 'run')
In [14]: df1
Out[14]:
run actual forecast
ids year
1 2013 10 NaN
2014 10 30
2015 130 120
2 2013 20 NaN
2014 40 50
2015 100 210
3 2013 20 NaN
2014 50 90
2015 190 150
In [15]: g1 = df1.groupby(level='ids', as_index=False)
In [16]: out_by = g1.apply(lambda x: x['actual'] - x['forecast'])
In [17]: out_by # hello levels bug, fixed in 0.13/master... yesterday :)
Out[17]:
ids ids year
1 1 2013 NaN
2014 -20
2015 10
2 2 2013 NaN
2014 -10
2015 -110
3 3 2013 NaN
2014 -40
2015 40
dtype: float64
你想要的结果是什么，但格式不正确（如果你不太讨厌的话，见下文[31]）...以下看起来有点像黑客（说得温和一点） ），但是这里：
In [21]: df2 = df.set_index(['ids', 'year', 'run'])
In [22]: out_by.index = out_by.index.droplevel(0)
In [23]: out_by_df = pd.DataFrame(out_by, columns=['revenue'])
In [24]: out_by_df['run'] = 'forecast'
In [25]: df2['chg_from_forecast'] = out_by_df.set_index('run', append=True)['revenue']
我们已经完成了......
In [26]: df2.reset_index()
Out[26]:
ids year run revenue chg_from_prev_year chg_from_forecast
0 1 2013 actual 10 NaN NaN
1 2 2013 actual 20 NaN NaN
2 3 2013 actual 20 NaN NaN
3 1 2014 forecast 30 NaN -20
4 2 2014 forecast 50 NaN -10
5 3 2014 forecast 90 NaN -40
6 1 2014 actual 10 0 NaN
7 2 2014 actual 40 20 NaN
8 3 2014 actual 50 30 NaN
9 1 2015 forecast 120 90 10
10 2 2015 forecast 210 160 -110
11 3 2015 forecast 150 60 40
12 1 2015 actual 130 120 NaN
13 2 2015 actual 100 60 NaN
14 3 2015 actual 190 140 NaN
注意：我认为chg_from_prev_year的前6个结果应为NaN。
但是，我认为你可能最好将它作为一个支点：
In [31]: df3 = df.pivot_table(['revenue', 'chg_from_prev_year'], ['ids', 'year'], 'run')
In [32]: df3['chg_from_forecast'] = g1.apply(lambda x: x['actual'] - x['forecast']).values
In [33]: df3
Out[33]:
revenue chg_from_prev_year chg_from_forecast
run actual forecast actual forecast
ids year
1 2013 10 NaN NaN NaN NaN
2014 10 30 0 NaN -20
2015 130 120 120 90 10
2 2013 20 NaN NaN NaN NaN
2014 40 50 20 NaN -10
2015 100 210 60 160 -110
3 2013 20 NaN NaN NaN NaN
2014 50 90 30 NaN -40
2015 190 150 140 60 40