在 pandas 数据框中查找目标值 [英] Find target values in pandas dataframe

问题描述

我有一个多级数据框df.作为列，我有不同的对象" 我分析.作为行索引，我有一个案例ID lc和时间t.

I have a multilevel dataframe df. As columns, I have different "objects" I analyze. As rows index , I have a Case ID lc, and time t.

对于每种情况lc，我都需要找到时间t(理想情况是内插，但是 最接近的值就足够了)，每个对象都达到目标值.

I need to find, for each case lc, the time t (ideally interpolated, but closest value is fine enough) at which each object reached a target value.

此目标值是给定对象在时间t==0的函数.

This target value is a function of the given object at time t==0.

import pandas as pd
print(pd.__version__)

0.16.2

虚拟数据集示例:

data = {1: {(1014, 0.0): 20.25,
     (1014, 0.0991): 19.08,
     (1014, 0.1991): 18.43,
     (1014, 0.2991): 19.03,
     (1014, 0.3991): 18.71,
     (1015, 0.0): 20.22,
     (1015, 0.0991): 19.3,
     (1015, 0.1991): 18.68,
     (1015, 0.2991): 18.22,
     (1015, 0.3991): 17.84,
     (1016, 0.0): 21.75,
     (1016, 0.0991): 19.97,
     (1016, 0.1991): 19.65,
     (1016, 0.2991): 19.29,
     (1016, 0.3991): 18.94
    },
 2: {(1014, 0.0): 29.11,
     (1014, 0.0991): 28.68,
     (1014, 0.1991): 28.27,
     (1014, 0.2991): 27.46,
     (1014, 0.3991): 26.96,
     (1015, 0.0): 29.22,
     (1015, 0.0991): 28.64,
     (1015, 0.1991): 28.18,
     (1015, 0.2991): 27.74,
     (1015, 0.3991): 27.25,
     (1016, 0.0): 29.17,
     (1016, 0.0991): 28.68,
     (1016, 0.1991): 28.17,
     (1016, 0.2991): 27.68,
     (1016, 0.3991): 27.18
    },
 3: {(1014, 0.0): 22.01,
     (1014, 0.0991): 21.5,
     (1014, 0.1991): 21.18,
     (1014, 0.2991): 20.58,
     (1014, 0.3991): 20.21,
     (1015, 0.0): 21.81,
     (1015, 0.0991): 21.46,
     (1015, 0.1991): 21.11,
     (1015, 0.2991): 20.78,
     (1015, 0.3991): 20.42,
     (1016, 0.0): 21.82,
     (1016, 0.0991): 21.49,
     (1016, 0.1991): 21.11,
     (1016, 0.2991): 20.75,
     (1016, 0.3991): 20.37
    }}

df = pd.DataFrame(data).sort()
df.index.names=['case', 't']

数据框看起来像这样:

                 1      2      3
case t                          
1014 0.0000  20.25  29.11  22.01
     0.0991  19.08  28.68  21.50
     0.1991  18.43  28.27  21.18
     0.2991  19.03  27.46  20.58
     0.3991  18.71  26.96  20.21
1015 0.0000  20.22  29.22  21.81
     0.0991  19.30  28.64  21.46
     0.1991  18.68  28.18  21.11
     0.2991  18.22  27.74  20.78
     0.3991  17.84  27.25  20.42
1016 0.0000  21.75  29.17  21.82
     0.0991  19.97  28.68  21.49
     0.1991  19.65  28.17  21.11
     0.2991  19.29  27.68  20.75
     0.3991  18.94  27.18  20.37

目标值是时间t==0的值的函数. 通常，对于半时段，这将是k = 0.5.对于当前样本，我们将取k = 0.926

Target values are a function of the values at time t==0. typically, this would be k=0.5 for half-time period. For the current sample,we will take k=0.926

由于对值进行了排序，因此可以针对每种情况采用第一行.

Since values are sorted, it is ok to take the first lines for each case.

targets = df.groupby(level='case').first() * 0.926
print(targets)

             1         2         3
case                              
1014  18.75150  26.95586  20.38126
1015  18.72372  27.05772  20.19606
1016  20.14050  27.01142  20.20532

现在，我该如何简单地构建以下数据框，该数据框显示了 每个对象达到上述计算的目标值的时间t?

Now, How could I simply build the following dataframe, which shows time t at wich each object reach target value calculated above?

             1         2         3
case                              
1014    0.3991    0.3991    0.2991
1015    0.1991    0.3991    0.3991
1016    0.0991    0.3991    0.3991

推荐答案

这些有点像骇客，让我们看看是否有更好的解决方案:

These are somewhat of a hack, let's see if there are better solutions:

In [36]:
targets['t']=0

In [37]:
df2 = df.reset_index().set_index('case') - targets

In [38]:
df3 = df2.groupby(df2.index).transform(lambda x: x.abs()==np.min(x.abs()))

In [39]:
df4 = pd.DataFrame({'1': df2.t[df3[1]],
                    '2': df2.t[df3[2]],
                    '3': df2.t[df3[3]]})

print df4

           1       2       3
case                        
1014  0.3991  0.3991  0.3991
1015  0.1991  0.3991  0.3991
1016  0.0991  0.3991  0.3991

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