链运营商识别值，其中记录是最接近数

Question

我有一个DataFramedf：

id Volume time_norm time_norm_ratio speed BPR_free_speed free_flow_speed capacity_speed dev_free_flow 
9SOUTHBOUND 1474 85 1.794392523 8.947916667 17.88 16.05607477 8.028037383 0.919879283 
9SOUTHBOUND 1375 17 1.158878505 13.85483871 17.88 16.05607477 8.028037383 5.826801327 
9SOUTHBOUND 1052 22 1.205607477 13.31782946 17.88 16.05607477 8.028037383 5.289792074 
9SOUTHBOUND 986 21 1.196261682 13.421875 17.88 16.05607477 8.028037383 5.393837617 
9SOUTHBOUND 1071 15 1.140186916 14.08196721 17.88 16.05607477 8.028037383 6.05392983 
9SOUTHBOUND 1206 34 1.317757009 12.18439716 17.88 16.05607477 8.028037383 4.15635978 
9SOUTHBOUND 1222 34 1.317757009 12.18439716 17.88 16.05607477 8.028037383 4.15635978 
9SOUTHBOUND 1408 33 1.308411215 12.27142857 17.88 16.05607477 8.028037383 4.243391188 
9SOUTHBOUND 1604 69 1.644859813 9.761363636 17.88 16.05607477 8.028037383 1.733326253 
9SOUTHBOUND 1731 124 2.158878505 7.437229437 17.88 16.05607477 8.028037383 -0.590807946 
9SOUTHBOUND 1596 640 6.981308411 2.299866131 17.88 16.05607477 8.028037383 -5.728171252 
9NORTHBOUND 449 17 1.17 14.66666667 17.88 17.16 8.58 6.086666667 
9NORTHBOUND 299 17 1.17 14.66666667 17.88 17.16 8.58 6.086666667 
9NORTHBOUND 241 18 1.18 14.54237288 17.88 17.16 8.58 5.962372881 
9NORTHBOUND 164 13 1.13 15.18584071 17.88 17.16 8.58 6.605840708 
9NORTHBOUND 142 16 1.16 14.79310345 17.88 17.16 8.58 6.213103448 
9NORTHBOUND 137 15 1.15 14.92173913 17.88 17.16 8.58 6.34173913 
9NORTHBOUND 196 13 1.13 15.18584071 17.88 17.16 8.58 6.605840708 

我想找到volume当速度是每个id最大速度的50％。为了做到这一点，我找到了每个ID的最大速度（free_flow_speed），计算了50％，并将其设置为free_flow_speed。为了确定哪个记录最接近50％，我创建了dev_free_flow列，这是给定的speed和free_flow_speed之间的差值。找到最接近于零的记录，对于每个id，应该标识要归因于cap_design值的记录。

因此，我想要创建一个新列cap_design这是volume当diff是最接近零，为每个id。

从我的最后一个问题，SO（我不是有一个美好的一天在这里）我已经创建：

df['cap_design'] = df['Volume'].where(df.groupby('id')['diff'].transform('min')) 

然而，这将返回Volume每该行的cap_design值，而不是体积dev_free_flow，每id最接近零值。我如何实现这一目标？

Answer 1

使用pd.Series.searchsorted()，可以获取索引，你应该在分类Series插入一个给定值维持秩序（的Series.max() 50％，你的情况），然后你可以使用在其他系列选择的匹配值（Volume）。因此，使用什么似乎是你的数据的相关子集：

   id Volume  speed 
13 9NORTHBOUND  241 14.542373 
11 9NORTHBOUND  449 14.666667 
12 9NORTHBOUND  299 14.666667 
15 9NORTHBOUND  142 14.793103 
16 9NORTHBOUND  137 14.921739 
14 9NORTHBOUND  164 15.185841 
17 9NORTHBOUND  196 15.185841 
10 9SOUTHBOUND 1596 2.299866 
9 9SOUTHBOUND 1731 7.437229 
0 9SOUTHBOUND 1474 8.947917 
8 9SOUTHBOUND 1604 9.761364 
5 9SOUTHBOUND 1206 12.184397 
6 9SOUTHBOUND 1222 12.184397 
7 9SOUTHBOUND 1408 12.271429 
2 9SOUTHBOUND 1052 13.317829 
3 9SOUTHBOUND  986 13.421875 
1 9SOUTHBOUND 1375 13.854839 
4 9SOUTHBOUND 1071 14.081967 

用途：

df = df.sort_values(['id', 'speed']) 
df.groupby('id').apply(lambda x: x.Volume.iloc[x.speed.searchsorted(x.speed.max()*.5)]) 

获得：

9NORTHBOUND 13  241 
9SOUTHBOUND 9  1731 
Name: Volume, dtype: int64 

如果你想要的结果作为一个新列，你可以这样做：

df['result'] = df.groupby('id', as_index=False).apply(lambda x: pd.Series(x.Volume.iloc[x.speed.searchsorted(x.speed.max()/2)].tolist() * len(x),index=x.index)).reset_index(level=0, drop=True) 

df.loc[:, ['id', 'Volume', 'speed', 'result']] 

      id Volume  speed result 
0 9NORTHBOUND  241 14.542373  241 
1 9NORTHBOUND  449 14.666667  241 
2 9NORTHBOUND  299 14.666667  241 
3 9NORTHBOUND  142 14.793103  241 
4 9NORTHBOUND  137 14.921739  241 
5 9NORTHBOUND  164 15.185841  241 
6 9NORTHBOUND  196 15.185841  241 
7 9SOUTHBOUND 1596 2.299866 1731 
8 9SOUTHBOUND 1731 7.437229 1731 
9 9SOUTHBOUND 1474 8.947917 1731 
10 9SOUTHBOUND 1604 9.761364 1731 
11 9SOUTHBOUND 1206 12.184397 1731 
12 9SOUTHBOUND 1222 12.184397 1731 
13 9SOUTHBOUND 1408 12.271429 1731 
14 9SOUTHBOUND 1052 13.317829 1731 
15 9SOUTHBOUND  986 13.421875 1731 
16 9SOUTHBOUND 1375 13.854839 1731 
17 9SOUTHBOUND 1071 14.081967 1731