鏈運營商識別值，其中記錄是最接近數

Question

我有一個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當速度是每個id最大速度的50％。爲了做到這一點，我找到了每個ID的最大速度（free_flow_speed），計算了50％，並將其設置爲free_flow_speed。爲了確定哪個記錄最接近50％，我創建了dev_free_flow列，這是給定的speed和free_flow_speed之間的差值。找到最接近於零的記錄，對於每個id，應該標識要歸因於cap_design值的記錄。

因此，我想要創建一個新列cap_design這是volume當diff是最接近零，爲每個id。

從我的最後一個問題，SO（我不是有一個美好的一天在這裏）我已經創建：

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

然而，這將返回Volume每該行的cap_design值，而不是體積dev_free_flow，每id最接近零值。我如何實現這一目標？

Answer 1

使用pd.Series.searchsorted()，可以獲取索引，你應該在分類Series插入一個給定值維持秩序（的Series.max() 50％，你的情況），然後你可以使用在其他系列選擇的匹配值（Volume）。因此，使用什麼似乎是你的數據的相關子集：

   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)]) 

獲得：

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

如果你想要的結果作爲一個新列，你可以這樣做：

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