熊貓數據框查找不同數據框中的值並指定值

Question

我有2個不同的數據框。 第一個是這樣的：

 joint label  x z  y pt 
0  1 NaN 50.4 0.0 -8.40 10 
1  2 shell 52.2 0.0 -8.40 20 
2  3 shell 54.0 0.0 -8.40 30 
3  4 shell 55.8 0.0 -8.40 40 
4  5 shell 57.6 0.0 -8.40 50 

和我的第二個數據幀的樣子：

 member joint1 joint2  joint1_pt  joint2_pt 
0   1  1  2    0    0 
1   2  2  3    0    0 
2   3  3  4    0    0 
3   4  4  5    0    0 

我想採取使用PT值是在一個特定的jointe對應和使用第二個數據幀如下所示：

 member joint1 joint2  joint1_pt  joint2_pt 
0   1  1  2    10    20 
1   2  2  3    20    30 
2   3  3  4    30    40 
3   4  4  5    40    50 

你能幫我一個例子/理想我該如何解決這個問題？ 提前謝謝！

Answer 1

你需要map從Series與set_index和to_dict創建dict爲指向P-robot的評論：

d = df1.set_index('joint')['pt'].to_dict() 
#mapping by Series works, but a bit slowier 
#d = df1.set_index('joint')['pt'] 
print (d) 
{1: 10, 2: 20, 3: 30, 4: 40, 5: 50} 

df2['joint1_pt'] = df2['joint1'].map(d) 
df2['joint2_pt'] = df2['joint2'].map(d) 
print (df2) 
    member joint1 joint2 joint1_pt joint2_pt 
0  1  1  2   10   20 
1  2  2  3   20   30 
2  3  3  4   30   40 
3  4  4  5   40   50 

Answer 2

您可以使用merge合併後將pt分配給joint1_pt & joint2_pt，最後刪除不需要的列。

df= pd.merge(df2,df1[['joint','pt']], right_on='joint',left_on='joint1',how='left') 
df= pd.merge(df,df1[['joint','pt']], right_on='joint',left_on='joint2',how='left') 
df[['joint1_pt','joint2_pt']] =df[['pt_x','pt_y']] 
df=df[['member','joint1','joint2','joint1_pt','joint2_pt']] 
print df 

輸出

member joint1 joint2 joint1_pt joint2_pt 
0  1  1  2   10   20 
1  2  2  3   20   30 
2  3  3  4   30   40 
3  4  4  5   40   50