Question

问题陈述：
我有一个数据框，如下所示：

   Description      Type      x      y  z
0       Branch  Actuated      0      0  0
1      Forward  Actuated   7.07   7.07  0
2    Backwards  Actuated   7.07  -2.93  0
3      Forward  Actuated  17.07  -2.93  0
4    Backwards  Actuated     10    -10  0
5      Forward  Actuated  17.07 -17.07  0
6          EOL  Actuated   7.07 -17.07  0
7      Forward  Actuated  -7.07  -7.07  0
8    Backwards  Actuated  -7.07   2.93  0
9      Forward  Actuated -17.07   2.93  0
10   Backwards  Actuated    -10     10  0
11     Forward  Actuated -17.07  17.07  0
12         EOL  Actuated  -7.07  17.07  0
13     Forward  Actuated     -0     10  0
14     Forward  Actuated     -0     20  0
15     Forward  Actuated     10      0  0

遇到EOL行时，算法需要反向查找第一行Branch，并将其插入EOL ro之后，如下所示：

   Description      Type      x      y  z
0       Branch  Actuated      0      0  0
1      Forward  Actuated   7.07   7.07  0
2    Backwards  Actuated   7.07  -2.93  0
3      Forward  Actuated  17.07  -2.93  0
4    Backwards  Actuated     10    -10  0
5      Forward  Actuated  17.07 -17.07  0
6          EOL  Actuated   7.07 -17.07  0
0       Branch  Actuated      0      0  0 <--
7      Forward  Actuated  -7.07  -7.07  0
8    Backwards  Actuated  -7.07   2.93  0
9      Forward  Actuated -17.07   2.93  0
10   Backwards  Actuated    -10     10  0
11     Forward  Actuated -17.07  17.07  0
12         EOL  Actuated  -7.07  17.07  0
0       Branch  Actuated      0      0  0 <--
13     Forward  Actuated     -0     10  0
14     Forward  Actuated     -0     20  0
15     Forward  Actuated     10      0  0

注意：反向搜索应基于原始数据框而不是增强的数据框。

问题： 如何有效地（资源松懈和快速）做到这一点？

Answer 1

使用merge_asof获取最后匹配的行，将concat，DataFrame.sort_index和最后DataFrame.reset_index与drop=True连接在一起，以防止索引值重复：

df1 = df[df['Description'] == 'Branch']
print (df1)
  Description      Type    x    y  z
0      Branch  Actuated  0.0  0.0  0

df2 = df[df['Description'] == 'EOL']
print (df2)
   Description      Type     x      y  z
6          EOL  Actuated  7.07 -17.07  0
12         EOL  Actuated -7.07  17.07  0

df3=pd.merge_asof(df2,df1,left_index=True,right_index=True,suffixes=('_',''))[df1.columns]
print (df3)
   Description      Type    x    y  z
6       Branch  Actuated  0.0  0.0  0
12      Branch  Actuated  0.0  0.0  0

df = pd.concat([df, df3]).sort_index().reset_index(drop=True)

print (df)
   Description      Type      x      y  z
0       Branch  Actuated   0.00   0.00  0
1      Forward  Actuated   7.07   7.07  0
2    Backwards  Actuated   7.07  -2.93  0
3      Forward  Actuated  17.07  -2.93  0
4    Backwards  Actuated  10.00 -10.00  0
5      Forward  Actuated  17.07 -17.07  0
6          EOL  Actuated   7.07 -17.07  0
7       Branch  Actuated   0.00   0.00  0
8      Forward  Actuated  -7.07  -7.07  0
9    Backwards  Actuated  -7.07   2.93  0
10     Forward  Actuated -17.07   2.93  0
11   Backwards  Actuated -10.00  10.00  0
12     Forward  Actuated -17.07  17.07  0
13         EOL  Actuated  -7.07  17.07  0
14      Branch  Actuated   0.00   0.00  0
15     Forward  Actuated  -0.00  10.00  0
16     Forward  Actuated  -0.00  20.00  0
17     Forward  Actuated  10.00   0.00  0

从数据框中的当前索引反向搜索

1 个答案: