根据表中另一列的最大值删除重复行

Question

我有一个像下面这样的大熊猫

在下面的df中，在索引0,1&2,3……& 500,501,502中发现了X&Y列中的重复值，第二轮开始时，索引1000, 1001 & 1002,1003 & ....1200,1201....it goes on中X&Y列中的重复值相同，但权重列中的权重不同。你知道吗

index     x         y         weight
0         59.644    10.72     0.69
1         59.644    10.72     0.82
2         57.822    10.13     0.75
3         57.822    10.13     0.68
4         57.822    10.13     0.20
.
.
500       53.252    10.85     0.15
501       53.252    10.85     0.95
502       53.252    10.85     0.69
.
.
1000      59.644    10.72     0.85
1001      59.644    10.72     0.73
1002      57.822    10.13     0.92
1003      57.822    10.13     0.15
.
.
.
1200       53.252    10.85     0.78
1201       53.252    10.85     1.098        

我的要求

I would like to have my df
1) Avoid repeated/duplicate row values in X & Y which has weight value less than 0.60

2) But still duplicates in X & Y column repeats, So now i want to compare the weight values between duplicate rows & remove the rows which has lesser weight.

3) If I use the below code, it removes all the duplicates between x & y

df_2.groupby(['X', 'Y'], as_index=False,sort=False)['weight'].max()

But I want to compare the first occured duplicates and remove them, then the 2nd, then 3rd and so on ..so that the continuity of duplicate value prevails after some rows. for better understanding, please refer the below required df

df应该是什么样子的：

index     x         y         weight
1         59.644    10.72     0.82
2         57.822    10.13     0.75
.
.
501      53.252    10.85      0.95
.
.
1000      59.644    10.72     0.85
.
1002      57.822    10.13     0.92
.
.
1201       53.252    10.85     1.098   
.
.

我尝试过使用if语句，但是代码行增加了。 我认为应该有一个替代的Python方式，使它更容易。（内置函数或使用numpy） 任何帮助都将不胜感激。你知道吗

Answer 1

与注释中提到的@Erfan一样，这里有必要通过helper Series对连续组进行分组：

x1 = df['x'].ne(df['x'].shift()).cumsum()
y1 = df['y'].ne(df['y'].shift()).cumsum()

df = df[df.groupby([x1, y1])['weight'].transform('max') == df['weight']]
print (df)
    index       x      y  weight
1       1  59.644  10.72   0.820
2       2  57.822  10.13   0.750
6     501  53.252  10.85   0.950
8    1000  59.644  10.72   0.850
10   1002  57.822  10.13   0.920
13   1201  53.252  10.85   1.098