如何根据不同列中的条件删除相同值的Pandas dataframe行

Question

我对Python和熊猫还不熟悉，所以请你耐心点。我想我有一个相当简单的问题要解决，但似乎做不好。 我有一个csv文件，我想用pandas数据帧编辑它。这些数据显示了从家到工作地点的流程，以及各个地点的ID以及纬度/经度坐标以及每个流程的值。在

id_home,name_home,lat_home,lon_home,id_work,work,lat_work,lon_work,value
1001,"Flensburg",54.78879007,9.4459971,1002,"Kiel",54.34189351,10.13048288,695
1001,"Flensburg",54.78879007,9.4459971,1003,"Lübeck, Hansestadt",53.88132436,10.72749774,106
1001,"Flensburg",54.78879007,9.4459971,1004,"Neumünster, Stadt",54.07797524,9.974475148,124
1001,"Flensburg",54.78879007,9.4459971,1051,"Dithmarschen",54.12904835,9.120139194,39
1001,"Flensburg",54.78879007,9.4459971,10,"Schleswig-Holstein",54.212,9.959,7618
1001,"Flensburg",54.78879007,9.4459971,1,"Schleswig-Holstein",54.20896049,9.957114419,7618
1001,"Flensburg",54.78879007,9.4459971,2000,"Hamburg, Freie und Hansestadt",53.57071859,9.943770215,567
1001,"Flensburg",54.78879007,9.4459971,20,"Hamburg",53.575,9.941,567
1001,"Flensburg",54.78879007,9.4459971,2,"Hamburg",53.57071859,9.943770215,567
1003,"Lübeck",53.88132436,10.72749774,100,"Saarland",49.379,6.979,25
1003,"Lübeck",53.88132436,10.72749774,10,"Saarland",54.212,9.959,25
1003,"Lübeck",53.88132436,10.72749774,11000,"Berlin, Stadt",52.50395948,13.39337765,274
1003,"Lübeck",53.88132436,10.72749774,110,"Berlin",52.507,13.405,274
1003,"Lübeck",53.88132436,10.72749774,11,"Berlin",52.50395948,13.39337765,274

我想删除所有相邻的具有相同值的重复行，只保留最后一行，其中id_work是一位数或两位数。应删除所有其他行。我怎样才能做到这一点？我基本上需要的是以下输出：

^{pr2}$

非常感谢你的帮助！在

Answer 1

^{}有一个keep参数，将其设置为last：

In [188]:
df.drop_duplicates(subset=['value'], keep='last')

Out[188]:
    id   name  value
0  345  name1    456
1   12  name2    220
5    2  name6    567

实际上，我认为以下是你想要的：

^{pr2}$

在这里，我们将删除具有重复值且“id”长度不是1的行标签细分：

In [198]:
df['value'].duplicated()

Out[198]:
0    False
1    False
2    False
3     True
4     True
5     True
Name: value, dtype: bool

In [199]:
df.loc[df['value'].duplicated(), 'value']

Out[199]:
3    567
4    567
5    567
Name: value, dtype: int64

In [200]:
df['value'].isin(df.loc[df['value'].duplicated(), 'value'].unique())

Out[200]:
0    False
1    False
2     True
3     True
4     True
5     True
Name: value, dtype: bool

In [201]:

(df['value'].isin(df.loc[df['value'].duplicated(), 'value'].unique())) & (df['id'].astype(str).str.len() != 1)

Out[201]:
0    False
1    False
2     True
3     True
4     True
5    False
dtype: bool

In [202]:
df.index[(df['value'].isin(df.loc[df['value'].duplicated(), 'value'].unique())) & (df['id'].astype(str).str.len() != 1)]

Out[202]:
Int64Index([2, 3, 4], dtype='int64')

所以上面使用^{}返回重复值，^{}只返回唯一的重复值，^{}为了测试成员资格，我们将'id'列转换为str，这样我们可以使用^{}测试长度，并使用布尔掩码来屏蔽索引标签。在