[英]removing reversed duplicates
My dataframe looks like this: 我的数据框看起来像这样:
df_in = pd.DataFrame(data={'mol1':['cpd1','cpd2', 'cpd3'], 'mol2': ['cpd2','cpd1', 'cpd4'], 'sim': [0.8,0.8,0.9]})
print(df_in)
mol1 mol2 sim
0 cpd1 cpd2 0.8
1 cpd2 cpd1 0.8
2 cpd3 cpd4 0.9
The pair (cpd1, cpd2) occurs twice although each element does not belong to the same column. 该对(cpd1,cpd2)出现两次,尽管每个元素不属于同一列。
I would like to get rid of these duplicates to end up with this: 我想摆脱这些重复,最终得到这个:
df_out = pd.DataFrame(data={'mol1':['cpd1', 'cpd3'], 'mol2': ['cpd2', 'cpd4'], 'sim': [0.8,0.9]})
print(df_out)
mol1 mol2 sim
0 cpd1 cpd2 0.8
1 cpd3 cpd4 0.9
If I ignore the third column, there is a solution describes in Pythonic way of removing reversed duplicates in list , but I have to preserve this column. 如果我忽略第三列,有一个解决方案以Pythonic方式描述删除列表中的反向重复项 ,但我必须保留此列。
You can use sorted
with apply
for columns from list cols
and then drop_duplicates
: 您可以使用
sorted
与apply
从列表列cols
然后drop_duplicates
:
cols = ['mol1','mol2']
df[cols] = df[cols].apply(sorted, axis=1)
df = df.drop_duplicates()
print (df)
mol1 mol2 sim
0 cpd1 cpd2 0.8
2 cpd3 cpd4 0.9
Similar solution with numpy.sort
: 与
numpy.sort
类似的解决方案:
cols = ['mol1','mol2']
df[cols] = np.sort(df[cols].values, axis=1)
df = df.drop_duplicates()
print (df)
mol1 mol2 sim
0 cpd1 cpd2 0.8
2 cpd3 cpd4 0.9
If need check duplicates only in cols
add parameter subset
: 如果需要仅在
cols
检查重复项添加参数subset
:
df = pd.DataFrame(
{'mol1':['cpd1','cpd2', 'cpd3'],
'mol2': ['cpd2', 'cpd1', 'cpd4'],
'sim': [0.7,0.8,0.9]})
print (df)
mol1 mol2 sim
0 cpd1 cpd2 0.7
1 cpd2 cpd1 0.8
2 cpd3 cpd4 0.9
cols = ['mol1','mol2']
df[cols] = np.sort(df[cols].values, axis=1)
df = df.drop_duplicates(subset=cols)
print (df)
mol1 mol2 sim
0 cpd1 cpd2 0.7
2 cpd3 cpd4 0.9
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.