假设我有一个使用 pandas.dataframe 的列，如下所示:
index fruits origin attribute
1 apple USA tasty
2 apple France yummy
3 apple USA juicy
4 apple England juicy
5 apple Japan normal
6 banana Canada nice
7 banana Italy good
.....
我想选择 yummy apple from France(2) 并从表中删除不匹配的 apples，如下所示:
index fruits origin attribute
1 apple France yummy
2 banana Canada nice
3 banana Italy good
.....
我认为以下应该有效。但事实并非如此:
df.drop(df[(df.fruits == "apple") & (df.origin != "France") | (df.fruits == "apple") & (df.attribute != "yummy")].index)
然后我尝试了以下方法，但也不起作用:
df = df[~df[(df.fruits == "apple") & (df.origin != "France") & (df.attribute != "yummy")]
小伙子们有什么帮助吗？
最佳答案
如果按匹配条件选择:
df[(df.fruits != 'apple') | ((df.fruits == 'apple') & (df.origin == 'France') & (df.attribute == 'yummy'))]
#index fruits origin attribute
#1 2 apple France yummy
#5 6 banana Canada nice
#6 7 banana Italy good
如果按不匹配条件删除:需要删除的是 fruits 为 apple 但 origin 与 France 不匹配的行或 attribute 与 yummy 不匹配:
df[~((df.fruits == 'apple') & ((df.origin != 'France') | (df.attribute != 'yummy')))]
# index fruits origin attribute
#1 2 apple France yummy
#5 6 banana Canada nice
#6 7 banana Italy good
关于Python Pandas : removing rows not matching multiple conditions from dataframe，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45558673/