python - 在 Pandas GroupBy 中查找组之间的重复值

Question

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                         'foo', 'bar', 'foo', 'foo'],
                   'B': ['one', 'one', 'two', 'three',
                         'two', 'two', 'one', 'three'],
                   'C': [3,4,5,8,10,12,14,12]})
df.groupby(['A','B']).sum()

如何查找 C 列中的值在其他组中是否也重复？(这里 12 在两组中重复)

Answer 1

想法是将MultIndex转换为3列DataFrame，然后DataFrame.pivot通过 DataFrame.dropna 删除非重复行常见值在索引中:

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                         'foo', 'bar', 'foo', 'foo'],
                   'B': ['one', 'one', 'two', 'three',
                         'two', 'two', 'one', 'three'],
                   'C': [3,4,5,8,10,12,14,12]})
df = df.groupby(['A','B']).sum()

common = df.reset_index().pivot('C','A','B').dropna().index
print (common)
Int64Index([12], dtype='int64', name='C')

然后如果想过滤原始数据使用 boolean indexing :

df = df[df['C'].isin(common)]
print (df)
            C
A   B        
bar two    12
foo three  12

如果希望公共(public)行至少在 2 组中重复，解决方案是:

print (df)  
     A      B   C
0  foo    one   3
1  bar    one   4
2  foo    two   3
3  bar  three   8
4  foo    two  14
5  bar    two  12
6  foo    one  14
7  foo  three  12
8  xxx    yyy   8

df = df.groupby(['A','B']).sum()
print (df)
            C
A   B        
bar one     4
    three   8 <- dupe per bar, three
    two    12 <- dupe per bar, two
foo one    17 <-17 is duplicated per group foo, one, so omited
    three  12 <- dupe per foo, three
    two    17 <-17 is duplicated per group foo, one, so omited
xxx yyy     8 <- dupe per xxx, yyy

<小时/>

common1 = (df.reset_index()
             .pivot_table(index='C',columns='A', values='B', aggfunc='size')
             .notna()
             .sum(axis=1)
            )
common1 = common1.index[common1.gt(1)]
print (common1)
Int64Index([8, 12], dtype='int64', name='C')

df1 = df[df['C'].isin(common1)]
print (df1)
            C
A   B        
bar three   8
    two    12
foo three  12
xxx yyy     8