CODEWITHSUNDEEP
pythonpython-3.xpandaspandas-groupby
Prassanth
Prassanth

Reputation: 69

Finding Duplicated value acorss groups in Pandas GroupBy

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()

enter image description here

How to find if the values in the C column is repeated in other groups as well? (Here 12 is repeated in both the groups)

Upvotes: 4

Views: 121

Answers (2)

jezrael
jezrael

Reputation: 862481

Idea is convert MultIndex to 3 columns DataFrame, then DataFrame.pivot with remove non duplicated rows by DataFrame.dropna and common values are in index:

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')

Then if want filter original data use boolean indexing:

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

If want common rows duplicated at least in 2 groups solution is:

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

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

Upvotes: 2

Valdi_Bo
Valdi_Bo

Reputation: 30971

To show a more instructive example, I added one row to your source DataFrame, so that it contains:

     A      B   C
0  foo    one   3
1  bar    one   4
2  foo    two   5
3  bar  three   8
4  foo    two  10
5  bar    two  12
6  foo    one  14
7  foo  three  12
8  xxx    yyy   8

I saved the result of grouping in another DataFrame:

df2 = df.groupby(['A','B']).sum()

so it contains:

            C
A   B        
bar one     4
    three   8
    two    12
foo one    17
    three  12
    two    15
xxx yyy     8

So as you can see, there are two repeted values in C: 12 and 8. Note that now the index in df2 is unique.

Then, to show the repeating values and their groups, run:

df2[df2.duplicated(keep=False)].sort_values('C')

getting:

            C
A   B        
bar three   8
xxx yyy     8
bar two    12
foo three  12

The above result shows all repeated values and groups (A and B) in which they are.

Upvotes: 2

Related Questions