Drop all duplicate rows across multiple columns in Python Pandas

Question

The pandas drop_duplicates function is great for \"uniquifying\" a dataframe. However, one of the keyword arguments to pass is take_last=True

Answer 1

Actually, drop rows 0 and 1 only requires (any observations containing matched A and C is kept.):

In [335]:

df['AC']=df.A+df.C
In [336]:

print df.drop_duplicates('C', take_last=True) #this dataset is a special case, in general, one may need to first drop_duplicates by 'c' and then by 'a'.
     A  B  C    AC
2  foo  1  B  fooB
3  bar  1  A  barA

[2 rows x 4 columns]

But I suspect what you really want is this (one observation containing matched A and C is kept.):

In [337]:

print df.drop_duplicates('AC')
     A  B  C    AC
0  foo  0  A  fooA
2  foo  1  B  fooB
3  bar  1  A  barA

[3 rows x 4 columns]

Edit:

Now it is much clearer, therefore:

In [352]:
DG=df.groupby(['A', 'C'])   
print pd.concat([DG.get_group(item) for item, value in DG.groups.items() if len(value)==1])
     A  B  C
2  foo  1  B
3  bar  1  A

[2 rows x 3 columns]