Duplicate rows in pandas DF

Question

I have a DF in Pandas, which looks like:

Letters Numbers
A       1
A       3
A       2
A       1
B       1
B       2
B       3
C       2
C       2


        

Answer 1

You can groupby these two columns and then calculate the sizes of the groups:

In [16]: df.groupby(['Letters', 'Numbers']).size()
Out[16]: 
Letters  Numbers
A        1          2
         2          1
         3          1
B        1          1
         2          1
         3          1
C        2          2
dtype: int64

To get a DataFrame like in your example output, you can reset the index with reset_index.

Answer 2

You can use a combination of groupby, transform and then drop_duplicates

In [84]:

df['Events'] = df.groupby('Letters')['Numbers'].transform(pd.Series.value_counts)
df.drop_duplicates()
Out[84]:
  Letters  Numbers  Events
0       A        1       2
1       A        3       1
2       A        2       1
4       B        1       1
5       B        2       1
6       B        3       1
7       C        2       2