Union of two pandas DataFrames

We use outer join to solve this -

df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df

   A        B
0  a  df1,df2
1  b      df1
2  c      df2

Use the command below:

df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')]) \
    .groupby('A') \
    .aggregate(list) \
    .reset_index()

The result will be:

   A      source
0  a  [df1, df2]
1  b       [df1]
2  c       [df2]

The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.

Merge with an indicator argument, and remap the result:

m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}

result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)

result
   A         B
0  a  df1, df2
1  b       df1
2  c       df2