Merging rows and adding columns based on matching index

[Pedroski55]

Stuck half way up this mountain, I have time. Playing with your data, I got this:

path2csv2 = '/home/pedro/myPython/pandas/csv_files/info2.csv'
df2 = pd.read_csv(path2csv2)
path2csv3 = '/home/pedro/myPython/pandas/csv_files/info3.csv'
df3 = pd.read_csv(path2csv3)
df2.update(df3)
df4 = pd.merge(df2, df3, left_on=['ID','name'], right_on = ['ID','name'])
df5 = pd.merge(df2, df3, how='left', on=['ID', 'name'])
df6 = pd.merge(df2, df3, how='outer', on=['ID', 'name'])

Output:
    ID   name    code  code 1 code 2 code 3
0  ABC   hans   apple   apple   pear   kale
1  ABC   hans    pear   apple   pear   kale
2  ABC   hans    kale   apple   pear   kale
3  DEF  holly  tomato  tomato    NaN    NaN
4  HIJ    jon  grapes  grapes    NaN    NaN

There are so many settings for pandas, you need to know exactly what you want. how='outer' seems to get a union of sets, whereas how='inner' seems to get an intersection of sets. But you can use many different methods to get what you want.

frames = [df2, df3]
result = pd.concat(frames)

Output:
result
    ID   name    code  code 1 code 2 code 3
0  ABC   hans   apple     NaN    NaN    NaN
1  ABC   hans    pear     NaN    NaN    NaN
2  ABC   hans    kale     NaN    NaN    NaN
3  DEF  holly  tomato     NaN    NaN    NaN
4  HIJ    jon  grapes     NaN    NaN    NaN
0  ABC   hans     NaN   apple   pear   kale
1  DEF  holly     NaN  tomato    NaN    NaN
2  HIJ    jon     NaN  grapes    NaN    NaN