Collapsing rows in a Pandas dataframe

Question

I\'m trying to collapse rows in a dataframe that contains a column of ID data and a number of columns that each hold a different string. It looks like groupby is the solution, b

Answer 1

You can use groupby with aggregation ''.join, sum or max:

#if blank values are NaN first replace to ''
df = df.fillna('')

df = df.groupby('ID').agg(''.join)
print (df)
     apples  pears  oranges
ID                         
101                 oranges
134  apples  pears         
576          pears  oranges
837  apples   

Also works:

df = df.fillna('')
df = df.groupby('ID').sum()
#alternatively max
#df = df.groupby('ID').max()
print (df)
     apples  pears  oranges
ID                         
101                 oranges
134  apples  pears         
576          pears  oranges
837  apples     

Also if need remove duplicates per group and per column add unique:

df = df.groupby('ID').agg(lambda x: ''.join(x.unique()))

Answer 2

Assuming blanks are ''

option 1
pivot_table

df.pivot_table(['apples', 'pears', 'oranges'], 'ID', aggfunc=''.join)

option 2
sort and take last row as '' will be sorted first

def f(df):
    return pd.DataFrame(np.sort(df.values, 0)[[-1]], [df.name], df.columns)

df.set_index(
    'ID', append=True
).groupby(level='ID', group_keys=False).apply(f)

---

Both yield

     apples  oranges  pears
ID                         
101          oranges       
134  apples           pears
576          oranges  pears
837  apples