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

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