Pandas DataFrame.groupby() to dictionary with multiple columns for value

Question

type(Table)
pandas.core.frame.DataFrame

Table
======= ======= =======
Column1 Column2 Column3
0       23      1
1       5       2
1       2       3
1       19               


        

Answer 1

One way is to create a new tup column and then create the dictionary.

df['tup'] = list(zip(df['Column2'], df['Column3']))
df.groupby('Column1')['tup'].apply(list).to_dict()

# {0: [(23, 1)],
#  1: [(5, 2), (2, 3), (19, 5)],
#  2: [(56, 1), (22, 2)],
#  3: [(2, 4), (14, 5)],
#  4: [(59, 1)],
#  5: [(44, 1), (1, 2), (87, 3)]}

@Psidom's solution is more efficient, but if performance isn't an issue use what makes more sense to you:

df = pd.concat([df]*10000)

def jp(df):
    df['tup'] = list(zip(df['Column2'], df['Column3']))
    return df.groupby('Column1')['tup'].apply(list).to_dict()

def psi(df):
    return df.groupby('Column1')[['Column2', 'Column3']].apply(lambda g: list(map(tuple, g.values.tolist()))).to_dict()

%timeit jp(df)   # 110ms
%timeit psi(df)  # 80ms