用途：

#unpivot by melt
df = df.melt('solution_id')
#create lists by split #
df['value'] = df['value'].str.split('#')
#repeat rows by value column
df = df.explode('value')
#counter for new columns names
df['g'] = df.groupby(['solution_id','variable']).cumcount().add(1)

#pivoting and sorting MultiIndex
df = (df.pivot('solution_id',['variable', 'g'], 'value')
         .sort_index(level=1, axis=1, sort_remaining=False))

#flatten MultiIndex
df.columns = df.columns.map(lambda x: f'{x[0]}_{x[1]}')

print (df)
            type_labour_1 labour_unit_1 est_labour_quantity_1  \
solution_id                                                     
10                  WorkA        Person                   2.0   
11                  WorkC        Person                   3.0   

            est_labour_costs_1 est_labour_total_costs_1 type_labour_2  \
solution_id                                                             
10                       300.0                    600.0         WorkB   
11                       300.0                    900.0         WorkD   

            labour_unit_2 est_labour_quantity_2 est_labour_costs_2  \
solution_id                                                          
10                 Person                   2.0             300.0.   
11                 Person                   2.0             300.0.   

            est_labour_total_costs_2  
solution_id                           
10                             600.0  
11                             600.0

您可以拆分字符串，分解和重塑：

df2 = (df
 .set_index('solution_id')
 .apply(lambda c: c.str.split('#'))
 .explode(list(df.columns[1:]))
 .assign(idx=lambda d: d.groupby(level=0).cumcount().add(1))
 .set_index('idx', append=True)
 .unstack('idx')
 .sort_index(axis=1, level='idx', sort_remaining=False)
)

df2.columns = [f'{a}_{b}' for a,b in df2.columns]

输出：

type labour_1 labour_unit_1 est_labour_quantity_1 est_labour_costs_1 est_labour_total_costs_1 type labour_2 labour_unit_2 est_labour_quantity_2 est_labour_costs_2 est_labour_total_costs_2
solution_id                                                                                                                                                                                            
10                  WorkA        Person                   2.0              300.0                    600.0         WorkB        Person                   2.0             300.0.                    600.0
11                  WorkC        Person                   3.0              300.0                    900.0         WorkD        Person                   2.0             300.0.                    600.0

或者，使用相同的初始分割，然后进行切片和级联的较短代码：

df2=(df
 .set_index('solution_id')
 .apply(lambda c: c.str.split('#'))
)
pd.concat([df2.apply(lambda c: c.str[i]).add_suffix(f'_{i+1}')
           for i in range(len(df2.iat[0,0]))], axis=1)