大熊貓GROUPBY GROUPBY後

df = pd.DataFrame({'A': [1,2,3,1,2,3], 'B': [10,10,11,10,10,15], 'key1':['a','b','a','b','c','c'],'key2':1}) 

df1 = pd.DataFrame({'A': [1,2,3,1,2,3], 'B': [100,100,110,100,100,150], 'key1':['a','c','b','a','a','c'],'key2':1}) 
dfn = pd.merge(df,df1,on='key2') 
dfn_grouped = dfn.groupby('key1_y') 

the list(dfn_grouped): 
[('a',  A_x B_x key1_x key2 A_y B_y key1_y 
    0  1 10  a  1 1 100  a 
    3  1 10  a  1 1 100  a 
    ... ...   ...  ... 
    33 3 15  c  1 1 100  a 
    34 3 15  c  1 2 100  a), 
    ('b',  A_x B_x key1_x key2 A_y B_y key1_y 
    2  1 10  a  1 3 110  b 
    8  2 10  b  1 3 110  b 
    14 3 11  a  1 3 110  b 
    20 1 10  b  1 3 110  b 
    26 2 10  c  1 3 110  b 
    32 3 15  c  1 3 110  b), 
    ('c',  A_x B_x key1_x key2 A_y B_y key1_y 
    1  1 10  a  1 2 100  c 
    ......  ... .... 
    35 3 15  c  1 3 150  c)]

現在我需要通過「key1_x」和CONCAT GROUPBY的dfn_grouped與dict像A_X：A_y大熊貓GROUPBY GROUPBY後

key1_y key1_x A_X:A_Y 
     b  a {'10':'110','11':110} 
     b  b {'10':110} 
     b  c {'10':110,'15':110} 
     // if A_x in dict append the A_y like: 
     // b  e {'10':[11,12]}

來源

2015-12-10 Vity Lin

這是你需要什麼？：

>> grouped = dfn.groupby(['key1_y','key1_x','A_x']) 

>> dfg = pd.DataFrame(grouped.apply(lambda x: [a for a in x.A_y])).reset_index() 
>> dfg.columns = [u'key1_y', u'key1_x', u'A_x', 'dic_values'] 
>> dfg['dic'] = [{a:b} for a,b in zip(dfg.A_x.values,dfg.dic_values.values)] 
>> dfg.drop(['A_x','dic_values'],1,inplace=True) 
>> g_dics = dfg.groupby(['key1_y','key1_x']).apply(lambda x: dict(sum(map(dict.items, [d for d in x.dic]), []))) 
>> pd.DataFrame(g_dics).reset_index()

來源

2015-12-10 11:50:26

大熊貓GROUPBY GROUPBY後

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