将带有列表的字典的数据框列转换为带有熊猫的单独列

Question

我有一个数据框，其中一列作为列表，另一列作为字典。然而，这并不一致。它也可以是单个元素或 NULL。此外，它们被解析为字符串类型。数据框如下所示：

df = pd.DataFrame({'item_id':[1,2,3,4],
'shop_id':[['S1', 'S2', 'S3', 'S4'],'S2','S3',['S1', 'S2', 'S3', 'S4']], 
'price':[{'10':['S1','S2'], '20':['S3'], '30':['S4']},'50','NaN',{'10':['S1','S2','S3'],'25':['S4']}]})

+-------+---------+--------------------+----------------------------------------------------+
| Index | item_id |     shop_id        |                      price                         |
+-------+---------+--------------------+----------------------------------------------------+
|     0 |       1 | '[S1, S2, S3, S4]' | '{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}' |
|     1 |       2 | 'S2'               | '50'                                               |
|     2 |       3 | 'S3'               | 'NaN'                                              |
|     3 |       4 | '[S1, S2, S3, S4]' | '{'10': ['S1', 'S2', 'S3'], '25': ['S4']}'         |
+-------+---------+--------------------+----------------------------------------------------+

我希望将其扩展为：

+-------+---------+---------+-------+
| Index | item_id | shop_id | price |
+-------+---------+---------+-------+
|     0 |       1 | S1      | 10    |
|     1 |       1 | S2      | 10    |
|     2 |       1 | S3      | 20    |
|     3 |       1 | S4      | 30    |
|     4 |       2 | S2      | 50    |
|     5 |       3 | S3      | NaN   |
|     6 |       4 | S1      | 10    |
|     7 |       4 | S2      | 10    |
|     8 |       4 | S3      | 10    |
|     9 |       4 | S4      | 25    |
+-------+---------+---------+-------+

实现这一目标的最佳方法是什么？任何建议表示赞赏。谢谢！

Answer 1

试试apply 和explode：

df['price'] = [[i for i in d.keys() for x in d[i]] if isinstance(d, dict) else [d] for d in df['price'].tolist()]
df = df.set_index('item_id').apply(pd.Series.explode, axis=0).reset_index()
print(df)

现在：

print(df)

愿意：

   item_id shop_id price
0        1      S1    10
1        1      S2    10
2        1      S3    20
3        1      S4    30
4        2      S2    50
5        3      S3   NaN
6        4      S1    10
7        4      S2    10
8        4      S3    10
9        4      S4    25

Answer 2

您好，我有一个部分解决方案和一个问题。

我的代码是：

df1 = pd.DataFrame({ 
    "item_id" : [1, 2, 3, 4] , 
    "shop_id" : [['S1','S2','S3','S4'], "S2", "S3", ['S1','S2','S3','S4']],
    "price" : [{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}, 50, np.nan, {'10': ['S1', 'S2', 'S3'], '25': ['S4']}] })
df1 = df1.explode('shop_id')
df1 = df1.reset_index(drop=True)

	item_id	shop_id	price
0	1	S1	{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}
1	1	S2	{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}
2	1	S3	{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}
3	1	S4	{'10': ['S1', 'S2'], '20': ['S3'], '30': ['S4']}
4	2	S2	50
5	3	S3	nan
6	4	S1	{'10': ['S1', 'S2', 'S3'], '25': ['S4']}
7	4	S2	{'10': ['S1', 'S2', 'S3'], '25': ['S4']}
8	4	S3	{'10': ['S1', 'S2', 'S3'], '25': ['S4']}
9	4	S4	{'10': ['S1', 'S2', 'S3'], '25': ['S4']}

对于价格列，您可以根据 shop_id 和 item_id 查找不同的表吗？如果没有，我可以继续尝试提出解决方案。