Python Pandas 将多列合并为一个字典列

Question

我有一个像这样的数据框（df_full）：

|cust_id|address    |store_id|email        |sales_channel|category|
-------------------------------------------------------------------
|1234567|123 Main St|10SjtT  |idk@gmail.com|ecom         |direct  |
|4567345|345 Main St|10SjtT  |101@gmail.com|instore      |direct  |
|1569457|876 Main St|51FstT  |404@gmail.com|ecom         |direct  |

我想将最后 4 个字段组合成一个元数据字段，它是一个像这样的字典：

|cust_id|address    |metadata                                                                                     |
-------------------------------------------------------------------------------------------------------------------
|1234567|123 Main St|{'store_id':'10SjtT', 'email':'idk@gmail.com','sales_channel':'ecom', 'category':'direct'}   |
|4567345|345 Main St|{'store_id':'10SjtT', 'email':'101@gmail.com','sales_channel':'instore', 'category':'direct'}|
|1569457|876 Main St|{'store_id':'51FstT', 'email':'404@gmail.com','sales_channel':'ecom', 'category':'direct'}   |

那可能吗？ 我在堆栈溢出方面看到了一些解决方案，但没有一个解决将超过 2 个字段组合到字典字段中的问题。

Answer 1

使用to_dict ，

columns = ['store_id', 'email', 'sales_channel', 'category']
df['metadata'] = df[columns].to_dict(orient='records')

如果你想drop原始列，

df = df.drop(columns=columns)

Answer 2

set_index

df.set_index(['cust_id', 'address']).apply(dict, axis=1).reset_index(name='metadata')

   cust_id      address                                           metadata
0  1234567  123 Main St  {'store_id': '10SjtT', 'email': 'idk@gmail.com...
1  4567345  345 Main St  {'store_id': '10SjtT', 'email': '101@gmail.com...
2  1569457  876 Main St  {'store_id': '51FstT', 'email': '404@gmail.com...

---

理解

dat = [(c, a, dict(zip([*df][2:], m))) for c, a, *m in zip(*map(df.get, df))]
pd.DataFrame(dat, df.index, [*df][:2] + ['metadata'])

   cust_id      address                                           metadata
0  1234567  123 Main St  {'store_id': '10SjtT', 'email': 'idk@gmail.com...
1  4567345  345 Main St  {'store_id': '10SjtT', 'email': '101@gmail.com...
2  1569457  876 Main St  {'store_id': '51FstT', 'email': '404@gmail.com...