Python Pandas 将多列合并成一个字典列 [英] Python Pandas merge multiple columns into a dictionary column
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问题描述
我有一个像这样的数据框 (df_full):
I have a dataframe (df_full) like so:
|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:
and I would like to combine the last 4 fields into one metadata field that is a dictionary like so:
|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'} |
这可能吗?我已经看到了一些关于堆栈溢出的解决方案，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.
is that possible? I've seen a few solutions around on stack overflow but none of them address combining more than 2 fields into a dictionary field.
推荐答案
使用 to_dict,
columns = ['store_id', 'email', 'sales_channel', 'category']
df['metadata'] = df[columns].to_dict(orient='records')
如果你想删除原始列，
And if you want to drop original columns,
df = df.drop(columns=columns)
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