Python 从DataFrame中解包未知对象

我是新手。我有一个数据框，其中包含一个我无法识别的对象，我需要将其解包并转换为一个新的单独数据框，以形成一个新的规范化结构

df的简化版本为：

   trasaction_id   customer_details
0   1       <customer {id:'A123', name: 'Tina'} as x >
0   2       <customer {id:'B456', name: 'Tony'} as x >
0   3       <customer {id:'C789', name: 'Tim'} as x >

Name: customer_details, dtype: object

---

这让我快发疯了。谢谢你的帮助

似乎您的对象/类具有属性

id

和

name

，因此您可以尝试获取

{'id': st.id, 'name': st.name}

也就是说

df['customer_details'] = df['customer_details'].apply(lambda x: {'id': x.id, 'name': x.name})

或直接连接到分离的列

df['id']   = df['customer_details'].apply(lambda x: x.id)
df['name'] = df['customer_details'].apply(lambda x: x.name)

---

示例代码：

import pandas as pd

class customer:
    def __init__(self, id_, name):
        self.id = id_
        self.name = name
    def __str__(self):
        return '<customer {{id: {}, name: {}}} as x>'.format(self.id, self.name)

data = {
    'trasaction_id': [1,2,3],
    'customer_details': [
        customer('A123', 'Tina'),
        customer('B456', 'Tony'),
        customer('C789', 'Tim')
    ],
}

df = pd.DataFrame(data)
print(df)

# ---

df['id'] = df['customer_details'].apply(lambda x: x.id)
df['name'] = df['customer_details'].apply(lambda x: x.name)
print(df)

df['customer_details'] = df['customer_details'].apply(lambda x: {'id': x.id, 'name': x.name})
print(df)

#new_df = pd.DataFrame( df['customer_details'].to_list() )

---

如果它是具有属性

id

和

name

的对象，那么

{'id'：st.id，'name'：st.name}

怎么样。但如果它只是普通字符串，那么您必须使用字符串函数或正则表达式切断字符串中的值。更详细的信息：

df['customer\u details']=df['customer\u details'].apply（lambda st:{'id'：st.id，'name'：st.name}）

谢谢@furas-我试过

df['customer\u details']=df['customer\u details']）。apply（lambda x:{'id'：x.id，'name'：x.name}）

但被告知该属性是字符串，并给出了错误

AttributeError:'str'对象没有属性'id'

如果您有字符串，则必须使用

re

从字符串中获取值。我添加了字符串示例。我尝试应用myfunc，现在得到以下结果：

TypeError:'NoneType'对象不是t subscriptable

在某些地方，您似乎有空单元格-

None

而不是字符串。或者字符串有不同的元素，它找不到模式

“id:'（.*），name:'（.*）}”

，它可能需要不同的模式。

import pandas as pd

class customer:
    def __init__(self, id_, name):
        self.id = id_
        self.name = name
    def __str__(self):
        return '<customer {{id: {}, name: {}}} as x>'.format(self.id, self.name)

data = {
    'trasaction_id': [1,2,3],
    'customer_details': [
        customer('A123', 'Tina'),
        customer('B456', 'Tony'),
        customer('C789', 'Tim')
    ],
}

df = pd.DataFrame(data)
print(df)

# ---

df['id'] = df['customer_details'].apply(lambda x: x.id)
df['name'] = df['customer_details'].apply(lambda x: x.name)
print(df)

df['customer_details'] = df['customer_details'].apply(lambda x: {'id': x.id, 'name': x.name})
print(df)

#new_df = pd.DataFrame( df['customer_details'].to_list() )

   trasaction_id                        customer_details
0              1  <customer {id: A123, name: Tina} as x>
1              2  <customer {id: B456, name: Tony} as x>
2              3   <customer {id: C789, name: Tim} as x>

   trasaction_id                        customer_details    id  name
0              1  <customer {id: A123, name: Tina} as x>  A123  Tina
1              2  <customer {id: B456, name: Tony} as x>  B456  Tony
2              3   <customer {id: C789, name: Tim} as x>  C789   Tim

   trasaction_id                customer_details    id  name
0              1  {'id': 'A123', 'name': 'Tina'}  A123  Tina
1              2  {'id': 'B456', 'name': 'Tony'}  B456  Tony
2              3   {'id': 'C789', 'name': 'Tim'}  C789   Tim

import pandas as pd
import re

data = {
    'trasaction_id': [1,2,3],
    'customer_details': [
        "<customer {id:'A123', name: 'Tina'} as x >",
        "<customer {id:'B456', name: 'Tony'} as x >",
        "<customer {id:'C789', name: 'Tim'} as x >",
    ]
}

df = pd.DataFrame(data)
print(df)

# ---

df['id'] = df['customer_details'].apply(lambda x: re.search("id:'(.*)',", x)[1])
df['name'] = df['customer_details'].apply(lambda x: re.search("name: '(.*)'}", x)[1])
print(df)

def myfunc(x):
    r = re.search("id:'(.*)', name: '(.*)'}", x)
    return {'id': r[1], 'name': r[2]}

df['customer_details'] = df['customer_details'].apply(myfunc)
print(df)

#new_df = pd.DataFrame( df['customer_details'].to_list() )