在Python中将有序字典转换为新列

在我的dataframe中，一些列采用OrderedDictionary格式。如何将它们转换为新列（不知道哪些列包含OrderedDictionary和OrderedDictionary中的元素）

示例列：

inventors
[OrderedDict([('@sequence', '001'), ('@app-type', 'applicant'), ('@designation', 'us-only'), ('addressbook', OrderedDict([('last-name', 'Nahm'), ('first-name', 'Seung Hoon'), ('address', OrderedDict([('city', 'Daejeon'), ('country', 'KR')]))])), ('residence', OrderedDict([('country', 'KR')]))]), OrderedDict([('@sequence', '002'), ('@app-type', 'applicant'), ('@designation', 'us-only'), ('addressbook', OrderedDict([('last-name', 'Jang'), ('first-name', 'Hoon Sik'), ('address', OrderedDict([('city', 'Daegu'), ('country', 'KR')]))])), ('residence', OrderedDict([('country', 'KR')]))])]

我想将其转换为以下数据帧（未写入所有列）：

---

在本例中，last_name和first_name来自另一个嵌套字典。在数据中，我不知道哪些列包含OrderedDictional，为了简单起见，我只包含了数据集中的一列，即inventors，您考虑过pandas函数了吗

# Create example dict
import collections
inventors = collections.OrderedDict()
inventors['@sequence'] =  "001"
inventors['@app-type'] =  "applicant"
inventors['@designation'] =  "us-only"
inventors['last-name'] =  "Nahm"
inventors['first-name'] =  "Seung Hoon"

# Import pandas to use from_dict function
import pandas as pd

# Use from_dict() function; include orient='index' for now to avoid index error
df = pd.DataFrame.from_dict(inventors, orient='index')

# Transpose for final output
df.T

---

df=pd.DataFrame（发明人[0]）

？你没有说

@

去哪里，也没有包括本身是嵌套字典的列让我来编辑这个问题

# Create example dict
import collections
inventors = collections.OrderedDict()
inventors['@sequence'] =  "001"
inventors['@app-type'] =  "applicant"
inventors['@designation'] =  "us-only"
inventors['last-name'] =  "Nahm"
inventors['first-name'] =  "Seung Hoon"

# Import pandas to use from_dict function
import pandas as pd

# Use from_dict() function; include orient='index' for now to avoid index error
df = pd.DataFrame.from_dict(inventors, orient='index')

# Transpose for final output
df.T