我一直在寻找修复即将deprIcated方法from_items与建议from_dict

pd.set_option('display.max_columns', 50)

pd.set_option('display.max_rows', 50)

Test_Data = [('originating_system_id', ['CL', 'CL', 'CL', 'CL']),

('security_type1', ['CORP', 'CORP', 'CORP', 'CORP']),

('state', ['Traded', 'Covered', 'Traded Away', 'Traded']),

('trading_book', ['LCAAAAA', 'NUBBBBB', 'EDFGSFG', 'PDFEFGR'])

]

df = pd.DataFrame.from_items(Test_Data)

print(df)

originating_system_id security_type1 state trading_book

0 CL CORP Traded LCAAAAA

1 CL CORP Covered NUBBBBB

2 CL CORP Traded Away EDFGSFG

3 CL CORP Traded PDFEFGR

当我改为from_dictdf赋值时：

df = pd.DataFrame.from_dict(Test_Data)

我希望应用过滤器时出现以下行错误：

m1 = ~df['trading_book'].str.startswith(tuple(prefixes))

KeyError: 'trading_book'

是from_dict结构不同？有替代品from_items吗？

解决办法：对我来说工作很好，将其转换为字典：

df = pd.DataFrame(dict(Test_Data))

#another alternative solution

#df = pd.DataFrame({a:b for a, b in Test_Data})

print(df)

originating_system_id rbc_security_type1 state trading_book

0 CL CORP Traded LCAAAAA

1 CL CORP Covered NUBBBBB

2 CL CORP Traded Away EDFGSFG

3 CL CORP Traded PDFEFGR

细节：

print(dict(Test_Data)

{'originating_system_id': ['CL', 'CL', 'CL', 'CL'],

'rbc_security_type1': ['CORP', 'CORP', 'CORP', 'CORP'],

'state': ['Traded', 'Covered', 'Traded Away', 'Traded'],

'trading_book': ['LCAAAAA', 'NUBBBBB', 'EDFGSFG', 'PDFEFGR']