阅读熊猫资料中的非结构化词典

Question

我正试图从我从json文件中读到的一组字典中创建一个熊猫数据。字典如下-

d1 = {"DisplayName": "Test_drive", "permissions": {"read": True, "read_acp": True, "write": True, "write_acp": True}}
d2= {"DisplayName": "Log delivery","URI": "http://test_drive.com/Logs", "permissions": {"read": False, "read_acp": True, "write": True, "write_acp": False}}

我正试着把这些放进一只熊猫的资料里。当我试着用数据格式读它们的时候-

df = pd.DataFrame(d) **or** df = pd.DataFrame.from_dict(d)

它产生这个-

                DisplayName  permissions
read       Test_drive         True
read_acp   Test_drive         True
write      Test_drive         True
write_acp  Test_drive         True

或者把它读成-

df1 = pd.DataFrame(d).Transpose()

它产生这个-

                         read          read_acp             write         write_acp
DisplayName  Test_drive  Test_drive  Test_drive  Test_drive
permissions              True              True              True              True

我正试着读这些字典，并把它们合并成一个数据集-

**DisplayName**              **read**          **read_acp**             **write**         **write_acp**         URI
Test_drive         True              True              True              True         NA
Log delivery            False             True              True             False         http://test_drive.com/Logs

有什么办法可以做到吗？

Answer 1

谢谢Vishnudev和Max Power的帮助。我想下面的答案给了我我想要得到的确切数据。

d1 = {"DisplayName": "Test_drive", "permissions": {"read": True, "read_acp": True, "write": True, "write_acp": True}}
d2= {"DisplayName": "Log delivery","URI": "http://test_drive.com/Logs", "permissions": {"read": False, "read_acp": True, "write": True, "write_acp": False}}
df = pd.concat([pd.Series(d1),pd.Series(d2)], axis=1).transpose()
df = pd.concat([df.drop(['permissions'], axis=1),df['permissions'].apply(pd.Series)],axis=1)

**DisplayName                         URI   read  read_acp  write  write_acp**
0    Test_drive                         NaN   True      True   True       True
1  Log delivery  http://test_drive.com/Logs  False      True   True      False

Answer 2

通过附加数据帧创建数据帧，然后使用透视对所需的结构进行整形。

df = pd.DataFrame.from_dict(d1).append(pd.DataFrame.from_dict(d2))
df.reset_index().pivot(index='DisplayName', columns='index', values='permissions')

包含URI

>>> df.reset_index().pivot(index='DisplayName', columns='index', values=['permissions', 'URI'])
             permissions                                                  URI                                                                                    
index               read read_acp write write_acp                        read                    read_acp                       write                   write_acp
DisplayName                                                                                                                                                      
Log delivery       False     True  True     False  http://test_drive.com/Logs  http://test_drive.com/Logs  http://test_drive.com/Logs  http://test_drive.com/Logs
Test_drive          True     True  True      True                         NaN                         NaN                         NaN                         NaN

Answer 3

import pandas as pd

# Input Data
d1 = {"DisplayName": "Test_drive", "permissions": {"read": True, "read_acp": True, "write": True, "write_acp": True}}
d2= {"DisplayName": "Log delivery","URI": "http://test_drive.com/Logs", "permissions": {"read": False, "read_acp": True, "write": True, "write_acp": False}}

# Convert to DataFrame
dicts = [d1, d2]
df_rows = [pd.DataFrame(d) for d in dicts]
df = pd.concat(df_rows, axis=0).reset_index(drop=False)

# Reshape As Desired
tp1 = df.pivot(index='DisplayName', columns='index', values='permissions')
answer = tp1.merge(df[['DisplayName', 'URI']].drop_duplicates(), 
                   how='left', 
                   left_index=True, 
                   right_on='DisplayName').set_index('DisplayName')

输出：

>>> answer
               read  read_acp  write  write_acp                         URI
DisplayName                                                                
Log delivery  False      True   True      False  http://test_drive.com/Logs
Test_drive     True      True   True       True                         NaN