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python - 如何使用具有不同字典和字典列表的数据来分解 Panda 列

问题描述

我有一个具有不同值集的熊猫数据框，例如第一个是列表或数组以及其他元素或不是

>>> df_3['integration-outbound:IntegrationEntity.integrationEntityDetails.supplier.forms.form.records.record']
0    [{'Internalid': '24348', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3127'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel4434'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5545'}]}}, {'Internalid': '24349', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3125'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel4268'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5418'}]}}, {'Internalid': '24350', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3122'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel425'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5221'}]}}]
0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      {'isDelete': 'false', 'fields': {'field': [{'id': 'S_EAST', 'value': 'N'}, {'id': 'W_EST', 'value': 'N'}, {'id': 'M_WEST', 'value': 'N'}, {'id': 'N_EAST', 'value': 'N'}, {'id': 'LOW_AREYOU_ASSET', 'value': '-1'}, {'id': 'LOW_SWART_PROG', 'value': '-1'}]}}
0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 {'isDelete': 'false', 'fields': {'field': {'id': 'LOW_COD_CONDUCT', 'value': '-1'}}}
0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     {'isDelete': 'false', 'fields': {'field': [{'id': 'LOW_SUPPLIER_TYPE', 'value': '2'}, {'id': 'LOW_DO_INT_BOTH', 'value': '1'}]}}

在此处输入图像描述

我想把它分解成多行。第一行是列表和其他行吗？

>>> type(df_3)
<class 'pandas.core.frame.DataFrame'>
>>> type(df_3['integration-outbound:IntegrationEntity.integrationEntityDetails.supplier.forms.form.records.record'])
<class 'pandas.core.series.Series'>

预期产出 -

{'Internalid': '24348', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3127'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel4434'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5545'}]}}
{'Internalid': '24349', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3125'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel4268'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5418'}]}}
{'Internalid': '24350', 'isDelete': 'false', 'fields': {'field': [{'id': 'CATEGOR_LEVEL_1', 'value': 'MR'}, {'id': 'LOW_PRODSERV', 'value': 'RES'}, {'id': 'LOW_LEVEL_2', 'value': 'keylevel221'}, {'id': 'LOW_LEVEL_3', 'value': 'keylevel3122'}, {'id': 'LOW_LEVEL_4', 'value': 'keylevel425'}, {'id': 'LOW_LEVEL_5', 'value': 'keylevel5221'}]}}]
{'isDelete': 'false', 'fields': {'field': [{'id': 'S_EAST', 'value': 'N'}, {'id': 'W_EST', 'value': 'N'}, {'id': 'M_WEST', 'value': 'N'}, {'id': 'N_EAST', 'value': 'N'}, {'id': 'LOW_AREYOU_ASSET', 'value': '-1'}, {'id': 'LOW_SWART_PROG', 'value': '-1'}]}}
{'isDelete': 'false', 'fields': {'field': {'id': 'LOW_COD_CONDUCT', 'value': '-1'}}}
{'isDelete': 'false', 'fields': {'field': [{'id': 'LOW_SUPPLIER_TYPE', 'value': '2'}, {'id': 'LOW_DO_INT_BOTH', 'value': '1'}]}}

我试图炸毁这些列

>>> df_3.explode('integration-outbound:IntegrationEntity.integrationEntityDetails.supplier.forms.form.records.record')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib64/python3.6/site-packages/pandas/core/frame.py", line 6318, in explode
    result = df[column].explode()
  File "/usr/local/lib64/python3.6/site-packages/pandas/core/series.py", line 3504, in explode
    values, counts = reshape.explode(np.asarray(self.array))
  File "pandas/_libs/reshape.pyx", line 129, in pandas._libs.reshape.explode
KeyError: 0

我可以遍历每一行并尝试找出它是否是一个列表并实现某些东西，但它似乎不正确

if str(type(df_3.loc[i,'{}'.format(c)])) == "<class 'list'>":

有什么方法可以对此类数据使用爆炸功能

标签： pythonpandasexplode

解决方案

使用 pandas-read-xml 的替代方法

from pandas_read_xml import flatten, fully_flatten

df = flatten(df)

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