给出以下样本数据(10条记录):
test_df = pd.DataFrame({"PN_id": ["745d626b", "745d626b", "fce503fb", "df3d727e", "df3d727e", "56c00531", "72ebb2b3", "5d1bc5d3", "72ebb2b3", "5c32fc8a", "5c32fc8a"],
"PN_raw": ['{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"90_P******_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"90_P******_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"and":[{"segment":"850c8d94-1236-45a1-93fc-08b0337b4059"}]},{"and":[{"tag":"All_S****_ES","group":"******_CRM"}]}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"All_S*****_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"All_S*****_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"and":[{"segment":"850c8d94-1236-45a1-93fc-08b0337b4059"}]},{"and":[{"tag":"All_S****_ES","group":"******_CRM"}]}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"P_90_or_S_90_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"P_90_or_S_90_ESLA","group":"******_CRM"}]}}',
'{"audience":{"and":[{"segment":"67537044-27db-4a0b-b5b7-362c9c5b2ba7"},{"tag":"BR","group":"ua_locale_country"},{"tag":"P_90_or_S_90_BR","group":"******_CRM"}]}}',
'{"audience":{"and":[{"and":[{"segment":"850c8d94-1236-45a1-93fc-08b0337b4059"}]},{"and":[{"tag":"P_90_or_S_90_ES","group":"******_CRM"}]}]}}',
'{"audience":{"and":[{"and":[{"segment":"850c8d94-1236-45a1-93fc-08b0337b4059"}]},{"and":[{"tag":"P_90_or_S_90_ES","group":"******_CRM"}]}]}}']})
我怎样才能达到以下期望的输出?(在同一DF内或在单独的DF上,我认为这是一种可能):
test_df_desired = pd.DataFrame({"PN_id":["745d626b", "745d626b", "fce503fb", "df3d727e", "df3d727e", "56c00531", "72ebb2b3", "5d1bc5d3", "72ebb2b3", "5c32fc8a", "5c32fc8a"],
"segment":["67537044-27db-4a0b-b5b7-362c9c5b2ba7", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "850c8d94-1236-45a1-93fc-08b0337b4059", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "850c8d94-1236-45a1-93fc-08b0337b4059", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "67537044-27db-4a0b-b5b7-362c9c5b2ba7", "850c8d94-1236-45a1-93fc-08b0337b4059", "850c8d94-1236-45a1-93fc-08b0337b4059"],
"tag_1":["BR", "BR", "All_S****_ES", "BR", "BR", "All_S****_ES", "BR", "P_90_or_S_90_ESLA", "BR", "P_90_or_S_90_ES", "P_90_or_S_90_ES"],
"group_1":["ua_locale_country", "ua_locale_country", "******_CRM", "ua_locale_country", "ua_locale_country", "******_CRM", "ua_locale_country", "******_CRM", "ua_locale_country", "******_CRM", "******_CRM"],
"tag_2":["90_P******_BR", "90_P******_BR", np.nan, "All_S*****_BR", "All_S*****_BR", np.nan, "P_90_or_S_90_BR", np.nan, "P_90_or_S_90_BR", np.nan, np.nan],
"group_2":["******_CRM", "******_CRM", np.nan, "******_CRM", "******_CRM", np.nan, "******_CRM", np.nan, "******_CRM", np.nan, np.nan]})
到目前为止,使用pd.json_normalize(test_df["PN_raw"].apply(ast.literal_eval), record_path = ["audience", "and"])
,我已经设法找到了dict路径结构为audience -> and
的记录,但是对于路径为audience -> and -> and
的记录,这是行不通的,我也无法绕过它添加record_path = ["audience", "and", "and"]
,我认为这是行不通的。我认为这需要通过循环序列来解决,如果它包含一个或两个+和,则根据它是否包含一个不同的函数来应用不同的函数
当前输出不仅在上面提到的行上“失败”,而且还存在将数据“转换”到正确行的问题(如果运行上面的行,您将看到我的意思)
我发现pandas混淆了JSON,我更喜欢用基本的python来处理它。我要说的是,JSON可以有如此不同的形状,以至于没有很好的方法来实现一个通用的“make_the_JSON_flat()”函数,尽管如果有这样的东西,我很想了解它
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