Pandas:根据列内的多个对象值选择行 [英] Pandas: Select rows based on multiple object values inside a column

问题描述

我有一个 Pandas 数据框，其中一列包含用户信息.此列的每条记录都是一个列表，该列表又包含用户信息的字典.像下面这样:

I have a pandas dataframe in which one of the column contains user information. Each record of this column is a list which in turn contains dictionaries of user information. Like the follwoing:

                                                USER                      id  
1  [{u'STATUS': u'INACTV', u'NAME': 'abc'},{u'STATUS': u'ACTV', u'NAME': 'xyz'}]  634618   
2  [{u'STATUS': u'INACTV', u'NAME': 'abc'},{u'STATUS': u'ACTV', u'NAME': 'xyz'}]  642054   
3  [{u'STATUS': u'ACTV', u'NAME': 'abc'},{u'STATUS': u'ACTV', u'NAME': 'xyz'}]  631426    

我只想选择 STATUS 为 ACTV 且 NAME 为 abc 的行.如何选择嵌套数据的行.所以在上面的df中只会选择第3行

I want to select only the rows where the STATUS is ACTV and the NAME is abc. How do I select rows where the data is nested. So in the above df only row 3 will be selected

推荐答案

我们可以将您的 df.USER 列解压到 pd.Panel 中，然后以这种方式找到行.很多开销.不值得！但是很酷……也许吧.我稍后再试.

We can unpack your df.USER column into a pd.Panel and find the rows that way. Lots of overhead. Not worth it! But cool... maybe. I'll try again later.

pn = pd.Panel({k: pd.DataFrame(v) for k, v in df.USER.iteritems()})
cond1 = pn.loc[:, :, 'STATUS'] == 'ACTV'
cond2 = pn.loc[:, :, 'NAME'] == 'abc'

df.loc[pn.loc[(cond1 & cond2).any(), :, :].items]

                                                USER      id
2  [{'STATUS': 'ACTV', 'NAME': 'abc'}, {'STATUS':...  631426

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