Question

在数据框的某些列上使用groupby之后，随后使用apply测试另一列中是否存在字符串，pandas仅返回被分组的列以及使用apply创建的最后一列。是否可以返回与groupby和test相关的所有列？例如，按会话线程的唯一标识符进行分组，并测试字符串是否在另一列中存在，然后包括数据框中存在但属于特定组的其他一些列？

我尝试过使用groupby，然后使用apply申请匿名功能。

df.head()

 shipment_id shipper_id courier_id  Question                                sender
0   14      9962    228898  Let's get your furbabys home Apple pet transpo...   courier
1   91919   190872  196838  Hi I'm kevin thims and I'm happy to do the job...   courier
2   92187   191128  196838  Hi I'm kevin thims and I'm happy to do the job...   shipper

unique_thread_indentifier = ['shipment_id', 'shipper_id', 'courier_id']
required_variables = ['shipment_id', 'shipper_id', 'courier_id', 'Question', 'sender']

df_new = (
    df
    .groupby(unique_thread_indentifier)[required_variables]
    .apply(lambda group: 'shipper' in group['sender'].unique())
    .to_frame(name='shipper_replied')
    .reset_index()
)

df_new.head()
    shipment_id shipper_id  courier_id  shipper_replied
0   14      9962            228898          False
1   91919   190872          196838          False
2   92187   191128          196838          True

我打算做的是在最终数据框中重新包含列Question和sender。预期输出如下：

 shipment_id shipper_id courier_id  Question                                sender        shipper_replied
0   14      9962    228898  Let's get your furbabys home Apple pet transpo...   courier       False
1   91919   190872  196838  Hi I'm kevin thims and I'm happy to do the job...   courier       False
2   92187   191128  196838  Hi I'm kevin thims and I'm happy to do the job...   shipper       True

Answer 1

我相信您需要GroupBy.transform：

df['shipper_replied'] = (df.groupby(unique_thread_indentifier)['sender']
                           .transform(lambda group: 'shipper' in group.unique()))

print (df)
   shipment_id  shipper_id  courier_id  \
0           14        9962      228898   
1        91919      190872      196838   
2        92187      191128      196838   

                                          Question   sender  shipper_replied  
0  Let's get your furbabys home Apple pet transpo.  courier            False  
1   Hi I'm kevin thims and I'm happy to do the job  courier            False  
2   Hi I'm kevin thims and I'm happy to do the job  shipper             True

另一种解决方案：

df['shipper_replied'] = (df.assign(new = df['sender'].eq('shipper'))
                           .groupby(unique_thread_indentifier)['new']
                           .transform('any'))

print (df)
   shipment_id  shipper_id  courier_id  \
0           14        9962      228898   
1        91919      190872      196838   
2        92187      191128      196838   

                                          Question   sender  shipper_replied  
0  Let's get your furbabys home Apple pet transpo.  courier            False  
1   Hi I'm kevin thims and I'm happy to do the job  courier            False  
2   Hi I'm kevin thims and I'm happy to do the job  shipper             True

如何强制熊猫申请返回父数据框的所有列？

1 个答案: