Question

我有一个数据框，可以使用下面给出的代码生成

data_file= pd.DataFrame({'person_id':[1,1,1,2,2,2,3,3,3],'ob.date': [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],
                     'observation': ['Age','interviewdate','marital_status','Age','interviewdate','marital_status','Age','interviewdate','marital_status'],
                     'answer': [21,'21/08/2017','Single',26,'11/03/2010','Single',41,'31/09/2012','Married'],
                     'visit.date': [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]
                     })

输入数据框如下图所示

我想做的是从与每个人对应的“答案”列中获取日期（采访日期）值，并将其放在同一人的“ ob.date”和“ visit.date”列中。

我尝试过滤数据框，但不确定如何继续进行。这种情况仅发生在已过滤的行中，但我希望将日期填充到原始或输入数据框中

df2 = data_file[(data_file.observation == 'interviewdate')]
df2.reset_index(inplace=True)
df3=data_file.merge(df2)
df3['ob.date']=df2['answer']
df3['visit.date']=df2['answer']

如何实现如下所示的输出？如您所见，每个人的采访数据都填在“ ob.date”和“ visit.date”列中

Answer 1

过滤后，用索引Series创建person_id，并用Series.map创建新列：

s = data_file[(data_file.observation == 'interviewdate')].set_index('person_id')['answer']
print (s)
person_id
1    21/08/2017
2    11/03/2010
3    31/09/2012
Name: answer, dtype: object

data_file['ob.date'] = data_file['person_id'].map(s)
data_file['visit.date'] = data_file['person_id'].map(s)
print (data_file)
   person_id     ob.date     observation      answer  visit.date
0          1  21/08/2017             Age          21  21/08/2017
1          1  21/08/2017   interviewdate  21/08/2017  21/08/2017
2          1  21/08/2017  marital_status      Single  21/08/2017
3          2  11/03/2010             Age          26  11/03/2010
4          2  11/03/2010   interviewdate  11/03/2010  11/03/2010
5          2  11/03/2010  marital_status      Single  11/03/2010
6          3  31/09/2012             Age          41  31/09/2012
7          3  31/09/2012   interviewdate  31/09/2012  31/09/2012
8          3  31/09/2012  marital_status     Married  31/09/2012

如果可能，请更改数据格式-使用DataFrame.pivot：

df = data_file.pivot('person_id','observation','answer')
print (df)
observation Age interviewdate marital_status
person_id                                   
1            21    21/08/2017         Single
2            26    11/03/2010         Single
3            41    31/09/2012        Married

使用Python熊猫根据条件将行值复制到另一列

1 个答案: