按 pandas (python) 中列名称的子字符串熔化列

Question

我有数据框：

         subject           A_target_word_gd  A_target_word_fd B_target_word_gd  B_target_word_fd  subject_type 
           1                      1             2                3                    4             mild 
           2                      11            12               13                  14             moderate

我想将它融化成一个数据框，看起来像：

     cond    subject    subject_type     value_type   value
      A         1        mild             gd           1           
      A         1        mild             fg           2           
      B         1        mild             gd           3            
      B         1        mild             fg           4  
      A         2        moderate         gd           11           
      A         2        moderate         fg           12           
      B         2        moderate         gd           13            
      B         2        moderate         fg           14          
...

...

意思是，根据列名的分隔符来融化。

最好的方法是什么？

Answer 1

这是我使用 melt and series.str.split() 的方式：

m = df.melt(['subject','subject_type'])
n = m['variable'].str.split('_',expand=True).iloc[:,[0,-1]]
n.columns = ['cond','value_type']
m = m.drop('variable',1).assign(**n).sort_values('subject')

print(m)

   subject subject_type  value cond value_type
0        1         mild      1    A         gd
2        1         mild      2    A         fd
4        1         mild      3    B         gd
6        1         mild      4    B         fd
1        2     moderate     11    A         gd
3        2     moderate     12    A         fd
5        2     moderate     13    B         gd
7        2     moderate     14    B         fd

Answer 2

另一种方法（与@anky_91 发布的内容非常相似。在他发布之前已经开始输入它，因此将其放在那里。）

new_df =pd.melt(df, id_vars=['subject_type','subject'], var_name='abc').sort_values(by=['subject', 'subject_type'])
new_df['cond']=new_df['abc'].apply(lambda x: (x.split('_'))[0])
new_df['value_type']=new_df.pop('abc').apply(lambda x: (x.split('_'))[-1])
new_df

输出

subject_type    subject     value   cond    value_type
0   mild              1     1          A    gd
2   mild              1     2          A    fd
4   mild              1     3          B    gd
6   mild              1     4          B    fd
1   moderate          2     11         A    gd
3   moderate          2     12         A    fd
5   moderate          2     13         B    gd
7   moderate          2     14         B    fd

Answer 3

将索引设置为 subject、subject_type。按字符串 _target_word_ 拆分列以创建多索引列。将轴重命名为专有名称和 stack 和 reset_index

df1 = df.set_index(['subject', 'subject_type'])
df1.columns = df1.columns.str.split('_target_word_', expand=True)
df_final = df1.rename_axis(['cond','value_type'],axis=1).stack([0,1]).reset_index(name='value')

Out[91]:
   subject subject_type cond value_type  value
0        1         mild    A         fd      2
1        1         mild    A         gd      1
2        1         mild    B         fd      4
3        1         mild    B         gd      3
4        2     moderate    A         fd     12
5        2     moderate    A         gd     11
6        2     moderate    B         fd     14
7        2     moderate    B         gd     13

Answer 4

首先将 DataFrame.set_index with DataFrame.stack and DataFrame.reset_index and then convert column with _ by Series.str.split 重塑为新列：

df = df.set_index(['subject','subject_type']).stack().reset_index(name='value')
df[['cond','value_type']] = df.pop('level_2').str.split('_', expand=True).iloc[:, [0,-1]]
print (df)
   subject subject_type  value cond value_type
0        1         mild      1    A         gd
1        1         mild      2    A         fd
2        1         mild      3    B         gd
3        1         mild      4    B         fd
4        2     moderate     11    A         gd
5        2     moderate     12    A         fd
6        2     moderate     13    B         gd
7        2     moderate     14    B         fd

按 pandas (python) 中列名称的子字符串熔化列

melt column by substring of the columns name in pandas (python)

dataframe

melt

pandas

data-science

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