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pandas - 根据其他两列向新列添加值

问题描述

我有一个看起来像这样的数据框。我创建了 3 个新列，它们将从其他列中获取值。我希望函数列将列分开，并为每个用户获取每个函数的总小时数。

 User         Function          Total hours   Damage Processing problem solve damages sweeper
 schae      Damage Processing      9.36
 Julie      Problem solve          9.70
 John       sweeper                18.9
 Dan       Damages                 1.83
 Dan       Damages                 1.83
 Julie     Damages                 1.83
 Dan       Problem solve           1.83

预期的输出看起来像

 User         Function          Total hours   Damage Processing  problem solve damages  sweeper
 schae     Damage Processing       9.36          9.36
 Julie     Problem solve           9.70                            9.70
 John      sweeper                 18.9                                                  18.9
 Dan       Damages                 1.83                                        1.83                                 
 Dan       sweeper                 1.83                                                  1.83
 Julie     Damages                 1.83                                        1.83
 Dan       Problem solve           1.83                            1.83

我想到了 pd.melt 但它抛出了一个错误值 var not present

res = pd.melt(result,id_vars = ['Function'],value_vars=['Total hours'])

标签： pandasmelt

解决方案

这是一种使用get_dummiesand的方法df.assign：

out = (df[['User','Function','Total hours']].assign(**pd.get_dummies(df['Function'])
       .mul(df['Total hours'],axis=0).replace(0,np.nan)))
print(out)

    User           Function  Total hours  Damage Processing  Damages  \
0  schae  Damage Processing         9.36               9.36      NaN   
1  Julie      Problem solve         9.70                NaN      NaN   
2   John            sweeper        18.90                NaN      NaN   
3    Dan            Damages         1.83                NaN     1.83   
4    Dan            Damages         1.83                NaN     1.83   
5  Julie            Damages         1.83                NaN     1.83   
6    Dan      Problem solve         1.83                NaN      NaN   

   Problem solve  sweeper  
0            NaN      NaN  
1           9.70      NaN  
2            NaN     18.9  
3            NaN      NaN  
4            NaN      NaN  
5            NaN      NaN  
6           1.83      NaN

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