使用column及其值在pandas数据框中创建一个新列

Question

I have a pandas data frame

 df = pd.DataFrame({'id':[1,2,3,4],
                    'attr1':[1,1,0,0],
                    'attr2':[0,1,1,0],
                    'attr3':[1,1,1,0],
                    'attr4':[1,1,1,1]})

I want to convert it to

Basically create a new variable which will contain previous dataframe columns if its value is 1

Answer 1

Use:

df1 = df.filter(like='attr')
df = df.drop(df1.columns, axis=1)
df['var'] = df1.dot(df1.columns + ' ').str.rstrip()
print (df)
   id                      var
0   1        attr1 attr3 attr4
1   2  attr1 attr2 attr3 attr4
2   3        attr2 attr3 attr4
3   4                    attr4

Explanation :

1. Filter columns by filter - only attribute columns
2. Remove columns by drop
3. Matrix multiplication by columns with DataFrame.dot
4. Last remove last whitespaces by rstrip

Alternative solution:

cols = df.columns[df.columns.str.startswith('attr')]
df = df.drop(cols, axis=1).assign(var=df[cols].dot(cols + ' ').str.rstrip())
print (df)
   id                      var
0   1        attr1 attr3 attr4
1   2  attr1 attr2 attr3 attr4
2   3        attr2 attr3 attr4
3   4                    attr4

For revert back use str.get_dummies :

df1 = df.join(df.pop('var').str.get_dummies(' '))
print (df1)
   id  attr1  attr2  attr3  attr4
0   1      1      0      1      1
1   2      1      1      1      1
2   3      0      1      1      1
3   4      0      0      0      1