根据 pandas 上另一列值的值创建一列

Question

I'm new with python and pandas and I'm struggling with a problem

Here is a dataset

data = {'col1': ['a','b','a','c'], 'col2': [None,None,'a',None], 'col3': [None,'a',None,'b'], 'col4': ['a',None,'b',None], 'col5': ['b','c','c',None]}
df = pd.DataFrame(data)

I need to create 3 columns based on the unique values of col1 to col4 and whenever the col1 or col2 or col3 or col4 have a value equals to the header of the new columns it should return 1 otherwise it should return 0

need a output like this

dataset output example:

data = {'col1': ['a','b','a','c'], 'col2': [None,None,'a',None], 'col3': [None,'a',None,'b'], 'col4': ['a',None,'b',None], 'col5': ['b','c','c',None], 'a':[1,1,1,0],'b':[0,1,1,1],'c':[0,1,1,1]}
df = pd.DataFrame(data)

I was able to create a new colum and set it to 1 using the code below

df['a'] = 0
df['a'] = (df['col1'] == 'a').astype(int)

but it works only with the first column, I would have to repeat it for all columns.

Is there a way to make it happens for all columns at once?

Answer 1

Check with pd.get_dummies and groupby

df = pd.concat([df,
                pd.get_dummies(df,prefix='',prefix_sep='').groupby(level=0,axis=1).max()],
                axis=1)
Out[377]: 
  col1  col2  col3  col4  col5  a  b  c
0    a  None  None     a     b  1  1  0
1    b  None     a  None     c  1  1  1
2    a     a  None     b     c  1  1  1
3    c  None     b  None  None  0  1  1

Answer 2

pd.concat([df, pd.get_dummies(df.stack().droplevel(1)).groupby(level=0).max()], axis=1)

result:

   col1 col2    col3    col4    col5    a   b   c
0   a   None    None    a       b       1   1   0
1   b   None    a       None    c       1   1   1
2   a   a       None    b       c       1   1   1
3   c   None    b       None    None    0   1   1