How to combine multiple pandas column object type values ​​into a single column, ignoring no?

Question

Run data file:

pd.DataFrame({'col1': ['one', 'None', 'None'], 'col2': ['None', 'None', 'six'], 'col3': ['None', 'eight', 'None']})

Final goal:

pd.DataFrame({'col4': ['one', 'eight', 'six']})

What I tried to do:

df['col1'].map(str)+df['col2'].map(str)+df['col3'].map(str)

How can I combine multiple pandas column object type values ​​into a single column, ignoring the None values? By the way, in this data set there will never be more than one value in the final cells of the data cells.

Answer 1

You have a string Nones and not actual null values, so you need to replace them first.

Option 1
replace / mask/ where+ fillna+agg

df.replace('None', np.nan).fillna('').agg(''.join, axis=1).to_frame('col4')

Or

df.mask(df.eq('None')).fillna('').agg(''.join, axis=1).to_frame('col4')

Or

df.where(df.ne('None')).fillna('').agg(''.join, axis=1).to_frame('col4')

    col4
0    one
1  eight
2    six

---

Option 2
replace +pd.notnull

v = df.replace('None', np.nan).values.ravel()
pd.DataFrame(v[pd.notnull(v)], columns=['col4'])

    col4
0    one
1  eight
2    six

---

Option 3
Solution using the superior Divakar feature justify:

pd.DataFrame(justify(df.values, invalid_val='None')[:, 0], columns=['col4'])

    col4
0    one
1  eight
2    six

( : , .)

def justify(a, invalid_val=0, axis=1, side='left'):    
    """
    Justifies a 2D array

    Parameters
    ----------
    A : ndarray
        Input array to be justified
    axis : int
        Axis along which justification is to be made
    side : str
        Direction of justification. It could be 'left', 'right', 'up', 'down'
        It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0.

    """

    if invalid_val is np.nan:
        mask = ~np.isnan(a)
    else:
        mask = a!=invalid_val
    justified_mask = np.sort(mask,axis=axis)
    if (side=='up') | (side=='left'):
        justified_mask = np.flip(justified_mask,axis=axis)
    out = np.full(a.shape, invalid_val, dtype='<U8')    # change to be made is here
    if axis==1:
        out[justified_mask] = a[mask]
    else:
        out.T[justified_mask.T] = a.T[mask.T]
    return out

Answer 2

, :

pd.DataFrame(df[df!='None'].stack().values, columns=['col4'])

    col4
0    one
1  eight
2    six

Answer 3

Or

df[df!='None'].fillna('').sum(1)
Out[1054]: 
0      one
1    eight
2      six
dtype: object

WITH list map

list(map(lambda x : ''.join(x) ,df.replace({'None':''}).values))
Out[1061]: ['one', 'eight', 'six']

Answer 4

df['col4']=df.apply(lambda x: x.max(),axis=1)