How to merge multiple pandas column object type values into one column while ignoring "None"?

Question

Starting dataframe:

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

End 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 merge multiple pandas column object type values into one column while ignoring "None" values? By the way, in this dataset, there will never end up being more than one value in the final dataframe cells.

Answer 1

You have string Nones, not actual null values, so you'll 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
A solution leveraging Divakar's excellent justify function:

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

    col4
0    one
1  eight
2    six

Reference
(Note, you will need to modify the function slightly to play nicely with string data.)

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

Another way, for the sake of giving you options:

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)