Melting multiple columns into one column

Question

I have been trying to melt this columns

d = {'key': [1,2,3,4,5], 'a': ['None','a', 'None','None','None'], 'b': ['None','None','b','None','None'],'c':['None','None','None','c','c']}

df = pd.DataFrame(d)

I need to look like this

key	letter
1	None
2	a
3	b
4	c
5	c

I tried:

df = pd.melt(df,id_vars=['key'], var_name = 'letters')

but i got:

key	letters	value
1	a	None
2	a	a
3	a	None
4	a	None
5	a	None
1	b	None
2	b	None
3	b	b
4	b	None
5	b	None
1	c	None
2	c	None
3	c	None
4	c	c
5	c	c

Answer 1

If need get first non None value per rows after key column use DataFrame.set_index with replace possible None strings with back filling missing values and selected first column by position, last use Series.reset_index:

df = (df.set_index('key')
        .replace('None', np.nan)
        .bfill(axis=1)
        .iloc[:, 0]
        .reset_index(name='letter')))
print (df)
   key letter
0    1    NaN
1    2      a
2    3      b
3    4      c
4    5      c

If possible multiple non None value per rows use:

d = {'key': [1,2,3,4,5], 
     'a': ['None','a', 'None','None','None'], 
     'b': ['None','b','b','None','None'],
     'c':['None','None','None','c','c']}

df = pd.DataFrame(d)

df = (df[['key']].join(df.set_index('key')
        .replace('None', np.nan)
        .stack()
        .groupby(level=0)
        .agg(','.join)
        .rename('letter'), on='key'))
print (df)
   key letter
0    1    NaN
1    2    a,b
2    3      b
3    4      c
4    5      c

Or:

df = (df.set_index('key')
        .replace('None', np.nan)
        .apply(lambda x: ','.join(x.dropna()), axis=1)
        .replace('', np.nan)
        .reset_index(name='letter'))

print (df)
   key letter
0    1    NaN
1    2    a,b
2    3      b
3    4      c
4    5      c