Nrthugu

I am trying to do a multiple column select then replace in pandas

df:

a  b  c  d  e

0  1  1  0  none

0  0  0  1  none

1  0  0  0  none

0  0  0  0  none

select where any or all of a, b, c, d are non zero

i, j = np.where(df)

s=pd.Series(dict(zip(zip(i, j), 

  df.columns[j]))).reset_index(-1, drop=True)

s:

0   b

0   c

1   d

2   a

Now I want to replace the values in column e by the series:

df['e'] = s.values

so that e looks like:

e:

b, c 

d

a

none

But the problem is that the lengths of the series are different to the number of rows in the dataframe.

Any idea on how I can do this?

Use DataFrame.dot for product with columns names, add rstrip, last add numpy.where for replace empty strings to None:

e = df.dot(df.columns + ', ').str.rstrip(', ')

df['e'] = np.where(e.astype(bool), e, None)

print (df)

   a  b  c  d     e

0  0  1  1  0  b, c

1  0  0  0  1     d

2  1  0  0  0     a

3  0  0  0  0  None

You can locate the 1's and use their locations as boolean indexes into the dataframe columns:

df['e'] = (df==1).apply(lambda x: df.columns[x], axis=1)

                 .str.join(",").replace('','none')

#   a  b  c  d     e

#0  0  1  1  0   b,c

#1  0  0  0  1     d

#2  1  0  0  0     a

#3  0  0  0  0  none