Create new columns and fill them depending on other in python

Question

ColG Col2 Col3 Len    Sign
G1   1    30   300    +
G2   20   80   200    +
G3   455  720  1000   -
G4   3    40   100    -
G4   2    90   130    +

and here is the idea, for each row, if the Sign is -, then do :

Len-Col2 > NewCol3
Len-Col3 > NewCol2

exemple

1000-720=280
1000-455=545
100-40=60
100-3=97

and get :

ColG Col2 Col3 Len    Sign NewCol2  NewCol3
G1   1    30   300    +    1        30
G2   20   80   200    +    20       80
G3   455  720  1000   -    280      545
G4   3    40   100    -    60       97
G4   2    90   130    +    2        90

Thank you for your help

It seems expected output has swapped col2, col3

jezrael
– jezrael

2020-04-28 11:00:25 +00:00
Commented Apr 28, 2020 at 11:00 — jezrael
– jezrael, Commented Apr 28, 2020 at 11:00
Oops yes I rectified, it is Len-Col2 that gives NewCol3

Grendel
– Grendel

2020-04-28 11:47:58 +00:00
Commented Apr 28, 2020 at 11:47 — Grendel
– Grendel, Commented Apr 28, 2020 at 11:47

jezrael · Accepted Answer · 2020-04-28 11:50:49Z

2

You need processing each column separately, e.g. compare by Series.eq with numpy.where:

m = df['Sign'].eq('-')

df['NewCol2'] = np.where(m, df['Len'].sub(df['Col3']), df['Col2'])
df['NewCol3'] = np.where(m, df['Len'].sub(df['Col2']), df['Col3'])

Or with Series.mask:

df['NewCol2'] = df['Col2'].mask(m, df['Len'].sub(df['Col3']))
df['NewCol3'] = df['Col3'].mask(m, df['Len'].sub(df['Col2']))

print (df)
  ColG  Col2  Col3   Len Sign  NewCol2  NewCol3
0   G1     1    30   300    +        1       30
1   G2    20    80   200    +       20       80
2   G3   455   720  1000    -      280      545
3   G4     3    40   100    -       60       97
4   G4     2    90   130    +        2       90

edited Apr 28, 2020 at 11:50

answered Apr 28, 2020 at 10:51

jezrael

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Comments

dzakyputra · Accepted Answer · 2020-04-28 10:51:21Z

1

If your dataframe is not too big, you can try this.

def new_col2(row):
  if row['Sign'] == '-':
    return row['Len'] - row['Çol2']
  return row['Col2']

def new_col3(row):
  if row['Sign'] == '-':
    return row['Len'] - row['Çol3']
  return row['Col3']

df['NewCol2'] = df.apply(new_col2, axis=1)
df['NewCol3'] = df.apply(new_col3, axis=1)

answered Apr 28, 2020 at 10:51

dzakyputra

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