How can I put a value in a column of a Dataframe based on oher columns (Python)?

Question

I have a dataframe df in pandas with the next data structure:

+------+------+
| col1 | col2 |
+------+------+
|  2   |  1   |
+------+------+
|  nan |  3   |
+------+------+
|  nan | nan  |
+------+------+

There are some values for columns col1 and col2 that are nan, and the others are Integers. What I would like to do is to add a new column col3 to my df dataframe where the values of the other columns are considered.

In this case, if col1 and col2 values for a row are integer values, the new value of col3 would be 0. If col1 is nan and col2 is not nan, col3 would have 1 value. And finally, if both col1 and col2 are nan, col3 would have 2 value.

How can I do it?

jezrael · Accepted Answer · 2021-04-08 11:00:00Z

1

Use numpy.select with conditions, default values is if col1 is not NaN and col2 is NaN values:

df = pd.DataFrame({'col1':[2, np.nan, np.nan, 5],
                   'col2':[1,3,np.nan, np.nan]})

m1 = df['col1'].isna()
m2 = df['col2'].isna()

df['out'] = np.select([~m1 & ~m2, m1 & ~m2, m1 & m2], [0,1,2], default=3)
print (df)
   col1  col2  out
0   2.0   1.0    0
1   NaN   3.0    1
2   NaN   NaN    2
3   5.0   NaN    3

edited Apr 8, 2021 at 11:00

answered Apr 8, 2021 at 10:52

jezrael

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