根据python中的条件使用第1行或第2行的值更新数据框列

Question

I have a dataframe and want to create a column based on a condition that populates the row with the value of a row in another column.

df = pd.DataFrame({'parent':[32, 3, 88, 9, 10, 23, 99, 23],
                   'id':[1, 2, 3, 4, 5, 6, 7, 8],
                   'flag':[True,True,False,True,False,True,True,True]})

I have tried to do this using np.where() but it doesn't update the value row by row but instead replaces all values within the column with the condition that is met.

df['res'] = np.where(df['flag'] == True, df['parent'], df['id'])

The dataframe I want to create looks as follows:

df = pd.DataFrame({'parent':[32, 3, 88, 9, 10, 23, 99, 23],
                   'id':[1, 2, 3, 4, 5, 6, 7, 8],
                   'flag':[True,True,False,True,False,True,True,True],
                   'res':[32, 3, 3, 9, 5, 23, 99, 23]})

Any ideas what I'm doing wrong? I'm new to python, so any help is much appreciated.

Answer 1

Just change this:

df['res'] = np.where(df['flag'] == True, output['parent'], output['id'])

to this:

df['res'] = np.where(df['flag'] == True, df['parent'], df['id'])

Answer 2

Fix your code change the output to df

df['res1'] = np.where(df['flag'] == True, df['parent'], df['id'])
df
Out[176]: 
   parent  id   flag  res  res1
0      32   1   True   32    32
1       3   2   True    3     3
2      88   3  False    3     3
3       9   4   True    9     9
4      10   5  False    5     5
5      23   6   True   23    23
6      99   7   True   99    99
7      23   8   True   23    23

Answer 3

As others have pointed out, you had a typo in your code. An alternative way the achieve the desired output is to use the apply method:

df['res'] = df.apply(lambda x : x['parent'] if x['flag'] else x['id'], 1)

Or

df['res'] = np.where(df['flag'], df['parent'], df['id'])

Output :

   parent  id   flag  res
0      32   1   True   32
1       3   2   True    3
2      88   3  False    3
3       9   4   True    9
4      10   5  False    5
5      23   6   True   23
6      99   7   True   99
7      23   8   True   23