Merging columns within a dataframe with pandas

Question

I'm trying to merge two different columns within a data frame. So if you have columns A and B, and you want A to remain the default value unless it is empty. If it is empty you want to use the value for B.

pd.merge looks like it only works when merging data frames, not columns within an existing single data frame.

| A     | B    |

| 2     | 4    |
| NaN   | 3    | 
| 5     | NaN  |
| NaN   | 6    | 
| 7     | 8    |

Desired Result:

|A|

|2|
|3|
|5|
|6|
|7|

df['A'].fillna(df['B']) ? Add some data to this question and expected output. — Scott Boston
– Scott Boston, Commented Jun 9, 2020 at 20:46
I added a data table to illustrate what I'm trying to accomplish. Thank you for taking the time to answer! — Russell Comer
– Russell Comer, Commented Jun 9, 2020 at 21:15

Caleb Courtney · Accepted Answer · 2020-06-10 00:41:17Z

1

Credit to Scott Boston for the comment on the OP:

import pandas as pd

df = pd.DataFrame( 
    {
        'A': [2, None, 5, None, 7], 
        'B': [4, 3, None, 6, 8] 
    } 
)

df.head()
"""
     A    B
0  2.0  4.0
1  NaN  3.0
2  5.0  NaN
3  NaN  6.0
4  7.0  8.0
"""

df['A'] = df['A'].fillna(df['B'])

df.head()
"""
     A    B
0  2.0  4.0
1  3.0  3.0
2  5.0  NaN
3  6.0  6.0
4  7.0  8.0
"""

answered Jun 10, 2020 at 0:41

Caleb Courtney

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2 Comments

Russell Comer Over a year ago

So question about this, it works but what do I use if the cell's value is "-"?

Caleb Courtney Over a year ago

You could do something along the lines of df.loc[df['A'] == '-', 'A'] = df.loc[df['A'] == '-', 'B'] In short, wherever A == '-', it will replace with the value in column B`.

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