I have a merged Pandas dataframe in the following format
| index | value_x | value_y |
|---|---|---|
| 0 | nan | 3 |
| 1 | 3 | nan |
| 2 | nan | nan |
| 3 | -1 | 1 |
| 4 | 6 | nan |
| 5 | nan | 6 |
| 6 | -1 | nan |
| 7 | -1 | 6 |
| 8 | nan | nan |
Since the original dataframes have the value field, therefore value_x and value_y column is gnerated during the merge process. I would like to merge the two columns so the final column would look like:
| index | value_x | value_y | value |
|---|---|---|---|
| 0 | nan | 3 | 3 |
| 1 | 3 | nan | 3 |
| 2 | nan | nan | nan |
| 3 | nan | 1 | 1 |
| 4 | 6 | nan | 6 |
| 5 | nan | 6 | 6 |
| 6 | -1 | nan | -1 |
| 7 | nan | 6 | 6 |
| 8 | nan | nan | nan |
In addition, I would like to know if I could avoid the column combining process during the merge process?
Thanks in advance
CodePudding user response：
You can use max
df["value"] = df[["value_x", "value_y"]].max(axis=1)
as this will pick the non-nan value for each row. For this question:
In addition, I would like to know if I could avoid the column combining process during the merge process?
the answer depends on what the two dataframes were before the merge.