The first dataframe(df1) is similar to this:
| Result | A | B | C |
|---|---|---|---|
| 2021-12-31 | False | True | True |
| 2022-01-01 | False | False | True |
| 2022-01-02 | False | True | False |
| 2022-01-03 | True | False | True |
df2 is an updated version of df1, the date data are new and the column names may be increased, which is similar to this:
| Result | A | B | C | D |
|---|---|---|---|---|
| 2022-01-04 | False | False | True | True |
| 2022-01-05 | True | False | True | True |
| 2022-01-06 | False | True | False | True |
| 2022-01-07 | False | False | True | True |
I want to integrate two databases, but I don't know how to do it。 I want to get a result similar to the following:
| Result | A | B | C | D |
|---|---|---|---|---|
| 2021-12-31 | False | True | True | NaN |
| 2022-01-01 | False | False | True | NaN |
| 2022-01-02 | False | True | False | NaN |
| 2022-01-03 | True | False | True | NaN |
| 2022-01-04 | False | False | True | True |
| 2022-01-05 | True | False | True | True |
| 2022-01-06 | False | True | False | True |
| 2022-01-07 | False | False | True | True |
Thank you very much!
CodePudding user response：
Use the concatenate function while ignoring indexes
df_new = pd.concat([df1, df2], ignore_index=True)
Any missing values will be 'NaN'.