I have 2 data frames
df1
| email | ack |
| -------- | -------------- |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
| [email protected] | 1 |
df2
| email | ack |name| date|
| -------- | -------------- |-------------- |-------------- |
|[email protected] | 0 |abc | 01/01/2022 |
| [email protected] | 0 |xyz | 01/02/2022 |
| [email protected] | 0 |mno | 01/03/2022 |
| [email protected] | 0 |pqr | 01/04/2022 |
| [email protected] | 0 |adam| 01/05/2022 |
| [email protected] | 0 |eve |01/06/2022|
| [email protected] | 0 |mary|01/07/2022|
| [email protected] | 0 |john|01/08/2022|
| [email protected] | 0 |kate|01/09/2022|
| [email protected] | 0 |matt|01/10/2022|
How do i merge the above two dataframes so as to replace the values in 'ack' column of df2 wherever applicable i.e., on email address.
result
df2
| email | ack |name| date|
| -------- | -------------- |-------------- |-------------- |
|[email protected] | 1 |abc|01/01/2022|
| [email protected] | 1 |xyz|01/02/2022|
| [email protected] | 1 |mno|01/03/2022|
| [email protected] | 1 |pqr|01/04/2022|
| [email protected] | 1 |adam|01/05/2022|
| [email protected] | 1 |eve|01/06/2022|
| [email protected] | 1 |mary|01/07/2022|
| [email protected] | 1 |john|01/08/2022|
| [email protected] | 0 |kate|01/09/2022|
| [email protected] | 0 |matt|01/10/2022|
I tried left join and outer join, it appended rows to existing rows.
CodePudding user response：
Assuming df1['ack'] is always 1, the following code should work:
df2.loc[df2['email'].isin(df1['email']), 'ack'] = 1
In English: If df2['email'] is found in df1['email'], set df2['ack'] = 1