BKS - 1 year ago 108

Python Question

I have the following two dataframes in pandas:

`DF1:`

AuthorID1 AuthorID2 Co-Authored

A1 A2 0

A1 A3 0

A1 A4 0

A2 A3 0

DF2:

AuthorID1 AuthorID2 Co-Authored

A1 A2 5

A2 A3 6

I would like (without looping and comparing) to find the matching AuthorID1 and AuthorID2 pairing in DF2 that exist in DF1 and update the column values accordingly. So the result for the above two tables would be the following:

`Resulting Updated DF1:`

AuthorID1 AuthorID2 Co-Authored

A1 A2 5

A1 A3 0

A1 A4 0

A2 A3 6

Is there a fast way to do this? As I have 7 millions rows in DF1 and looping and comparing would just take forever.

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

You can use `update`

:

```
df1.update(df2)
print (df1)
AuthorID1 AuthorID2 Co-Authored
0 A1 A2 5.0
1 A2 A3 6.0
2 A1 A4 0.0
3 A2 A3 0.0
```

Sample:

```
df1 = pd.DataFrame({'new': {0: 7, 1: 8, 2: 1, 3: 3},
'AuthorID2': {0: 'A2', 1: 'A3', 2: 'A4', 3: 'A3'},
'AuthorID1': {0: 'A1', 1: 'A1', 2: 'A1', 3: 'A2'},
'Co-Authored': {0: 0, 1: 0, 2: 0, 3: 0}})
df2 = pd.DataFrame({'AuthorID2': {0: 'A2', 1: 'A3'},
'AuthorID1': {0: 'A1', 1: 'A2'},
'Co-Authored': {0: 5, 1: 6}})
AuthorID1 AuthorID2 Co-Authored new
0 A1 A2 0 7
1 A1 A3 0 8
2 A1 A4 0 1
3 A2 A3 0 3
print (df2)
AuthorID1 AuthorID2 Co-Authored
0 A1 A2 5
1 A2 A3 6
df1.update(df2)
print (df1)
AuthorID1 AuthorID2 Co-Authored new
0 A1 A2 5.0 7
1 A2 A3 6.0 8
2 A1 A4 0.0 1
3 A2 A3 0.0 3
```

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**