I have two dataframes

First dataframe, df1 

```
    UserMasterId    Status  Count
  0 1296.0               5  5
  1 1316.0               5  9
  2 1325.0               5  14
  3 1332.0               5  5
  4 1337.0               5  44
  5 1342.0               5  2
  6 1344.0               5  18

```

Second dataframe, df2

```
    UserMasterId    Status  Count
  0 1325.0               0  2
  1 1332.0               0  1
  2 1337.0               0  1
  3 1342.0               0  3
  4 1344.0               0  1

```

Here we have same ID in both dataframes.. 

When I use **Concat**,

```
result = pd.concat([df1, df2], axis=1, sort=True)
result

```

The output I get is,

```
    UserMasterId    Status  Count   UserMasterId    Status  Count
0   1296.0               5      5         1325.0       0.0    2.0
1   1316.0               5      9         1332.0       0.0    1.0
2   1325.0               5     14         1337.0       0.0    1.0
3   1332.0               5      5         1342.0       0.0    3.0
4   1337.0               5     44         1344.0       0.0    1.0
5   1342.0               5      2            NaN       NaN    NaN
6   1344.0               5     18            NaN       NaN    NaN

```

When i use **Merge**,

```
result = pd.merge(df1,df2[['UserMasterId', 'Count','Status']],on='UserMasterId')
result.head()

```

The output is,

```
    UserMasterId    Status_x    Count_x Count_y Status_y
0         1325.0           5         14      2         0
1         1332.0           5          5      1         0
2         1337.0           5         44      1         0
3         1342.0           5          2      3         0
4         1344.0           5         18      1         0

```

This removes the ID that is not common in df1 & df2.

I don't want to remove the ID that is not common to both dataframes.
I want the output like this,

```
UserMasterId    Status_x    Count_x Count_y Status_y
0     1296.0           5          5      NA       NA
1     1316.0           5          9      NA       NA
2     1325.0           5         14      2         0
3     1332.0           5          5      1         0
4     1337.0           5         44      1         0
5     1342.0           5          2      3         0
6     1344.0           5         18      1         0

```

Can anyone help me out?