Sure, running the following Python3 code for some randomly generated data-frames I can provide you an example:
```{python}
import pandas as pd 
import numpy as np

df1 = pd.DataFrame({"A": ["foo", "bar", "foe", "baz", "kaz"], "VAL": np.random.rand(5)})
df2 = pd.DataFrame({"B": ["foe", "baz", "foo", "kaz", "bar"], "VAL": np.random.rand(5)})
```
So `df1` is 
```{python}
df1
```
|   |  A    |   VAL|
| --- | --- | --- |
|0 | foo | 0.494726|
|1 | bar | 0.059523|
|2 | foe | 0.741398|
|3 | baz | 0.555508|
|4 | kaz | 0.907680|
And 
```{python}
df2
```
| |    B      | VAL|
|--- | ---| ---|
|0 | foe  | 0.241034 |
|1  | baz |  0.116969 | 
| 2 | foo  | 0.826898 |
| 3 | kaz | 0.364501 |
| 4 | bar | 0.638705 |

Now, merging as described above using pandas, we get 
```
pd.merge(df1, df2, left_on="A", right_on="B")
```  
|    | A   |  VAL_x    | B   |  VAL_y |
| --- | --- | --- | ---| ---|
| 0 | foo | 0.494726  | foo | 0.826898 |
| 1 | bar | 0.059523 | bar | 0.638705 |
| 2  | foe  | 0.741398 | foe | 0.241034 | 
| 3  | baz | 0.555508  | baz | 0.116969 |
| 4 |  kaz |  0.907680 | kaz  | 0.364501 |

Hope this examples is good enough, feel free to ask for more, maybe adding further information so that I can provide better results!
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