There are field_1 in both tables, we want to get rows field_3 from table_2 if the field_1 shows in Table_1.

Data

Table_1:

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| field_1 | field_2 |
|---------|:--------|
| 1       | apple   |
| 1       | pearl   |
| 2       | banana  |
| 3       | apple   |
| 4       | peach   |

Table_2:

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| field_1 | field_3 |
|---------|:--------|
| 2       | train1  |
| 1       | bus1    |
| 2       | train3  |
| 5       | metro1  |
| 4       | tram1   |
| 1       | bus2    |
| 5       | metro2  |
| 5       | metro3  |

In this case, the field_1 values in table_1 are 1, 2, 3, 4.

The output should be:

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| field_1 | field_3 |
|---------|:--------|
| 2       | train1  |
| 1       | bus1    |
| 2       | train3  |
| 4       | tram1   |
| 1       | bus2    |

Code

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import pandas as pd
table_1_path = 'path\\table_1.txt'
table_2_path = 'path\\table_2.txt'
 
table_1 = pd.read_csv(table_1_path)
table_2 = pd.read_csv(table_2_path)
 
value = table_1['field_1']
# To select rows whose column value is in an iterable, use .isin()
output = table_2.loc[table_2['field_1'].isin(value)]

print output
#-------------------------
   field_1   field_3
0        2   train1 
1        1   bus1   
2        2   train3 
4        4   tram1  
5        1   bus2   
[Finished in 1.0s]

References

[1] Select rows from a DataFrame based on values in a column in pandas
[2] Pandas document | Different Choices for Indexing