I have two dataframes (DF and DF2). **Anyobody could help me in understand how can I combine these two dataframes and make them look like this third one (DF3)?** I presented a simple example, but I need this to compile dataframes that include different samples (or observations). Eventually, there are samples that emcompass the same group of variables. But most of the cases, the samples present different variables. Each column corresponds to one sample.

Any help is welcome!

DF -

```
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
        'age': [42, 52, 36, 24, 73],
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
print(df)

```

DF2 -

```
raw_data2 = {'first_name': ['Molly', 'Jake'],
             'civil_status': ['Single', 'Single']}
df2 = pd.DataFrame(raw_data2, columns = ['first_name', 'civil_status'])
print(df2)´´´

```

DF3 -

```
raw_data3 = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
        'age': [42, 52, 36, 24, 73],
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70],
             'civil_status': ['NaN', 'Single', 'NaN', 'Single', 'NaN']}
df3 = pd.DataFrame(raw_data3, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore',
                                         'civil_status'])
print(df3)

```