I am trying to create a new column in a pandas data frame by and calculating the value from existing columns. 

I have 3 existing columns ("launched\_date", "item\_published\_at", "item\_created\_at")

However, my "if row[column\_name] is not None:" statement is allowing columns with NaN value and not skipping to the next statement. 

In the code below, I would not expect the value of "nan" to be printed after the first conditional, I would expect something like "2018-08-17"

```
df['adjusted_date'] = df.apply(lambda row: adjusted_date(row), axis=1)

def adjusted_launch(row): 
    if row['launched_date']is not None:
        print(row['launched_date'])
        exit()
        adjusted_date = date_to_time_in_timezone(row['launched_date']) 

    elif row['item_published_at'] is not None:
        adjusted_date = row['item_published_at']#make datetime in PST
    else:
        adjusted_date = row['item_created_at'] #make datetime in PST
    return adjusted_date

```

How can I structure this conditional statement correctly?