Today I got asked if you can index in to rows returned by ibm_db_dbi by column name. While this doesn't come out of the box1, it can be done pretty easily.

Here's some bog-standard Python code that executes a query and prints out each row:

import ibm_db_dbi as db2 

cur = db2.connect().cursor()
cur.execute('select * from example')

for row in cur:

This produces the output below:

(1, 'Bob', 'Sales')
(2, 'Joe', 'Accounting')
(3, 'Vera', 'Development')
(4, 'Yu', 'Support')

Notice that each row is a Python tuple and thus can only be indexed by column index. However, PEP 249 requires the column names (and other metadata) to be stored in the cursor description attribute. With that information, we can easily map column index to column name with that. In fact, with a quick Google search, I found a recipe in the Python Cookbook that does just that:

def fields(cursor):
    """ Given a DB API 2.0 cursor object that has been executed, returns
    a dictionary that maps each field name to a column index; 0 and up. """
    results = {}
    column = 0
    for d in cursor.description:
        results[d[0]] = column
        column = column + 1

    return results

field_map = fields(cur)

for row in cur:

The downfall of this approach is that we have to first generate a column index map and then every time we want to index by name, we have to look up the column name in the map to find its column index. We can certainly do better with the help of Python generators! A generator implements the Iterator Pattern, giving back a value when __next__() is called until a StopIteration exception is thrown. This is usually done implicitly using a for loop and not called directly. We can create a generator class which returns a dictionary, mapping the values in the tuple to their column name using the descriptions in the cursor object. Since a cursor object is itself a generator, it's easy to write a simple wrapper:

class CursorByName():
    def __init__(self, cursor):
        self._cursor = cursor
    def __iter__(self):
        return self

    def __next__(self):
        row = self._cursor.__next__()

        return { description[0]: row[col] for col, description in enumerate(self._cursor.description) }
for row in CursorByName(cur):

All the magic happens in the __next__ function. It simply calls the self._cursor.__next__() to get the next row, while letting the StopIteration bubble up to the caller. We then use a dict comprehension to map the items in the tuple to a dictionary. We use the enumerate built-in function to loop through each column description in the cursor description along with its column index. Now each row you get back is a dictionary:

{'ID': 1, 'NAME': 'Bob', 'DEPT': 'Sales'}
{'ID': 2, 'NAME': 'Joe', 'DEPT': 'Accounting'}
{'ID': 3, 'NAME': 'Vera', 'DEPT': 'Development'}
{'ID': 4, 'NAME': 'Yu', 'DEPT': 'Support'}

An interesting idea would be to create a class which acts as both a dict and tuple, such that you could mimic db2_fetch_both from the PHP ibm_db2 interface. While you could just add column index keys to the dictionary above, but you will lose out on slicing and other sequence operations that you can do on tuples.

1 You can actually do this by using ibm_db directly by calling either ibm_db.fetch_assoc or ibm_db.fetch_both, but using ibm_db is a pain and you lose out on all the PEP 249 goodness as well.