gistfile1.py

>>> from cities import citiesToInsert
>>> for city in citiesToInsert:
...     db.cities.insert(city)
... 
ObjectId('51313de546c19709f3ce00eb')
ObjectId('51313de546c19709f3ce00ec')
ObjectId('51313de546c19709f3ce00ed')
ObjectId('51313de546c19709f3ce00ee')
ObjectId('51313de546c19709f3ce00ef')
ObjectId('51313de546c19709f3ce00f0')
ObjectId('51313de546c19709f3ce00f1')
ObjectId('51313de546c19709f3ce00f2')
ObjectId('51313de546c19709f3ce00f3')
ObjectId('51313de546c19709f3ce00f4')
ObjectId('51313ea146c19709f3ce00f5')
ObjectId('51313ea246c19709f3ce00f6')
ObjectId('51313ea246c19709f3ce00f7')

# and so on, for ~900 lines

>>> for city in db.cities.find():
...     print city
... 
{u'_id': ObjectId('51313de546c19709f3ce00eb'), u'censusName': u'Abbeville, LA Micropolitan Statistical Area', u'censusCBSACode': u'10020'}
{u'_id': ObjectId('51313de546c19709f3ce00ec'), u'censusName': u'Aberdeen, SD Micropolitan Statistical Area', u'censusCBSACode': u'10100'}
{u'_id': ObjectId('51313de546c19709f3ce00ed'), u'censusName': u'Aberdeen, WA Micropolitan Statistical Area', u'censusCBSACode': u'10140'}
{u'_id': ObjectId('51313de546c19709f3ce00ee'), u'censusName': u'Abilene, TX Metropolitan Statistical Area', u'censusCBSACode': u'10180'}
{u'_id': ObjectId('51313de546c19709f3ce00ef'), u'censusName': u'Ada, OK Micropolitan Statistical Area', u'censusCBSACode': u'10220'}
{u'_id': ObjectId('51313de546c19709f3ce00f0'), u'censusName': u'Adjuntas, PR Micropolitan Statistical Area', u'censusCBSACode': u'10260'}
{u'_id': ObjectId('51313de546c19709f3ce00f1'), u'censusName': u'Adrian, MI Micropolitan Statistical Area', u'censusCBSACode': u'10300'}
{u'_id': ObjectId('51313de546c19709f3ce00f2'), u'censusName': u'Aguadilla-Isabela-San Sebasti\xe1n, PR Metropolitan Statistical Area', u'censusCBSACode': u'10380'}
{u'_id': ObjectId('51313de546c19709f3ce00f3'), u'censusName': u'Akron, OH Metropolitan Statistical Area', u'censusCBSACode': u'10420'}
{u'_id': ObjectId('51313de546c19709f3ce00f4'), u'censusName': u'Alamogordo, NM Micropolitan Statistical Area', u'censusCBSACode': u'10460'}
{u'_id': ObjectId('51313ea146c19709f3ce00f5'), u'censusName': u'Albany, GA Metropolitan Statistical Area', u'censusCBSACode': u'10500'}
{u'_id': ObjectId('51313ea246c19709f3ce00f6'), u'censusName': u'Albany-Lebanon, OR Micropolitan Statistical Area', u'censusCBSACode': u'10540'}
{u'_id': ObjectId('51313ea246c19709f3ce00f7'), u'censusName': u'Albany-Schenectady-Troy, NY Metropolitan Statistical Area', u'censusCBSACode': u'10580'}
{u'_id': ObjectId('51313ea246c19709f3ce00f8'), u'censusName': u'Albemarle, NC Micropolitan Statistical Area', u'censusCBSACode': u'10620'}
{u'_id': ObjectId('51313ea246c19709f3ce00f9'), u'censusName': u'Albert Lea, MN Micropolitan Statistical Area', u'censusCBSACode': u'10660'} 

# and so on, for ~900 lines