Key/Value Store Shortcuts Signal support pwiz, a model generator Schema Migrations Reflection Database URL CSV Utils Connection pool Read Slaves Test Utils pskel Flask Utils API Reference Models Fields Query ews-digest-with-boolean-query- parser/]. Using peewee to explore CSV files [http://charlesleifer.com/blog/using-peewee-to-explore-csv- files/]. Structuring Flask apps with Peewee [http://charlesleifer basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with

and Peewee. • Personalized news digest (with a boolean query parser!). • Using peewee to explore CSV files. • Structuring Flask apps with Peewee. • Creating a lastpass clone with Flask and Peewee. basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with accomplish the above, without resorting to hacks, is to use the Model.insert_many() API: data = load_user_csv() fields = [User.id, User.username] with db.atomic(): User.insert_many(data, fields=fields).execute() when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with accomplish the above, without resorting to hacks, is to use the Model.insert_many() API: data = load_user_csv() fields = [User.id, User.username] with db.atomic(): User.insert_many(data, fields=fields).execute() when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop Removed Extensions The following extensions are no longer included in the playhouse: berkeleydb csv_utils djpeewee gfk kv pskel read_slave SQLite Extension The SQLite extension module’s VirtualModel

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop Removed Extensions The following extensions are no longer included in the playhouse: berkeleydb csv_utils djpeewee gfk kv pskel read_slave SQLite Extension The SQLite extension module’s VirtualModel

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop Removed Extensions The following extensions are no longer included in the playhouse: berkeleydb csv_utils djpeewee gfk kv pskel read_slave SQLite Extension The SQLite extension module’s VirtualModel

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop Removed Extensions The following extensions are no longer included in the playhouse: • berkeleydb • csv_utils • djpeewee • gfk • kv • pskel • read_slave SQLite Extension The SQLite extension module’s

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop Removed Extensions The following extensions are no longer included in the playhouse: • berkeleydb • csv_utils • djpeewee • gfk • kv • pskel • read_slave SQLite Extension The SQLite extension module’s

basis, you can simply tell peewee to turn off auto_increment during the import: data = load_user_csv() # load up a bunch of data User._meta.auto_increment = False # turn off auto incrementing IDs with accomplish the above, without resorting to hacks, is to use the Model.insert_many() API: data = load_user_csv() fields = [User.id, User.username] with db.atomic(): User.insert_many(data, fields=fields).execute() when iterating over large result sets. # Let's assume we've got 10 million stat objects to dump to a csv file. stats = Stat.select() # Our imaginary serializer class serializer = CSVSerializer() # Loop