This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover:
If you’d like something a bit more meaty, there is a thorough tutorial on creating a “twitter”-style web app using peewee and the Flask framework.
I strongly recommend opening an interactive shell session and running the code. That way you can get a feel for typing in queries.
Model classes, fields and model instances all map to database concepts:
|Model class||Database table|
|Field instance||Column on a table|
|Model instance||Row in a database table|
When starting to a project with peewee, it’s typically best to begin with your data model, by defining one or more
from peewee import * db = SqliteDatabase('people.db') class Person(Model): name = CharField() birthday = DateField() is_relative = BooleanField() class Meta: database = db # This model uses the "people.db" database.
Note that we named our model
Person instead of
People. This is a convention you should follow – even though the table will contain multiple people, we always name the class using the singular form.
There are lots of field types suitable for storing various types of data. Peewee handles converting between pythonic values those used by the database, so you can use Python types in your code without having to worry.
Things get interesting when we set up relationships between models using foreign keys (wikipedia). This is easy to do with peewee:
class Pet(Model): owner = ForeignKeyField(Person, related_name='pets') name = CharField() animal_type = CharField() class Meta: database = db # this model uses the "people.db" database
Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice since it will reveal any errors with your database connection immediately, as opposed to some arbitrary time later when the first query is executed. It is also good to close the connection when you are done – for instance, a web app might open a connection when it receives a request, and close the connection when it sends the response.
We’ll begin by creating the tables in the database that will store our data. This will create the tables with the appropriate columns, indexes, sequences, and foreign key constraints:
>>> db.create_tables([Person, Pet])
>>> from datetime import date >>> uncle_bob = Person(name='Bob', birthday=date(1960, 1, 15), is_relative=True) >>> uncle_bob.save() # bob is now stored in the database 1
When you call
save(), the number of rows modified is returned.
You can also add a person by calling the
create() method, which returns a model instance:
>>> grandma = Person.create(name='Grandma', birthday=date(1935, 3, 1), is_relative=True) >>> herb = Person.create(name='Herb', birthday=date(1950, 5, 5), is_relative=False)
To update a row, modify the model instance and call
save() to persist the changes. Here we will change Grandma’s name and then save the changes in the database:
>>> grandma.name = 'Grandma L.' >>> grandma.save() # Update grandma's name in the database. 1
Now we have stored 3 people in the database. Let’s give them some pets. Grandma doesn’t like animals in the house, so she won’t have any, but Herb is an animal lover:
>>> bob_kitty = Pet.create(owner=uncle_bob, name='Kitty', animal_type='cat') >>> herb_fido = Pet.create(owner=herb, name='Fido', animal_type='dog') >>> herb_mittens = Pet.create(owner=herb, name='Mittens', animal_type='cat') >>> herb_mittens_jr = Pet.create(owner=herb, name='Mittens Jr', animal_type='cat')
After a long full life, Mittens sickens and dies. We need to remove him from the database:
>>> herb_mittens.delete_instance() # he had a great life 1
The return value of
delete_instance() is the number of rows removed from the database.
Uncle Bob decides that too many animals have been dying at Herb’s house, so he adopts Fido:
>>> herb_fido.owner = uncle_bob >>> herb_fido.save() >>> bob_fido = herb_fido # rename our variable for clarity
The real strength of our database is in how it allows us to retrieve data through queries. Relational databases are excellent for making ad-hoc queries.
Getting single records¶
Let’s retrieve Grandma’s record from the database. To get a single record from the database, use
>>> grandma = Person.select().where(Person.name == 'Grandma L.').get()
We can also use the equivalent shorthand
>>> grandma = Person.get(Person.name == 'Grandma L.')
Lists of records¶
Let’s list all the people in the database:
>>> for person in Person.select(): ... print person.name, person.is_relative ... Bob True Grandma L. True Herb False
Let’s list all the cats and their owner’s name:
>>> query = Pet.select().where(Pet.animal_type == 'cat') >>> for pet in query: ... print pet.name, pet.owner.name ... Kitty Bob Mittens Jr Herb
There is a big problem with the previous query: because we are accessing
pet.owner.name and we did not select this value in our original query, peewee will have to perform an additional query to retrieve the pet’s owner. This behavior is referred to as N+1 and it should generally be avoided.
We can avoid the extra queries by selecting both Pet and Person, and adding a join.
>>> query = (Pet ... .select(Pet, Person) ... .join(Person) ... .where(Pet.animal_type == 'cat')) >>> for pet in query: ... print pet.name, pet.owner.name ... Kitty Bob Mittens Jr Herb
Let’s get all the pets owned by Bob:
>>> for pet in Pet.select().join(Person).where(Person.name == 'Bob'): ... print pet.name ... Kitty Fido
We can do another cool thing here to get bob’s pets. Since we already have an object to represent Bob, we can do this instead:
>>> for pet in Pet.select().where(Pet.owner == uncle_bob): ... print pet.name
Let’s make sure these are sorted alphabetically by adding an
>>> for pet in Pet.select().where(Pet.owner == uncle_bob).order_by(Pet.name): ... print pet.name ... Fido Kitty
Let’s list all the people now, youngest to oldest:
>>> for person in Person.select().order_by(Person.birthday.desc()): ... print person.name, person.birthday ... Bob 1960-01-15 Herb 1950-05-05 Grandma L. 1935-03-01
Now let’s list all the people and some info about their pets:
>>> for person in Person.select(): ... print person.name, person.pets.count(), 'pets' ... for pet in person.pets: ... print ' ', pet.name, pet.animal_type ... Bob 2 pets Kitty cat Fido dog Grandma L. 0 pets Herb 1 pets Mittens Jr cat
Once again we’ve run into a classic example of N+1 query behavior. We can avoid this by performing a JOIN and aggregating the records:
>>> subquery = Pet.select(fn.COUNT(Pet.id)).where(Pet.owner == Person.id) >>> query = (Person ... .select(Person, Pet, subquery.alias('pet_count')) ... .join(Pet, JOIN.LEFT_OUTER) ... .order_by(Person.name)) >>> for person in query.aggregate_rows(): # Note the `aggregate_rows()` call. ... print person.name, person.pet_count, 'pets' ... for pet in person.pets: ... print ' ', pet.name, pet.animal_type ... Bob 2 pets Kitty cat Fido dog Grandma L. 0 pets Herb 1 pets Mittens Jr cat
Even though we created the subquery separately, only one query is actually executed.
Finally, let’s do a complicated one. Let’s get all the people whose birthday was either:
- before 1940 (grandma)
- after 1959 (bob)
>>> d1940 = date(1940, 1, 1) >>> d1960 = date(1960, 1, 1) >>> query = (Person ... .select() ... .where((Person.birthday < d1940) | (Person.birthday > d1960))) ... >>> for person in query: ... print person.name, person.birthday ... Bob 1960-01-15 Grandma L. 1935-03-01
Now let’s do the opposite. People whose birthday is between 1940 and 1960:
>>> query = (Person ... .select() ... .where((Person.birthday > d1940) & (Person.birthday < d1960))) ... >>> for person in query: ... print person.name, person.birthday ... Herb 1950-05-05
One last query. This will use a SQL function to find all people whose names start with either an upper or lower-case G:
>>> expression = (fn.Lower(fn.Substr(Person.name, 1, 1)) == 'g') >>> for person in Person.select().where(expression): ... print person.name ... Grandma L.
We’re done with our database, let’s close the connection:
This is just the basics! You can make your queries as complex as you like.
All the other SQL clauses are available as well, such as:
Check the documentation on Querying for more info.
Working with existing databases¶
If you already have a database, you can autogenerate peewee models using pwiz, a model generator. For instance, if I have a postgresql database named charles_blog, I might run:
python -m pwiz -e postgresql charles_blog > blog_models.py