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conda install

sqlite-minutils

[!TIP]

Where to find the complete documentation for this library

If you want to learn about everything this project can do, we recommend reading the Python library section of the sqlite-utils project here.

This project wouldn’t exist without Simon Willison and his excellent sqlite-utils project. Most of this project is his code, with some minor changes made to it.

Install

pip install sqlite-minutils

Use

First, import the sqlite-miniutils library. Through the use of the all attribute in our Python modules by using import * we only bring in the Database, Queryable, Table, View classes. There’s no risk of namespace pollution.

from sqlite_minutils.db import *

Then we create a SQLite database. For the sake of convienance we’re doing it in-memory with the :memory: special string. If you wanted something more persistent, name it something not surrounded by colons, data.db is a common file name.

db = Database(":memory:")

Let’s drop (aka ‘delete’) any tables that might exist. These docs also serve as a test harness, and we want to make certain we are starting with a clean slate. This also serves as a handy sneak preview of some of the features of this library.

for t in db.tables: t.drop()

User tables are a handy way to create a useful example with some real-world meaning. To do this, we first instantiate the users table object:

users = Table(db, 'Users')
users

<Table Users (does not exist yet)>

The table doesn’t exist yet, so let’s add some columns via the Table.create method:

users.create(columns=dict(id=int, name=str, age=int))
users

<Table Users (id, name, age)>

What if we need to change the table structure?

For example User tables often include things like password field. Let’s add that now by calling create again, but this time with transform=True. We should now see that the users table now has the pwd:str field added.

users.create(columns=dict(id=int, name=str, age=int, pwd=str), transform=True, pk='id')
users

<Table Users (id, name, age, pwd)>

print(db.schema)

CREATE TABLE "Users" (
   [id] INTEGER PRIMARY KEY,
   [name] TEXT,
   [age] INTEGER,
   [pwd] TEXT
);

Queries

Let’s add some users to query:

users.insert(dict(name='Raven', age=8, pwd='s3cret'))
users.insert(dict(name='Magpie', age=5, pwd='supersecret'))
users.insert(dict(name='Crow', age=12, pwd='verysecret'))
users.insert(dict(name='Pigeon', age=3, pwd='keptsecret'))
users.insert(dict(name='Eagle', age=7, pwd='s3cr3t'))

<Table Users (id, name, age, pwd)>

A simple unfiltered select can be executed using rows property on the table object.

users.rows

<generator object Queryable.rows_where at 0x10849f6f0>

Let’s iterate over that generator to see the results:

[o for o in users.rows]

[{'id': 1, 'name': 'Raven', 'age': 8, 'pwd': 's3cret'},
 {'id': 2, 'name': 'Magpie', 'age': 5, 'pwd': 'supersecret'},
 {'id': 3, 'name': 'Crow', 'age': 12, 'pwd': 'verysecret'},
 {'id': 4, 'name': 'Pigeon', 'age': 3, 'pwd': 'keptsecret'},
 {'id': 5, 'name': 'Eagle', 'age': 7, 'pwd': 's3cr3t'}]

Filtering can be done via the rows_where function:

[o for o in users.rows_where('age > 3')]

[{'id': 1, 'name': 'Raven', 'age': 8, 'pwd': 's3cret'},
 {'id': 2, 'name': 'Magpie', 'age': 5, 'pwd': 'supersecret'},
 {'id': 3, 'name': 'Crow', 'age': 12, 'pwd': 'verysecret'},
 {'id': 5, 'name': 'Eagle', 'age': 7, 'pwd': 's3cr3t'}]

We can also limit the results:

[o for o in users.rows_where('age > 3', limit=2)]

[{'id': 1, 'name': 'Raven', 'age': 8, 'pwd': 's3cret'},
 {'id': 2, 'name': 'Magpie', 'age': 5, 'pwd': 'supersecret'}]

The offset keyword can be combined with the limit keyword.

[o for o in users.rows_where('age > 3', limit=2, offset=1)]

[{'id': 2, 'name': 'Magpie', 'age': 5, 'pwd': 'supersecret'},
 {'id': 3, 'name': 'Crow', 'age': 12, 'pwd': 'verysecret'}]

The offset must be used with limit or raise a ValueError:

try:
    [o for o in users.rows_where(offset=1)]
except ValueError as e:
    print(e)

Cannot use offset without limit