# fastlite


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

`fastlite` provides some little quality-of-life improvements for
interactive use of the wonderful
[sqlite-utils](https://sqlite-utils.datasette.io/) library. It’s likely
to be particularly of interest to folks using Jupyter.

## Install

    pip install fastlite

## Overview

``` python
from fastlite import *
from fastcore.utils import *
from fastcore.net import urlsave
```

We demonstrate `fastlite`‘s features here using the ’chinook’ sample
database.

``` python
url = 'https://github.com/lerocha/chinook-database/raw/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite'
path = Path('chinook.sqlite')
if not path.exists(): urlsave(url, path)

db = database("chinook.sqlite")
```

Databases have a `t` property that lists all tables:

``` python
dt = db.t
dt
```

    Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track

You can use this to grab a single table…:

``` python
artist = dt.artists
artist
```

    <Table artists (does not exist yet)>

``` python
artist = dt.Artist
artist
```

    <Table Artist (ArtistId, Name)>

…or multiple tables at once:

``` python
dt['Artist','Album','Track','Genre','MediaType']
```

    [<Table Artist (ArtistId, Name)>,
     <Table Album (AlbumId, Title, ArtistId)>,
     <Table Track (TrackId, Name, AlbumId, MediaTypeId, GenreId, Composer, Milliseconds, Bytes, UnitPrice)>,
     <Table Genre (GenreId, Name)>,
     <Table MediaType (MediaTypeId, Name)>]

It also provides auto-complete in Jupyter, IPython, and nearly any other
interactive Python environment:

<img src="index_files/figure-commonmark/8905c5f8-1-image.png"
width="180" />

You can check if a table is in the database already:

``` python
'Artist' in dt
```

    True

Column work in a similar way to tables, using the `c` property:

``` python
ac = artist.c
ac
```

    ArtistId, Name

Auto-complete works for columns too:

<img src="index_files/figure-commonmark/50e8220b-1-image.png"
width="140" />

Columns, tables, and view stringify in a format suitable for including
in SQL statements. That means you can use auto-complete in f-strings.

``` python
qry = f"select * from {artist} where {ac.Name} like 'AC/%'"
print(qry)
```

    select * from "Artist" where "Artist"."Name" like 'AC/%'

You can view the results of a select query using `q`:

``` python
db.q(qry)
```

    [{'ArtistId': 1, 'Name': 'AC/DC'}]

Views can be accessed through the `v` property:

``` python
album = dt.Album

acca_sql = f"""select {album}.*
from {album} join {artist} using (ArtistId)
where {ac.Name} like 'AC/%'"""

db.create_view("AccaDaccaAlbums", acca_sql, replace=True)
acca_dacca = db.q(f"select * from {db.v.AccaDaccaAlbums}")
acca_dacca
```

    [{'AlbumId': 1,
      'Title': 'For Those About To Rock We Salute You',
      'ArtistId': 1},
     {'AlbumId': 4, 'Title': 'Let There Be Rock', 'ArtistId': 1}]

## Dataclass support

A `dataclass` type with the names, types, and defaults of the tables is
created using `dataclass()`:

``` python
album_dc = album.dataclass()
```

Let’s try it:

``` python
album_obj = album_dc(**acca_dacca[0])
album_obj
```

    Album(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1)

You can get the definition of the dataclass using fastcore’s
`dataclass_src` – everything is treated as nullable, in order to handle
auto-generated database values:

``` python
src = dataclass_src(album_dc)
hl_md(src, 'python')
```

``` python
@dataclass
class Album:
    AlbumId: int | None = None
    Title: str | None = None
    ArtistId: int | None = None
```

Because `dataclass()` is dynamic, you won’t get auto-complete in editors
like vscode – it’ll only work in dynamic environments like Jupyter and
IPython. For editor support, you can export the full set of dataclasses
to a module, which you can then import from:

``` python
create_mod(db, 'db_dc')
```

``` python
from db_dc import Track
Track()
```

    Track(TrackId=None, Name=None, AlbumId=None, MediaTypeId=None, GenreId=None, Composer=None, Milliseconds=None, Bytes=None, UnitPrice=None)

Indexing into a table does a query on primary key:

``` python
dt.Track[1]
```

    Track(TrackId=1, Name='For Those About To Rock (We Salute You)', AlbumId=1, MediaTypeId=1, GenreId=1, Composer='Angus Young, Malcolm Young, Brian Johnson', Milliseconds=343719, Bytes=11170334, UnitPrice=0.99)

There’s a shortcut to select from a table – just call it as a function.
If you’ve previously called `dataclass()`, returned iterms will be
constructed using that class by default. There’s lots of params you can
check out, such as `limit`:

``` python
album(limit=2)
```

    [Album(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1),
     Album(AlbumId=2, Title='Balls to the Wall', ArtistId=2)]

Pass a truthy value as `with_pk` and you’ll get tuples of primary keys
and records:

``` python
album(with_pk=1, limit=2)
```

    [(1,
      Album(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1)),
     (2, Album(AlbumId=2, Title='Balls to the Wall', ArtistId=2))]

Indexing also uses the dataclass by default:

``` python
album[5]
```

    Album(AlbumId=5, Title='Big Ones', ArtistId=3)

If you set `xtra` fields, then indexing is also filtered by those. As a
result, for instance in this case, nothing is returned since album 5 is
not created by artist 1:

``` python
album.xtra(ArtistId=1)

try: album[5]
except NotFoundError: print("Not found")
```

    Not found

The same filtering is done when using the table as a callable:

``` python
album()
```

    [Album(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1),
     Album(AlbumId=4, Title='Let There Be Rock', ArtistId=1)]

## Core design

The following methods accept `**kwargs`, passing them along to the first
`dict` param:

- `create`
- `transform`
- `transform_sql`
- `update`
- `insert`
- `upsert`
- `lookup`

We can access a table that doesn’t actually exist yet:

``` python
cats = dt.cats
cats
```

    <Table cats (does not exist yet)>

We can use keyword arguments to now create that table:

``` python
cats.create(id=int, name=str, weight=float, uid=int, pk='id')
hl_md(cats.schema, 'sql')
```

``` sql
CREATE TABLE [cats] (
   [id] INTEGER PRIMARY KEY,
   [name] TEXT,
   [weight] FLOAT,
   [uid] INTEGER
)
```

It we set `xtra` then the additional fields are used for `insert`,
`update`, and `delete`:

``` python
cats.xtra(uid=2)
cat = cats.insert(name='meow', weight=6)
```

The inserted row is returned, including the xtra ‘uid’ field.

``` python
cat
```

    {'id': 1, 'name': 'meow', 'weight': 6.0, 'uid': 2}

Using `**` in `update` here doesn’t actually achieve anything, since we
can just pass a `dict` directly – it’s just to show that it works:

``` python
cat['name'] = "moo"
cat['uid'] = 1
cats.update(**cat)
cats()
```

    [{'id': 1, 'name': 'moo', 'weight': 6.0, 'uid': 2}]

Attempts to update or insert with xtra fields are ignored.

An error is raised if there’s an attempt to update a record not matching
`xtra` fields:

``` python
cats.xtra(uid=1)
try: cats.update(**cat)
except NotFoundError: print("Not found")
```

    Not found

This all also works with dataclasses:

``` python
cats.xtra(uid=2)
cats.dataclass()
cat = cats[1]
cat
```

    Cats(id=1, name='moo', weight=6.0, uid=2)

``` python
cats.drop()
cats
```

    <Table cats (does not exist yet)>

Alternatively, you can create a table from a class. If it’s not already
a dataclass, it will be converted into one. In either case, the
dataclass will be created (or modified) so that `None` can be passed to
any field (this is needed to support fields such as automatic row ids).

``` python
class Cat: id:int; name:str; weight:float; uid:int
```

``` python
cats = db.create(Cat)
```

``` python
hl_md(cats.schema, 'sql')
```

``` sql
CREATE TABLE [cat] (
   [id] INTEGER PRIMARY KEY,
   [name] TEXT,
   [weight] FLOAT,
   [uid] INTEGER
)
```

``` python
cat = Cat(name='咪咪', weight=9)
cats.insert(cat)
```

    Cat(id=1, name='咪咪', weight=9.0, uid=None)

``` python
cats.drop()
```

## Manipulating data

We try to make the following methods as flexible as possible. Wherever
possible, they support Python dictionaries, dataclasses, and classes.

### .insert()

Creates a record. Returns an instance of the updated record.

Insert using a dictionary.

``` python
cats.insert({'name': 'Rex', 'weight': 12.2})
```

    Cat(id=1, name='Rex', weight=12.2, uid=UNSET)

Insert using a dataclass.

``` python
CatDC = cats.dataclass()
cats.insert(CatDC(name='Tom', weight=10.2))
```

    Cat(id=2, name='Tom', weight=10.2)

Insert using a standard Python class

``` python
cat = cats.insert(Cat(name='Jerry', weight=5.2))
```

### .update()

Updates a record using a Python dict, dataclass, or object, and returns
an instance of the updated record.

Updating from a Python dict:

``` python
cats.update(dict(id=cat.id, name='Jerry', weight=6.2))
```

    Cat(id=3, name='Jerry', weight=6.2)

Updating from a dataclass:

``` python
cats.update(CatDC(id=cat.id, name='Jerry', weight=6.3))
```

    Cat(id=3, name='Jerry', weight=6.3)

Updating using a class:

``` python
cats.update(Cat(id=cat.id, name='Jerry', weight=5.7))
```

    Cat(id=3, name='Jerry', weight=5.7)

### .delete()

Removing data is done by providing the primary key value of the record.

``` python
# Farewell Jerry!
cats.delete(cat.id)
```

    <Table cat (id, name, weight)>

### Multi-field primary keys

Pass a collection of strings to create a multi-field pk:

``` python
class PetFood: catid:int; food:str; qty:int
petfoods = db.create(PetFood, pk=['catid','food'])
print(petfoods.schema)
```

    CREATE TABLE [pet_food] (
       [catid] INTEGER,
       [food] TEXT,
       [qty] INTEGER,
       PRIMARY KEY ([catid], [food])
    )

You can index into these using multiple values:

``` python
pf = petfoods.insert(PetFood(1, 'tuna', 2))
petfoods[1,'tuna']
```

    PetFood(catid=1, food='tuna', qty=2)

Updates work in the usual way:

``` python
pf.qty=3
petfoods.update(pf)
```

    PetFood(catid=1, food='tuna', qty=3)

You can also use `upsert` to update if the key exists, or insert
otherwise:

``` python
pf.qty=1
petfoods.upsert(pf)
petfoods()
```

    [PetFood(catid=1, food='tuna', qty=1)]

``` python
pf.food='salmon'
petfoods.upsert(pf)
petfoods()
```

    [PetFood(catid=1, food='tuna', qty=1), PetFood(catid=1, food='salmon', qty=1)]

`delete` takes a tuple of keys:

``` python
petfoods.delete((1, 'tuna'))
petfoods()
```

    [PetFood(catid=1, food='salmon', qty=1)]

## Diagrams

If you have [graphviz](https://pypi.org/project/graphviz/) installed,
you can create database diagrams. Pass a subset of tables to just
diagram those. You can also adjust the size and aspect ratio.

``` python
diagram(db.t['Artist','Album','Track','Genre','MediaType'], size=8, ratio=0.4)
```

![](index_files/figure-commonmark/cell-50-output-1.svg)

### Importing CSV/TSV/etc

------------------------------------------------------------------------

<a
href="https://github.com/AnswerDotAI/fastlite/blob/main/fastlite/core.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>

### Database.import_file

``` python

def import_file(
    table_name, file, format:NoneType=None, pk:NoneType=None, alter:bool=False
):

```

*Import path or handle `file` to new table `table_name`*

You can pass a file name, string, bytes, or open file handle to
`import_file` to import a CSV:

``` python
db = Database(":memory:")
csv_data = """id,name,age
1,Alice,30
2,Bob,25
3,Charlie,35"""

table = db.import_file("people", csv_data)
table()
```

    [{'id': 1, 'name': 'Alice', 'age': 30},
     {'id': 2, 'name': 'Bob', 'age': 25},
     {'id': 3, 'name': 'Charlie', 'age': 35}]