# fastduck
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
`fastduck` provides some development experience improvements for the
standard `duckdb` python API.
## Install
``` sh
pip install fastduck
```
## How to use
~~import fastduck as fuck~~
``` python
from fastduck import database
```
``` python
db = database('../data/chinook.duckdb')
db
```
DuckDBPyConnection (chinook_main)
``` python
dt = db.t
dt
```
(chinook_main) Tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track, todos
You can use this to grab a single table…
``` python
artist = dt.Artist
artist
```
#### DuckDBPyRelation BASE TABLE chinook.main.Artist
| ArtistId | Name |
|:---------|:----------------------|
| 1 | AC/DC |
| 2 | Accept |
| 3 | Aerosmith |
| … | … |
| 274 | Nash Ensemble |
| 275 | Philip Glass Ensemble |
275 rows x 2 cols
``` python
customer = dt['Customer']
customer
```
#### DuckDBPyRelation BASE TABLE chinook.main.Customer
| CustomerId | FirstName | LastName | Company | Address | City | State | Country | PostalCode | Phone | Fax | Email | SupportRepId |
|:-----------|:----------|:-----------|:-------------------------------------------------|:--------------------------------|:--------------------|:------|:--------|:-----------|:-------------------|:-------------------|:-------------------------|:-------------|
| 1 | Luís | Gonçalves | Embraer - Empresa Brasileira de Aeronáutica S.A. | Av. Brigadeiro Faria Lima, 2170 | São José dos Campos | SP | Brazil | 12227-000 | +55 (12) 3923-5555 | +55 (12) 3923-5566 | luisg@embraer.com.br | 3 |
| 2 | Leonie | Köhler | | Theodor-Heuss-Straße 34 | Stuttgart | | Germany | 70174 | +49 0711 2842222 | | leonekohler@surfeu.de | 5 |
| 3 | François | Tremblay | | 1498 rue Bélanger | Montréal | QC | Canada | H2G 1A7 | +1 (514) 721-4711 | | ftremblay@gmail.com | 3 |
| … | … | … | … | … | … | … | … | … | … | … | … | … |
| 58 | Manoj | Pareek | | 12,Community Centre | Delhi | | India | 110017 | +91 0124 39883988 | | manoj.pareek@rediff.com | 3 |
| 59 | Puja | Srivastava | | 3,Raj Bhavan Road | Bangalore | | India | 560001 | +91 080 22289999 | | puja_srivastava@yahoo.in | 3 |
59 rows x 13 cols
… or multiple tables at once:
``` python
dt['Artist', 'Album', 'Genre']
```
[DuckDBPyRelation BASE TABLE chinook.main.Artist
,
DuckDBPyRelation BASE TABLE chinook.main.Album
,
DuckDBPyRelation BASE TABLE chinook.main.Genre
]
It also provides auto-complete in Jupyter, IPython and nearly any other
interactive Python environment:
<img src="/nbs/images/autocomplete.png" width="400"
alt="Autocomplete in Jupyter" />
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, artist.columns
```
(chinook.main.Artist Columns: ArtistId, Name, ['ArtistId', 'Name'])
Auto-complete works for columns too:
<img src="/nbs/images/columns_complete.png" width="300"
alt="Columns autocomplete in Jupyter" />
The tables and views of a database got some interesting new attributes….
``` python
artist.meta
```
{'base': DuckDBPyConnection (chinook_main),
'catalog': 'chinook',
'schema': 'main',
'name': 'Artist',
'type': 'BASE TABLE',
'comment': None,
'shape': (275, 2)}
``` python
artist.model
```
[{'name': 'ArtistId',
'type': 'INTEGER',
'nullable': False,
'default': None,
'pk': True},
{'name': 'Name',
'type': 'VARCHAR',
'nullable': True,
'default': None,
'pk': False}]
``` python
artist.cls, type(artist.cls)
```
(fastduck.core.Artist, type)
`duckdb` replacement scans keep working and are wonderful for usage in
SQL statements:
``` python
db.sql("select * from artist where artist.Name like 'AC/%'")
```
#### DuckDBPyRelation
| ArtistId | Name |
|---------:|:------|
| 1 | AC/DC |
1 rows x 2 cols
You can view the results of a query as records
``` python
db.sql("select * from artist where artist.Name like 'AC/%'").to_recs()
```
[{'ArtistId': 1, 'Name': 'AC/DC'}]
or as a list of lists
``` python
db.sql("select * from artist where artist.Name like 'AC/%'").to_list()
```
[[1, 'AC/DC']]
And you there is also an alias for `sql` with `to_recs` simply called
`q`
``` python
db.q("select * from artist where artist.Name like 'AC/%'")
```
[{'ArtistId': 1, 'Name': 'AC/DC'}]
#### Dataclass support
As we briefly saw, a `dataclass` type with the names, types and defaults
of the table is added to the Relation:
``` python
abm = db.t.Album
art = db.t.Artist
acca_sql = f"""
select abm.*
from abm join art using (ArtistID)
where art.Name like 'AC/%'
"""
acca_dacca = db.q(acca_sql)
acca_dacca
```
[{'AlbumId': 1,
'Title': 'For Those About To Rock We Salute You',
'ArtistId': 1},
{'AlbumId': 4, 'Title': 'Let There Be Rock', 'ArtistId': 1}]
``` python
let_b_rock_obj = abm.cls(**acca_dacca[-1])
let_b_rock_obj
```
Album(AlbumId=4, Title='Let There Be Rock', 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
from fastcore.xtras import hl_md, dataclass_src
src = dataclass_src(db.t.Album.cls)
hl_md(src, 'python')
```
``` python
@dataclass
class Album:
AlbumId: int32 = None
Title: str = None
ArtistId: int32 = None
```
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"description": "# fastduck\n\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n`fastduck` provides some development experience improvements for the\nstandard `duckdb` python API.\n\n## Install\n\n``` sh\npip install fastduck\n```\n\n## How to use\n\n~~import fastduck as fuck~~\n\n``` python\nfrom fastduck import database\n```\n\n``` python\ndb = database('../data/chinook.duckdb')\ndb\n```\n\n DuckDBPyConnection (chinook_main)\n\n``` python\ndt = db.t\ndt\n```\n\n (chinook_main) Tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track, todos\n\nYou can use this to grab a single table\u2026\n\n``` python\nartist = dt.Artist\nartist\n```\n\n#### DuckDBPyRelation BASE TABLE chinook.main.Artist\n\n| ArtistId | Name |\n|:---------|:----------------------|\n| 1 | AC/DC |\n| 2 | Accept |\n| 3 | Aerosmith |\n| \u2026 | \u2026 |\n| 274 | Nash Ensemble |\n| 275 | Philip Glass Ensemble |\n\n275 rows x 2 cols\n\n``` python\ncustomer = dt['Customer']\ncustomer\n```\n\n#### DuckDBPyRelation BASE TABLE chinook.main.Customer\n\n| CustomerId | FirstName | LastName | Company | Address | City | State | Country | PostalCode | Phone | Fax | Email | SupportRepId |\n|:-----------|:----------|:-----------|:-------------------------------------------------|:--------------------------------|:--------------------|:------|:--------|:-----------|:-------------------|:-------------------|:-------------------------|:-------------|\n| 1 | Lu\u00eds | Gon\u00e7alves | Embraer - Empresa Brasileira de Aeron\u00e1utica S.A. | Av. Brigadeiro Faria Lima, 2170 | S\u00e3o Jos\u00e9 dos Campos | SP | Brazil | 12227-000 | +55 (12) 3923-5555 | +55 (12) 3923-5566 | luisg@embraer.com.br | 3 |\n| 2 | Leonie | K\u00f6hler | | Theodor-Heuss-Stra\u00dfe 34 | Stuttgart | | Germany | 70174 | +49 0711 2842222 | | leonekohler@surfeu.de | 5 |\n| 3 | Fran\u00e7ois | Tremblay | | 1498 rue B\u00e9langer | Montr\u00e9al | QC | Canada | H2G 1A7 | +1 (514) 721-4711 | | ftremblay@gmail.com | 3 |\n| \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 | \u2026 |\n| 58 | Manoj | Pareek | | 12,Community Centre | Delhi | | India | 110017 | +91 0124 39883988 | | manoj.pareek@rediff.com | 3 |\n| 59 | Puja | Srivastava | | 3,Raj Bhavan Road | Bangalore | | India | 560001 | +91 080 22289999 | | puja_srivastava@yahoo.in | 3 |\n\n59 rows x 13 cols\n\n\u2026 or multiple tables at once:\n\n``` python\ndt['Artist', 'Album', 'Genre']\n```\n\n [DuckDBPyRelation BASE TABLE chinook.main.Artist \n ,\n DuckDBPyRelation BASE TABLE chinook.main.Album \n ,\n DuckDBPyRelation BASE TABLE chinook.main.Genre \n ]\n\nIt also provides auto-complete in Jupyter, IPython and nearly any other\ninteractive Python environment:\n\n<img src=\"/nbs/images/autocomplete.png\" width=\"400\"\nalt=\"Autocomplete in Jupyter\" />\n\nYou can check if a table is in the database already:\n\n``` python\n'Artist' in dt\n```\n\n True\n\nColumn work in a similar way to tables, using the `c` property:\n\n``` python\nac = artist.c\nac, artist.columns\n```\n\n (chinook.main.Artist Columns: ArtistId, Name, ['ArtistId', 'Name'])\n\nAuto-complete works for columns too:\n\n<img src=\"/nbs/images/columns_complete.png\" width=\"300\"\nalt=\"Columns autocomplete in Jupyter\" />\n\nThe tables and views of a database got some interesting new attributes\u2026.\n\n``` python\nartist.meta\n```\n\n {'base': DuckDBPyConnection (chinook_main),\n 'catalog': 'chinook',\n 'schema': 'main',\n 'name': 'Artist',\n 'type': 'BASE TABLE',\n 'comment': None,\n 'shape': (275, 2)}\n\n``` python\nartist.model\n```\n\n [{'name': 'ArtistId',\n 'type': 'INTEGER',\n 'nullable': False,\n 'default': None,\n 'pk': True},\n {'name': 'Name',\n 'type': 'VARCHAR',\n 'nullable': True,\n 'default': None,\n 'pk': False}]\n\n``` python\nartist.cls, type(artist.cls)\n```\n\n (fastduck.core.Artist, type)\n\n`duckdb` replacement scans keep working and are wonderful for usage in\nSQL statements:\n\n``` python\ndb.sql(\"select * from artist where artist.Name like 'AC/%'\")\n```\n\n#### DuckDBPyRelation\n\n| ArtistId | Name |\n|---------:|:------|\n| 1 | AC/DC |\n\n1 rows x 2 cols\n\nYou can view the results of a query as records\n\n``` python\ndb.sql(\"select * from artist where artist.Name like 'AC/%'\").to_recs()\n```\n\n [{'ArtistId': 1, 'Name': 'AC/DC'}]\n\nor as a list of lists\n\n``` python\ndb.sql(\"select * from artist where artist.Name like 'AC/%'\").to_list()\n```\n\n [[1, 'AC/DC']]\n\nAnd you there is also an alias for `sql` with `to_recs` simply called\n`q`\n\n``` python\ndb.q(\"select * from artist where artist.Name like 'AC/%'\")\n```\n\n [{'ArtistId': 1, 'Name': 'AC/DC'}]\n\n#### Dataclass support\n\nAs we briefly saw, a `dataclass` type with the names, types and defaults\nof the table is added to the Relation:\n\n``` python\nabm = db.t.Album\nart = db.t.Artist\nacca_sql = f\"\"\"\nselect abm.* \nfrom abm join art using (ArtistID)\nwhere art.Name like 'AC/%'\n\"\"\"\nacca_dacca = db.q(acca_sql)\nacca_dacca\n```\n\n [{'AlbumId': 1,\n 'Title': 'For Those About To Rock We Salute You',\n 'ArtistId': 1},\n {'AlbumId': 4, 'Title': 'Let There Be Rock', 'ArtistId': 1}]\n\n``` python\nlet_b_rock_obj = abm.cls(**acca_dacca[-1])\nlet_b_rock_obj\n```\n\n Album(AlbumId=4, Title='Let There Be Rock', ArtistId=1)\n\nYou can get the definition of the dataclass using fastcore\u2019s\n`dataclass_src` \u2013 everything is treated as nullable, in order to handle\nauto-generated database values:\n\n``` python\nfrom fastcore.xtras import hl_md, dataclass_src\n\nsrc = dataclass_src(db.t.Album.cls)\nhl_md(src, 'python')\n```\n\n``` python\n@dataclass\nclass Album:\n AlbumId: int32 = None\n Title: str = None\n ArtistId: int32 = None\n```\n",
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