a db package that doesn't suck
Project description
What is it?
db.py is an easier way to interact with your databases. It makes it easier to explore tables, columns, views, etc. It puts the emphasis on user interaction, information display, and providing easy to use helper functions.
db.py uses `pandas <http://pandas.pydata.org/>`__ to manage data, so if you’re already using pandas, db.py should feel pretty natural. It’s also fully compatible with the IPython Notebook, so not only is db.py extremely functional, it’s also pretty.
Execute queries
>>> db.query_from_file("myscript.sql")
_id datetime user_id n
0 1290000 10/Jun/2014:18:21:27 +0000 0000015b37cd0964 1
1 9120009 23/Jun/2014:02:11:21 +0000 00006e01a6419822 1
2 1683874 23/Jun/2014:02:11:48 +0000 00006e01a6419822 2
3 2562153 23/Jun/2014:02:12:57 +0000 00006e01a6419822 3
4 393019 14/Jun/2014:16:05:18 +0000 000099d569e3a216 1
5 3542568 14/Jun/2014:16:06:02 +0000 000099d569e3a216 2
Fully compatible with predictive type
>>> db.tables.
db.tables.Album db.tables.Customer db.tables.Genre db.tables.InvoiceLine db.tables.Playlist db.tables.Track
db.tables.Artist db.tables.Employee db.tables.Invoice db.tables.MediaType db.tables.PlaylistTrack db.tables.tables
Friendly displays
>>> db.tables.Track
+------------------------------+
| Track |
+--------------+---------------+
| Column | Type |
+--------------+---------------+
| TrackId | INTEGER |
| Name | NVARCHAR(200) |
| AlbumId | INTEGER |
| MediaTypeId | INTEGER |
| GenreId | INTEGER |
| Composer | NVARCHAR(220) |
| Milliseconds | INTEGER |
| Bytes | INTEGER |
| UnitPrice | NUMERIC(10,2) |
+--------------+---------------+
Directly integrated with pandas
>>> db.tables.Track.head()
TrackId Name AlbumId MediaTypeId \
0 1 For Those About To Rock (We Salute You) 1 1
1 2 Balls to the Wall 2 2
2 3 Fast As a Shark 3 2
3 4 Restless and Wild 3 2
4 5 Princess of the Dawn 3 2
5 6 Put The Finger On You 1 1
GenreId Composer Milliseconds \
0 1 Angus Young, Malcolm Young, Brian Johnson 343719
1 1 None 342562
2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Ho... 230619
3 1 F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. D... 252051
4 1 Deaffy & R.A. Smith-Diesel 375418
5 1 Angus Young, Malcolm Young, Brian Johnson 205662
Bytes UnitPrice
0 11170334 0.99
1 5510424 0.99
2 3990994 0.99
3 4331779 0.99
4 6290521 0.99
5 6713451 0.99
Search your schema
>>> db.find_column("*Id*")
+---------------+---------------+---------+
| Table | Column Name | Type |
+---------------+---------------+---------+
| Album | AlbumId | INTEGER |
| Album | ArtistId | INTEGER |
| Artist | ArtistId | INTEGER |
| Customer | SupportRepId | INTEGER |
| Customer | CustomerId | INTEGER |
| Employee | EmployeeId | INTEGER |
| Genre | GenreId | INTEGER |
| Invoice | InvoiceId | INTEGER |
| Invoice | CustomerId | INTEGER |
| InvoiceLine | InvoiceId | INTEGER |
| InvoiceLine | TrackId | INTEGER |
| InvoiceLine | InvoiceLineId | INTEGER |
| MediaType | MediaTypeId | INTEGER |
| Playlist | PlaylistId | INTEGER |
| PlaylistTrack | TrackId | INTEGER |
| PlaylistTrack | PlaylistId | INTEGER |
| Track | MediaTypeId | INTEGER |
| Track | TrackId | INTEGER |
| Track | AlbumId | INTEGER |
| Track | GenreId | INTEGER |
+---------------+---------------+---------+
Quickstart
Installation
$ pip install db.py
Demo
>>> from db import DemoDB # or connect to your own using DB. see below
>>> db = DemoDB() # comes from: http://chinookdatabase.codeplex.com/
>>> db.tables
+---------------+----------------------------------------------------------------------------------+
| Table | Columns |
+---------------+----------------------------------------------------------------------------------+
| Album | AlbumId, Title, ArtistId |
| Artist | ArtistId, Name |
| Customer | CustomerId, FirstName, LastName, Company, Address, City, State, Country, PostalC |
| | ode, Phone, Fax, Email, SupportRepId |
| Employee | EmployeeId, LastName, FirstName, Title, ReportsTo, BirthDate, HireDate, Address, |
| | City, State, Country, PostalCode, Phone, Fax, Email |
| Genre | GenreId, Name |
| Invoice | InvoiceId, CustomerId, InvoiceDate, BillingAddress, BillingCity, BillingState, B |
| | illingCountry, BillingPostalCode, Total |
| InvoiceLine | InvoiceLineId, InvoiceId, TrackId, UnitPrice, Quantity |
| MediaType | MediaTypeId, Name |
| Playlist | PlaylistId, Name |
| PlaylistTrack | PlaylistId, TrackId |
| Track | TrackId, Name, AlbumId, MediaTypeId, GenreId, Composer, Milliseconds, Bytes, Uni |
| | tPrice |
+---------------+----------------------------------------------------------------------------------+
>>> db.tables.Customer
+-----------------------------+
| Customer |
+--------------+--------------+
| Column | Type |
+--------------+--------------+
| CustomerId | INTEGER |
| FirstName | NVARCHAR(40) |
| LastName | NVARCHAR(20) |
| Company | NVARCHAR(80) |
| Address | NVARCHAR(70) |
| City | NVARCHAR(40) |
| State | NVARCHAR(40) |
| Country | NVARCHAR(40) |
| PostalCode | NVARCHAR(10) |
| Phone | NVARCHAR(24) |
| Fax | NVARCHAR(24) |
| Email | NVARCHAR(60) |
| SupportRepId | INTEGER |
+--------------+--------------+
>>> db.tables.Customer.sample()
CustomerId FirstName LastName \
0 4 Bjørn Hansen
1 26 Richard Cunningham
2 1 Luís Gonçalves
3 21 Kathy Chase
4 6 Helena Holý
5 14 Mark Philips
6 49 Stanisław Wójcik
7 19 Tim Goyer
8 45 Ladislav Kovács
9 8 Daan Peeters
Company \
0 None
1 None
2 Embraer - Empresa Brasileira de Aeronáutica S.A.
3 None
4 None
5 Telus
6 None
7 Apple Inc.
8 None
9 None
Address City State Country \
0 Ullevålsveien 14 Oslo None Norway
1 2211 W Berry Street Fort Worth TX USA
2 Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil
3 801 W 4th Street Reno NV USA
4 Rilská 3174/6 Prague None Czech Republic
5 8210 111 ST NW Edmonton AB Canada
6 Ordynacka 10 Warsaw None Poland
7 1 Infinite Loop Cupertino CA USA
8 Erzsébet krt. 58. Budapest None Hungary
9 Grétrystraat 63 Brussels None Belgium
PostalCode Phone Fax \
0 0171 +47 22 44 22 22 None
1 76110 +1 (817) 924-7272 None
2 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566
3 89503 +1 (775) 223-7665 None
4 14300 +420 2 4177 0449 None
5 T6G 2C7 +1 (780) 434-4554 +1 (780) 434-5565
6 00-358 +48 22 828 37 39 None
7 95014 +1 (408) 996-1010 +1 (408) 996-1011
8 H-1073 None None
9 1000 +32 02 219 03 03 None
Email SupportRepId
0 bjorn.hansen@yahoo.no 4
1 ricunningham@hotmail.com 4
2 luisg@embraer.com.br 3
3 kachase@hotmail.com 5
4 hholy@gmail.com 5
5 mphilips12@shaw.ca 5
6 stanisław.wójcik@wp.pl 4
7 tgoyer@apple.com 3
8 ladislav_kovacs@apple.hu 3
9 daan_peeters@apple.be 4
>>> db.find_column("*Name*")
+-----------+-------------+---------------+
| Table | Column Name | Type |
+-----------+-------------+---------------+
| Artist | Name | NVARCHAR(120) |
| Customer | FirstName | NVARCHAR(40) |
| Customer | LastName | NVARCHAR(20) |
| Employee | FirstName | NVARCHAR(20) |
| Employee | LastName | NVARCHAR(20) |
| Genre | Name | NVARCHAR(120) |
| MediaType | Name | NVARCHAR(120) |
| Playlist | Name | NVARCHAR(120) |
| Track | Name | NVARCHAR(200) |
+-----------+-------------+---------------+
>>> db.find_table("A*")
+--------+--------------------------+
| Table | Columns |
+--------+--------------------------+
| Album | AlbumId, Title, ArtistId |
| Artist | ArtistId, Name |
+--------+--------------------------+
>>> db.query("select * from Artist limit 10;")
ArtistId Name
0 1 AC/DC
1 2 Accept
2 3 Aerosmith
3 4 Alanis Morissette
4 5 Alice In Chains
5 6 Antônio Carlos Jobim
6 7 Apocalyptica
7 8 Audioslave
8 9 BackBeat
9 10 Billy Cobham
How To
Connecting to a Database
The DB() object
Arguments
username: your username
password: your password
hostname: hostname of the database (i.e. localhost, dw.mardukas.com, ec2-54-191-289-254.us-west-2.compute.amazonaws.com)
port: port the database is running on (i.e. 5432)
dbname: name of the database (i.e. hanksdb)
filename: path to sqlite database (i.e. baseball-archive-2012.sqlite, employees.db)
dbtype: type of database you’re connecting to (postgres, mysql, sqlite, redshfit)
profile: name of the profile you want to use to connect. using this negates the need to specify any other arguments
exclude_system_tables: whether or not to load schema information for internal tables. for example, postgres has a bunch of tables prefixed with pg_ that you probably don’t actually care about. on the other had if you’re administrating a database, you might want to query these tables
>>> from db import DB
>>> db = DB(username="greg", password="secret", hostname="localhost",
dbtype="postgres")
Saving a profile
>>> from db import DB
>>> db = DB(username="greg", password="secret", hostname="localhost",
dbtype="postgres")
>>> db.save_credentials() # this will save to "default"
>>> db.save_credentials(profile="local_pg")
Connecting from a profile
>>> from db import DB
>>> db = DB() # this loads "default" profile
>>> db = DB(profile="local_pg")
Executing Queries
From a string
>>> df1 = db.query("select * from Artist;")
>>> df2 = db.query("select * from Album;")
From a file
>>> db.query_from_file("myscript.sql")
>>> df = db.query_from_file("myscript.sql")
Searching for Tables and Columns
Tables
>>> db.find_table("A*")
+--------+--------------------------+
| Table | Columns |
+--------+--------------------------+
| Album | AlbumId, Title, ArtistId |
| Artist | ArtistId, Name |
+--------+--------------------------+
>>> results = db.find_table("tmp*") # returns all tables prefixed w/ tmp
>>> results = db.find_table("prod_*") # returns all tables prefixed w/ prod_
>>> results = db.find_table("*Invoice*") # returns all tables containing trans
>>> results = db.find_table("*") # returns everything
Columns
>>> db.find_column("*Id*")
+---------------+---------------+---------+
| Table | Column Name | Type |
+---------------+---------------+---------+
| Album | AlbumId | INTEGER |
| Album | ArtistId | INTEGER |
| Artist | ArtistId | INTEGER |
| Customer | SupportRepId | INTEGER |
| Customer | CustomerId | INTEGER |
| Employee | EmployeeId | INTEGER |
| Genre | GenreId | INTEGER |
| Invoice | InvoiceId | INTEGER |
| Invoice | CustomerId | INTEGER |
| InvoiceLine | InvoiceId | INTEGER |
| InvoiceLine | TrackId | INTEGER |
| InvoiceLine | InvoiceLineId | INTEGER |
| MediaType | MediaTypeId | INTEGER |
| Playlist | PlaylistId | INTEGER |
| PlaylistTrack | TrackId | INTEGER |
| PlaylistTrack | PlaylistId | INTEGER |
| Track | MediaTypeId | INTEGER |
| Track | TrackId | INTEGER |
| Track | AlbumId | INTEGER |
| Track | GenreId | INTEGER |
+---------------+---------------+---------+
>>> results = db.find_column("tmp*") # returns all columns prefixed w/ tmp
>>> results = db.find_column("*trans*") # returns all columns containing trans
>>> results = db.find_column("*trans*", datatype="varchar") # returns all columns containing trans that are varchars
>>> results = db.find_column("*trans*", datatype=["varchar", "float8"]) # returns all columns that are varchars or float8
>>> results = db.find_column("*") # returns everything
TODO
[x] Switch to newer version of pandas sql api
[ ] Add database support
[x] postgres
[x] sqlite
[x] redshift
[x] mysql
[ ] mssql (going to be a little trickier since i don’t have one)
[x] publish examples to nbviewer
[x] improve documentation and readme
[x] add sample database to distrobution
[ ] push to Redshift
[ ] “joins to” for columns
[x] postgres
[x] sqlite
[x] redshift
[x] mysql
[ ] mssql
[ ] patsy formulas
[x] profile w/ limit
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