db.py is an easier way to interact with your databases

Related tags

Database Driversdb.py
Overview

db.py

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 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.

Blog Post

Databases Supported

  • PostgreSQL
  • MySQL
  • SQLite
  • Redshift
  • MS SQL Server
  • Oracle

db.py let's you...

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
+-------------------------------------------------------------+
|                            Album                            |
+----------+---------------+-----------------+----------------+
| Column   | Type          | Foreign Keys    | Reference Keys |
+----------+---------------+-----------------+----------------+
| AlbumId  | INTEGER       |                 | Track.AlbumId  |
| Title    | NVARCHAR(160) |                 |                |
| ArtistId | INTEGER       | Artist.ArtistId |                |
+----------+---------------+-----------------+----------------+

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

Create queries using Handlebars style templates

q = """
SELECT
    '{{ name }}' as table_name, sum(1) as cnt
FROM
    {{ name }}
GROUP BY
    table_name
"""
data = [
  {"name": "Album"},
  {"name": "Artist"},
  {"name": "Track"}
]
db.query(q, data=data)
  table_name   cnt
0      Album   347
1     Artist   275
2      Track  3503

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 |
+---------------+---------------+---------+

IPython Notebook friendly

Quickstart

Installation

db.py is on PyPi.

$ pip install db.py

The database libraries being used under the hood are optional dependencies (if you use mysql, you probably don't care about installing psycopg2). Based on the databases you're using, you'll need one (or many) of the following:

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         | Foreign Keys        | Reference Keys     |
+--------------+--------------+---------------------+--------------------+
| CustomerId   | INTEGER      |                     | Invoice.CustomerId |
| 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      | Employee.EmployeeId |                    |
+--------------+--------------+---------------------+--------------------+
>>> 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, redshift)
  • 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
  • limit: default number of records to return in a query. This is used by the DB.query method. You can override it by adding limit={X} to the query method, or by passing an argument to DB(). None indicates that there will be no limit (That's right, you'll be limitless. Bradley Cooper style.)
>>> 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")

List your profiles

>>> from db import list_profiles
>>> list_profiles()
{'demo': {u'dbname': None,
  u'dbtype': u'sqlite',
  u'filename': u'/Users/glamp/repos/yhat/opensource/db.py/db/data/chinook.sqlite',
  u'hostname': u'localhost',
  u'password': None,
  u'port': 5432,
  u'username': None},
 'muppets': {u'dbname': u'muppetdb',
  u'dbtype': u'postgres',
  u'filename': None,
  u'hostname': u'muppets.yhathq.com',
  u'password': None,
  u'port': 5432,
  u'username': u'kermit'}}

Remove a profile

>>> remove_profile('demo')

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("Name") # returns all columns named "Name"
+-----------+-------------+---------------+
| Table     | Column Name | Type          |
+-----------+-------------+---------------+
| Artist    |     Name    | NVARCHAR(120) |
| Genre     |     Name    | NVARCHAR(120) |
| MediaType |     Name    | NVARCHAR(120) |
| Playlist  |     Name    | NVARCHAR(120) |
| Track     |     Name    | NVARCHAR(200) |
+-----------+-------------+---------------+
>>> db.find_column("*Id") # returns all columns ending w/ 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 |
+---------------+---------------+---------+
>>> db.find_column("*Address*") # returns all columns containing Address
+----------+----------------+--------------+
| Table    |  Column Name   | Type         |
+----------+----------------+--------------+
| Customer |    Address     | NVARCHAR(70) |
| Employee |    Address     | NVARCHAR(70) |
| Invoice  | BillingAddress | NVARCHAR(70) |
+----------+----------------+--------------+
# returns all columns containing Address that are varchars
>>> db.find_column("*Address*", data_type="NVARCHAR(70)")
# returns all columns have an "e" and are NVARCHAR/INTEGERS
>>> db.find_column("*e*", data_type=["NVARCHAR(70)", "INTEGER"])

Tests

To run individual tests:

$ python -m unittest test_module.TestClass.test_method

To run all the tests:

$ python -m unittest discover <path_to_tests_folder> -v

Contributing

See either the TODO below or Adding a Database.

TODO

  • Switch to newer version of pandas sql api
  • Add database support
    • postgres
    • sqlite
    • redshift
    • mysql
    • mssql (going to be a little trickier since i don't have one)
  • publish examples to nbviewer
  • improve documentation and readme
  • add sample database to distrobution
  • push to Redshift
  • "joins to" for columns
    • postgres
    • sqlite
    • redshift
    • mysql
    • mssql
  • intelligent display of number/size returned in query
  • patsy formulas
  • profile w/ limit

image

Owner
yhat
yhat
GitHub Repository
Official Python low-level client for Elasticsearch

Python Elasticsearch Client Official low-level client for Elasticsearch. Its goal is to provide common ground for all Elasticsearch-related code in Py

elastic 3.8k Jan 01, 2023
Pony Object Relational Mapper

Downloads Pony Object-Relational Mapper Pony is an advanced object-relational mapper. The most interesting feature of Pony is its ability to write que

3.1k Jan 04, 2023
A simple wrapper to make a flat file drop in raplacement for mongodb out of TinyDB

Purpose A simple wrapper to make a drop in replacement for mongodb out of tinydb. This module is an attempt to add an interface familiar to those curr

180 Jan 01, 2023
Dinamopy is a python helper library for dynamodb

Dinamopy is a python helper library for dynamodb. You can define your access patterns in a json file and can use dynamic method names to make operations.

Rasim Andıran 2 Jul 18, 2022
Micro ODM for MongoDB

Beanie - is an asynchronous ODM for MongoDB, based on Motor and Pydantic. It uses an abstraction over Pydantic models and Motor collections to work wi

Roman 993 Jan 03, 2023
Asynchronous interface for peewee ORM powered by asyncio

peewee-async Asynchronous interface for peewee ORM powered by asyncio. Important notes Since version 0.6.0a only peewee 3.5+ is supported If you still

05Bit 666 Dec 30, 2022
asyncio compatible driver for elasticsearch

asyncio client library for elasticsearch aioes is a asyncio compatible library for working with Elasticsearch The project is abandoned aioes is not su

97 Sep 05, 2022
Find graph motifs using intuitive notation

d o t m o t i f Find graph motifs using intuitive notation DotMotif is a library that identifies subgraphs or motifs in a large graph. It looks like t

APL BRAIN 45 Jan 02, 2023
A tiny python web application based on Flask to set, get, expire, delete keys of Redis database easily with direct link at the browser.

First Redis Python (CRUD) A tiny python web application based on Flask to set, get, expire, delete keys of Redis database easily with direct link at t

Max Base 9 Dec 24, 2022
Logica is a logic programming language that compiles to StandardSQL and runs on Google BigQuery.

Logica: language of Big Data Logica is an open source declarative logic programming language for data manipulation. Logica is a successor to Yedalog,

Evgeny Skvortsov 1.5k Dec 30, 2022
dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.

dbd: database prototyping tool dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL d

Zdenek Svoboda 47 Dec 07, 2022
Py2neo is a comprehensive toolkit for working with Neo4j from within Python applications or from the command line.

Py2neo Py2neo is a client library and toolkit for working with Neo4j from within Python applications and from the command line. The library supports b

Nigel Small 1.2k Jan 02, 2023
edaSQL is a library to link SQL to Exploratory Data Analysis and further more in the Data Engineering.

edaSQL is a python library to bridge the SQL with Exploratory Data Analysis where you can connect to the Database and insert the queries. The query results can be passed to the EDA tool which can giv

Tamil Selvan 8 Dec 12, 2022
CouchDB client built on top of aiohttp (asyncio)

aiocouchdb source: https://github.com/aio-libs/aiocouchdb documentation: http://aiocouchdb.readthedocs.org/en/latest/ license: BSD CouchDB client buil

aio-libs 53 Apr 05, 2022
A simple python package that perform SQL Server Source Control and Auto Deployment.

deploydb Deploy your database objects automatically when the git branch is updated. Production-ready! ⚙️ Easy-to-use 🔨 Customizable 🔧 Installation I

Mert Güvençli 10 Dec 07, 2022
A Python DB-API and SQLAlchemy dialect to Google Spreasheets

Note: shillelagh is a drop-in replacement for gsheets-db-api, with many additional features. You should use it instead. If you're using SQLAlchemy all

Beto Dealmeida 185 Jan 01, 2023
pandas-gbq is a package providing an interface to the Google BigQuery API from pandas

pandas-gbq pandas-gbq is a package providing an interface to the Google BigQuery API from pandas Installation Install latest release version via conda

Google APIs 348 Jan 03, 2023
sync/async MongoDB ODM, yes.

μMongo: sync/async ODM μMongo is a Python MongoDB ODM. It inception comes from two needs: the lack of async ODM and the difficulty to do document (un)

Scille 428 Dec 29, 2022
Python PostgreSQL adapter to stream results of multi-statement queries without a server-side cursor

streampq Stream results of multi-statement PostgreSQL queries from Python without server-side cursors. Has benefits over some other Python PostgreSQL

Department for International Trade 6 Oct 31, 2022
PubMed Mapper: A Python library that map PubMed XML to Python object

pubmed-mapper: A Python Library that map PubMed XML to Python object 中文文档 1. Philosophy view UML Programmatically access PubMed article is a common ta

灵魂工具人 33 Dec 08, 2022