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Catherine Devlin

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Databases taste better with Python.
Updated: 15 hours 20 min ago

%sql: To Pandas and Back

Tue, 2014-08-26 05:03

A Pandas DataFrame has a nice to_sql(table_name, sqlalchemy_engine) method that saves itself to a database.

The only trouble is that coming up with the SQLAlchemy Engine object is a little bit of a pain, and if you're using the IPython %sql magic, your %sql session already has an SQLAlchemy engine anyway. So I created a bogus PERSIST pseudo-SQL command that simply calls to_sql with the open database connection:

%sql PERSIST mydataframe

The result is that your data can make a very convenient round-trip from your database, to Pandas and whatever transformations you want to apply there, and back to your database:

In [1]: %load_ext sql

In [2]: %sql postgresql://@localhost/
Out[2]: u'Connected: @'

In [3]: ohio = %sql select * from cities_of_ohio;
246 rows affected.

In [4]: df = ohio.DataFrame()

In [5]: montgomery = df[df['county']=='Montgomery County']

In [6]: %sql PERSIST montgomery
Out[6]: u'Persisted montgomery'

In [7]: %sql SELECT * FROM montgomery
11 rows affected.
[(27L, u'Brookville', u'5,884', u'Montgomery County'),
(54L, u'Dayton', u'141,527', u'Montgomery County'),
(66L, u'Englewood', u'13,465', u'Montgomery County'),
(81L, u'Germantown', u'6,215', u'Montgomery County'),
(130L, u'Miamisburg', u'20,181', u'Montgomery County'),
(136L, u'Moraine', u'6,307', u'Montgomery County'),
(157L, u'Oakwood', u'9,202', u'Montgomery County'),
(180L, u'Riverside', u'25,201', u'Montgomery County'),
(210L, u'Trotwood', u'24,431', u'Montgomery County'),
(220L, u'Vandalia', u'15,246', u'Montgomery County'),
(230L, u'West Carrollton', u'13,143', u'Montgomery County')]

auto-generate SQLAlchemy models

Mon, 2014-07-28 15:30

PyOhio gave my lightning talk on ddlgenerator a warm reception, and Brandon Lorenz got me thinking, and PyOhio sprints filled my with py-drenaline, and now ddlgenerator can inspect your data and spit out SQLAlchemy model definitions for you:

$ cat merovingians.yaml
name: Clovis I
from: 486
to: 511
name: Childebert I
from: 511
to: 558
$ ddlgenerator --inserts sqlalchemy merovingians.yaml

from sqlalchemy import create_engine, Column, Integer, Table, Unicode
engine = create_engine(r'sqlite:///:memory:')
metadata = MetaData(bind=engine)

merovingians = Table('merovingians', metadata,
Column('name', Unicode(length=12), nullable=False),
Column('reign_from', Integer(), nullable=False),
Column('reign_to', Integer(), nullable=False),

conn = engine.connect()
inserter = merovingians.insert()
conn.execute(inserter, **{'name': 'Clovis I', 'reign_from': 486, 'reign_to': 511})
conn.execute(inserter, **{'name': 'Childebert I', 'reign_from': 511, 'reign_to': 558})

Brandon's working on a pull request to provide similar functionality for Django models!


Tue, 2014-07-01 10:37

Yesterday was my first day at 18F!

What is 18F? We're a small, little-known government organization that works outside the usual channels to accomplish special projects. It involves black outfits and a lot of martial arts.

Kidding! Sort of. 18F is a new agency within the GSA that does citizen-focused work for other parts of the U.S. Government, working small, quick projects to make information more accessible. We're using all the tricks: small teams, agile development, rapid iteration, open-source software, test-first, continuous integration. We do our work in the open.

Sure, this is old hat to you, faithful blog readers. But bringing it into government IT work is what makes it exciting. We're hoping that the techniques we use will ripple out beyond the immediate projects we work on, popularizing them throughout government IT and helping efficiency and responsiveness throughout. This is a chance to put all the techniques I've learned from you to work for all of us. Who wouldn't love to get paid to work for the common good?

Obviously, this is still my personal blog, so nothing I say about 18F counts as official information. Just take it as my usual enthusiastic babbling.


Fri, 2014-05-23 15:09

I've had it on github for a while, but I finally released ddlgenerator to PyPI.

I've been frustrated for years that there was no good open-source way to set up RDBMS tables from flat data files. Sure, you could import the data - after setting up the DDL by hand. ddlgenerator handles that; in fact, you can go from zero, setting up and populating a table in a single line. Nothing up my sleeve:

$ psql -c "SELECT * FROM knights"
ERROR: relation "knights" does not exist
LINE 1: SELECT * FROM knights
$ ddlgenerator --inserts postgresql knights.yaml | psql
$ psql -c "SELECT * FROM knights"
name | dob | kg | brave
Lancelot | 0471-01-09 00:00:00 | 82.0000 | t
Gawain | | 69.2000 | t
Robin | 0471-01-09 00:00:00 | | f
Reepacheep | | 0.0691 | t

This is a fairly complex tool so I'm sure you'll be using the bug tracker. But I hope you'll enjoy it nonetheless!


Wed, 2014-05-21 10:50

I went down a refactoring rabbit hole on ddl-generator and ended up pulling out the portion that pulls in data from various file formats. Perhaps it will be useful to others.

>>> from data_dispenser.sources import Source
>>> for row in Source('animals.csv'):
... print(row)
OrderedDict([('name', 'Alfred'), ('species', 'wart hog'), ('kg', '22'), ('notes', 'loves turnips')])
OrderedDict([('name', 'Gertrude'), ('species', 'polar bear'), ('kg', '312.7'), ('notes', 'deep thinker')])
OrderedDict([('name', 'Emily'), ('species', 'salamander'), ('kg', '0.3'), ('notes', '')])

Basically, I wanted a consistent way to consume rows of data - no matter where those rows come from. Right now, JSON, CSV, YAML, etc. all require separate libraries, each with its own API. This abstracts all that out, for reading purposes; now each data source is just a Source.

I'd love bug reports, and sample files to test against. And feel free to contribute patches! For example, it wouldn't be hard to add MS Excel as a data source.