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Query treasury.io through Python, returning Pandas DataFrames

Project Description
pytreasuryio
======
Access .. _treasury.io: http://treasury.io from Python.

This is a package consisting of a single, simple function for submitting ``SQL`` queries to .. _treasury.io: http://treasury.io from ``python``. While you could simply copy-and-paste the function from script-to-script, this makes it quicker and easier to get up and running!

It also has some helpers to make a Twitter bot from the treasury.io data.

Installation
--------
Install with pip.::

pip install treasuryio

Example
---------

Basic query
~~~~~~~~~
Send an SQL query and receive a pandas data frame.::

# Operating cash balances for May 22, 2013
import treasuryio
sql = 'SELECT * FROM "t1" WHERE "date" = \'2013-05-22\';'
treasuryio.query(sql)

Twitter bot
~~~~~~~~~
Write a ``~/.twitter.yml`` file.::

consumer_key: oeshaoduhsaousaoeuhts
consumer_secret: b233tsao-enuhsaoehsunoesudtuhoelaouhs2uo
access_token: 2349081293-astoehusatoehusaoeustahoeuhh2AOEUTAouhc
access_token_secret: 9023uonshesuaHONETuoeuoeouo0eOHNEuhOuoeu

Define a function that produces the text of the tweet, and decorate it with the
``@treasurio.tweet`` decorator.::

import treasuryio
import humanize
import math

MIL = 1e6

# Helpers to humanize numbers / dates
def human_number(num):
return humanize.intword(int(math.ceil(num))).lower()

def human_date(date):
return humanize.naturalday(date).title()

@treasuryio.tweet
def total_debt_tweet():
df = treasuryio.query('SELECT date, close_today FROM t3c WHERE (item LIKE \'%subject to limit%\' AND year = 2013 AND month >=1) ORDER BY date DESC')

# determine length of DataFrame
end = len(df)-1

# extract current amount and amount at the beginning of the year
current_amt = df['close_today'][0]*MIL
previous_amt = df['close_today'][end]*MIL

# calculate change
delta = abs(current_amt - previous_amt)

# generate word to represnet the direction of change
if current_amt > previous_amt:
change = "increased"
elif current_amt < previous_amt:
change = "decreased"

# humanize values
# Notice the included ``human_date`` and ``human_number`` functions which simplify these values for you
current_date = human_date(df['date'][0])
amt = human_number(current_amt)
delta = human_number(delta)
previous_date = human_date(df['date'][end])

# generate tweet
vals = (current_date, amt, change, previous_date, 'http://treasury.io')
return "As of %s, the US Gov is $%s in debt. This amount has %s since %s - %s" % vals

Then just run it.::

total_debt_tweet()

You can get fancy by switching the functions that you use.::

import treasuryio
import random

@treasurio.tweet
def tweet_a():
# ...

@treasurio.tweet
def tweet_b():
# ...

@treasurio.tweet
def tweet_c():
# ...

random.choice([tweet_a, tweet_b, tweet_c])()
Release History

Release History

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0.0.4

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