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adamgreenhall / df2json.py
Last active October 12, 2019 21:13 — forked from ameliagreenhall/df2json.py
Convert pandas.DataFrame to JSON (and optionally write the JSON blob to a file).
"""
tiny script to convert a pandas data frame into a JSON object
"""
import json as json
def df_to_json(df, filename=''):
x = df.reset_index().T.to_dict().values()
if filename:
with open(filename, 'w+') as f: f.write(json.dumps(x))
@adamgreenhall
adamgreenhall / pandas-reg.ipynb
Created November 8, 2012 22:16
pandas regularization
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@adamgreenhall
adamgreenhall / stocks-bonds.ipynb
Created November 5, 2012 01:02
testing pandas yahoo financial data fetcher
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@adamgreenhall
adamgreenhall / median-401k-balances.csv
Created October 27, 2012 04:30
retirement savings data
State Growth 2008 2009 2010 2011
UT 26% 15366.84 10640.13 14512.30 19384.97
DE 38% 26968.08 19269.31 24448.17 37208.37
RI 14% 24220.50 17278.61 22368.74 27630.56
GA 30% 18642.63 13752.71 17506.62 24232.52
AZ 36% 13217.43 8984.05 12118.08 17951.43
AR 21% 13600.70 11097.47 12670.71 16436.84
AL 14% 14941.88 11072.41 13428.64 17072.95
AK 3% 16175.87 12507.99 16233.93 16622.15
OR 37% 23216.79 17183.07 20924.93 31754.11
@adamgreenhall
adamgreenhall / analogue_distances.json
Created October 16, 2012 22:32
analogues display test
[
{
"color": "#1f77b4",
"key": "2011-01-01",
"values": [
[
1210291200,
0.0004590374261905092
],
[
@adamgreenhall
adamgreenhall / generators.csv
Created October 13, 2012 00:36
We can't make this file beautiful and searchable because it's too large.
SEQGEN09,PSTATABB,PNAME,ORISPL,GENID,NUMBLR,GENSTAT,PRMVR,FUELG1,NAMEPCAP,CFACT,GENNTAN,GENNTOZ,GENERSRC,GENYRONL
1.0,AK,Alakanuk,57053,UNIT1,0.0,OP,IC,JF,0.40000000000000002,0.0,0.0,0.0,,2008.0
2.0,AK,Alakanuk,57053,UNIT2,0.0,OP,IC,JF,0.5,0.0,0.0,0.0,,1999.0
3.0,AK,Alakanuk,57053,UNIT3,0.0,OP,IC,JF,0.40000000000000002,0.0,0.0,0.0,,1993.0
4.0,AK,Anchorage 1,75,D1,0.0,OP,IC,DFO,1.1000000000000001,0.0,0.0,0.0,,1956.0
5.0,AK,Anchorage 1,75,D2,0.0,OP,IC,DFO,1.1000000000000001,0.0,0.0,0.0,,1947.0
6.0,AK,Anchorage 1,75,1,0.0,OP,GT,NG,12.5,0.0,0.0,0.0,,1962.0
7.0,AK,Anchorage 1,75,2,0.0,OP,GT,NG,12.5,0.0,0.0,0.0,,1962.0
8.0,AK,Anchorage 1,75,3R,0.0,OP,GT,NG,48.899999999999999,0.0,0.0,0.0,,2007.0
9.0,AK,Anchorage 1,75,4,0.0,OP,GT,NG,27.0,0.0,0.0,0.0,,1972.0
@adamgreenhall
adamgreenhall / inheritance_test.py
Created October 8, 2012 21:35
memory leaks in coopr sets
import objgraph
class BaseClass(object):
def some_method(args): print args
class DerivedClass(BaseClass):
def __init__(self, *args):
print 'new derived class'
self.args = args
@adamgreenhall
adamgreenhall / gist:3802678
Created September 29, 2012 00:03
[{"values": [{"y": 0, "x": "January"}, {"y": 0, "x": "February"}, {"y": 0, "x": "March"}, {"y": 0, "x": "April"}, {"y": 6, "x": "May"}, {"y": 5, "x": "June"}, {"y": 7, "x": "July"}, {"y": 5, "x": "August"}, {"y": 2, "x": "September"}, {"y": 4, "x": "October"}, {"y": 2, "x": "November"}, {"y": 7, "x": "December"}], "key": "2007"}, {"values": [{"y": 2, "x": "January"}, {"y": 3, "x": "February"}, {"y": 9, "x": "March"}, {"y": 5, "x": "April"}, {"y": 7, "x": "May"}, {"y": 11, "x": "June"}, {"y": 10, "x": "July"}, {"y": 3, "x": "August"}, {"y": 4, "x": "September"}, {"y": 3, "x": "October"}, {"y": 8, "x": "November"}, {"y": 1, "x": "December"}], "key": "2008"}, {"values": [{"y": 3, "x": "January"}, {"y": 3, "x": "February"}, {"y": 7, "x": "March"}, {"y": 5, "x": "April"}, {"y": 1, "x": "May"}, {"y": 1, "x": "June"}, {"y": 6, "x": "July"}, {"y": 8, "x": "August"}, {"y": 6, "x": "September"}, {"y": 2, "x": "October"}, {"y": 3, "x": "November"}, {"y": 8, "x": "December"}], "key": "2009"}, {"values": [{"y": 4, "x": "J
@adamgreenhall
adamgreenhall / index.html
Created August 8, 2012 17:50 — forked from mbostock/.block
D3 and Custom Data Attributes
<!DOCTYPE html>
<html>
<head>
<script type="text/javascript" src="http://mbostock.github.com/d3/d3.js?2.4.6"></script>
</head>
<body>
<ul id="list">
<li data-username="shawnbot">Shawn Allen</li>
<li data-username="mbostock">Mike Bostock</li>
</ul>
@adamgreenhall
adamgreenhall / pw_test.py
Created June 1, 2012 21:55
getting started with pyomo Piecewise linearization model building
from coopr import pyomo
model =pyomo.ConcreteModel()
times=[1,2,3,4,5]
model.t =pyomo.Set(initialize=times)
model.x =pyomo.Var(model.t, bounds=(0,3))
model.y =pyomo.Var(model.t, bounds=(0,300))
x_discrete=[0,0.5,1,1.5,2,2.5,3]
xD=dict((t,x_discrete) for t in times)