In [6]:
%matplotlib inline
import datetime
import pandas as pd
import sqlalchemy as sa
from sqlalchemy import *
import pandas as pd
import os
import numpy as np
import operator
import math
import random
from pylab import *
import json
import matplotlib
import matplotlib.pyplot as pyplt
Month accessors
In [3]:
##for accessing all months
months = ['01','02','03','04','05','06','07','08','09','10','11']
days = {'01':31,'02':28,'03':31,'04':30,'05':31,'06':30,'07':31,'08':31,'09':30,'10':31,'11':30}
Sensitive db info below!!!
In [7]:
##engine information
user_name = 'USERNAME'
pw='PASSWORD'
host = "db.wiki-energy.org"
port = "5432"
db = "postgres"
Function for getting the all data for group 1 houses for a certain month (ex: month="01" is jan). returns an array of dataframes with all data for 25 houses
In [8]:
def query_month(month):
# exectute query
query = """select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_"""+month
url = "postgresql"+"://"+user_name+":"+pw+"@"+host+":"+port+"/"+db
eng = create_engine(url)
eng.echo=True
eng_object=eng.execute(query)
# get data
df = pd.DataFrame.from_records(eng_object.fetchall())
df.columns = eng_object.keys()
# create a dic
dfs_dict = {}
dfs=df.groupby('dataid')
for df in dfs:
dfs_dict[df[0]]=df[1].drop('dataid',1)
return dfs_dict
In [11]:
#example, takes a couple of minutes to run. fetches january data.
dataframes = query_month("01")
2014-06-25 11:20:38,683 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-25 11:20:38,685 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-25 11:20:38,776 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-25 11:20:38,778 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-25 11:20:38,873 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-25 11:20:38,874 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-25 11:20:38,925 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-25 11:20:38,927 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-25 11:20:39,062 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-25 11:20:39,063 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-25 11:20:39,156 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
2014-06-25 11:20:39,158 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
Out[11]:
{936: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 59520 to 62495
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 1334: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 19475 to 29759
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 1697: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 35712 to 38691
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 1714: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 17856 to 20839
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 2156: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 50592 to 53567
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 2158: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 5952 to 8927
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
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drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
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lights_plugs1 2976 non-null values
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lights_plugs4 2976 non-null values
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microwave1 2976 non-null values
office1 2976 non-null values
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outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
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poollight1 2976 non-null values
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security1 2976 non-null values
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unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 2829: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 56544 to 59519
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
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drye1 2976 non-null values
dryg1 2976 non-null values
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garage1 2976 non-null values
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kitchenapp1 2976 non-null values
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lights_plugs1 2976 non-null values
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security1 2976 non-null values
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unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 4031: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 29760 to 32740
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
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freezer1 2976 non-null values
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garage1 2976 non-null values
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heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
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kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
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lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
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microwave1 2976 non-null values
office1 2976 non-null values
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outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
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unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 5395: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 53568 to 56543
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 5746: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 65472 to 68447
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 5785: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 8928 to 11903
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 5949: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 29930 to 35711
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 6673: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 68448 to 71424
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 6941: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 47616 to 50591
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 7731: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 2976 to 5951
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 7989: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 44640 to 47615
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 8197: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 11904 to 14883
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 8669: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 18343 to 23811
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9160: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 35948 to 41663
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9654: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 0 to 2975
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9737: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 23812 to 26794
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9923: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 41664 to 44639
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9932: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 62496 to 65471
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9934: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 12409 to 17855
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72), 9937: <class 'pandas.core.frame.DataFrame'>
Int64Index: 2976 entries, 70560 to 74399
Data columns (total 73 columns):
id 2976 non-null values
utc_15min 2976 non-null values
air1 2976 non-null values
air2 2976 non-null values
air3 2976 non-null values
airwindowunit1 2976 non-null values
aquarium1 2976 non-null values
bathroom1 2976 non-null values
bathroom2 2976 non-null values
bedroom1 2976 non-null values
bedroom2 2976 non-null values
bedroom3 2976 non-null values
bedroom4 2976 non-null values
bedroom5 2976 non-null values
car1 0 non-null values
clotheswasher1 2976 non-null values
clotheswasher_dryg1 2976 non-null values
diningroom1 2976 non-null values
diningroom2 2976 non-null values
dishwasher1 2976 non-null values
disposal1 2976 non-null values
drye1 2976 non-null values
dryg1 2976 non-null values
freezer1 2976 non-null values
furnace1 2976 non-null values
furnace2 2976 non-null values
garage1 2976 non-null values
garage2 2976 non-null values
heater1 2976 non-null values
housefan1 2976 non-null values
icemaker1 2976 non-null values
jacuzzi1 2976 non-null values
kitchen1 2976 non-null values
kitchen2 2976 non-null values
kitchenapp1 2976 non-null values
kitchenapp2 2976 non-null values
lights_plugs1 2976 non-null values
lights_plugs2 2976 non-null values
lights_plugs3 2976 non-null values
lights_plugs4 2976 non-null values
lights_plugs5 2976 non-null values
lights_plugs6 2976 non-null values
livingroom1 2976 non-null values
livingroom2 2976 non-null values
microwave1 2976 non-null values
office1 2976 non-null values
outsidelights_plugs1 2976 non-null values
outsidelights_plugs2 2976 non-null values
oven1 2976 non-null values
oven2 2976 non-null values
pool1 2976 non-null values
pool2 2976 non-null values
poollight1 2976 non-null values
poolpump1 2976 non-null values
pump1 2976 non-null values
range1 2976 non-null values
refrigerator1 2976 non-null values
refrigerator2 2976 non-null values
security1 2976 non-null values
shed1 2976 non-null values
sprinkler1 2976 non-null values
subpanel1 2976 non-null values
subpanel2 2976 non-null values
unknown1 2976 non-null values
unknown2 2976 non-null values
unknown3 2976 non-null values
unknown4 2976 non-null values
use 2976 non-null values
utilityroom1 2976 non-null values
venthood1 2976 non-null values
waterheater1 2976 non-null values
waterheater2 2976 non-null values
winecooler1 2976 non-null values
dtypes: int64(1), object(72)}
In [6]:
def plot_ac_over_one_month(d,month):
pyplt.clf()
day = days[month]
x=[]
for key,val in d.iteritems():
a1 = [float(y) for y in list(val['air1'])]
a2 = [float(y) + z for (y,z) in zip(list(val['air2']),a1)]
a3 = [float(y) + z for (y,z) in zip(list(val['air3']),a2)]
x = [float(y) + z for (y,z) in zip(list(val['airwindowunit1']),a3)]
x = [y/25.0 for y in x]
l = list(range(0,len(x),(len(x)/day)))
ax = pyplt.figure(figsize=(20,10)).add_subplot(111)
ax.set_xticks(l)
ax.set_xticklabels(range(1,day+1))
title = 'ac_'+month
pyplt.title(title)
ax.plot(x)
pyplt.savefig(title+'.png')
#pyplt.show()
In [7]:
def plot_heat_over_one_month(d,month):
pyplt.clf()
day = days[month]
x=[]
for key,val in d.iteritems():
x = [float(y) for y in list(val['heater1'])]
x = [y/25.0 for y in x]
l = list(range(0,len(x),(len(x)/day)))
ax = pyplt.figure(figsize=(20,10)).add_subplot(111)
ax.set_xticks(l)
ax.set_xticklabels(range(1,day+1))
title = 'heat_'+month
pyplt.title(title)
ax.plot(x)
pyplt.savefig(title+'.png')
#pyplt.show()
In [8]:
##plot air conditioning for one month
##for a home add all the columns
'''
x=[]
for key,val in dfs_dict.iteritems():
a1 = [float(y) for y in list(val['air1'])]
a2 = [float(y) + z for (y,z) in zip(list(val['air2']),a1)]
a3 = [float(y) + z for (y,z) in zip(list(val['air3']),a2)]
x = [float(y) + z for (y,z) in zip(list(val['airwindowunit1']),a3)]
x = [float(y) for y in list(val['heat1'])]
x = [y/25.0 for y in x]
#print x
'''
Out[8]:
"\nx=[]\nfor key,val in dfs_dict.iteritems():\n a1 = [float(y) for y in list(val['air1'])]\n a2 = [float(y) + z for (y,z) in zip(list(val['air2']),a1)]\n a3 = [float(y) + z for (y,z) in zip(list(val['air3']),a2)]\n x = [float(y) + z for (y,z) in zip(list(val['airwindowunit1']),a3)]\n x = [float(y) for y in list(val['heat1'])]\nx = [y/25.0 for y in x]\n#print x\n"
In [9]:
##for plotting purposes
'''
l = list(range(0,len(x),(len(x)/31)))
len(l)
pyplt.clf()
'''
Out[9]:
'\nl = list(range(0,len(x),(len(x)/31)))\nlen(l)\npyplt.clf()\n'
In [10]:
#pyplt.clf()
In [11]:
##plotting
##should make this a method
'''
ax = pyplt.figure(figsize=(20,10)).add_subplot(111)
ax.set_xticks(l)
ax.set_xticklabels(range(1,32))
ax.plot(x)
pyplt.savefig('October_avg.pdf')
pyplt.show()
'''
Out[11]:
"\nax = pyplt.figure(figsize=(20,10)).add_subplot(111)\nax.set_xticks(l)\nax.set_xticklabels(range(1,32))\nax.plot(x)\npyplt.savefig('October_avg.pdf')\n\npyplt.show()\n"
In [12]:
#test_building=dfs_dict[8197]
In [12]:
def get_sums(house):
house=house.drop('id',axis=1)
#house=house.drop('dataid',axis=1)
house=house.drop('utc_15min',axis=1)
sums={}
# print(house.columns)
for col in house.columns:
sums[str(col)]=float(house[col].sum())
return sums
In [13]:
#get_sums(dfs_dict[8197])
In [14]:
def hvacs(house):
other = 0
hvacs={'air1':0,'air2':0,'air3':0,'airwindowunit1':0,'furnace1':0,'furnace2':0,'heater1':0,'housefan1':0,'venthood1':0, 'other':0,'use':0}
house2 = house.drop('id',axis=1)
# house2=house2.drop('dataid',axis=1)
house2=house2.drop('utc_15min',axis=1)
#print(house2.columns)
count = 0
for col in house2.columns:
if str(col) in hvacs:
hvacs[str(col)]=float(house2[col].sum())
elif col!='use':
hvacs['other']=float(hvacs['other']) + float(house2[col].sum())
if not math.isnan(float(house2[col].sum())):
count = count + float(house2[col].sum())
hvacs['other']=count
else:
hvacs['use']=float(house2[col].sum())
total_air = hvacs['air1']+hvacs['air2']+hvacs['air3']+hvacs['airwindowunit1']
hvacs['air']=total_air
hvacs.pop('air1',None)
hvacs.pop('air2',None)
hvacs.pop('air3',None)
hvacs.pop('airwindowunit1',None)
hvacs['furnaces']= hvacs['furnace1']+hvacs['furnace2']
hvacs.pop('furnace1',None)
hvacs.pop('furnace2',None)
#print(count)
return hvacs
In [16]:
'''
x=sum(val for (key,val) in hvacs.iteritems() if key!='other')
print(x)
print(1-(hvacs['other']/(x+hvacs['other'])))
'''
Out[16]:
"\nx=sum(val for (key,val) in hvacs.iteritems() if key!='other')\nprint(x)\nprint(1-(hvacs['other']/(x+hvacs['other'])))\n"
In [17]:
#funhouse = d[d.keys()[0]]
In [15]:
#check that use is the total use?
count=0
funhouset = funhouse
funhouset = funhouset.drop('id',axis=1)
# house2=house2.drop('dataid',axis=1)
funhouset=funhouset.drop('utc_15min',axis=1)
for col in funhouset.columns:
if col!='use':
if not math.isnan(float(funhouset[col].sum())):
count = count + float(funhouset[col].sum())
print count
print float(funhouset['use'].sum())
3843.059
7600.19
In [37]:
for month in months:
d = query_month(month)
plot_heat_over_one_month(d,month)
2014-06-16 15:58:59,395 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 15:58:59,396 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 15:58:59,485 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 15:58:59,486 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 15:58:59,577 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 15:58:59,578 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 15:58:59,623 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 15:58:59,625 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 15:58:59,748 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 15:58:59,750 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 15:58:59,841 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
2014-06-16 15:58:59,842 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:21,758 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:01:21,759 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:21,847 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:01:21,849 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:21,936 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:01:21,937 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:21,983 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:01:21,984 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:22,121 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:01:22,123 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:01:22,212 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
2014-06-16 16:01:22,213 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:25,807 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:03:25,810 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:26,090 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:03:26,091 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:26,327 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:03:26,328 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:26,448 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:03:26,449 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:26,846 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:03:26,847 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:03:27,112 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
2014-06-16 16:03:27,113 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:05:58,687 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:05:58,688 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:05:58,957 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:05:58,958 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:05:59,277 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:05:59,278 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:05:59,453 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:05:59,454 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:05:59,933 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:05:59,933 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:06:00,237 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
2014-06-16 16:06:00,238 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:39,568 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:08:39,569 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:39,875 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:08:39,876 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:40,174 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:08:40,174 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:40,319 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:08:40,319 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:40,767 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:08:40,768 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:08:41,050 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
2014-06-16 16:08:41,051 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:21,738 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:11:21,740 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:21,834 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:11:21,834 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:21,923 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:11:21,924 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:21,968 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:11:21,969 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:22,098 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:11:22,099 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:11:22,193 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
2014-06-16 16:11:22,194 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:50,629 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:13:50,630 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:50,894 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:13:50,895 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:51,151 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:13:51,152 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:51,278 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:13:51,280 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:51,675 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:13:51,676 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:13:51,936 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
2014-06-16 16:13:51,937 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:51,043 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:16:51,045 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:51,334 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:16:51,335 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:51,666 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:16:51,667 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:51,789 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:16:51,790 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:51,991 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:16:51,992 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:16:52,124 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
2014-06-16 16:16:52,125 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:48,914 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:19:48,915 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:49,009 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:19:49,009 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:49,099 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:19:49,100 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:49,145 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:19:49,147 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:49,279 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:19:49,280 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:19:49,374 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
2014-06-16 16:19:49,375 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:17,248 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:22:17,249 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:17,407 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:22:17,408 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:17,571 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:22:17,572 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:17,652 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:22:17,653 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:17,880 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:22:17,882 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:22:18,096 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
2014-06-16 16:22:18,097 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:08,918 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 16:24:08,919 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:09,049 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 16:24:09,050 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:09,168 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:24:09,169 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:09,247 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 16:24:09,248 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:09,641 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 16:24:09,642 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 16:24:09,921 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
2014-06-16 16:24:09,922 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
<matplotlib.figure.Figure at 0x15ec6e3d0>
<matplotlib.figure.Figure at 0x133f67890>
<matplotlib.figure.Figure at 0x16834ced0>
<matplotlib.figure.Figure at 0x115069810>
<matplotlib.figure.Figure at 0x135863710>
<matplotlib.figure.Figure at 0x10b3c6a50>
<matplotlib.figure.Figure at 0x112cc1b10>
<matplotlib.figure.Figure at 0x118bd2f90>
<matplotlib.figure.Figure at 0x14c372d50>
<matplotlib.figure.Figure at 0x148e4ea90>
<matplotlib.figure.Figure at 0x133963850>
In [8]:
d = query_month('01')
2014-06-17 09:57:17,037 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-17 09:57:17,038 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-17 09:57:17,127 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-17 09:57:17,128 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-17 09:57:17,212 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-17 09:57:17,213 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-17 09:57:17,261 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-17 09:57:17,262 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-17 09:57:17,392 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-17 09:57:17,393 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-17 09:57:17,481 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
2014-06-17 09:57:17,482 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
In [12]:
hvacs_t = hvacs(d[d.keys()[0]])
In [63]:
plot_hvacs = {key:val for (key,val) in hvacs.iteritems() if val!=0}
In [64]:
fracs = [i for i in plot_hvacs.values()]
labels = [k for k in plot_hvacs.keys()]
In [67]:
ax = pyplt.figure(figsize=(6,6))
pyplt.pie(fracs,labels=labels)
pyplt.title('Percent Hvac')
savefig('test.png')
In [13]:
hvacs_test={'air':0,'furnaces':0,'heater1':0,'housefan1':0,'venthood1':0, 'other':0}
for val in d.values():
h=hvacs(val)
for (k,v) in h.iteritems():
hvacs_test[k]=hvacs_test[k]+h[k]
hvacs_test
Out[13]:
{'air': 6135.7829999999985,
'furnaces': 6210.662,
'heater1': 478.561,
'housefan1': 0.073,
'other': 116378.40100000001,
'venthood1': 0.0}
In [14]:
hvacs_test = {k:(val/25) for (k,val) in hvacs_test.iteritems()}
In [15]:
##should make this faster
def get_pie_month(month):
pyplt.clf()
d = query_month(month)
hvacs_test={'air':0,'furnaces':0,'heater1':0,'housefan1':0,'venthood1':0, 'other':0}
for val in d.values():
h=hvacs(val)
h.pop('use',None)
for (k,v) in h.iteritems():
hvacs_test[k]=hvacs_test[k]+h[k]
hvacs_test = {k:(val/25) for (k,val) in hvacs_test.iteritems()}
plot_hvacs = {key:val for (key,val) in hvacs_test.iteritems() if val!=0}
fracs = [i for i in plot_hvacs.values()]
labels = [k for k in plot_hvacs.keys()]
ax = pyplt.figure(figsize=(6,6))
pyplt.pie(fracs,labels=labels)
title = 'Percent_Hvac_'+month
pyplt.title(title)
savefig(title+'.png')
In [26]:
get_pie_month('11')
2014-06-18 08:51:35,159 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-18 08:51:35,161 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-18 08:51:35,253 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-18 08:51:35,254 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-18 08:51:35,343 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-18 08:51:35,344 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-18 08:51:35,391 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-18 08:51:35,392 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-18 08:51:35,524 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-18 08:51:35,525 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-18 08:51:35,616 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
2014-06-18 08:51:35,616 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
<matplotlib.figure.Figure at 0x10404ead0>
In [24]:
##for all the houses return the highest appliances
def get_highest(house):
sums= get_sums(house)
sums.pop('use',None)
sums.pop('car1',None)
sorted_sums = sorted(sums.iteritems(), key=operator.itemgetter(1), reverse = True)
return sorted_sums
In [30]:
##seeing all of the appliances for
def get_top_5_all(d):
k = random.choice(d.keys())
count_highest = {str(k): 0 for k in d[k].columns}
for house in d.values():
apps=get_highest(house)
for i in range(5):
count_highest[apps[i][0]]=count_highest[apps[i][0]]+1
sorted_sums = sorted(count_highest.iteritems(), key=operator.itemgetter(1), reverse = True)
return sorted_sums[:5]
In [116]:
h=get_top_5_all()
In [25]:
#month is a string
#d is the dictionary for that month,
#where the keys are the house ids and the values are dataframes
def write_month(month,d):
dir_path = os.path.join(os.getcwd(), 'ranked_apps/' )
#os.makedirs(dir_path)
filename = month+'.json'
text = []
for (h_id,house) in d.iteritems():
highs_dict = get_highest(house)
#text_dict[h_id] = highs_dict[2:7]
# highs_dict.pop('use',None)
# highs_dict.pop('car1',None)
try_this = {h_id:highs_dict[:5]}
text.append(str(try_this)+'\n\n')
with open(os.path.join(dir_path, filename), 'w') as outfile:
outfile.writelines(text)
outfile.close()
In [85]:
###check for cars - how many people have cars
cars = []
cars_sanity = []
for (house_id,house) in dfs_dict.iteritems():
cars_sanity.append(house['car1'].sum())
if not(math.isnan(house['car1'].sum())):
cars.append(house_id)
cars
#cars_sanity
Out[85]:
[]
In [ ]:
##is any data missing?
In [182]:
percent_hvac(dfs_dict[9934])
Index([u'air1', u'air2', u'air3', u'airwindowunit1', u'aquarium1', u'bathroom1', u'bathroom2', u'bedroom1', u'bedroom2', u'bedroom3', u'bedroom4', u'bedroom5', u'car1', u'clotheswasher1', u'clotheswasher_dryg1', u'diningroom1', u'diningroom2', u'dishwasher1', u'disposal1', u'drye1', u'dryg1', u'freezer1', u'furnace1', u'furnace2', u'garage1', u'garage2', u'heater1', u'housefan1', u'icemaker1', u'jacuzzi1', u'kitchen1', u'kitchen2', u'kitchenapp1', u'kitchenapp2', u'lights_plugs1', u'lights_plugs2', u'lights_plugs3', u'lights_plugs4', u'lights_plugs5', u'lights_plugs6', u'livingroom1', u'livingroom2', u'microwave1', u'office1', u'outsidelights_plugs1', u'outsidelights_plugs2', u'oven1', u'oven2', u'pool1', u'pool2', u'poollight1', u'poolpump1', u'pump1', u'range1', u'refrigerator1', u'refrigerator2', u'security1', u'shed1', u'sprinkler1', u'subpanel1', u'subpanel2', u'unknown1', u'unknown2', u'unknown3', u'unknown4', u'use', u'utilityroom1', u'venthood1', u'waterheater1', u'waterheater2', u'winecooler1'], dtype='object')
Out[182]:
{'air1': Decimal('1414.214'),
'air2': Decimal('0'),
'air3': Decimal('0'),
'airwindowunit1': Decimal('0'),
'furnace1': Decimal('308.475'),
'furnace2': Decimal('31.683'),
'heater1': Decimal('0'),
'housefan1': Decimal('0.073'),
'other': 0,
'venthood1': Decimal('0')}
In [26]:
##get all months and output the top five appliances for all houses in that month
for month in months:
d = query_month(month)
write_month(month,d)
2014-06-16 09:22:47,442 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:22:47,443 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:22:47,536 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:22:47,537 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:22:47,627 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:22:47,628 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:22:47,672 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:22:47,673 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:22:47,801 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:22:47,802 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:22:47,887 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
2014-06-16 09:22:47,888 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,448 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:25:28,449 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,535 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:25:28,536 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,629 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:25:28,630 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,680 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:25:28,682 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,815 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:25:28,816 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:25:28,903 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
2014-06-16 09:25:28,904 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,510 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:27:36,511 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,595 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:27:36,596 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,685 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:27:36,686 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,730 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:27:36,732 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,870 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:27:36,871 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:27:36,960 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
2014-06-16 09:27:36,961 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:05,863 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:30:05,864 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:05,957 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:30:05,958 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:06,047 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:30:06,048 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:06,092 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:30:06,094 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:06,231 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:30:06,233 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:30:06,318 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
2014-06-16 09:30:06,319 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,193 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:32:25,194 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,281 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:32:25,282 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,373 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:32:25,374 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,421 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:32:25,422 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,571 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:32:25,572 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:32:25,658 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
2014-06-16 09:32:25,659 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,285 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:34:56,287 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,375 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:34:56,376 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,465 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:34:56,466 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,512 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:34:56,513 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,655 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:34:56,656 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:34:56,741 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
2014-06-16 09:34:56,742 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:16,609 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:37:16,610 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:16,702 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:37:16,703 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:16,809 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:37:16,810 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:16,889 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:37:16,890 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:17,120 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:37:17,120 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:37:17,349 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
2014-06-16 09:37:17,350 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:44,602 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:39:44,604 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:44,689 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:39:44,690 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:44,780 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:39:44,781 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:44,829 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:39:44,829 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:44,964 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:39:44,965 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:39:45,060 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
2014-06-16 09:39:45,061 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:13,730 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:42:13,731 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:13,821 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:42:13,822 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:13,906 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:42:13,907 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:13,956 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:42:13,957 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:14,085 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:42:14,086 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:42:14,175 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
2014-06-16 09:42:14,176 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,372 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:44:34,373 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,465 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:44:34,466 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,557 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:44:34,559 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,605 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:44:34,606 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,734 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:44:34,735 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:44:34,827 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
2014-06-16 09:44:34,828 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,326 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:46:24,327 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,415 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:46:24,416 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,510 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:46:24,511 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,557 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:46:24,558 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,684 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:46:24,685 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:46:24,772 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
2014-06-16 09:46:24,773 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,297 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 09:48:42,298 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,632 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 09:48:42,633 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,722 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:48:42,723 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,771 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 09:48:42,773 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,902 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 09:48:42,903 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:42,991 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_12
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_12
2014-06-16 09:48:42,992 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 09:48:43,042 INFO sqlalchemy.engine.base.Engine ROLLBACK
INFO:sqlalchemy.engine.base.Engine:ROLLBACK
---------------------------------------------------------------------------
ProgrammingError Traceback (most recent call last)
<ipython-input-26-5c48eb3ffb70> in <module>()
1 ##get all months and output the top five appliances for all houses in that month
2 for month in months:
----> 3 d = query_month(month)
4 write_month(month,d)
<ipython-input-7-33ba5dc70d56> in query_month(month)
4 eng = create_engine(url)
5 eng.echo=True
----> 6 eng_object=eng.execute(query)
7 df = pd.DataFrame.from_records(eng_object.fetchall())
8 df.columns = eng_object.keys()
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in execute(self, statement, *multiparams, **params)
1680
1681 connection = self.contextual_connect(close_with_result=True)
-> 1682 return connection.execute(statement, *multiparams, **params)
1683
1684 def scalar(self, statement, *multiparams, **params):
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in execute(self, object, *multiparams, **params)
710 """
711 if isinstance(object, util.string_types[0]):
--> 712 return self._execute_text(object, multiparams, params)
713 try:
714 meth = object._execute_on_connection
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_text(self, statement, multiparams, params)
859 statement,
860 parameters,
--> 861 statement, parameters
862 )
863 if self._has_events or self.engine._has_events:
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
945 parameters,
946 cursor,
--> 947 context)
948
949 if self._has_events or self.engine._has_events:
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1106 self.dialect.dbapi.Error,
1107 connection_invalidated=self._is_disconnect),
-> 1108 exc_info
1109 )
1110
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/util/compat.pyc in raise_from_cause(exception, exc_info)
183 exc_info = sys.exc_info()
184 exc_type, exc_value, exc_tb = exc_info
--> 185 reraise(type(exception), exception, tb=exc_tb)
186
187 if py3k:
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
938 statement,
939 parameters,
--> 940 context)
941 except Exception as e:
942 self._handle_dbapi_exception(
/Users/sabina/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/default.pyc in do_execute(self, cursor, statement, parameters, context)
433
434 def do_execute(self, cursor, statement, parameters, context=None):
--> 435 cursor.execute(statement, parameters)
436
437 def do_execute_no_params(self, cursor, statement, context=None):
ProgrammingError: (ProgrammingError) relation "PecanStreet_CuratedSets.group1_disaggregated_2013_12" does not exist
LINE 1: select * from "PecanStreet_CuratedSets".group1_disaggregated...
^
'select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_12' {}
In [31]:
##get most common 5 highs
dir_path = os.path.join(os.getcwd(), 'ranked_apps/' )
for month in months:
filename = month+'_most_common_5.json'
d = query_month(month)
text = (str(get_top_5_all(d))+'\n\n')
with open(os.path.join(dir_path, filename), 'w') as outfile:
outfile.writelines(text)
outfile.close()
2014-06-16 10:37:22,835 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:37:22,836 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:37:23,051 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:37:23,052 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:37:23,255 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:37:23,257 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:37:23,372 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:37:23,374 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:37:23,840 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:37:23,841 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:37:24,080 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_01
2014-06-16 10:37:24,081 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:21,629 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:41:21,630 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:21,926 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:41:21,927 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:22,256 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:41:22,258 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:22,425 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:41:22,426 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:22,814 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:41:22,816 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:41:23,069 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_02
2014-06-16 10:41:23,070 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:02,446 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:45:02,447 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:02,728 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:45:02,729 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:03,010 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:45:03,011 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:03,058 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:45:03,059 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:03,191 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:45:03,192 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:45:03,281 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_03
2014-06-16 10:45:03,282 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:16,987 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:49:16,988 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:17,240 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:49:17,242 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:17,470 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:49:17,471 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:17,608 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:49:17,609 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:18,043 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:49:18,044 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:49:18,320 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_04
2014-06-16 10:49:18,321 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:48,206 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:52:48,208 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:48,517 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:52:48,518 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:48,839 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:52:48,840 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:48,954 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:52:48,956 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:49,090 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:52:49,092 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:52:49,180 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_05
2014-06-16 10:52:49,181 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,193 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 10:56:51,194 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,469 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 10:56:51,470 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,655 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:56:51,656 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,701 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 10:56:51,702 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,833 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 10:56:51,834 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 10:56:51,928 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_06
2014-06-16 10:56:51,929 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:31,898 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 11:00:31,899 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:32,219 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 11:00:32,220 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:32,530 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:00:32,531 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:32,601 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:00:32,602 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:32,832 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 11:00:32,833 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:00:33,013 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_07
2014-06-16 11:00:33,014 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:20,521 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 11:04:20,522 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:20,777 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 11:04:20,778 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:21,041 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:04:21,042 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:21,167 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:04:21,168 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:21,574 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 11:04:21,575 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:04:21,855 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_08
2014-06-16 11:04:21,856 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,448 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 11:08:11,450 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,542 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 11:08:11,543 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,632 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:08:11,633 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,682 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:08:11,683 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,816 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 11:08:11,817 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:08:11,903 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_09
2014-06-16 11:08:11,904 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:52,117 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 11:11:52,118 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:52,426 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 11:11:52,427 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:52,758 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:11:52,759 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:52,885 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:11:52,887 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:53,235 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 11:11:53,236 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:11:53,494 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_10
2014-06-16 11:11:53,495 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:32,839 INFO sqlalchemy.engine.base.Engine select version()
INFO:sqlalchemy.engine.base.Engine:select version()
2014-06-16 11:14:32,840 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:33,030 INFO sqlalchemy.engine.base.Engine select current_schema()
INFO:sqlalchemy.engine.base.Engine:select current_schema()
2014-06-16 11:14:33,031 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:33,300 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:14:33,301 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:33,438 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1
2014-06-16 11:14:33,439 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:33,840 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings
INFO:sqlalchemy.engine.base.Engine:show standard_conforming_strings
2014-06-16 11:14:33,841 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
2014-06-16 11:14:34,113 INFO sqlalchemy.engine.base.Engine select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
INFO:sqlalchemy.engine.base.Engine:select * from "PecanStreet_CuratedSets".group1_disaggregated_2013_11
2014-06-16 11:14:34,114 INFO sqlalchemy.engine.base.Engine {}
INFO:sqlalchemy.engine.base.Engine:{}
In [ ]:
def pie(month):
hvacs = percent_hvacs(hv)
labels = []
fracs = []
total_air =
Content source: dssg/wikienergy
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