Residential Sector Energy Consumption Estimates, 1949– 2012

Residential Sector Energy Consumption Estimates, 1949– 2012 Source: http://www.eia.gov/totalenergy/data/annual/#consumption



In [78]:

    
from pandas import *

import pandas
pandas.__version__

import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns



In [79]:

    
import pandas as pd



In [6]:

    
# cleaned version from 11-6-14
#Res_Energy_Consumption_1949_2012 = read_csv('MER_T02_02-10-27-2014 _v1.csv')
Res_Energy_Consumption_1949_2012 = read_csv('MER_T02_02-11-6-2014 _v1.csv')
# http://www.eia.gov/totalenergy/data/annual/#consumption
# 2.1B   Residential Sector Energy Consumption Estimates, 1949– 2012



In [7]:

    
Res_Energy_Consumption_1949_2012.head(10)









    Out[7]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      0
       CLRCBUS
       194913
       1271.551
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1949
       13
    
    
      1
       CLRCBUS
       195013
       1261.267
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1950
       13
    
    
      2
       CLRCBUS
       195113
       1158.679
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1951
       13
    
    
      3
       CLRCBUS
       195213
       1079.206
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1952
       13
    
    
      4
       CLRCBUS
       195313
        965.664
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1953
       13
    
    
      5
       CLRCBUS
       195413
        858.255
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1954
       13
    
    
      6
       CLRCBUS
       195513
        867.431
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1955
       13
    
    
      7
       CLRCBUS
       195613
        838.910
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1956
       13
    
    
      8
       CLRCBUS
       195713
        653.734
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1957
       13
    
    
      9
       CLRCBUS
       195813
        663.496
       1
       Coal Consumed by the Residential Sector
       Trillion Btu
       1958
       13



In [8]:

    
#http://stackoverflow.com/questions/893417/item-frequency-count-in-python
##from collections import Counter
##words = "apple banana apple strawberry banana lemon"
##freqs = Counter(words.split())
##print(freqs)
#Counter({'apple': 2, 'banana': 2, 'strawberry': 1, 'lemon': 1})



In [9]:

    
#http://stackoverflow.com/questions/893417/item-frequency-count-in-python
##from collections import Counter
#freqs = Counter(Res_Energy_Consumption_1949_2012.Value.split())
##freqs = Counter(Res_Energy_Consumption_1949_2012.Value)
#freqs = Counter(Value.split())
##print(freqs)
#Counter({.......})

#works
# sorted.



In [10]:

    
#Valuetest = Res_Energy_Consumption_1949_2012.Value
#Valuetest.count('Not Available')
# Does not work.



In [11]:

    
words = 'the quick brown fox jumps over the lazy gray dog'
words.count('z')
#output: 1









    Out[11]:





1



In [12]:

    
Res_Energy_Consumption_1949_2012.tail(10)









    Out[12]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      6746
       TERCBUS
       201310
        1368.665
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2013
       10
    
    
      6747
       TERCBUS
       201311
        1712.673
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2013
       11
    
    
      6748
       TERCBUS
       201312
        2402.746
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2013
       12
    
    
      6749
       TERCBUS
       201313
       21286.839
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2013
       13
    
    
      6750
       TERCBUS
       201401
        2773.735
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        1
    
    
      6751
       TERCBUS
       201402
        2330.009
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        2
    
    
      6752
       TERCBUS
       201403
        2063.318
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        3
    
    
      6753
       TERCBUS
       201404
        1425.137
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        4
    
    
      6754
       TERCBUS
       201405
        1346.358
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        5
    
    
      6755
       TERCBUS
       201406
        1501.449
       12
       Total Energy Consumed by the Residential Sector
       Trillion Btu
       2014
        6



In [13]:

    
print round(-1123.456789, -1)
#1120.0



In [14]:

    
# http://localhost:8888/notebooks/laptop-repos/Pandas_TestFiles/PandasTour.ipynb
#Res_Energy_Consumption_1949_2012.fillna(0)



In [15]:

    
# http://learnpythonthehardway.org/book/ex30.html
# if cars > people:
 #   print "We should take the cars."
#elif cars < people:
#    print "We should not take the cars."
#else:
#    print "We can't decide."

##if Res_Energy_Consumption_1949_2012.Value > 5:
 ##   print "Not Available."
##else:
  ##  print Res_Energy_Consumption_1949_2012.Value
    
    #Valuetest = Res_Energy_Consumption_1949_2012.Value
#Valuetest.count('Not Available')
# Does not work.

In [85]: df = DataFrame({'a': [0, 1, 2, 3, 4], 'b': [5, 6, 7, 8, 9]}) In [86]: df.replace({'a': 0, 'b': 5}, 100) Out[86]: a b 0 100 100 1 1 6 2 2 7 3 3 8 4 4 9



In [16]:

    
#valuetest = Res_Energy_Consumption_1949_2012.Value
#http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook-missing-data
Revised= Res_Energy_Consumption_1949_2012.Value.replace({'Value': "Not Available"}, "NaN")



In [23]:

    
print Revised









    



0     1271.551
1     1261.267
2     1158.679
3     1079.206
4      965.664
5      858.255
6      867.431
7      838.910
8      653.734
9      663.496
10     573.321
11     585.284
12     533.611
13     520.794
14     438.442
...
6741     1341.095
6742     1504.229
6743     1748.855
6744     1674.095
6745     1468.586
6746     1368.665
6747     1712.673
6748     2402.746
6749    21286.839
6750     2773.735
6751     2330.009
6752     2063.318
6753     1425.137
6754     1346.358
6755     1501.449
Name: Value, Length: 6756, dtype: float64



In [24]:

    
Revised.dtype









    Out[24]:





dtype('float64')



In [18]:

    
#http://stackoverflow.com/questions/893417/item-frequency-count-in-python
from collections import Counter
#freqs = Counter(Res_Energy_Consumption_1949_2012.Value.split())
freqs = Counter(Revised)
#freqs = Counter(Value.split())
print(freqs)









    



Counter({82.384: 28, 78.137: 21, 32.274000000000001: 21, 3.363: 17, 49.259999999999998: 17, 79.725999999999999: 16, 83.013999999999996: 15, 77.076999999999998: 14, 31.506: 14, 35.573999999999998: 14, 71.995000000000005: 14, 46.013999999999996: 14, 72.191999999999993: 14, 61.836999999999996: 14, 36.521000000000001: 14, 83.004999999999995: 14, 52.814999999999998: 14, 40.794000000000004: 14, 44.163999999999994: 14, 73.890000000000001: 14, 30.074000000000002: 14, 85.781000000000006: 14, 36.131: 14, 75.616: 12, 31.233000000000001: 12, 0.54200000000000004: 11, 4.8210000000000006: 11, 47.671000000000006: 10, 3.2549999999999999: 10, 0.72799999999999998: 9, 29.103999999999999: 8, 34.426000000000002: 8, 59.841999999999999: 8, 71.507000000000005: 8, 42.740000000000002: 8, 69.863: 8, 0.42499999999999999: 8, 69.671999999999997: 8, 51.111000000000004: 8, 5.3399999999999999: 8, 0.501: 8, 80.328000000000003: 8, 35.341999999999999: 8, 30.489000000000001: 8, 0.59299999999999997: 8, 74.590000000000003: 8, 34.965000000000003: 8, 39.478000000000002: 8, 44.530000000000001: 8, 7.5650000000000004: 7, 0.57799999999999996: 7, 4.8019999999999996: 7, 13.030999999999999: 7, 43.451999999999998: 7, 52.816000000000003: 7, 50.183999999999997: 7, 0.65400000000000003: 7, 38.219000000000001: 7, 37.369999999999997: 7, 40.734000000000002: 7, 9.6890000000000001: 7, 4.9820000000000002: 7, 2.786: 7, 0.56100000000000005: 7, 4.8780000000000001: 7, 59.792999999999999: 7, 5.4299999999999997: 7, 0.46700000000000003: 7, 3.125: 7, 54.207999999999998: 7, 39.808999999999997: 7, 57.186999999999998: 7, 39.185000000000002: 7, 33.123000000000005: 7, 39.213000000000001: 7, 50.048999999999999: 7, 48.841999999999999: 7, 5.9120000000000008: 7, 4.7219999999999995: 7, 34.726999999999997: 7, 5.3849999999999998: 7, 33.972999999999999: 7, 18.597999999999999: 7, 35.670999999999999: 7, 54.696999999999996: 7, 71.221999999999994: 7, 1.554: 7, 38.012: 7, 5.4550000000000001: 7, 6.7970000000000006: 7, 4.9210000000000003: 7, 45.738: 7, 42.466000000000001: 7, 4.8719999999999999: 7, 5.3579999999999997: 7, 0.72199999999999998: 7, 1.1859999999999999: 7, 31.425000000000001: 7, 38.358000000000004: 7, 52.496000000000002: 7, 1.3500000000000001: 7, 39.878999999999998: 7, 46.711999999999996: 7, 5.2060000000000004: 7, 5.4670000000000005: 7, 42.792000000000002: 7, 41.431000000000004: 7, 50.18: 7, 0.86699999999999999: 7, 15.769: 7, 37.209000000000003: 7, 5.0430000000000001: 7, 2.2359999999999998: 7, 5.1289999999999996: 7, 0.52700000000000002: 7, 5.5079999999999991: 7, 51.808: 7, 42.623999999999995: 7, 1.1040000000000001: 7, 0.80299999999999994: 7, 1.8680000000000001: 7, 0.63700000000000001: 7, 54.450000000000003: 7, 51.838000000000001: 7, 4.4530000000000003: 7, 54.613: 7, 3.3539999999999996: 7, 5.3300000000000001: 5, 34.521000000000001: 5, 45.204999999999998: 5, 1.3069999999999999: 5, 5.7210000000000001: 4, 37.920999999999999: 4, 36.786000000000001: 4, 4.7149999999999999: 4, 0.83900000000000008: 4, 44.262: 4, 38.591999999999999: 4, 40.094999999999999: 4, 37.119999999999997: 4, 74.411000000000001: 4, 4.5700000000000003: 4, 33.606999999999999: 4, 850.0: 4, 970.0: 4, 51.111999999999995: 4, 4.9630000000000001: 4, 41.096000000000004: 4, 50.802: 4, 38.524999999999999: 4, 50.137: 4, 5.29: 4, 55.341999999999999: 4, 36.009: 4, 32.876999999999995: 4, 47.266000000000005: 4, 36.985999999999997: 4, 68.923999999999992: 4, 50.166000000000004: 4, 4.6470000000000002: 4, 30.410999999999998: 4, 52.851000000000006: 4, 0.70499999999999996: 4, 12.609999999999999: 4, 37.948: 4, 17.998000000000001: 4, 7.3210000000000006: 4, 2.1640000000000001: 4, 0.45200000000000001: 4, 0.52500000000000002: 4, 57.863999999999997: 4, 6.5779999999999994: 4, 9.3759999999999994: 4, 39.420000000000002: 4, 2.6960000000000002: 4, 0.57399999999999995: 4, 41.411999999999999: 4, 52.933: 4, 5.2110000000000003: 4, 0.48499999999999999: 4, 5.0380000000000003: 4, 0.61599999999999999: 4, 36.164000000000001: 4, 15.26: 4, 5.1849999999999996: 4, 80.335999999999999: 4, 52.693000000000005: 4, 1.1479999999999999: 4, 4.3090000000000002: 4, 1.504: 4, 1.8080000000000001: 4, 0.69900000000000007: 4, 4.7199999999999998: 4, 3.0249999999999999: 4, 1.0680000000000001: 4, 48.433999999999997: 4, 0.41100000000000003: 4, 0.55899999999999994: 4, 52.458999999999996: 4, 0.51000000000000001: 4, 5.1679999999999993: 4, 48.561000000000007: 4, 4.7619999999999996: 4, 42.049999999999997: 4, 0.63300000000000001: 4, 41.248999999999995: 4, 5.2539999999999996: 4, 48.564999999999998: 4, 4.8810000000000002: 4, 32.055: 4, 5.2789999999999999: 4, 0.77700000000000002: 4, 4.6660000000000004: 4, 3.2460000000000004: 4, 380.0: 3, 420.0: 3, 39.600000000000001: 3, 21.388000000000002: 3, 29.151: 3, 920.0: 3, 3.0380000000000003: 3, 74.012: 3, 44.493000000000002: 3, 70.575000000000003: 3, 958.21199999999999: 2, 775.06600000000003: 2, 560.08400000000006: 2, 430.0: 2, 1005.529: 2, 580.0: 2, 425.40800000000002: 2, 910.0: 2, 980.0: 2, 72.103999999999999: 2, 65.204999999999998: 2, 799.74800000000005: 2, 1.5800000000000001: 2, 0.61899999999999999: 2, 381.87400000000002: 2, 2.4609999999999999: 2, 32.985999999999997: 2, 621.84900000000005: 2, 541.78300000000002: 2, 433.97300000000001: 2, 2.6549999999999998: 2, 425.51099999999997: 2, 481.63400000000001: 2, 1.6769999999999998: 2, 646.92599999999993: 2, 27.164000000000001: 2, 77.478999999999999: 2, 0.94299999999999995: 2, 1.857: 2, 831.947: 2, 899.16399999999999: 2, 2.7400000000000002: 2, 33.279000000000003: 2, 0.45000000000000001: 2, 47.703000000000003: 2, 586.86400000000003: 2, 1.6669999999999998: 2, 55.851999999999997: 2, 1.075: 2, 1055.1860000000001: 2, 32.634: 2, 468.14999999999998: 2, 38.161999999999999: 2, 688.447: 2, 499.05800000000005: 2, 738.70600000000002: 2, 28.456999999999997: 2, 870.0: 2, 536.96699999999998: 2, 1.109: 2, 71.623999999999995: 2, 67.349999999999994: 2, 0.52200000000000002: 2, 0.74900000000000011: 2, 66.739999999999995: 2, 1010.0: 2, 701.81200000000001: 2, 0.65500000000000003: 2, 415.05300000000005: 2, 39.890000000000001: 2, 127.62299999999999: 2, 507.29400000000004: 2, 379.77600000000001: 2, 77.650000000000006: 2, 400.77699999999999: 2, 20.698: 2, 454.97399999999999: 2, 520.0: 2, 370.952: 2, 626.63: 2, 354.096: 2, 320.41800000000001: 2, 0.6409999999999999: 2, 1.7749999999999999: 2, 728.07600000000002: 2, 41.561000000000007: 2, 2.375: 1, 0.625: 1, 1130.9080000000001: 1, 350.36400000000003: 1, nan: 1, 5.0: 1, nan: 1, 291.26499999999999: 1, 8.0: 1, 489.154: 1, 324.67900000000003: 1, 13.0: 1, 14.0: 1, nan: 1, 3.6319999999999997: 1, nan: 1, 0.78299999999999992: 1, nan: 1, 388.70800000000003: 1, 4405.1559999999999: 1, 135.03200000000001: 1, 1.329: 1, 238.929: 1, 261.22399999999999: 1, 316.02199999999999: 1, nan: 1, 456.03699999999998: 1, 302.34500000000003: 1, 1687.2550000000001: 1, 492.89499999999998: 1, 2.9580000000000002: 1, 613.03899999999999: 1, 7.0: 1, 251.75099999999998: 1, 7711.3599999999997: 1, nan: 1, 270.19799999999998: 1, 281.48099999999999: 1, 300.41500000000002: 1, 3.5760000000000001: 1, 2.1869999999999998: 1, 401.21300000000002: 1, 204.93799999999999: 1, 2613.0389999999998: 1, 649.48699999999997: 1, nan: 1, 3557.0149999999999: 1, 0.93099999999999994: 1, 857.93700000000001: 1, nan: 1, 1056.2839999999999: 1, 1071.191: 1, 2570.712: 1, nan: 1, 238.77099999999999: 1, 929.29999999999995: 1, 357.99000000000001: 1, 147.334: 1, 1283.742: 1, nan: 1, 607.84100000000001: 1, 910.875: 1, 3.0069999999999997: 1, 2747.7790000000005: 1, 1052.1880000000001: 1, 232.80599999999998: 1, 1243.279: 1, 323.34800000000001: 1, 1510.4079999999999: 1, nan: 1, 6345.4989999999998: 1, 6775.6099999999997: 1, 66.84899999999999: 1, 235.167: 1, 349.40300000000002: 1, 215.12900000000002: 1, 36.020000000000003: 1, 272.72800000000001: 1, 361.41800000000001: 1, 6650.835: 1, 8.5239999999999991: 1, 662.75199999999995: 1, nan: 1, 22.0: 1, 1388.1229999999998: 1, 136.0: 1, 448.05099999999999: 1, 274.685: 1, 164.96200000000002: 1, 301.084: 1, 302.60900000000004: 1, 857.84699999999998: 1, 1010.0839999999999: 1, 335.89300000000003: 1, 105.684: 1, 157.035: 1, 30.629999999999999: 1, nan: 1, 578.73000000000002: 1, 239.99700000000001: 1, 1567.816: 1, nan: 1, 940.72800000000007: 1, 875.44600000000003: 1, 651.12300000000005: 1, 0.68200000000000005: 1, 628.173: 1, 5010.0609999999997: 1, nan: 1, 1294.481: 1, 162.566: 1, 178.0: 1, 196.94099999999997: 1, 120.895: 1, 79.555999999999997: 1, 203.554: 1, nan: 1, 1501.4489999999998: 1, nan: 1, 101.361: 1, 841.404: 1, 1863.4479999999999: 1, nan: 1, 439.97399999999999: 1, 2052.123: 1, 150.26400000000001: 1, 3.4950000000000001: 1, 687.39300000000003: 1, 674.90899999999999: 1, 651.93799999999999: 1, 9690.7129999999997: 1, 35.417999999999999: 1, 126.551: 1, 1483.5229999999999: 1, 181.27900000000002: 1, 19970.898000000001: 1, 436.95499999999998: 1, 107.04000000000001: 1, 1630.1229999999998: 1, 45.691000000000003: 1, 316.63200000000001: 1, 174.602: 1, 6184.2130000000006: 1, nan: 1, 365.27800000000002: 1, 157.56: 1, 144.28899999999999: 1, 212.80900000000003: 1, 404.22199999999998: 1, 216.13200000000001: 1, 0.996: 1, 1502.7560000000001: 1, 190.09400000000002: 1, 312.49000000000001: 1, 3014.5419999999999: 1, 229.34099999999998: 1, 1.131: 1, 304.94400000000002: 1, 798.3660000000001: 1, 6086.991: 1, nan: 1, nan: 1, 193.965: 1, 224.90200000000002: 1, 576.85400000000004: 1, 7129.1419999999998: 1, 315.68200000000002: 1, 1.3149999999999999: 1, 267.41300000000001: 1, 737.94399999999996: 1, 365.01400000000001: 1, 1596.7370000000001: 1, 994.96399999999994: 1, 157.06799999999998: 1, 1905.085: 1, 915.28699999999992: 1, 774.44600000000003: 1, nan: 1, 431.35599999999999: 1, 7662.7250000000004: 1, 402.673: 1, 2800.3270000000002: 1, 762.029: 1, 1798.8489999999999: 1, 241.833: 1, 1213.98: 1, 647.03499999999997: 1, 37.313000000000002: 1, 951.55100000000004: 1, 266.02299999999997: 1, 0.57499999999999996: 1, nan: 1, 2.4319999999999999: 1, 869.36199999999997: 1, 4.9400000000000004: 1, 1849.9189999999999: 1, 249.322: 1, nan: 1, 956.29100000000005: 1, nan: 1, 147.666: 1, 1.5680000000000001: 1, 352.49199999999996: 1, 0.64400000000000002: 1, 5.3010000000000002: 1, nan: 1, 542.70800000000008: 1, 758.45500000000004: 1, 675.50600000000009: 1, 1036.742: 1, 945.26600000000008: 1, 134.70099999999999: 1, 6923.3739999999998: 1, 331.13800000000003: 1, 118.604: 1, 2.2130000000000001: 1, 4.7589999999999995: 1, 4735.0829999999996: 1, nan: 1, 783.404: 1, 1.577: 1, 597.54999999999995: 1, 19.317999999999998: 1, 631.40800000000002: 1, nan: 1, nan: 1, 232.13499999999999: 1, 2478.7719999999999: 1, nan: 1, nan: 1, nan: 1, 332.52600000000001: 1, 484.15199999999999: 1, nan: 1, 292.55099999999999: 1, 154.33799999999999: 1, 953.68499999999995: 1, 278.94999999999999: 1, 692.78399999999999: 1, 390.0: 1, 254.32599999999999: 1, nan: 1, nan: 1, 965.66399999999999: 1, 0.45299999999999996: 1, 209.93000000000001: 1, 400.0: 1, 499.01999999999998: 1, nan: 1, 877.72699999999998: 1, 1125.1589999999999: 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200.25599999999997: 1, 325.15199999999999: 1, 111.32700000000001: 1, nan: 1, 229.935: 1, 4711.2510000000002: 1, 655.01999999999998: 1, nan: 1, 76.063000000000002: 1, 308.04200000000003: 1, 224.87099999999998: 1, 3.5789999999999997: 1, 1161.1689999999999: 1, 129.66800000000001: 1, 137.06700000000001: 1, 30.684999999999999: 1, nan: 1, 135.48099999999999: 1, 100.90100000000001: 1, 537.06299999999999: 1, 1135.6420000000001: 1, 1857.6610000000001: 1, 251.11199999999999: 1, 415.77800000000002: 1, 363.85900000000004: 1, 8247.603000000001: 1, nan: 1, 360.16199999999998: 1, 818.745: 1, 572.34100000000001: 1, 1.8159999999999998: 1, 599.28999999999996: 1, 1003.4450000000001: 1, 94.082000000000008: 1, nan: 1, 58.658999999999999: 1, 673.68399999999997: 1, 535.93600000000004: 1, 230.935: 1, 102.23299999999999: 1, 110.63200000000001: 1, 378.95400000000001: 1, 99.213999999999999: 1, 88.995000000000005: 1, 296.15100000000001: 1, 1908.3889999999999: 1, 153.52600000000001: 1, 0.66900000000000004: 1, 638.72300000000007: 1, nan: 1, 384.29699999999997: 1, 2362.3789999999999: 1, 89.896000000000001: 1, 103.69799999999999: 1, 6531.0360000000001: 1, 474.36000000000001: 1, 51.167999999999999: 1, 106.431: 1, 427.24000000000001: 1, 474.18900000000002: 1, 425.01900000000001: 1, 326.69200000000001: 1, 488.83499999999998: 1, 1674.729: 1, 206.803: 1, 356.54400000000004: 1, nan: 1, 240.202: 1, 582.34699999999998: 1, 2158.1570000000002: 1, 781.54899999999998: 1, 125.02600000000001: 1, 1041.6569999999999: 1, 318.44599999999997: 1, 452.286: 1, 1450.0610000000001: 1, 0.72400000000000009: 1, 710.89199999999994: 1, 1149.2080000000001: 1, 230.40599999999998: 1, 239.23699999999999: 1, 191.636: 1, 7111.7510000000002: 1, 64.055999999999997: 1})



In [25]:

    
Res_Energy_Consumption_1949_2012.Value.cumsum()









    Out[25]:





0      1271.551
1      2532.818
2      3691.497
3      4770.703
4      5736.367
5      6594.622
6      7462.053
7      8300.963
8      8954.697
9      9618.193
10    10191.514
11    10776.798
12    11310.409
13    11831.203
14    12269.645
...
6741    4910989.493
6742    4912493.722
6743    4914242.577
6744    4915916.672
6745    4917385.258
6746    4918753.923
6747    4920466.596
6748    4922869.342
6749    4944156.181
6750    4946929.916
6751    4949259.925
6752    4951323.243
6753    4952748.380
6754    4954094.738
6755    4955596.187
Name: Value, Length: 6756, dtype: float64



In [ ]:



In [20]:

    
print round(Revised,-1)









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-20-c078e1c61bdc> in <module>()
----> 1 print round(Revised,-1)

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in wrapper(self)
     71             return converter(self.iloc[0])
     72         raise TypeError(
---> 73             "cannot convert the series to {0}".format(str(converter)))
     74     return wrapper
     75 

TypeError: cannot convert the series to <type 'float'>



In [28]:

    
Res_Energy_Consumption_1949_2012.Value.dtype









    Out[28]:





dtype('float64')



In [26]:

    
print round(Res_Energy_Consumption_1949_2012.Value, -1)









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-93764190f00d> in <module>()
----> 1 print round(Res_Energy_Consumption_1949_2012.Value, -1)

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in wrapper(self)
     71             return converter(self.iloc[0])
     72         raise TypeError(
---> 73             "cannot convert the series to {0}".format(str(converter)))
     74     return wrapper
     75 

TypeError: cannot convert the series to <type 'float'>

Quick Data Analysis with Pandas # Advice From Here: # http://pbpython.com/simple-data-analysis.html



In [22]:

    
# use the pivot table function 

#report = SALES.pivot_table(values=['quantity'],index=['Account Name'],columns=['category'], aggfunc=np.sum)
#report.head(n=10)
## report = Res_Energy_Consumption_1949_2012.pivot_table(values=['Value'],index=['Description'],columns=['Value'], aggfunc=np.sum)
## report.head(n=10)

report = Res_Energy_Consumption_1949_2012.pivot_table(values=['Value'],index=['Description'], aggfunc=np.sum)
report.head(n=10)

# need to remove "columns=['Value']," since we don't have additional specification.
# need to convert the columns to floats so that they will be added?









    Out[22]:






  
    
      
      Value
    
    
      Description
      
    
  
  
    
      Biomass Energy Consumed by the Residential Sector
         63565.665
    
    
      Coal Consumed by the Residential Sector
         16806.390
    
    
      Electricity Retail Sales to the Residential Sector
        303964.024
    
    
      Geothermal Energy Consumed by the Residential Sector
           818.356
    
    
      Natural Gas Consumed by the Residential Sector (Excluding Supplemental Gaseous Fuels)
        477851.187
    
    
      Petroleum Consumed by the Residential Sector
        182442.105
    
    
      Residential Sector Electrical System Energy Losses
        682645.859
    
    
      Solar/PV Energy Consumed by the Residential Sector
          4110.424
    
    
      Total Energy Consumed by the Residential Sector
       1732203.987
    
    
      Total Fossil Fuels Consumed by the Residential Sector
        677099.677



In [ ]:

    
# chart selected categories

Create a dataframe of just TFFCRS.



In [30]:

    
#Total Fossil Fuels Consumed by the Residential Sector	677099.677= TFFCRS
# girls = names[names.sex == 'girl']
#Create a dataframe of just TFCRS.
TFFCRS=  Res_Energy_Consumption_1949_2012[Res_Energy_Consumption_1949_2012.Description == 'Total Fossil Fuels Consumed by the Residential Sector']



In [32]:

    
TFFCRS.head(10)
# only 6 months in 2014
# So skip that year.









    Out[32]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      1689
       FFRCBUS
       194913
       3405.248
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1949
       13
    
    
      1690
       FFRCBUS
       195013
       3823.808
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1950
       13
    
    
      1691
       FFRCBUS
       195113
       4146.264
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1951
       13
    
    
      1692
       FFRCBUS
       195213
       4259.029
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1952
       13
    
    
      1693
       FFRCBUS
       195313
       4220.568
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1953
       13
    
    
      1694
       FFRCBUS
       195413
       4462.559
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1954
       13
    
    
      1695
       FFRCBUS
       195513
       4832.746
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1955
       13
    
    
      1696
       FFRCBUS
       195613
       5100.677
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1956
       13
    
    
      1697
       FFRCBUS
       195713
       5042.088
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1957
       13
    
    
      1698
       FFRCBUS
       195813
       5436.923
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1958
       13



In [34]:

    
TFFCRS.to_csv("TFFCRS.csv", index=False)
#can just start from here now.
#have it local, cleaned.



In [33]:

    
TFFCRS.describe









    Out[33]:





<bound method DataFrame.describe of           MSN  YYYYMM     Value  Column_Order  \
1689  FFRCBUS  194913  3405.248             4   
1690  FFRCBUS  195013  3823.808             4   
1691  FFRCBUS  195113  4146.264             4   
1692  FFRCBUS  195213  4259.029             4   
1693  FFRCBUS  195313  4220.568             4   
1694  FFRCBUS  195413  4462.559             4   
1695  FFRCBUS  195513  4832.746             4   
1696  FFRCBUS  195613  5100.677             4   
1697  FFRCBUS  195713  5042.088             4   
1698  FFRCBUS  195813  5436.923             4   
1699  FFRCBUS  195913  5541.385             4   
1700  FFRCBUS  196013  6024.205             4   
1701  FFRCBUS  196113  6188.746             4   
1702  FFRCBUS  196213  6519.627             4   
1703  FFRCBUS  196313  6552.846             4   
1704  FFRCBUS  196413  6612.693             4   
1705  FFRCBUS  196513  6811.272             4   
1706  FFRCBUS  196613  7045.412             4   
1707  FFRCBUS  196713  7277.387             4   
1708  FFRCBUS  196813  7507.234             4   
1709  FFRCBUS  196913  7827.744             4   
1710  FFRCBUS  197013  7921.629             4   
1711  FFRCBUS  197113  8045.577             4   
1712  FFRCBUS  197213  8247.603             4   
1713  FFRCBUS  197301  1283.742             4   
1714  FFRCBUS  197302  1122.848             4   
1715  FFRCBUS  197303   940.288             4   
1716  FFRCBUS  197304   680.527             4   
1717  FFRCBUS  197305   514.522             4   
1718  FFRCBUS  197306   356.544             4   
...       ...     ...       ...           ...   
2222  FFRCBUS  201203   493.454             4   
2223  FFRCBUS  201204   349.403             4   
2224  FFRCBUS  201205   233.107             4   
2225  FFRCBUS  201206   190.094             4   
2226  FFRCBUS  201207   173.826             4   
2227  FFRCBUS  201208   181.452             4   
2228  FFRCBUS  201209   185.240             4   
2229  FFRCBUS  201210   310.548             4   
2230  FFRCBUS  201211   565.721             4   
2231  FFRCBUS  201212   767.291             4   
2232  FFRCBUS  201213  5137.410             4   
2233  FFRCBUS  201301  1021.368             4   
2234  FFRCBUS  201302   884.460             4   
2235  FFRCBUS  201303   786.602             4   
2236  FFRCBUS  201304   458.182             4   
2237  FFRCBUS  201305   260.914             4   
2238  FFRCBUS  201306   183.701             4   
2239  FFRCBUS  201307   172.098             4   
2240  FFRCBUS  201308   172.799             4   
2241  FFRCBUS  201309   186.631             4   
2242  FFRCBUS  201310   293.006             4   
2243  FFRCBUS  201311   605.985             4   
2244  FFRCBUS  201312   966.726             4   
2245  FFRCBUS  201313  5992.084             4   
2246  FFRCBUS  201401  1160.318             4   
2247  FFRCBUS  201402   963.498             4   
2248  FFRCBUS  201403   801.434             4   
2249  FFRCBUS  201404   412.368             4   
2250  FFRCBUS  201405   261.224             4   
2251  FFRCBUS  201406   185.211             4   

                                            Description          Unit  YYYY  \
1689  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1949   
1690  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1950   
1691  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1951   
1692  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1952   
1693  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1953   
1694  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1954   
1695  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1955   
1696  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1956   
1697  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1957   
1698  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1958   
1699  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1959   
1700  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1960   
1701  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1961   
1702  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1962   
1703  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1963   
1704  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1964   
1705  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1965   
1706  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1966   
1707  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1967   
1708  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1968   
1709  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1969   
1710  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1970   
1711  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1971   
1712  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1972   
1713  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1714  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1715  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1716  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1717  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1718  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
...                                                 ...           ...   ...   
2222  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2223  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2224  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2225  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2226  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2227  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2228  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2229  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2230  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2231  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2232  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2233  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2234  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2235  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2236  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2237  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2238  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2239  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2240  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2241  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2242  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2243  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2244  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2245  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   
2246  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   
2247  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   
2248  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   
2249  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   
2250  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   
2251  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2014   

      MM  
1689  13  
1690  13  
1691  13  
1692  13  
1693  13  
1694  13  
1695  13  
1696  13  
1697  13  
1698  13  
1699  13  
1700  13  
1701  13  
1702  13  
1703  13  
1704  13  
1705  13  
1706  13  
1707  13  
1708  13  
1709  13  
1710  13  
1711  13  
1712  13  
1713   1  
1714   2  
1715   3  
1716   4  
1717   5  
1718   6  
...   ..  
2222   3  
2223   4  
2224   5  
2225   6  
2226   7  
2227   8  
2228   9  
2229  10  
2230  11  
2231  12  
2232  13  
2233   1  
2234   2  
2235   3  
2236   4  
2237   5  
2238   6  
2239   7  
2240   8  
2241   9  
2242  10  
2243  11  
2244  12  
2245  13  
2246   1  
2247   2  
2248   3  
2249   4  
2250   5  
2251   6  

[563 rows x 8 columns]>



In [36]:

    
TFFCRS.groupby('YYYY')['Value'].describe()
#summary for each name
# a series method.









    Out[36]:





YYYY       
1949  count       1.000
      mean     3405.248
      std           NaN
      min      3405.248
      25%      3405.248
      50%      3405.248
      75%      3405.248
      max      3405.248
1950  count       1.000
      mean     3823.808
      std           NaN
      min      3823.808
      25%      3823.808
      50%      3823.808
      75%      3823.808
...
2013  mean      921.888923
      std      1557.094966
      min       172.098000
      25%       186.631000
      50%       458.182000
      75%       884.460000
      max      5992.084000
2014  count       6.000000
      mean      630.675500
      std       400.759693
      min       185.211000
      25%       299.010000
      50%       606.901000
      75%       922.982000
      max      1160.318000
Length: 528, dtype: float64



In [37]:

    
TFFCRS.groupby('YYYY')['Value'].mean()
#summary for each name
# a series method









    Out[37]:





YYYY
1949    3405.248
1950    3823.808
1951    4146.264
1952    4259.029
1953    4220.568
1954    4462.559
1955    4832.746
1956    5100.677
1957    5042.088
1958    5436.923
1959    5541.385
1960    6024.205
1961    6188.746
1962    6519.627
1963    6552.846
...
2000    1026.240615
2001     989.324846
2002     994.545231
2003    1041.412462
2004    1001.958000
2005     985.593923
2006     877.939538
2007     938.061385
2008     975.440462
2009     930.028846
2010     923.716231
2011     901.109846
2012     790.429615
2013     921.888923
2014     630.675500
Name: Value, Length: 66, dtype: float64



In [39]:

    
'''
need to clean 2014 data from my analysis
'''









    Out[39]:





'\nneed to clean 2014 data from my analysis\n'



In [31]:

    
#Regression
import statsmodels.formula.api as smf
model = smf.ols('Value ~ YYYY', data=TFFCRS)
results = model.fit()
results.summary()
# why is year negatively correlated?
# should increase over time- is there a total that is messing up things?









    Out[31]:





OLS Regression Results

  Dep. Variable:           Value         R-squared:             0.074 


  Model:                    OLS          Adj. R-squared:        0.073 


  Method:              Least Squares     F-statistic:           45.02 


  Date:              Sat, 08 Nov 2014    Prob (F-statistic):  4.77e-11 


  Time:                  15:02:13        Log-Likelihood:      -5024.2 


  No. Observations:          563         AIC:                1.005e+04


  Df Residuals:              561         BIC:                1.006e+04


  Df Model:                    1                                      




               coef      std err       t       P>|t|  [95.0% Conf. Int.] 


  Intercept   7.701e+04   1.13e+04      6.816   0.000   5.48e+04  9.92e+04


  YYYY         -38.0583      5.672     -6.710   0.000    -49.199   -26.917




  Omnibus:        261.867    Durbin-Watson:         1.371 


  Prob(Omnibus):   0.000     Jarque-Bera (JB):    900.937 


  Skew:            2.296     Prob(JB):           2.31e-196


  Kurtosis:        7.162     Cond. No.           2.93e+05



In [40]:

    
df = TFFCRS[TFFCRS.YYYY == 2013].sort_index(by='MM',ascending=False)
#nneed to skip 13
# or do the analyis where MMM =13



In [41]:

    
df









    Out[41]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      2245
       FFRCBUS
       201313
       5992.084
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       13
    
    
      2244
       FFRCBUS
       201312
        966.726
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       12
    
    
      2243
       FFRCBUS
       201311
        605.985
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       11
    
    
      2242
       FFRCBUS
       201310
        293.006
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       10
    
    
      2241
       FFRCBUS
       201309
        186.631
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        9
    
    
      2240
       FFRCBUS
       201308
        172.799
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        8
    
    
      2239
       FFRCBUS
       201307
        172.098
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        7
    
    
      2238
       FFRCBUS
       201306
        183.701
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        6
    
    
      2237
       FFRCBUS
       201305
        260.914
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        5
    
    
      2236
       FFRCBUS
       201304
        458.182
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        4
    
    
      2235
       FFRCBUS
       201303
        786.602
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        3
    
    
      2234
       FFRCBUS
       201302
        884.460
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        2
    
    
      2233
       FFRCBUS
       201301
       1021.368
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        1



In [ ]:



In [61]:

    
#Do the analysis by yearly data (full year)
Yearly_TFFCRS = TFFCRS[TFFCRS.MM == 13].sort_index(by='YYYY',ascending=True)
#need to skip 13
# or do the analyis where MMM =1



In [62]:

    
Yearly_TFFCRS.describe









    Out[62]:





<bound method DataFrame.describe of           MSN  YYYYMM     Value  Column_Order  \
1689  FFRCBUS  194913  3405.248             4   
1690  FFRCBUS  195013  3823.808             4   
1691  FFRCBUS  195113  4146.264             4   
1692  FFRCBUS  195213  4259.029             4   
1693  FFRCBUS  195313  4220.568             4   
1694  FFRCBUS  195413  4462.559             4   
1695  FFRCBUS  195513  4832.746             4   
1696  FFRCBUS  195613  5100.677             4   
1697  FFRCBUS  195713  5042.088             4   
1698  FFRCBUS  195813  5436.923             4   
1699  FFRCBUS  195913  5541.385             4   
1700  FFRCBUS  196013  6024.205             4   
1701  FFRCBUS  196113  6188.746             4   
1702  FFRCBUS  196213  6519.627             4   
1703  FFRCBUS  196313  6552.846             4   
1704  FFRCBUS  196413  6612.693             4   
1705  FFRCBUS  196513  6811.272             4   
1706  FFRCBUS  196613  7045.412             4   
1707  FFRCBUS  196713  7277.387             4   
1708  FFRCBUS  196813  7507.234             4   
1709  FFRCBUS  196913  7827.744             4   
1710  FFRCBUS  197013  7921.629             4   
1711  FFRCBUS  197113  8045.577             4   
1712  FFRCBUS  197213  8247.603             4   
1725  FFRCBUS  197313  7871.212             4   
1738  FFRCBUS  197413  7536.958             4   
1751  FFRCBUS  197513  7564.221             4   
1764  FFRCBUS  197613  7909.501             4   
1777  FFRCBUS  197713  7651.836             4   
1790  FFRCBUS  197813  7638.094             4   
...       ...     ...       ...           ...   
1868  FFRCBUS  198413  6230.899             4   
1881  FFRCBUS  198513  6138.219             4   
1894  FFRCBUS  198613  5986.190             4   
1907  FFRCBUS  198713  6073.374             4   
1920  FFRCBUS  198813  6446.785             4   
1933  FFRCBUS  198913  6589.516             4   
1946  FFRCBUS  199013  5916.202             4   
1959  FFRCBUS  199113  6073.429             4   
1972  FFRCBUS  199213  6244.245             4   
1985  FFRCBUS  199313  6528.036             4   
1998  FFRCBUS  199413  6389.085             4   
2011  FFRCBUS  199513  6345.499             4   
2024  FFRCBUS  199613  6854.495             4   
2037  FFRCBUS  199713  6531.036             4   
2050  FFRCBUS  199813  5961.746             4   
2063  FFRCBUS  199913  6313.783             4   
2076  FFRCBUS  200013  6670.213             4   
2089  FFRCBUS  200113  6430.080             4   
2102  FFRCBUS  200213  6464.162             4   
2115  FFRCBUS  200313  6768.494             4   
2128  FFRCBUS  200413  6512.558             4   
2141  FFRCBUS  200513  6405.628             4   
2154  FFRCBUS  200613  5706.282             4   
2167  FFRCBUS  200713  6096.872             4   
2180  FFRCBUS  200813  6339.689             4   
2193  FFRCBUS  200913  6044.486             4   
2206  FFRCBUS  201013  6003.429             4   
2219  FFRCBUS  201113  5856.595             4   
2232  FFRCBUS  201213  5137.410             4   
2245  FFRCBUS  201313  5992.084             4   

                                            Description          Unit  YYYY  \
1689  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1949   
1690  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1950   
1691  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1951   
1692  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1952   
1693  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1953   
1694  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1954   
1695  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1955   
1696  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1956   
1697  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1957   
1698  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1958   
1699  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1959   
1700  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1960   
1701  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1961   
1702  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1962   
1703  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1963   
1704  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1964   
1705  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1965   
1706  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1966   
1707  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1967   
1708  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1968   
1709  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1969   
1710  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1970   
1711  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1971   
1712  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1972   
1725  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1973   
1738  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1974   
1751  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1975   
1764  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1976   
1777  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1977   
1790  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1978   
...                                                 ...           ...   ...   
1868  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1984   
1881  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1985   
1894  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1986   
1907  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1987   
1920  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1988   
1933  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1989   
1946  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1990   
1959  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1991   
1972  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1992   
1985  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1993   
1998  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1994   
2011  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1995   
2024  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1996   
2037  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1997   
2050  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1998   
2063  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  1999   
2076  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2000   
2089  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2001   
2102  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2002   
2115  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2003   
2128  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2004   
2141  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2005   
2154  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2006   
2167  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2007   
2180  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2008   
2193  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2009   
2206  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2010   
2219  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2011   
2232  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2012   
2245  Total Fossil Fuels Consumed by the Residential...  Trillion Btu  2013   

      MM  
1689  13  
1690  13  
1691  13  
1692  13  
1693  13  
1694  13  
1695  13  
1696  13  
1697  13  
1698  13  
1699  13  
1700  13  
1701  13  
1702  13  
1703  13  
1704  13  
1705  13  
1706  13  
1707  13  
1708  13  
1709  13  
1710  13  
1711  13  
1712  13  
1725  13  
1738  13  
1751  13  
1764  13  
1777  13  
1790  13  
...   ..  
1868  13  
1881  13  
1894  13  
1907  13  
1920  13  
1933  13  
1946  13  
1959  13  
1972  13  
1985  13  
1998  13  
2011  13  
2024  13  
2037  13  
2050  13  
2063  13  
2076  13  
2089  13  
2102  13  
2115  13  
2128  13  
2141  13  
2154  13  
2167  13  
2180  13  
2193  13  
2206  13  
2219  13  
2232  13  
2245  13  

[65 rows x 8 columns]>



In [63]:

    
Yearly_TFFCRS









    Out[63]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      1689
       FFRCBUS
       194913
       3405.248
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1949
       13
    
    
      1690
       FFRCBUS
       195013
       3823.808
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1950
       13
    
    
      1691
       FFRCBUS
       195113
       4146.264
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1951
       13
    
    
      1692
       FFRCBUS
       195213
       4259.029
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1952
       13
    
    
      1693
       FFRCBUS
       195313
       4220.568
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1953
       13
    
    
      1694
       FFRCBUS
       195413
       4462.559
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1954
       13
    
    
      1695
       FFRCBUS
       195513
       4832.746
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1955
       13
    
    
      1696
       FFRCBUS
       195613
       5100.677
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1956
       13
    
    
      1697
       FFRCBUS
       195713
       5042.088
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1957
       13
    
    
      1698
       FFRCBUS
       195813
       5436.923
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1958
       13
    
    
      1699
       FFRCBUS
       195913
       5541.385
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1959
       13
    
    
      1700
       FFRCBUS
       196013
       6024.205
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1960
       13
    
    
      1701
       FFRCBUS
       196113
       6188.746
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1961
       13
    
    
      1702
       FFRCBUS
       196213
       6519.627
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1962
       13
    
    
      1703
       FFRCBUS
       196313
       6552.846
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1963
       13
    
    
      1704
       FFRCBUS
       196413
       6612.693
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1964
       13
    
    
      1705
       FFRCBUS
       196513
       6811.272
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1965
       13
    
    
      1706
       FFRCBUS
       196613
       7045.412
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1966
       13
    
    
      1707
       FFRCBUS
       196713
       7277.387
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1967
       13
    
    
      1708
       FFRCBUS
       196813
       7507.234
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1968
       13
    
    
      1709
       FFRCBUS
       196913
       7827.744
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1969
       13
    
    
      1710
       FFRCBUS
       197013
       7921.629
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1970
       13
    
    
      1711
       FFRCBUS
       197113
       8045.577
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1971
       13
    
    
      1712
       FFRCBUS
       197213
       8247.603
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1972
       13
    
    
      1725
       FFRCBUS
       197313
       7871.212
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1973
       13
    
    
      1738
       FFRCBUS
       197413
       7536.958
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1974
       13
    
    
      1751
       FFRCBUS
       197513
       7564.221
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1975
       13
    
    
      1764
       FFRCBUS
       197613
       7909.501
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1976
       13
    
    
      1777
       FFRCBUS
       197713
       7651.836
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1977
       13
    
    
      1790
       FFRCBUS
       197813
       7638.094
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1978
       13
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      1868
       FFRCBUS
       198413
       6230.899
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1984
       13
    
    
      1881
       FFRCBUS
       198513
       6138.219
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1985
       13
    
    
      1894
       FFRCBUS
       198613
       5986.190
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1986
       13
    
    
      1907
       FFRCBUS
       198713
       6073.374
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1987
       13
    
    
      1920
       FFRCBUS
       198813
       6446.785
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1988
       13
    
    
      1933
       FFRCBUS
       198913
       6589.516
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1989
       13
    
    
      1946
       FFRCBUS
       199013
       5916.202
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1990
       13
    
    
      1959
       FFRCBUS
       199113
       6073.429
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1991
       13
    
    
      1972
       FFRCBUS
       199213
       6244.245
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1992
       13
    
    
      1985
       FFRCBUS
       199313
       6528.036
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1993
       13
    
    
      1998
       FFRCBUS
       199413
       6389.085
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1994
       13
    
    
      2011
       FFRCBUS
       199513
       6345.499
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1995
       13
    
    
      2024
       FFRCBUS
       199613
       6854.495
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1996
       13
    
    
      2037
       FFRCBUS
       199713
       6531.036
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1997
       13
    
    
      2050
       FFRCBUS
       199813
       5961.746
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1998
       13
    
    
      2063
       FFRCBUS
       199913
       6313.783
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       1999
       13
    
    
      2076
       FFRCBUS
       200013
       6670.213
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2000
       13
    
    
      2089
       FFRCBUS
       200113
       6430.080
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2001
       13
    
    
      2102
       FFRCBUS
       200213
       6464.162
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2002
       13
    
    
      2115
       FFRCBUS
       200313
       6768.494
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2003
       13
    
    
      2128
       FFRCBUS
       200413
       6512.558
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2004
       13
    
    
      2141
       FFRCBUS
       200513
       6405.628
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2005
       13
    
    
      2154
       FFRCBUS
       200613
       5706.282
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2006
       13
    
    
      2167
       FFRCBUS
       200713
       6096.872
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2007
       13
    
    
      2180
       FFRCBUS
       200813
       6339.689
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2008
       13
    
    
      2193
       FFRCBUS
       200913
       6044.486
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2009
       13
    
    
      2206
       FFRCBUS
       201013
       6003.429
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2010
       13
    
    
      2219
       FFRCBUS
       201113
       5856.595
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2011
       13
    
    
      2232
       FFRCBUS
       201213
       5137.410
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2012
       13
    
    
      2245
       FFRCBUS
       201313
       5992.084
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       13
    
  

65 rows × 8 columns

explore by group The first concept we deal with here is pandas groupby. The idea is to group a dataframe by the values of a particular factor variable. The documentation can be found here: http://pandas.pydata.org/pandas-docs/dev/groupby.html. What we do here ia a technique called Split, Apply, and Combine.

split

region_groupby = df.groupby('region') print type(region_groupby) region_groupby.head()



In [64]:

    
#split
Yearly_TFFCRS.Description_groupby = df.groupby('Description')
print type(Yearly_TFFCRS.Description_groupby)
Yearly_TFFCRS.Description_groupby.head(100)
#something wrong here--- taking just 2013-- review naming below.









    



<class 'pandas.core.groupby.DataFrameGroupBy'>






    Out[64]:






  
    
      
      MSN
      YYYYMM
      Value
      Column_Order
      Description
      Unit
      YYYY
      MM
    
  
  
    
      2245
       FFRCBUS
       201313
       5992.084
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       13
    
    
      2244
       FFRCBUS
       201312
        966.726
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       12
    
    
      2243
       FFRCBUS
       201311
        605.985
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       11
    
    
      2242
       FFRCBUS
       201310
        293.006
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
       10
    
    
      2241
       FFRCBUS
       201309
        186.631
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        9
    
    
      2240
       FFRCBUS
       201308
        172.799
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        8
    
    
      2239
       FFRCBUS
       201307
        172.098
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        7
    
    
      2238
       FFRCBUS
       201306
        183.701
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        6
    
    
      2237
       FFRCBUS
       201305
        260.914
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        5
    
    
      2236
       FFRCBUS
       201304
        458.182
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        4
    
    
      2235
       FFRCBUS
       201303
        786.602
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        3
    
    
      2234
       FFRCBUS
       201302
        884.460
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        2
    
    
      2233
       FFRCBUS
       201301
       1021.368
       4
       Total Fossil Fuels Consumed by the Residential...
       Trillion Btu
       2013
        1



In [60]:

    
Yearly_TFFCRS.Description_groupby.describe()









    Out[60]:






  
    
      
      
      YYYYMM
      Value
      Column_Order
      YYYY
      MM
    
    
      Description
      
      
      
      
      
      
    
  
  
    
      Total Fossil Fuels Consumed by the Residential Sector
      count
           13.00000
         13.000000
       13
         13
       13.00000
    
    
      mean
       201307.00000
        921.888923
        4
       2013
        7.00000
    
    
      std
            3.89444
       1557.094966
        0
          0
        3.89444
    
    
      min
       201301.00000
        172.098000
        4
       2013
        1.00000
    
    
      25%
       201304.00000
        186.631000
        4
       2013
        4.00000
    
    
      50%
       201307.00000
        458.182000
        4
       2013
        7.00000
    
    
      75%
       201310.00000
        884.460000
        4
       2013
       10.00000
    
    
      max
       201313.00000
       5992.084000
        4
       2013
       13.00000



In [ ]:



In [68]:

    
'''
from
http://localhost:8888/notebooks/Dropbox/laptop-repos/OliveOilMachineLearning/Olives_at_BDF.ipynb
'''
#seaborn
#ggplot for python, but seaborn is similar -- passive grid.
# one thing for each region
#a groupby for plots
#using pandas.

g=sns.FacetGrid(Yearly_TFFCRS, col="Description")
#####g.map(plt.scatter);
g.map(plt.scatter,"Value", "MM");
##not working.









    



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-68-1d6b9aeebae6> in <module>()
     11 g=sns.FacetGrid(Yearly_TFFCRS, col="Description")
     12 #####g.map(plt.scatter);
---> 13 g.map(plt.scatter,"Value", "MM");
     14 #region one is very distinct on the x axis.
     15 # might be easy to split up south(1) from the others.

/home/perry/anaconda/lib/python2.7/site-packages/seaborn/axisgrid.pyc in map(self, func, *args, **kwargs)
    436 
    437         # Finalize the annotations and layout
--> 438         self._finalize_grid(args[:2])
    439 
    440         return self

/home/perry/anaconda/lib/python2.7/site-packages/seaborn/axisgrid.pyc in _finalize_grid(self, axlabels)
    523         self.set_axis_labels(*axlabels)
    524         self.set_titles()
--> 525         self.fig.tight_layout()
    526 
    527     def facet_axis(self, row_i, col_j):

/home/perry/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc in tight_layout(self, renderer, pad, h_pad, w_pad, rect)
   1605                                          rect=rect)
   1606 
-> 1607         self.subplots_adjust(**kwargs)
   1608 
   1609 

/home/perry/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc in subplots_adjust(self, *args, **kwargs)
   1465 
   1466         """
-> 1467         self.subplotpars.update(*args, **kwargs)
   1468         for ax in self.axes:
   1469             if not isinstance(ax, SubplotBase):

/home/perry/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc in update(self, left, bottom, right, top, wspace, hspace)
    215             if self.left >= self.right:
    216                 reset()
--> 217                 raise ValueError('left cannot be >= right')
    218 
    219             if self.bottom >= self.top:

ValueError: left cannot be >= right



In [72]:

    
#Yearly_TFFCRS.Description.plot()
#does not work
##Yearly_TFFCRS.Value.plot()
# why from 1700 to 2300?
##TFFCRS.Value.plot()
# why from 1700 to 2300?
#up and down given yearly and monthly data.
Yearly_TFFCRS.Value.plot()









    Out[72]:





<matplotlib.axes.AxesSubplot at 0x7f6aa943bc90>



In [75]:

    
grouped = Yearly_TFFCRS.Value.groupby('MM')['Value']
Yearly_TFFCRS['year_rank'] = grouped.transform(Series.rank)
idf = Yearly_TFFCRS.set_index(['Value', 'MM']) 
#idf.ix['Wesley']
idf.ix['Total Fossil Fuels Consumed by the Residential Sector'].year_rank.plot()
#KEY ERROR









    



---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-75-8f9605c56bfb> in <module>()
----> 1 grouped = Yearly_TFFCRS.Value.groupby('MM')['Value']
      2 Yearly_TFFCRS['year_rank'] = grouped.transform(Series.rank)
      3 idf = Yearly_TFFCRS.set_index(['Value', 'MM'])
      4 #idf.ix['Wesley']
      5 idf.ix['Total Fossil Fuels Consumed by the Residential Sector'].year_rank.plot()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
   2871         axis = self._get_axis_number(axis)
   2872         return groupby(self, by, axis=axis, level=level, as_index=as_index,
-> 2873                        sort=sort, group_keys=group_keys, squeeze=squeeze)
   2874 
   2875     def asfreq(self, freq, method=None, how=None, normalize=False):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
   1187         raise TypeError('invalid type: %s' % type(obj))
   1188 
-> 1189     return klass(obj, by, **kwds)
   1190 
   1191 

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze)
    379         if grouper is None:
    380             grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis,
--> 381                                                     level=level, sort=sort)
    382 
    383         self.obj = obj

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
   2108             exclusions.append(gpr)
   2109             name = gpr
-> 2110             gpr = obj[gpr]
   2111 
   2112         if isinstance(gpr, Categorical) and len(gpr) != len(obj):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
    500     def __getitem__(self, key):
    501         try:
--> 502             result = self.index.get_value(self, key)
    503 
    504             if not np.isscalar(result):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
   1398 
   1399         try:
-> 1400             return self._engine.get_value(s, k)
   1401         except KeyError as e1:
   1402             if len(self) > 0 and self.inferred_type in ['integer','boolean']:

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:3080)()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2809)()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3732)()

KeyError: 'MM'



In [83]:

    
#new dataframe-- key advantage of pandas
#plot
pd.value_counts(df.areastring, sort=False).plot(kind="bar");
#style from seaborn
#softgriding
#easy to read.
#could use additional labels.









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-83-0750d02b115a> in <module>()
      1 #new dataframe-- key advantage of pandas
      2 #plot
----> 3 pd.value_counts(df.areastring, sort=False).plot(kind="bar");
      4 #style from seaborn
      5 #softgriding

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
   1934                 return self[name]
   1935             raise AttributeError("'%s' object has no attribute '%s'" %
-> 1936                                  (type(self).__name__, name))
   1937 
   1938     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'areastring'



In [81]:

    
#birdseye, quick view of the data
pd.crosstab(Yearly_TFFCRS.Value, Yearly_TFFCRS.YYYY)









    Out[81]:






  
    
      YYYY
      1949
      1950
      1951
      1952
      1953
      1954
      1955
      1956
      1957
      1958
      ...
      2004
      2005
      2006
      2007
      2008
      2009
      2010
      2011
      2012
      2013
    
    
      Value
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      3405.248
       1
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      3823.808
       0
       1
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      4146.264
       0
       0
       1
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      4220.568
       0
       0
       0
       0
       1
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      4259.029
       0
       0
       0
       1
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      4462.559
       0
       0
       0
       0
       0
       1
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      4832.746
       0
       0
       0
       0
       0
       0
       1
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5042.088
       0
       0
       0
       0
       0
       0
       0
       0
       1
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5100.677
       0
       0
       0
       0
       0
       0
       0
       1
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5137.410
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       1
       0
    
    
      5436.923
       0
       0
       0
       0
       0
       0
       0
       0
       0
       1
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5541.385
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5706.282
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       1
       0
       0
       0
       0
       0
       0
       0
    
    
      5856.595
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       1
       0
       0
    
    
      5861.823
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5916.202
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5961.746
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
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       0
       0
    
    
      5986.190
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
    
    
      5992.084
       0
       0
       0
       0
       0
       0
       0
       0
       0
       0
      ...
       0
       0
       0
       0
       0
       0
       0
       0
       0
       1
    
    
      6003.429
       0
       0
       0
       0
       0
       0
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       0
       0
       0
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       0
       0
       0
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       1
       0
       0
       0
    
    
      6024.205
       0
       0
       0
       0
       0
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       0
       0
       0
       0
      ...
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       0
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       0
       0
       0
    
    
      6044.486
       0
       0
       0
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       0
       0
       0
       0
       0
      ...
       0
       0
       0
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       0
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       0
       0
    
    
      6073.374
       0
       0
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      6073.429
       0
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      6096.872
       0
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      ...
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       0
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       0
    
    
      6138.219
       0
       0
       0
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      6175.285
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      6176.587
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      6230.899
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      ...
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      ...
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      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      6405.628
       0
       0
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      6430.080
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      6552.846
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      6589.405
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      6589.516
       0
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65 rows × 65 columns



In [82]:

    
Yearly_TFFCRS.dtypes









    Out[82]:





MSN              object
YYYYMM            int64
Value           float64
Column_Order      int64
Description      object
Unit             object
YYYY              int64
MM                int64
dtype: object



In [69]:

    
# now rank of over time
# from http://localhost:8888/notebooks/Dropbox/laptop-repos/Babynames2/PlayingwithBabynames.ipynb
#Yearly_TFFCRS.Description
grouped = Yearly_TFFCRS.Description.groupby('YYYY')['Value']
Yearly_TFFCRS.Description['YYYY_rank'] = grouped.transform(Series.rank)
Yearly_TFFCRS.Description[Yearly_TFFCRS.Description.name == 'Total Fossil Fuels Consumed by the Residential Sector'].YYYY_rank.plot()

#
#-c:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
#Try using .loc[row_index,col_indexer] = value instead

# but still works, I guess.

# but my y axis is different. proportion rather than rank?









    



---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-69-3d8b6974a243> in <module>()
      2 # from http://localhost:8888/notebooks/Dropbox/laptop-repos/Babynames2/PlayingwithBabynames.ipynb
      3 #Yearly_TFFCRS.Description
----> 4 grouped = Yearly_TFFCRS.Description.groupby('YYYY')['Value']
      5 Yearly_TFFCRS.Description['YYYY_rank'] = grouped.transform(Series.rank)
      6 Yearly_TFFCRS.Description[Yearly_TFFCRS.Description.name == 'Total Fossil Fuels Consumed by the Residential Sector'].YYYY_rank.plot()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
   2871         axis = self._get_axis_number(axis)
   2872         return groupby(self, by, axis=axis, level=level, as_index=as_index,
-> 2873                        sort=sort, group_keys=group_keys, squeeze=squeeze)
   2874 
   2875     def asfreq(self, freq, method=None, how=None, normalize=False):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
   1187         raise TypeError('invalid type: %s' % type(obj))
   1188 
-> 1189     return klass(obj, by, **kwds)
   1190 
   1191 

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze)
    379         if grouper is None:
    380             grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis,
--> 381                                                     level=level, sort=sort)
    382 
    383         self.obj = obj

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
   2108             exclusions.append(gpr)
   2109             name = gpr
-> 2110             gpr = obj[gpr]
   2111 
   2112         if isinstance(gpr, Categorical) and len(gpr) != len(obj):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
    500     def __getitem__(self, key):
    501         try:
--> 502             result = self.index.get_value(self, key)
    503 
    504             if not np.isscalar(result):

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
   1398 
   1399         try:
-> 1400             return self._engine.get_value(s, k)
   1401         except KeyError as e1:
   1402             if len(self) > 0 and self.inferred_type in ['integer','boolean']:

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:3080)()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2809)()

/home/perry/anaconda/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3732)()

KeyError: 'YYYY'



In [59]:

    
#Regression
import statsmodels.formula.api as smf
model = smf.ols('Value ~ YYYY', data=Yearly_TFFCRS)
results = model.fit()
results.summary()
# 4% of variance explained. Very low.
# Year is statistically significant at the 10% CI, not at the 5% (barely) Could do one-directional.
# YYYY	13.1862	6.736	1.957	0.055	-0.275 26.648

# 65 observations--- compare to 13-- above--which is wrong.









    Out[59]:





OLS Regression Results

  Dep. Variable:           Value         R-squared:             0.057


  Model:                    OLS          Adj. R-squared:        0.042


  Method:              Least Squares     F-statistic:           3.832


  Date:              Sat, 08 Nov 2014    Prob (F-statistic):   0.0547 


  Time:                  15:51:28        Log-Likelihood:      -541.44


  No. Observations:           65         AIC:                   1087.


  Df Residuals:               63         BIC:                   1091.


  Df Model:                    1                                     




               coef      std err       t       P>|t|  [95.0% Conf. Int.] 


  Intercept  -1.984e+04   1.33e+04     -1.487   0.142  -4.65e+04  6824.665


  YYYY          13.1862      6.736      1.957   0.055     -0.275    26.648




  Omnibus:         0.048    Durbin-Watson:         0.105


  Prob(Omnibus):   0.976    Jarque-Bera (JB):      0.164


  Skew:            0.059    Prob(JB):              0.921


  Kurtosis:        2.785    Cond. No.           2.09e+05

Pivot Tables # Advice From Here: http://pbpython.com/simple-graphing-pandas.html



In [ ]:



In [ ]:



In [25]:

    
#a hierarchical index
Res_Energy_Consumption_1949_2012.groupby(['MM']).size().ix[13]

# a series.









    Out[25]:





780



In [26]:

    
#names.groupby(['sex','year']).size()
#names.groupby(['year','sex']).size().ix[2000]
Res_Energy_Consumption_1949_2012.groupby(['YYYY','Description']).size().ix[1980]
#a hierarchical index
# a series.









    Out[26]:





Description
Biomass Energy Consumed by the Residential Sector               13
Coal Consumed by the Residential Sector                         13
Electricity Retail Sales to the Residential Sector              13
Geothermal Energy Consumed by the Residential Sector            13
Natural Gas Consumed by the Residential Sector (Excluding Supplemental Gaseous Fuels)    13
Petroleum Consumed by the Residential Sector                    13
Residential Sector Electrical System Energy Losses              13
Solar/PV Energy Consumed by the Residential Sector              13
Total Energy Consumed by the Residential Sector                 13
Total Fossil Fuels Consumed by the Residential Sector           13
Total Primary Energy Consumed by the Residential Sector         13
Total Renewable Energy Consumed by the Residential Sector       13
dtype: int64



In [18]:

    
import numpy as np



In [19]:

    
from __future__ import division



In [20]:

    
Res_Energy_Consumption_1949_2012['YYYYfl'] = np.float32(Res_Energy_Consumption_1949_2012.YYYY)



In [37]:

    
Res_Energy_Consumption_1949_2012['MMfl'] = np.float32(Res_Energy_Consumption_1949_2012.MM)



In [27]:

    
Res_Energy_Consumption_1949_2012['Valuefl'] = np.double(Res_Energy_Consumption_1949_2012.Value)
# fixed no with text removed in csv



In [29]:

    
#boys[boys.year== 2000].prop.idxmax()
# gives the index of the maximum value: idmax
#boys.ix[boys[boys.year== 2000].prop.idxmax()]
# for the whole row
#Res_Energy_Consumption_1949_2012

Res_Energy_Consumption_1949_2012.ix[Res_Energy_Consumption_1949_2012[Res_Energy_Consumption_1949_2012.MMfl== 13].YYYYfl.idxmax()]


# this works: .MMfl== 13].YYYYfl.i
# but not this: .MMfl== 13].Valuefl.i
#because I can't get Value to convert to float. Is it in a bigger format? or should float constrain it?

#ValueError: could not convert string to float: Not Available
# why not available here?

#stanford engineering python classes online?









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-29-bfee83745abb> in <module>()
      5 #Res_Energy_Consumption_1949_2012
      6 
----> 7 Res_Energy_Consumption_1949_2012.ix[Res_Energy_Consumption_1949_2012[Res_Energy_Consumption_1949_2012.MMfl== 13].YYYYfl.idxmax()]
      8 
      9 

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
   1841                 return self[name]
   1842             raise AttributeError("'%s' object has no attribute '%s'" %
-> 1843                                  (type(self).__name__, name))
   1844 
   1845     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'MMfl'



In [28]:

    
def get_max_record(group):
    return group.ix[group.YYYY.idxmax()]
get_max_record(Res_Energy_Consumption_1949_2012) 

## Need to figure out how to take all the 13s for each year.









    Out[28]:





MSN                                             CLRCBUS
YYYYMM                                           201401
Value                                               NaN
Column_Order                                          1
Description     Coal Consumed by the Residential Sector
Unit                                       Trillion Btu
YYYY                                               2014
MM                                                    1
Valuefl                                             NaN
Name: 557, dtype: object



In [ ]:

    
#Res_Energy_Consumption_1949_2012
# df name



In [ ]:

    
#Rename a column
# http://stackoverflow.com/questions/11346283/renaming-columns-in-pandas 

# df.columns.values[0]='XX' 
#Res_Primary_E_Consumption.columns.values[1]='NaturalGas'



In [ ]:

    
#Rename a column
# http://stackoverflow.com/questions/11346283/renaming-columns-in-pandas 

# df.columns.values[0]='XX' 
#Res_Primary_E_Consumption.columns.values[-3]='MYTOTAL'



In [ ]:



In [ ]:

    
# split by year-- proportion in each year and stack them.
# 
#def get_max_record(group):
 #   return group.ix[group.prop.idxmax()]
#result =boys.groupby('year').apply(get_max_record)     
# my full data is not showing due to html, which I cannot remove.
# diversity of data going up.

def get_max_record(group):
    return group.ix[group.Value.idxmax()]
result = Res_Energy_Consumption_1949_2012.groupby('YYYYfl').apply(get_max_record)   

#why am I getting this error? The TOTAL column has been renamed to YESTOTAL.
#AttributeError: 'DataFrame' object has no attribute 'YESTOTAL'
#YESTOTAL works beow with result.

#no error when returned to TOTAL.



In [35]:

    
# split by year-- proportion in each year and stack them.


def get_max_record(group):
    return group.ix[group.Value.idxmax()]
Value_result =Res_Energy_Consumption_1949_2012.groupby('YYYY', 'Description').apply(get_max_record)









    



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-35-c0a0391c6323> in <module>()
      4 def get_max_record(group):
      5     return group.ix[group.Value.idxmax()]
----> 6 Value_result =Res_Energy_Consumption_1949_2012.groupby('YYYY', 'Description').apply(get_max_record)

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
   2769 
   2770         from pandas.core.groupby import groupby
-> 2771         axis = self._get_axis_number(axis)
   2772         return groupby(self, by, axis=axis, level=level, as_index=as_index,
   2773                        sort=sort, group_keys=group_keys, squeeze=squeeze)

/home/perry/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_axis_number(self, axis)
    293                 pass
    294         raise ValueError('No axis named {0} for object type {1}'
--> 295                          .format(axis, type(self)))
    296 
    297     def _get_axis_name(self, axis):

ValueError: No axis named Description for object type <class 'pandas.core.frame.DataFrame'>



In [36]:

    
# split by year-- proportion in each year and stack them.
# 
#def get_max_record(group):
 #   return group.ix[group.prop.idxmax()]
#result =boys.groupby('year').apply(get_max_record)     
# my full data is not showing due to html, which I cannot remove.
# diversity of data going up.

# NaturalGas
##def get_max_record(group):
  #  return group.ix[group.NaturalGas_perc.idxmax()]
##    return group.ix[group.Renewable_perc.idxmax()]
##NG_result =Res_Primary_E_Consumption.groupby('Year').apply(get_max_record)     

#Renewable_perc
#    return group.ix[group.NaturalGas.idxmax()]
# ??? AttributeError: 'DataFrame' object has no attribute 'NaturalGas'
# Why Not? It has TOTAL

#    return group.ix[group.NaturalGas_perc.idxmax()]
#ValueError: invalid literal for float(): 30%
# does it have this. but is it yeilding an error on this?

Total change over time



In [ ]:

    
NG_result.NaturalGas



In [38]:

    
#http://allendowney.blogspot.com/
# from http://allendowney.blogspot.com/
#Value by year.
###NEEDS TO BE DONE BY EACH DESCRIPTION TYPE. and compared.
# also chart each comparison.
import statsmodels.formula.api as smf
model = smf.ols('Value ~ YYYY', data=Res_Energy_Consumption_1949_2012)
results = model.fit()
results.summary()









    Out[38]:





OLS Regression Results

  Dep. Variable:           Value         R-squared:             0.011 


  Model:                    OLS          Adj. R-squared:        0.011 


  Method:              Least Squares     F-statistic:           70.37 


  Date:              Thu, 06 Nov 2014    Prob (F-statistic):  6.03e-17 


  Time:                  16:06:08        Log-Likelihood:      -55958. 


  No. Observations:         6208         AIC:                1.119e+05


  Df Residuals:             6206         BIC:                1.119e+05


  Df Model:                    1                                      




               coef      std err       t       P>|t|  [95.0% Conf. Int.] 


  Intercept   3.262e+04   3793.855      8.599   0.000   2.52e+04  4.01e+04


  YYYY         -15.9716      1.904     -8.389   0.000    -19.704   -12.239




  Omnibus:        7084.299    Durbin-Watson:          1.398 


  Prob(Omnibus):    0.000     Jarque-Bera (JB):   593911.548


  Skew:             6.046     Prob(JB):                0.00 


  Kurtosis:        49.366     Cond. No.            3.00e+05



In [30]:

    
result.TOTAL.plot()
# linear increase since modeled after 2010.
# but hits later









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-30-01aec75c7861> in <module>()
----> 1 result.TOTAL.plot()
      2 # linear increase since modeled after 2010.
      3 # but hits later

NameError: name 'result' is not defined



In [26]:

    
result.NaturalGas.plot()
# linear increase since modeled after 2010.
# but hits later









    Out[26]:





<matplotlib.axes.AxesSubplot at 0x7f24321809d0>



In [41]:

    
result[:50]









    Out[41]:






  
    
      
      Year
      NaturalGas
      NaturalGas_perc
      Petroleum
      Petroleum_perc
      Coal
      Coal_perc
      Renewable
      Renewable_perc
      Electricity_Sales
      Electricity_Losses
      Electricity_Total
      Electricity_Total_perc
      TOTAL
      TOTAL_perc
      Change_since2010
    
    
      Year
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1980
       1980
       4.79
       30%
       1.72
       11%
       0.03
       0%
       0.85
       5%
       2.45
        5.89
        8.33
       53%
       15.72
       100%
           -
    
    
      1981
       1981
       4.57
       30%
       1.52
       10%
       0.03
       0%
       0.87
       6%
       2.46
        5.77
        8.24
       54%
       15.23
       100%
           -
    
    
      1982
       1982
       4.68
       30%
       1.42
        9%
       0.03
       0%
       0.97
       6%
       2.49
        5.89
        8.38
       54%
       15.48
       100%
           -
    
    
      1983
       1983
       4.45
       29%
       1.33
        9%
       0.03
       0%
       0.97
       6%
       2.56
        6.03
        8.59
       56%
       15.38
       100%
           -
    
    
      1984
       1984
       4.64
       29%
       1.51
       10%
       0.04
       0%
       0.98
       6%
       2.66
        6.07
        8.73
       55%
       15.90
       100%
           -
    
    
      1985
       1985
       4.51
       28%
       1.55
       10%
       0.04
       0%
       1.01
       6%
       2.71
        6.21
        8.92
       56%
       16.02
       100%
           -
    
    
      1986
       1986
       4.38
       28%
       1.52
       10%
       0.04
       0%
       0.92
       6%
       2.79
        6.27
        9.07
       57%
       15.94
       100%
           -
    
    
      1987
       1987
       4.40
       27%
       1.60
       10%
       0.04
       0%
       0.85
       5%
       2.90
        6.42
        9.32
       58%
       16.21
       100%
           -
    
    
      1988
       1988
       4.72
       28%
       1.66
       10%
       0.04
       0%
       0.91
       5%
       3.05
        6.75
        9.79
       57%
       17.12
       100%
           -
    
    
      1989
       1989
       4.88
       28%
       1.64
        9%
       0.03
       0%
       0.97
       5%
       3.09
        7.13
       10.22
       58%
       17.76
       100%
           -
    
    
      1990
       1990
       4.47
       26%
       1.37
        8%
       0.03
       0%
       0.64
       4%
       3.15
        7.24
       10.39
       61%
       16.91
       100%
           -
    
    
      1991
       1991
       4.64
       27%
       1.36
        8%
       0.03
       0%
       0.67
       4%
       3.26
        7.42
       10.68
       61%
       17.37
       100%
           -
    
    
      1992
       1992
       4.78
       28%
       1.39
        8%
       0.03
       0%
       0.70
       4%
       3.19
        7.22
       10.41
       60%
       17.31
       100%
           -
    
    
      1993
       1993
       5.05
       28%
       1.42
        8%
       0.03
       0%
       0.61
       3%
       3.39
        7.68
       11.08
       61%
       18.18
       100%
           -
    
    
      1994
       1994
       4.94
       27%
       1.39
        8%
       0.02
       0%
       0.58
       3%
       3.44
        7.70
       11.14
       62%
       18.07
       100%
           -
    
    
      1995
       1995
       4.94
       27%
       1.35
        7%
       0.02
       0%
       0.58
       3%
       3.56
        8.03
       11.59
       63%
       18.49
       100%
           -
    
    
      1996
       1996
       5.35
       27%
       1.46
        7%
       0.02
       0%
       0.60
       3%
       3.69
        8.35
       12.04
       62%
       19.47
       100%
           -
    
    
      1997
       1997
       5.09
       27%
       1.40
        7%
       0.02
       0%
       0.49
       3%
       3.67
        8.27
       11.94
       63%
       18.93
       100%
           -
    
    
      1998
       1998
       4.64
       25%
       1.28
        7%
       0.01
       0%
       0.44
       2%
       3.86
        8.69
       12.55
       66%
       18.92
       100%
           -
    
    
      1999
       1999
       4.82
       25%
       1.43
        7%
       0.01
       0%
       0.46
       2%
       3.91
        8.88
       12.79
       66%
       19.52
       100%
           -
    
    
      2000
       2000
       5.07
       25%
       1.52
        7%
       0.01
       0%
       0.49
       2%
       4.07
        9.20
       13.27
       65%
       20.36
       100%
           -
    
    
      2001
       2001
       4.87
       24%
       1.50
        7%
       0.01
       0%
       0.43
       2%
       4.10
        9.08
       13.18
       66%
       20.00
       100%
           -
    
    
      2002
       2002
       4.98
       24%
       1.43
        7%
       0.01
       0%
       0.44
       2%
       4.32
        9.57
       13.89
       67%
       20.74
       100%
           -
    
    
      2003
       2003
       5.20
       25%
       1.49
        7%
       0.01
       0%
       0.46
       2%
       4.35
        9.55
       13.90
       66%
       21.06
       100%
           -
    
    
      2004
       2004
       4.97
       24%
       1.49
        7%
       0.01
       0%
       0.47
       2%
       4.41
        9.70
       14.10
       67%
       21.05
       100%
           -
    
    
      2005
       2005
       4.94
       23%
       1.42
        7%
       0.01
       0%
       0.49
       2%
       4.64
       10.08
       14.72
       68%
       21.58
       100%
           -
    
    
      2006
       2006
       4.46
       22%
       1.20
        6%
       0.01
       0%
       0.45
       2%
       4.61
        9.91
       14.53
       70%
       20.64
       100%
           -
    
    
      2007
       2007
       4.83
       22%
       1.22
        6%
       0.01
       0%
       0.50
       2%
       4.75
       10.19
       14.94
       69%
       21.49
       100%
           -
    
    
      2008
       2008
       5.00
       23%
       1.21
        6%
       0.01
       0%
       0.53
       2%
       4.71
       10.08
       14.79
       69%
       21.53
       100%
           -
    
    
      2009
       2009
       4.88
       23%
       1.14
        5%
       0.01
       0%
       0.52
       2%
       4.66
        9.80
       14.45
       69%
       20.99
       100%
           -
    
    
      2010
       2010
       5.06
       23%
       1.22
        6%
       0.01
       0%
       0.45
       2%
       4.95
       10.39
       15.34
       69%
       22.07
       100%
           -
    
    
      2011
       2011
       5.05
       23%
       1.19
        5%
       0.01
       0%
       0.47
       2%
       4.94
       10.45
       15.39
       70%
       22.10
       100%
        0.1%
    
    
      2012
       2012
       5.17
       24%
       1.19
        5%
       0.01
       0%
       0.50
       2%
       4.84
       10.07
       14.91
       68%
       21.77
       100%
       -0.7%
    
    
      2013
       2013
       4.99
       23%
       1.13
        5%
       0.01
       0%
       0.48
       2%
       4.79
        9.86
       14.66
       69%
       21.26
       100%
       -1.2%
    
    
      2014
       2014
       5.00
       24%
       1.11
        5%
       0.01
       0%
       0.49
       2%
       4.79
        9.75
       14.54
       69%
       21.15
       100%
       -1.1%
    
    
      2015
       2015
       4.99
       24%
       1.08
        5%
       0.01
       0%
       0.51
       2%
       4.79
        9.68
       14.47
       69%
       21.06
       100%
       -0.9%
    
    
      2016
       2016
       4.98
       24%
       1.06
        5%
       0.01
       0%
       0.53
       2%
       4.82
        9.72
       14.54
       69%
       21.13
       100%
       -0.7%
    
    
      2017
       2017
       4.98
       23%
       1.05
        5%
       0.01
       0%
       0.53
       2%
       4.87
        9.85
       14.72
       69%
       21.28
       100%
       -0.5%
    
    
      2018
       2018
       4.96
       23%
       1.03
        5%
       0.01
       0%
       0.53
       2%
       4.93
        9.95
       14.88
       69%
       21.41
       100%
       -0.4%
    
    
      2019
       2019
       4.95
       23%
       1.02
        5%
       0.01
       0%
       0.53
       2%
       4.98
       10.06
       15.05
       70%
       21.56
       100%
       -0.3%
    
    
      2020
       2020
       4.95
       23%
       1.01
        5%
       0.01
       0%
       0.54
       2%
       5.02
       10.15
       15.17
       70%
       21.66
       100%
       -0.2%
    
    
      2021
       2021
       4.93
       23%
       1.00
        5%
       0.01
       0%
       0.54
       2%
       5.07
       10.22
       15.29
       70%
       21.77
       100%
       -0.1%
    
    
      2022
       2022
       4.92
       22%
       0.99
        5%
       0.01
       0%
       0.54
       2%
       5.13
       10.31
       15.44
       71%
       21.89
       100%
       -0.1%
    
    
      2023
       2023
       4.90
       22%
       0.98
        4%
       0.01
       0%
       0.54
       2%
       5.18
       10.44
       15.62
       71%
       22.04
       100%
        0.0%
    
    
      2024
       2024
       4.89
       22%
       0.97
        4%
       0.01
       0%
       0.54
       2%
       5.24
       10.55
       15.79
       71%
       22.19
       100%
        0.0%
    
    
      2025
       2025
       4.88
       22%
       0.95
        4%
       0.01
       0%
       0.54
       2%
       5.30
       10.70
       16.00
       71%
       22.39
       100%
        0.1%
    
    
      2026
       2026
       4.87
       22%
       0.95
        4%
       0.01
       0%
       0.54
       2%
       5.37
       10.78
       16.14
       72%
       22.51
       100%
        0.1%
    
    
      2027
       2027
       4.86
       21%
       0.94
        4%
       0.01
       0%
       0.54
       2%
       5.43
       10.88
       16.31
       72%
       22.65
       100%
        0.2%
    
    
      2028
       2028
       4.85
       21%
       0.93
        4%
       0.01
       0%
       0.54
       2%
       5.49
       10.96
       16.45
       72%
       22.78
       100%
        0.2%
    
    
      2029
       2029
       4.85
       21%
       0.92
        4%
       0.01
       0%
       0.55
       2%
       5.56
       11.03
       16.59
       72%
       22.91
       100%
        0.2%



In [43]:

    
# df.[variablename].cumsum()

Res_Primary_E_Consumption.TOTAL.cumsum()[:130]
#first 130









    Out[43]:





0       15.72
1       30.95
2       46.43
3       61.81
4       77.71
5       93.73
6      109.67
7      125.88
8      143.00
9      160.76
10     177.67
11     195.04
12     212.35
13     230.53
14     248.60
15     267.09
16     286.56
17     305.49
18     324.41
19     343.93
20     364.29
21     384.29
22     405.03
23     426.09
24     447.14
25     468.72
26     489.36
27     510.85
28     532.38
29     553.37
30     575.44
31     597.54
32     619.31
33     640.57
34     661.72
35     682.78
36     703.91
37     725.19
38     746.60
39     768.16
40     789.82
41     811.59
42     833.48
43     855.52
44     877.71
45     900.10
46     922.61
47     945.26
48     968.04
49     990.95
50    1014.01
51    1037.20
52    1060.50
53    1083.93
54    1107.49
55    1131.18
Name: TOTAL, Length: 56, dtype: float64



In [ ]:



In [46]:

    
TestData2 = read_csv('TestData2.csv')



In [25]:

    
# pandas join function is merge
#merge?
merge(Res_Primary_E_Consumption, TestData2, on= [ 'Year'])
#simple join on year 
#inner join is default









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-25-d073424a4fd8> in <module>()
      1 # pandas join function is merge
      2 #merge?
----> 3 merge(Res_Primary_E_Consumption, TestData2, on= [ 'Year'])
      4 #simple join on year
      5 #inner join is default

NameError: name 'Res_Primary_E_Consumption' is not defined

# note that many to many joins create the cartesian product of the two tables.



In [51]:

    
merged = merge(Res_Primary_E_Consumption, TestData2, on= [ 'Year'])



In [72]:

    
from __future__ import division



In [93]:

    
merged['NG'] = np.float32(merged.NaturalGas)



In [95]:

    
merged['TTL']  = np.float32(merged.TOTAL)



In [101]:

    
merged['RNW']  = np.float32(merged.Renewable)



In [101]:

    
merged['RNW']  = np.float32(merged.Renewable)



In [98]:

    
# Create a new proportion variable
merged['NG_percent'] = (merged.NG / merged.TTL)



In [106]:

    
#Create a new variable
merged['RNW_percent'] = (merged.RNW / merged.TTL)



In [ ]:



In [53]:

    
# total test.. Create a new column called TOTALtest,
# need to add the number of persons
merged['TOTALtest'] = np.floor(merged.TOTAL * merged.Col2)

#decimal numbers so use npl.floor



In [99]:

    
merged.head()









    Out[99]:






  
    
      
      Year
      NaturalGas
      NaturalGas_perc
      Petroleum
      Petroleum_perc
      Coal
      Coal_perc
      Renewable
      Renewable_perc
      Electricity_Sales
      ...
      TOTAL
      TOTAL_perc
      Change_since2010
      Col1
      Col2
      Col3
      TOTALtest
      NG_percent
      NG
      TTL
    
  
  
    
      0
       1980
       4.79
       30%
       1.72
       11%
       0.03
       0%
       0.85
       5%
       2.45
      ...
       15.72
       100%
       -
       1
       0.229849
       1
       75
       0.304707
       4.79
       15.72
    
    
      1
       1981
       4.57
       30%
       1.52
       10%
       0.03
       0%
       0.87
       6%
       2.46
      ...
       15.23
       100%
       -
       2
       0.003063
       2
       69
       0.300066
       4.57
       15.23
    
    
      2
       1982
       4.68
       30%
       1.42
        9%
       0.03
       0%
       0.97
       6%
       2.49
      ...
       15.48
       100%
       -
       3
       0.418250
       1
       72
       0.302326
       4.68
       15.48
    
    
      3
       1983
       4.45
       29%
       1.33
        9%
       0.03
       0%
       0.97
       6%
       2.56
      ...
       15.38
       100%
       -
       4
       0.689310
       2
       68
       0.289337
       4.45
       15.38
    
    
      4
       1984
       4.64
       29%
       1.51
       10%
       0.04
       0%
       0.98
       6%
       2.66
      ...
       15.90
       100%
       -
       5
       0.432489
       3
       73
       0.291824
       4.64
       15.90
    
  

5 rows × 23 columns



In [ ]:

    
# total test.. Create a new column called TOTALtest,
# need to add the number of persons
merged['TOTALtest'] = np.floor(merged.TOTAL * merged.Col2)

#decimal numbers so use npl.floor



In [57]:

    
merged.groupby(['Year', 'TOTALtest'])['Col3'].sum()
#for each year TOTALtest combo-- the total number of Col3.
#hierarchical labeling.









    Out[57]:





Year  TOTALtest
1980  3             1
1981  0             2
1982  6             1
1983  10            2
1984  6             3
1985  15            4
1986  3             5
1987  9             6
1988  10            7
1989  5             8
1990  6             9
1991  2            10
1992  5            11
1993  0            12
1994  11           13
1995  11           14
1996  17           15
1997  9            16
1998  15           17
1999  11           18
2000  13           19
2001  10           20
2002  0            21
2003  7            22
2004  11           23
2005  19           24
2006  17           25
2007  6            26
2008  9            27
2009  18           28
2010  19           29
2011  7            30
2012  6            31
2013  9            32
2014  17           33
2015  17           34
2016  12           35
2017  0            36
2018  16           37
2019  4            38
2020  13           39
2021  11           40
2022  20           41
2023  19           42
2024  7            43
2025  12           44
2026  2            45
2027  12           46
2028  5            47
2029  0            48
2030  1            49
2031  7            50
2032  15           51
2033  19           52
2034  2            53
2035  3            54
Name: Col3, Length: 56, dtype: int64



In [24]:

    
# Regression Test-- 
#made up independent variable.
import statsmodels.formula.api as smf
model = smf.ols('merged.TOTAL ~ merged.Col2', data=merged)
results_merged = model.fit()
results_merged.summary()









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-24-1dd5e67828a3> in <module>()
      2 #made up independent variable.
      3 import statsmodels.formula.api as smf
----> 4 model = smf.ols('merged.TOTAL ~ merged.Col2', data=merged)
      5 results_merged = model.fit()
      6 results_merged.summary()

NameError: name 'merged' is not defined



In [ ]:



In [84]:

    
#result = _   # underscore in ipython: the output of the last statement, because we don't want to compute again the same thing.



In [108]:

    
#result['Change_since2010'].plot()
#result['TTL'].plot()



In [23]:

    
#import numpy as np
#import matplotlib.pyplot as plt

# evenly sampled time at 200ms intervals
#t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
#plt.plot(merged.Year, merged.TOTAL, 'r--')
#plt.show()

plt.plot(merged.Year, merged.TOTAL, 'rs', merged.Year,merged.NaturalGas, 'b--', merged.Year,merged.Coal, 'k--',merged.Year,merged.Renewable, 'g--',
         merged.Year,merged.Electricity_Total, 'm--')
plt.ylabel('MMBtu')
plt.xlabel('Year')
plt.title('US Residential Energy Use (MMBtu)',fontsize=16)
plt.show()

#result['merged.TOTAL',
#'NaturalGas',
#'Petroleum',
#'Coal',
#'Renewable',
#'Electricity_Total'].plot()









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-23-0f641c68de85> in <module>()
      9 #plt.show()
     10 
---> 11 plt.plot(merged.Year, merged.TOTAL, 'rs', merged.Year,merged.NaturalGas, 'b--', merged.Year,merged.Coal, 'k--',merged.Year,merged.Renewable, 'g--',
     12          merged.Year,merged.Electricity_Total, 'm--')
     13 plt.ylabel('MMBtu')

NameError: name 'merged' is not defined



In [96]:

    
mgirls = merge(girls, births)  # inner join by default.



In [97]:

    
mgirls['persons'] = np.floor(mgirls.prop * mgirls.births)



In [98]:

    
gpersons = mgirls.set_index(['year', 'name']).persons   # Select out persons



In [144]:

    
type(merged.TTL)  # One dimensional series.









    Out[144]:





pandas.core.series.Series



In [145]:

    
result = _   # underscore in ipython: the output of the last statement, because we don't want to compute again the same thing.



In [146]:

    
#pandas.DataFrame.mean
#DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
#Return the mean of the values for the requested axis
merged.TTL.mean()









    Out[146]:





20.199642811502731



In [33]:

    
#pandas.DataFrame.mean
#DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
#Return the mean of the values for the requested axis
Res_Primary_E_Consumption.mean()









    Out[33]:





Year                  2007.500000
NaturalGas               4.853214
Petroleum                1.232321
Coal                     0.016607
Renewable                0.599643
Electricity_Sales        4.341429
Electricity_Losses       9.158393
Electricity_Total       13.500179
TOTAL                   20.199643
dtype: float64



In [34]:

    
#pandas.DataFrame.mean
#DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
#Return the mean of the values for the requested axis
Res_Primary_E_Consumption.TOTAL.mean()









    Out[34]:





20.199642857142855

pandas.DataFrame.mean DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) Return the mean of the values for the requested axis Parameters : axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use everything, then use only numeric data Returns : mean : Series or DataFrame (if level specified) Res_Primary_E_Consumption.TOTAL.mean(axis=None, skipna=None, level=None, numeric_only=None)



In [35]:

    
Res_Primary_E_Consumption.TOTAL.mean(axis=None, skipna=None, level=None, numeric_only=None)









    Out[35]:





20.199642857142855



In [ ]:

	MSN	YYYYMM	Value	Column_Order	Description	Unit	YYYY	MM
0	CLRCBUS	194913	1271.551	1	Coal Consumed by the Residential Sector	Trillion Btu	1949	13
1	CLRCBUS	195013	1261.267	1	Coal Consumed by the Residential Sector	Trillion Btu	1950	13
2	CLRCBUS	195113	1158.679	1	Coal Consumed by the Residential Sector	Trillion Btu	1951	13
3	CLRCBUS	195213	1079.206	1	Coal Consumed by the Residential Sector	Trillion Btu	1952	13
4	CLRCBUS	195313	965.664	1	Coal Consumed by the Residential Sector	Trillion Btu	1953	13
5	CLRCBUS	195413	858.255	1	Coal Consumed by the Residential Sector	Trillion Btu	1954	13
6	CLRCBUS	195513	867.431	1	Coal Consumed by the Residential Sector	Trillion Btu	1955	13
7	CLRCBUS	195613	838.910	1	Coal Consumed by the Residential Sector	Trillion Btu	1956	13
8	CLRCBUS	195713	653.734	1	Coal Consumed by the Residential Sector	Trillion Btu	1957	13
9	CLRCBUS	195813	663.496	1	Coal Consumed by the Residential Sector	Trillion Btu	1958	13

	MSN	YYYYMM	Value	Column_Order	Description	Unit	YYYY	MM
6746	TERCBUS	201310	1368.665	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2013	10
6747	TERCBUS	201311	1712.673	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2013	11
6748	TERCBUS	201312	2402.746	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2013	12
6749	TERCBUS	201313	21286.839	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2013	13
6750	TERCBUS	201401	2773.735	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	1
6751	TERCBUS	201402	2330.009	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	2
6752	TERCBUS	201403	2063.318	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	3
6753	TERCBUS	201404	1425.137	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	4
6754	TERCBUS	201405	1346.358	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	5
6755	TERCBUS	201406	1501.449	12	Total Energy Consumed by the Residential Sector	Trillion Btu	2014	6

	Value
Description
Biomass Energy Consumed by the Residential Sector	63565.665
Coal Consumed by the Residential Sector	16806.390
Electricity Retail Sales to the Residential Sector	303964.024
Geothermal Energy Consumed by the Residential Sector	818.356
Natural Gas Consumed by the Residential Sector (Excluding Supplemental Gaseous Fuels)	477851.187
Petroleum Consumed by the Residential Sector	182442.105
Residential Sector Electrical System Energy Losses	682645.859
Solar/PV Energy Consumed by the Residential Sector	4110.424
Total Energy Consumed by the Residential Sector	1732203.987
Total Fossil Fuels Consumed by the Residential Sector	677099.677

	MSN	YYYYMM	Value	Column_Order	Description	Unit	YYYY	MM
1689	FFRCBUS	194913	3405.248	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1949	13
1690	FFRCBUS	195013	3823.808	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1950	13
1691	FFRCBUS	195113	4146.264	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1951	13
1692	FFRCBUS	195213	4259.029	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1952	13
1693	FFRCBUS	195313	4220.568	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1953	13
1694	FFRCBUS	195413	4462.559	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1954	13
1695	FFRCBUS	195513	4832.746	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1955	13
1696	FFRCBUS	195613	5100.677	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1956	13
1697	FFRCBUS	195713	5042.088	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1957	13
1698	FFRCBUS	195813	5436.923	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	1958	13

Dep. Variable:	Value	R-squared:	0.074
Model:	OLS	Adj. R-squared:	0.073
Method:	Least Squares	F-statistic:	45.02
Date:	Sat, 08 Nov 2014	Prob (F-statistic):	4.77e-11
Time:	15:02:13	Log-Likelihood:	-5024.2
No. Observations:	563	AIC:	1.005e+04
Df Residuals:	561	BIC:	1.006e+04
Df Model:	1

	coef	std err	t	P>\|t\|	[95.0% Conf. Int.]
Intercept	7.701e+04	1.13e+04	6.816	0.000	5.48e+04 9.92e+04
YYYY	-38.0583	5.672	-6.710	0.000	-49.199 -26.917

Omnibus:	261.867	Durbin-Watson:	1.371
Prob(Omnibus):	0.000	Jarque-Bera (JB):	900.937
Skew:	2.296	Prob(JB):	2.31e-196
Kurtosis:	7.162	Cond. No.	2.93e+05

	MSN	YYYYMM	Value	Column_Order	Description	Unit	YYYY	MM
2245	FFRCBUS	201313	5992.084	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	13
2244	FFRCBUS	201312	966.726	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	12
2243	FFRCBUS	201311	605.985	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	11
2242	FFRCBUS	201310	293.006	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	10
2241	FFRCBUS	201309	186.631	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	9
2240	FFRCBUS	201308	172.799	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	8
2239	FFRCBUS	201307	172.098	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	7
2238	FFRCBUS	201306	183.701	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	6
2237	FFRCBUS	201305	260.914	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	5
2236	FFRCBUS	201304	458.182	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	4
2235	FFRCBUS	201303	786.602	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	3
2234	FFRCBUS	201302	884.460	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	2
2233	FFRCBUS	201301	1021.368	4	Total Fossil Fuels Consumed by the Residential...	Trillion Btu	2013	1

		YYYYMM	Value	Column_Order	YYYY	MM
Description
Total Fossil Fuels Consumed by the Residential Sector	count	13.00000	13.000000	13	13	13.00000
	mean	201307.00000	921.888923	4	2013	7.00000
	std	3.89444	1557.094966	0	0	3.89444
	min	201301.00000	172.098000	4	2013	1.00000
	25%	201304.00000	186.631000	4	2013	4.00000
	50%	201307.00000	458.182000	4	2013	7.00000
	75%	201310.00000	884.460000	4	2013	10.00000
	max	201313.00000	5992.084000	4	2013	13.00000

Omnibus:	0.048	Durbin-Watson:	0.105
Prob(Omnibus):	0.976	Jarque-Bera (JB):	0.164
Skew:	0.059	Prob(JB):	0.921
Kurtosis:	2.785	Cond. No.	2.09e+05

Omnibus:	7084.299	Durbin-Watson:	1.398
Prob(Omnibus):	0.000	Jarque-Bera (JB):	593911.548
Skew:	6.046	Prob(JB):	0.00
Kurtosis:	49.366	Cond. No.	3.00e+05

	Year	NaturalGas	NaturalGas_perc	Petroleum	Petroleum_perc	Coal	Coal_perc	Renewable	Renewable_perc	Electricity_Sales	Electricity_Losses	Electricity_Total	Electricity_Total_perc	TOTAL	TOTAL_perc	Change_since2010
Year
1980	1980	4.79	30%	1.72	11%	0.03	0%	0.85	5%	2.45	5.89	8.33	53%	15.72	100%	-
1981	1981	4.57	30%	1.52	10%	0.03	0%	0.87	6%	2.46	5.77	8.24	54%	15.23	100%	-
1982	1982	4.68	30%	1.42	9%	0.03	0%	0.97	6%	2.49	5.89	8.38	54%	15.48	100%	-
1983	1983	4.45	29%	1.33	9%	0.03	0%	0.97	6%	2.56	6.03	8.59	56%	15.38	100%	-
1984	1984	4.64	29%	1.51	10%	0.04	0%	0.98	6%	2.66	6.07	8.73	55%	15.90	100%	-
1985	1985	4.51	28%	1.55	10%	0.04	0%	1.01	6%	2.71	6.21	8.92	56%	16.02	100%	-
1986	1986	4.38	28%	1.52	10%	0.04	0%	0.92	6%	2.79	6.27	9.07	57%	15.94	100%	-
1987	1987	4.40	27%	1.60	10%	0.04	0%	0.85	5%	2.90	6.42	9.32	58%	16.21	100%	-
1988	1988	4.72	28%	1.66	10%	0.04	0%	0.91	5%	3.05	6.75	9.79	57%	17.12	100%	-
1989	1989	4.88	28%	1.64	9%	0.03	0%	0.97	5%	3.09	7.13	10.22	58%	17.76	100%	-
1990	1990	4.47	26%	1.37	8%	0.03	0%	0.64	4%	3.15	7.24	10.39	61%	16.91	100%	-
1991	1991	4.64	27%	1.36	8%	0.03	0%	0.67	4%	3.26	7.42	10.68	61%	17.37	100%	-
1992	1992	4.78	28%	1.39	8%	0.03	0%	0.70	4%	3.19	7.22	10.41	60%	17.31	100%	-
1993	1993	5.05	28%	1.42	8%	0.03	0%	0.61	3%	3.39	7.68	11.08	61%	18.18	100%	-
1994	1994	4.94	27%	1.39	8%	0.02	0%	0.58	3%	3.44	7.70	11.14	62%	18.07	100%	-
1995	1995	4.94	27%	1.35	7%	0.02	0%	0.58	3%	3.56	8.03	11.59	63%	18.49	100%	-
1996	1996	5.35	27%	1.46	7%	0.02	0%	0.60	3%	3.69	8.35	12.04	62%	19.47	100%	-
1997	1997	5.09	27%	1.40	7%	0.02	0%	0.49	3%	3.67	8.27	11.94	63%	18.93	100%	-
1998	1998	4.64	25%	1.28	7%	0.01	0%	0.44	2%	3.86	8.69	12.55	66%	18.92	100%	-
1999	1999	4.82	25%	1.43	7%	0.01	0%	0.46	2%	3.91	8.88	12.79	66%	19.52	100%	-
2000	2000	5.07	25%	1.52	7%	0.01	0%	0.49	2%	4.07	9.20	13.27	65%	20.36	100%	-
2001	2001	4.87	24%	1.50	7%	0.01	0%	0.43	2%	4.10	9.08	13.18	66%	20.00	100%	-
2002	2002	4.98	24%	1.43	7%	0.01	0%	0.44	2%	4.32	9.57	13.89	67%	20.74	100%	-
2003	2003	5.20	25%	1.49	7%	0.01	0%	0.46	2%	4.35	9.55	13.90	66%	21.06	100%	-
2004	2004	4.97	24%	1.49	7%	0.01	0%	0.47	2%	4.41	9.70	14.10	67%	21.05	100%	-
2005	2005	4.94	23%	1.42	7%	0.01	0%	0.49	2%	4.64	10.08	14.72	68%	21.58	100%	-
2006	2006	4.46	22%	1.20	6%	0.01	0%	0.45	2%	4.61	9.91	14.53	70%	20.64	100%	-
2007	2007	4.83	22%	1.22	6%	0.01	0%	0.50	2%	4.75	10.19	14.94	69%	21.49	100%	-
2008	2008	5.00	23%	1.21	6%	0.01	0%	0.53	2%	4.71	10.08	14.79	69%	21.53	100%	-
2009	2009	4.88	23%	1.14	5%	0.01	0%	0.52	2%	4.66	9.80	14.45	69%	20.99	100%	-
2010	2010	5.06	23%	1.22	6%	0.01	0%	0.45	2%	4.95	10.39	15.34	69%	22.07	100%	-
2011	2011	5.05	23%	1.19	5%	0.01	0%	0.47	2%	4.94	10.45	15.39	70%	22.10	100%	0.1%
2012	2012	5.17	24%	1.19	5%	0.01	0%	0.50	2%	4.84	10.07	14.91	68%	21.77	100%	-0.7%
2013	2013	4.99	23%	1.13	5%	0.01	0%	0.48	2%	4.79	9.86	14.66	69%	21.26	100%	-1.2%
2014	2014	5.00	24%	1.11	5%	0.01	0%	0.49	2%	4.79	9.75	14.54	69%	21.15	100%	-1.1%
2015	2015	4.99	24%	1.08	5%	0.01	0%	0.51	2%	4.79	9.68	14.47	69%	21.06	100%	-0.9%
2016	2016	4.98	24%	1.06	5%	0.01	0%	0.53	2%	4.82	9.72	14.54	69%	21.13	100%	-0.7%
2017	2017	4.98	23%	1.05	5%	0.01	0%	0.53	2%	4.87	9.85	14.72	69%	21.28	100%	-0.5%
2018	2018	4.96	23%	1.03	5%	0.01	0%	0.53	2%	4.93	9.95	14.88	69%	21.41	100%	-0.4%
2019	2019	4.95	23%	1.02	5%	0.01	0%	0.53	2%	4.98	10.06	15.05	70%	21.56	100%	-0.3%
2020	2020	4.95	23%	1.01	5%	0.01	0%	0.54	2%	5.02	10.15	15.17	70%	21.66	100%	-0.2%
2021	2021	4.93	23%	1.00	5%	0.01	0%	0.54	2%	5.07	10.22	15.29	70%	21.77	100%	-0.1%
2022	2022	4.92	22%	0.99	5%	0.01	0%	0.54	2%	5.13	10.31	15.44	71%	21.89	100%	-0.1%
2023	2023	4.90	22%	0.98	4%	0.01	0%	0.54	2%	5.18	10.44	15.62	71%	22.04	100%	0.0%
2024	2024	4.89	22%	0.97	4%	0.01	0%	0.54	2%	5.24	10.55	15.79	71%	22.19	100%	0.0%
2025	2025	4.88	22%	0.95	4%	0.01	0%	0.54	2%	5.30	10.70	16.00	71%	22.39	100%	0.1%
2026	2026	4.87	22%	0.95	4%	0.01	0%	0.54	2%	5.37	10.78	16.14	72%	22.51	100%	0.1%
2027	2027	4.86	21%	0.94	4%	0.01	0%	0.54	2%	5.43	10.88	16.31	72%	22.65	100%	0.2%
2028	2028	4.85	21%	0.93	4%	0.01	0%	0.54	2%	5.49	10.96	16.45	72%	22.78	100%	0.2%
2029	2029	4.85	21%	0.92	4%	0.01	0%	0.55	2%	5.56	11.03	16.59	72%	22.91	100%	0.2%