In [74]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
In [2]:
fn1 = 'EPA emissions.txt'
fn2 = 'may_generator2016.xlsx'
fn3 = 'EIA923_Schedules_2_3_4_5_M_10_2016.xlsx'
In [3]:
emissions = pd.read_csv(fn1)
In [4]:
emissions.head()
Out[4]:
State
Facility Name
Facility ID (ORISPL)
Unit ID
Associated Stacks
Month
Year
Program(s)
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
NERC Region
Unit Type
Fuel Type (Primary)
AL
AMEA Sylacauga Plant
56018
1
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
28.00
1149.0
NaN
0.004
0.0759
0.473
732.800
12428.200
4
SERC
Combustion turbine
Pipeline Natural Gas
NaN
AL
AMEA Sylacauga Plant
56018
2
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
17.00
642.0
NaN
0.002
0.0809
0.283
412.300
6984.600
4
SERC
Combustion turbine
Pipeline Natural Gas
NaN
AL
Barry
3
1
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
11.0
NaN
0.005
0.0080
0.001
14.213
240.050
4
SERC
Tangentially-fired
Pipeline Natural Gas
NaN
AL
Barry
3
2
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
11.0
NaN
0.005
0.0080
0.001
14.213
240.050
4
SERC
Tangentially-fired
Pipeline Natural Gas
NaN
AL
Barry
3
4
NaN
5
2016
ARP, MATS, CSNOX, CSNOXOS, CSSO2G2
725.25
116336.0
NaN
572.537
0.4319
265.224
128402.000
1251483.675
4
SERC
Tangentially-fired
Coal
NaN
From the read_csv docstring:
index_col : int or sequence or False, default None
Column to use as the row labels of the DataFrame. If a sequence is given, a
MultiIndex is used. If you have a malformed file with delimiters at the end
of each line, you might consider index_col=False to force pandas to _not_
use the first column as the index (row names)
In [7]:
emissions = pd.read_csv(fn1, index_col=False)
In [8]:
emissions.head()
Out[8]:
State
Facility Name
Facility ID (ORISPL)
Unit ID
Associated Stacks
Month
Year
Program(s)
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
NERC Region
Unit Type
Fuel Type (Primary)
0
AL
AMEA Sylacauga Plant
56018
1
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
28.00
1149.0
NaN
0.004
0.0759
0.473
732.800
12428.200
4
SERC
Combustion turbine
Pipeline Natural Gas
1
AL
AMEA Sylacauga Plant
56018
2
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
17.00
642.0
NaN
0.002
0.0809
0.283
412.300
6984.600
4
SERC
Combustion turbine
Pipeline Natural Gas
2
AL
Barry
3
1
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
11.0
NaN
0.005
0.0080
0.001
14.213
240.050
4
SERC
Tangentially-fired
Pipeline Natural Gas
3
AL
Barry
3
2
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
11.0
NaN
0.005
0.0080
0.001
14.213
240.050
4
SERC
Tangentially-fired
Pipeline Natural Gas
4
AL
Barry
3
4
NaN
5
2016
ARP, MATS, CSNOX, CSNOXOS, CSSO2G2
725.25
116336.0
NaN
572.537
0.4319
265.224
128402.000
1251483.675
4
SERC
Tangentially-fired
Coal
In [9]:
emissions.tail()
Out[9]:
State
Facility Name
Facility ID (ORISPL)
Unit ID
Associated Stacks
Month
Year
Program(s)
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
NERC Region
Unit Type
Fuel Type (Primary)
3919
WY
Neil Simpson II (CT2)
55477
CT2
NaN
5
2016
ARP
2.16
69.00
NaN
0.000
0.0485
0.020
39.911
672.960
8
NaN
Combustion turbine
Pipeline Natural Gas
3920
WY
Wygen I
55479
001
NaN
5
2016
ARP, MATS
662.62
58852.88
NaN
24.672
0.1298
43.135
68889.530
656851.004
8
NaN
Dry bottom wall-fired boiler
Coal
3921
WY
Wygen II
56319
001
NaN
5
2016
ARP, MATS
744.00
69215.00
NaN
17.378
0.0534
20.303
79420.900
757259.300
8
WECC
Dry bottom wall-fired boiler
Coal
3922
WY
Wygen III
56596
001
NaN
5
2016
ARP, MATS
581.64
62642.20
NaN
16.041
0.0481
12.642
67689.944
645401.544
8
WECC
Dry bottom wall-fired boiler
Coal
3923
WY
Wyodak
6101
BW91
NaN
5
2016
ARP, MATS
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
8
WECC
Dry bottom wall-fired boiler
Coal
In [10]:
emissions.columns
Out[10]:
Index([u'State', u' Facility Name', u' Facility ID (ORISPL)', u' Unit ID',
u' Associated Stacks', u' Month', u' Year', u' Program(s)',
u' Operating Time', u' Gross Load (MW-h)', u' Steam Load (1000lb)',
u' SO2 (tons)', u' Avg. NOx Rate (lb/MMBtu)', u' NOx (tons)',
u' CO2 (short tons)', u' Heat Input (MMBtu)', u' EPA Region',
u' NERC Region', u' Unit Type', u' Fuel Type (Primary)'],
dtype='object')
In [12]:
emissions.columns = [name.strip() for name in emissions.columns]
In [13]:
emissions.columns
Out[13]:
Index([u'State', u'Facility Name', u'Facility ID (ORISPL)', u'Unit ID',
u'Associated Stacks', u'Month', u'Year', u'Program(s)',
u'Operating Time', u'Gross Load (MW-h)', u'Steam Load (1000lb)',
u'SO2 (tons)', u'Avg. NOx Rate (lb/MMBtu)', u'NOx (tons)',
u'CO2 (short tons)', u'Heat Input (MMBtu)', u'EPA Region',
u'NERC Region', u'Unit Type', u'Fuel Type (Primary)'],
dtype='object')
In [14]:
emissions.dtypes
Out[14]:
State object
Facility Name object
Facility ID (ORISPL) int64
Unit ID object
Associated Stacks object
Month int64
Year int64
Program(s) object
Operating Time float64
Gross Load (MW-h) float64
Steam Load (1000lb) float64
SO2 (tons) float64
Avg. NOx Rate (lb/MMBtu) float64
NOx (tons) float64
CO2 (short tons) float64
Heat Input (MMBtu) float64
EPA Region int64
NERC Region object
Unit Type object
Fuel Type (Primary) object
dtype: object
In [15]:
type(emissions)
Out[15]:
pandas.core.frame.DataFrame
In [16]:
type(emissions['State'])
Out[16]:
pandas.core.series.Series
In [18]:
emissions.loc[:,'State']
Out[18]:
0 AL
1 AL
2 AL
3 AL
4 AL
5 AL
6 AL
7 AL
8 AL
9 AL
10 AL
11 AL
12 AL
13 AL
14 AL
15 AL
16 AL
17 AL
18 AL
19 AL
20 AL
21 AL
22 AL
23 AL
24 AL
25 AL
26 AL
27 AL
28 AL
29 AL
..
3894 WV
3895 WV
3896 WV
3897 WV
3898 WV
3899 WY
3900 WY
3901 WY
3902 WY
3903 WY
3904 WY
3905 WY
3906 WY
3907 WY
3908 WY
3909 WY
3910 WY
3911 WY
3912 WY
3913 WY
3914 WY
3915 WY
3916 WY
3917 WY
3918 WY
3919 WY
3920 WY
3921 WY
3922 WY
3923 WY
Name: State, dtype: object
In [20]:
emissions.loc[:5,'State':'Operating Time']
Out[20]:
State
Facility Name
Facility ID (ORISPL)
Unit ID
Associated Stacks
Month
Year
Program(s)
Operating Time
0
AL
AMEA Sylacauga Plant
56018
1
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
28.00
1
AL
AMEA Sylacauga Plant
56018
2
NaN
5
2016
CSNOX, CSNOXOS, CSSO2G2, ARP
17.00
2
AL
Barry
3
1
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
3
AL
Barry
3
2
CS0AAN
5
2016
ARP, CSNOX, CSNOXOS, CSSO2G2
2.75
4
AL
Barry
3
4
NaN
5
2016
ARP, MATS, CSNOX, CSNOXOS, CSSO2G2
725.25
5
AL
Barry
3
5
MS5C, MS5D, MS5E
5
2016
ARP, MATS, CSNOX, CSNOXOS, CSSO2G2
588.00
In [21]:
emissions.iloc[:5,:3]
Out[21]:
State
Facility Name
Facility ID (ORISPL)
0
AL
AMEA Sylacauga Plant
56018
1
AL
AMEA Sylacauga Plant
56018
2
AL
Barry
3
3
AL
Barry
3
4
AL
Barry
3
In [24]:
emissions.groupby('Facility ID (ORISPL)')
Out[24]:
<pandas.core.groupby.DataFrameGroupBy object at 0x11307ba50>
In [27]:
facility_emiss = emissions.groupby('Facility ID (ORISPL)').sum()
facility_emiss
Out[27]:
Month
Year
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
Facility ID (ORISPL)
3
40
16128
3844.00
1048450.25
NaN
595.911
0.5760
411.165
677078.976
8471790.125
32
7
10
4032
924.00
NaN
222159.75
0.990
0.2067
13.814
15771.300
266053.425
8
8
15
6048
1837.25
505777.25
NaN
125.662
0.5375
440.348
531418.603
5179513.525
12
9
5
2016
20.60
541.68
NaN
0.003
0.2751
1.504
NaN
10441.833
6
10
55
22176
188.25
8897.75
NaN
48.579
0.8474
10.162
11340.124
144258.150
44
26
25
10080
744.00
495791.00
NaN
173.698
0.0646
165.370
524604.900
5113099.400
20
47
65
26208
20.00
177.00
NaN
0.333
4.4379
0.722
192.400
2765.600
52
50
10
4032
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
8
51
5
2016
553.48
262392.63
NaN
1081.656
0.2080
241.863
270128.775
2481213.560
6
54
45
18144
254.45
15652.65
NaN
0.057
0.3753
2.787
11101.898
186799.693
36
56
15
6048
759.23
112793.47
NaN
63.902
0.5125
167.878
129301.129
1260257.488
12
59
5
2016
321.07
18822.00
NaN
16.264
0.2297
24.957
21946.742
209245.816
7
60
10
4032
753.95
36111.00
NaN
176.503
0.2169
39.165
44877.292
427905.242
14
87
5
2016
744.00
82439.00
NaN
34.069
0.3811
161.168
88806.200
846735.000
6
108
5
2016
660.18
160882.24
NaN
95.013
0.1813
151.412
173507.748
1654354.527
7
113
15
6048
992.93
120001.76
NaN
57.635
0.6462
149.985
135364.206
1319344.965
27
116
10
4032
247.40
8653.84
NaN
0.032
0.2216
7.130
6368.940
107186.519
18
117
15
6048
623.34
109950.39
NaN
0.257
0.0754
7.739
50958.633
857449.725
27
118
5
2016
37.80
1491.10
NaN
0.007
0.0507
0.447
1333.907
22445.386
9
120
15
6048
748.53
35334.66
NaN
0.120
0.1839
26.270
23956.571
403122.659
27
124
5
2016
7.70
360.90
NaN
0.009
0.0468
0.081
298.620
5024.270
9
126
20
8064
1101.56
76803.54
NaN
2.119
0.5604
69.574
50133.139
822072.677
36
127
5
2016
401.58
129794.52
NaN
73.983
0.2907
203.293
145337.271
1385756.804
6
130
20
8064
1518.03
822856.12
NaN
370.759
0.2077
289.871
860955.931
8391402.264
16
136
10
4032
1451.60
689645.88
NaN
477.829
0.1152
187.062
682395.436
6651035.344
8
141
15
6048
67.49
4296.61
NaN
0.014
0.5300
11.359
2730.960
45960.324
27
147
5
2016
611.23
130042.08
NaN
0.277
0.0101
3.965
54824.884
922519.859
9
160
20
8064
1970.04
185441.25
NaN
20.620
1.0331
359.939
185387.782
1905952.909
36
165
10
4032
603.34
191538.88
NaN
506.266
0.2807
170.876
239423.987
2290169.297
12
170
5
2016
638.74
139956.89
NaN
0.422
0.2584
239.594
83529.374
1405547.366
6
...
...
...
...
...
...
...
...
...
...
...
...
57037
10
4032
1398.25
368847.25
NaN
0.850
0.0271
17.634
168415.150
2833845.350
8
57073
5
2016
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9
57074
5
2016
407.00
NaN
NaN
0.015
0.0110
0.298
3187.900
54046.000
9
57075
5
2016
248.00
NaN
NaN
0.007
0.0110
0.149
1602.200
27151.000
9
57241
5
2016
42.34
2051.00
NaN
0.090
0.0555
1.154
3480.042
33986.189
4
57267
20
8064
18.74
2488.57
NaN
0.008
0.0434
0.123
1712.645
28817.647
36
57349
5
2016
393.61
100963.68
NaN
0.207
0.0089
2.387
40922.021
688589.496
3
57482
40
16128
451.97
29914.41
NaN
0.088
0.1731
2.092
17568.752
295618.850
72
57483
20
8064
345.92
10334.14
NaN
0.036
0.0692
0.712
6236.594
104945.273
36
57515
25
10080
823.60
65754.07
NaN
0.174
0.1513
5.561
34605.858
582304.228
45
57703
15
6048
733.14
19706.25
NaN
0.060
0.0540
0.859
12035.438
202527.156
24
57839
10
4032
1101.30
357284.80
NaN
0.639
0.0110
5.838
126516.225
2128891.010
4
57865
15
6048
81.55
10928.02
NaN
0.034
0.1633
2.704
6779.163
114074.151
18
57881
15
6048
580.92
20205.20
NaN
0.062
0.0855
1.784
12307.201
207098.166
24
57943
15
6048
1753.37
60954.81
NaN
0.183
0.0481
3.916
36090.079
607288.728
24
57978
5
2016
16.81
2461.69
NaN
0.008
0.0201
0.119
1625.035
27347.542
9
58001
20
8064
1485.62
417253.89
NaN
0.935
0.0426
12.778
185161.751
3115749.902
24
58005
10
4032
994.60
292270.75
NaN
0.644
0.0166
7.113
127678.269
2148462.119
12
58054
5
2016
743.03
53385.00
NaN
1.455
0.0584
19.356
69154.702
664207.371
1
58079
10
4032
1268.00
385188.46
NaN
0.756
0.0128
7.746
149796.191
2520589.201
4
58122
5
2016
2.57
61.56
NaN
0.000
0.0775
0.048
43.560
732.625
9
58235
5
2016
119.55
7160.00
NaN
0.020
0.0191
0.384
3909.089
65770.895
2
58260
15
6048
1824.11
705418.26
NaN
1.468
0.0321
17.479
290920.901
4895295.214
9
58284
5
2016
16.62
483.78
NaN
0.001
0.0250
0.032
289.595
4874.589
6
58325
5
2016
7.93
471.64
NaN
0.002
0.0457
0.147
390.728
6576.714
6
58471
10
4032
81.30
8786.34
NaN
0.142
0.1028
1.593
6244.180
105066.232
12
58478
5
2016
47.25
1823.50
NaN
0.005
0.0215
0.148
968.200
16293.125
6
58562
10
4032
615.09
40172.06
NaN
0.111
0.0296
2.077
21929.390
368989.001
12
59926
10
4032
24.63
2794.92
NaN
0.015
0.0320
0.806
2972.500
50368.700
10
70454
10
4032
62.78
7178.74
NaN
0.026
0.1711
3.388
5164.599
86899.023
8
1208 rows × 11 columns
In [31]:
def correct_region(row):
num_units = row['Month'] / 5
region = row['EPA Region'] / num_units
return int(region)
In [33]:
facility_emiss.loc[:,'EPA Region'] = facility_emiss.apply(correct_region, axis=1)
facility_emiss = facility_emiss.iloc[:,2:]
facility_emiss
Out[33]:
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
Facility ID (ORISPL)
3
3844.00
1048450.25
NaN
595.911
0.5760
411.165
677078.976
8471790.125
4
7
924.00
NaN
222159.75
0.990
0.2067
13.814
15771.300
266053.425
4
8
1837.25
505777.25
NaN
125.662
0.5375
440.348
531418.603
5179513.525
4
9
20.60
541.68
NaN
0.003
0.2751
1.504
NaN
10441.833
6
10
188.25
8897.75
NaN
48.579
0.8474
10.162
11340.124
144258.150
4
26
744.00
495791.00
NaN
173.698
0.0646
165.370
524604.900
5113099.400
4
47
20.00
177.00
NaN
0.333
4.4379
0.722
192.400
2765.600
4
50
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4
51
553.48
262392.63
NaN
1081.656
0.2080
241.863
270128.775
2481213.560
6
54
254.45
15652.65
NaN
0.057
0.3753
2.787
11101.898
186799.693
4
56
759.23
112793.47
NaN
63.902
0.5125
167.878
129301.129
1260257.488
4
59
321.07
18822.00
NaN
16.264
0.2297
24.957
21946.742
209245.816
7
60
753.95
36111.00
NaN
176.503
0.2169
39.165
44877.292
427905.242
7
87
744.00
82439.00
NaN
34.069
0.3811
161.168
88806.200
846735.000
6
108
660.18
160882.24
NaN
95.013
0.1813
151.412
173507.748
1654354.527
7
113
992.93
120001.76
NaN
57.635
0.6462
149.985
135364.206
1319344.965
9
116
247.40
8653.84
NaN
0.032
0.2216
7.130
6368.940
107186.519
9
117
623.34
109950.39
NaN
0.257
0.0754
7.739
50958.633
857449.725
9
118
37.80
1491.10
NaN
0.007
0.0507
0.447
1333.907
22445.386
9
120
748.53
35334.66
NaN
0.120
0.1839
26.270
23956.571
403122.659
9
124
7.70
360.90
NaN
0.009
0.0468
0.081
298.620
5024.270
9
126
1101.56
76803.54
NaN
2.119
0.5604
69.574
50133.139
822072.677
9
127
401.58
129794.52
NaN
73.983
0.2907
203.293
145337.271
1385756.804
6
130
1518.03
822856.12
NaN
370.759
0.2077
289.871
860955.931
8391402.264
4
136
1451.60
689645.88
NaN
477.829
0.1152
187.062
682395.436
6651035.344
4
141
67.49
4296.61
NaN
0.014
0.5300
11.359
2730.960
45960.324
9
147
611.23
130042.08
NaN
0.277
0.0101
3.965
54824.884
922519.859
9
160
1970.04
185441.25
NaN
20.620
1.0331
359.939
185387.782
1905952.909
9
165
603.34
191538.88
NaN
506.266
0.2807
170.876
239423.987
2290169.297
6
170
638.74
139956.89
NaN
0.422
0.2584
239.594
83529.374
1405547.366
6
...
...
...
...
...
...
...
...
...
...
57037
1398.25
368847.25
NaN
0.850
0.0271
17.634
168415.150
2833845.350
4
57073
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9
57074
407.00
NaN
NaN
0.015
0.0110
0.298
3187.900
54046.000
9
57075
248.00
NaN
NaN
0.007
0.0110
0.149
1602.200
27151.000
9
57241
42.34
2051.00
NaN
0.090
0.0555
1.154
3480.042
33986.189
4
57267
18.74
2488.57
NaN
0.008
0.0434
0.123
1712.645
28817.647
9
57349
393.61
100963.68
NaN
0.207
0.0089
2.387
40922.021
688589.496
3
57482
451.97
29914.41
NaN
0.088
0.1731
2.092
17568.752
295618.850
9
57483
345.92
10334.14
NaN
0.036
0.0692
0.712
6236.594
104945.273
9
57515
823.60
65754.07
NaN
0.174
0.1513
5.561
34605.858
582304.228
9
57703
733.14
19706.25
NaN
0.060
0.0540
0.859
12035.438
202527.156
8
57839
1101.30
357284.80
NaN
0.639
0.0110
5.838
126516.225
2128891.010
2
57865
81.55
10928.02
NaN
0.034
0.1633
2.704
6779.163
114074.151
6
57881
580.92
20205.20
NaN
0.062
0.0855
1.784
12307.201
207098.166
8
57943
1753.37
60954.81
NaN
0.183
0.0481
3.916
36090.079
607288.728
8
57978
16.81
2461.69
NaN
0.008
0.0201
0.119
1625.035
27347.542
9
58001
1485.62
417253.89
NaN
0.935
0.0426
12.778
185161.751
3115749.902
6
58005
994.60
292270.75
NaN
0.644
0.0166
7.113
127678.269
2148462.119
6
58054
743.03
53385.00
NaN
1.455
0.0584
19.356
69154.702
664207.371
1
58079
1268.00
385188.46
NaN
0.756
0.0128
7.746
149796.191
2520589.201
2
58122
2.57
61.56
NaN
0.000
0.0775
0.048
43.560
732.625
9
58235
119.55
7160.00
NaN
0.020
0.0191
0.384
3909.089
65770.895
2
58260
1824.11
705418.26
NaN
1.468
0.0321
17.479
290920.901
4895295.214
3
58284
16.62
483.78
NaN
0.001
0.0250
0.032
289.595
4874.589
6
58325
7.93
471.64
NaN
0.002
0.0457
0.147
390.728
6576.714
6
58471
81.30
8786.34
NaN
0.142
0.1028
1.593
6244.180
105066.232
6
58478
47.25
1823.50
NaN
0.005
0.0215
0.148
968.200
16293.125
6
58562
615.09
40172.06
NaN
0.111
0.0296
2.077
21929.390
368989.001
6
59926
24.63
2794.92
NaN
0.015
0.0320
0.806
2972.500
50368.700
5
70454
62.78
7178.74
NaN
0.026
0.1711
3.388
5164.599
86899.023
4
1208 rows × 9 columns
In [34]:
capacity = pd.read_excel(fn2, sheetname='Operating', header=1)
In [35]:
capacity.head()
Out[35]:
Entity ID
Entity Name
Plant ID
Plant Name
Sector
Plant State
Generator ID
Nameplate Capacity (MW)
Net Summer Capacity (MW)
Technology
...
Status
Planned Derate Year
Planned Derate Month
Planned Derate of Summer Capacity (MW)
Planned Uprate Year
Planned Uprate Month
Planned Uprate of Summer Capacity (MW)
County
Latitude
Longitude
0
195
Alabama Power Co
2.0
Bankhead Dam
Electric Utility
AL
1
53.9
56
Conventional Hydroelectric
...
(OP) Operating
Tuscaloosa
33.4587
-87.3568
1
195
Alabama Power Co
3.0
Barry
Electric Utility
AL
1
153.1
55
Natural Gas Steam Turbine
...
(OP) Operating
Mobile
31.0069
-88.0103
2
195
Alabama Power Co
3.0
Barry
Electric Utility
AL
2
153.1
55
Natural Gas Steam Turbine
...
(OP) Operating
Mobile
31.0069
-88.0103
3
195
Alabama Power Co
3.0
Barry
Electric Utility
AL
4
403.7
362
Conventional Steam Coal
...
(OP) Operating
Mobile
31.0069
-88.0103
4
195
Alabama Power Co
3.0
Barry
Electric Utility
AL
5
788.8
726.5
Conventional Steam Coal
...
(OP) Operating
Mobile
31.0069
-88.0103
5 rows × 26 columns
In [36]:
capacity.tail()
Out[36]:
Entity ID
Entity Name
Plant ID
Plant Name
Sector
Plant State
Generator ID
Nameplate Capacity (MW)
Net Summer Capacity (MW)
Technology
...
Status
Planned Derate Year
Planned Derate Month
Planned Derate of Summer Capacity (MW)
Planned Uprate Year
Planned Uprate Month
Planned Uprate of Summer Capacity (MW)
County
Latitude
Longitude
20183
60205
Currin Solar, LLC
60403.0
Currin Solar, LLC
IPP Non-CHP
NC
CSPV
5.2
5.2
Solar Photovoltaic
...
(OP) Operating
Granville
36.2896
-78.6333
20184
60163
Soltage LLC
60411.0
Coventry Photovoltaic, LLC
IPP Non-CHP
VT
COVEN
2.2
2.2
Solar Photovoltaic
...
(OP) Operating
Orleans
44.9102
-72.2198
20185
60227
ORNI 37 LLC
60419.0
Don A Campbell 2 Geothermal
IPP Non-CHP
NV
DAC2G
25.0
16.2
Geothermal
...
(OP) Operating
Mineral
38.8358
-118.325
20186
57081
WGL Energy Systems, Inc
60425.0
Skidmore College
IPP Non-CHP
NY
SO049
2.0
2
Solar Photovoltaic
...
(OP) Operating
Saratoga
43.0937
-73.8113
20187
NOTES:\nCapacity from facilities with a total ...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
5 rows × 26 columns
In [37]:
capacity.drop(20187, inplace=True)
In [38]:
capacity.loc[:,'Plant ID'] = capacity.loc[:,'Plant ID'].astype(int)
In [39]:
capacity.head()
Out[39]:
Entity ID
Entity Name
Plant ID
Plant Name
Sector
Plant State
Generator ID
Nameplate Capacity (MW)
Net Summer Capacity (MW)
Technology
...
Status
Planned Derate Year
Planned Derate Month
Planned Derate of Summer Capacity (MW)
Planned Uprate Year
Planned Uprate Month
Planned Uprate of Summer Capacity (MW)
County
Latitude
Longitude
0
195
Alabama Power Co
2
Bankhead Dam
Electric Utility
AL
1
53.9
56
Conventional Hydroelectric
...
(OP) Operating
Tuscaloosa
33.4587
-87.3568
1
195
Alabama Power Co
3
Barry
Electric Utility
AL
1
153.1
55
Natural Gas Steam Turbine
...
(OP) Operating
Mobile
31.0069
-88.0103
2
195
Alabama Power Co
3
Barry
Electric Utility
AL
2
153.1
55
Natural Gas Steam Turbine
...
(OP) Operating
Mobile
31.0069
-88.0103
3
195
Alabama Power Co
3
Barry
Electric Utility
AL
4
403.7
362
Conventional Steam Coal
...
(OP) Operating
Mobile
31.0069
-88.0103
4
195
Alabama Power Co
3
Barry
Electric Utility
AL
5
788.8
726.5
Conventional Steam Coal
...
(OP) Operating
Mobile
31.0069
-88.0103
5 rows × 26 columns
In [40]:
capacity.columns
Out[40]:
Index([u'Entity ID', u'Entity Name', u'Plant ID', u'Plant Name', u'Sector',
u'Plant State', u'Generator ID', u'\nNameplate Capacity (MW)',
u'\n \nNet Summer Capacity (MW)', u'Technology', u'Energy Source Code',
u'Prime Mover Code', u'Operating Month', u'Operating Year',
u'Planned Retirement Month', u'Planned Retirement Year', u'Status',
u'Planned Derate Year', u'Planned Derate Month',
u'Planned Derate of Summer Capacity (MW)', u'Planned Uprate Year',
u'Planned Uprate Month', u'Planned Uprate of Summer Capacity (MW)',
u'County', u'Latitude', u'Longitude'],
dtype='object')
In [41]:
capacity.columns = [name.strip() for name in capacity.columns]
In [42]:
capacity.columns
Out[42]:
Index([u'Entity ID', u'Entity Name', u'Plant ID', u'Plant Name', u'Sector',
u'Plant State', u'Generator ID', u'Nameplate Capacity (MW)',
u'Net Summer Capacity (MW)', u'Technology', u'Energy Source Code',
u'Prime Mover Code', u'Operating Month', u'Operating Year',
u'Planned Retirement Month', u'Planned Retirement Year', u'Status',
u'Planned Derate Year', u'Planned Derate Month',
u'Planned Derate of Summer Capacity (MW)', u'Planned Uprate Year',
u'Planned Uprate Month', u'Planned Uprate of Summer Capacity (MW)',
u'County', u'Latitude', u'Longitude'],
dtype='object')
In [43]:
PA_cap = capacity.loc[capacity['Plant State']=='PA',:]
PA_cap
Out[43]:
Entity ID
Entity Name
Plant ID
Plant Name
Sector
Plant State
Generator ID
Nameplate Capacity (MW)
Net Summer Capacity (MW)
Technology
...
Status
Planned Derate Year
Planned Derate Month
Planned Derate of Summer Capacity (MW)
Planned Uprate Year
Planned Uprate Month
Planned Uprate of Summer Capacity (MW)
County
Latitude
Longitude
5223
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
1A
25.5
15
Petroleum Liquids
...
(OP) Operating
Allegheny
40.4649
-80.0438
5224
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
2A
65.3
46
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5225
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
2B
65.3
48
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5226
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
3
65.3
49
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5227
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
ST4
144.0
101
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5228
17235
NRG REMA LLC
3109
Hamilton (PA)
IPP Non-CHP
PA
1
19.6
18
Petroleum Liquids
...
(OP) Operating
Adams
39.9087
-76.9885
5229
17235
NRG REMA LLC
3110
Hunterstown
IPP Non-CHP
PA
1
20.0
18
Petroleum Liquids
...
(OP) Operating
Adams
39.8662
-77.1648
5230
17235
NRG REMA LLC
3110
Hunterstown
IPP Non-CHP
PA
2
20.0
17
Petroleum Liquids
...
(OP) Operating
Adams
39.8662
-77.1648
5231
17235
NRG REMA LLC
3110
Hunterstown
IPP Non-CHP
PA
3
20.0
18
Petroleum Liquids
...
(OP) Operating
Adams
39.8662
-77.1648
5232
17235
NRG REMA LLC
3111
Mountain
IPP Non-CHP
PA
1
27.0
18
Petroleum Liquids
...
(OP) Operating
Cumberland
40.1229
-77.1723
5233
17235
NRG REMA LLC
3111
Mountain
IPP Non-CHP
PA
2
27.0
18
Petroleum Liquids
...
(OP) Operating
Cumberland
40.1229
-77.1723
5234
17235
NRG REMA LLC
3112
Orrtanna
IPP Non-CHP
PA
1
27.0
18
Petroleum Liquids
...
(OP) Operating
Adams
39.8442
-77.3508
5235
17235
NRG REMA LLC
3113
Portland (PA)
IPP Non-CHP
PA
3
18.0
13
Petroleum Liquids
...
(OP) Operating
Northampton
40.9102
-75.0794
5236
17235
NRG REMA LLC
3113
Portland (PA)
IPP Non-CHP
PA
4
20.0
17
Petroleum Liquids
...
(OP) Operating
Northampton
40.9102
-75.0794
5237
17235
NRG REMA LLC
3113
Portland (PA)
IPP Non-CHP
PA
5
156.0
134
Petroleum Liquids
...
(OP) Operating
Northampton
40.9102
-75.0794
5238
17235
NRG REMA LLC
3114
Shawnee (PA)
IPP Non-CHP
PA
1
20.0
17
Petroleum Liquids
...
(OP) Operating
Monroe
41.0612
-75.0582
5239
17235
NRG REMA LLC
3115
Titus
IPP Non-CHP
PA
4
18.0
13
Petroleum Liquids
...
(OP) Operating
Berks
40.3056
-75.9081
5240
17235
NRG REMA LLC
3115
Titus
IPP Non-CHP
PA
5
18.0
14
Petroleum Liquids
...
(OP) Operating
Berks
40.3056
-75.9081
5241
17235
NRG REMA LLC
3116
Tolna
IPP Non-CHP
PA
1
27.0
18
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
York
39.7606
-76.6353
5242
17235
NRG REMA LLC
3116
Tolna
IPP Non-CHP
PA
2
27.0
18
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
York
39.7606
-76.6353
5243
56065
York Haven Power Company LLC
3117
York Haven
IPP Non-CHP
PA
1
19.6
19
Conventional Hydroelectric
...
(OP) Operating
York
40.1136
-76.712
5244
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
1
936.0
850
Conventional Steam Coal
...
(OP) Operating
Indiana
40.3842
-79.0611
5245
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
2
936.0
850
Conventional Steam Coal
...
(OP) Operating
Indiana
40.3842
-79.0611
5246
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
A
3.0
2.8
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
Indiana
40.3842
-79.0611
5247
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
B
3.0
2.8
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
Indiana
40.3842
-79.0611
5248
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
C
3.0
2.8
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
Indiana
40.3842
-79.0611
5249
15873
GenOn Northeast Management Company
3118
Conemaugh
IPP Non-CHP
PA
D
3.0
2.8
Petroleum Liquids
...
(SB) Standby/Backup: available for service but...
Indiana
40.3842
-79.0611
5250
17235
NRG REMA LLC
3120
Blossburg
IPP Non-CHP
PA
1
24.0
16
Natural Gas Fired Combustion Turbine
...
(OP) Operating
Tioga
41.7065
-77.0818
5251
58615
NRG Homer City Services LLC
3122
Homer City Generating Station
IPP Non-CHP
PA
1
660.0
624.5
Conventional Steam Coal
...
(OP) Operating
Indiana
40.5128
-79.1961
5252
58615
NRG Homer City Services LLC
3122
Homer City Generating Station
IPP Non-CHP
PA
2
660.0
617.5
Conventional Steam Coal
...
(OP) Operating
Indiana
40.5128
-79.1961
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
18480
58438
Sunstream Energy LLC
58443
Martin Limestone Solar Array
IPP Non-CHP
PA
GEN1
1.0
1
Solar Photovoltaic
...
(OP) Operating
Lancaster
40.1078
-76.0617
18526
58463
Dart Container Corp
58476
Dart Container Corp
Industrial CHP
PA
LFGT1
5.6
4.6
Landfill Gas
...
(OP) Operating
Lancaster
40.0865
-76.176
18527
58463
Dart Container Corp
58476
Dart Container Corp
Industrial CHP
PA
LFGT2
5.6
4.6
Landfill Gas
...
(OP) Operating
Lancaster
40.0865
-76.176
18549
7477
Granger Electric Co
58497
L&S Sweetners
IPP Non-CHP
PA
GEN1
1.6
1.6
Landfill Gas
...
(OP) Operating
Lancaster
40.0906
-76.1456
18550
7477
Granger Electric Co
58497
L&S Sweetners
IPP Non-CHP
PA
GEN2
1.6
1.6
Landfill Gas
...
(OP) Operating
Lancaster
40.0906
-76.1456
18598
56645
Aqua America Inc
58535
Pickering Solar
IPP Non-CHP
PA
1
1.5
1.4
Solar Photovoltaic
...
(OP) Operating
Chester
40.1206
-75.4881
18645
58527
Gettysburg Energy and Nutrient Rec Facility LLC
58565
Gettysburg Energy & Nutrient Rec Facility
IPP Non-CHP
PA
GENRF
3.3
2.5
Other Waste Biomass
...
(OP) Operating
Adams
39.9503
-77.1253
18785
58617
Mahoning Creek Hydroelectric Company LLC
58685
Mahoning Creek Hydroelectric Project
IPP Non-CHP
PA
8107
6.0
6.7
Conventional Hydroelectric
...
(OP) Operating
Armstrong
40.9211
-79.2817
18818
58652
Roundtop Energy LLC
58715
Roundtop
IPP Non-CHP
PA
GEN1
4.4
4.2
Natural Gas Internal Combustion Engine
...
(OP) Operating
Susquehanna
41.6578
-76.0492
18819
58652
Roundtop Energy LLC
58715
Roundtop
IPP Non-CHP
PA
GEN2
4.4
4.2
Natural Gas Internal Combustion Engine
...
(OP) Operating
Susquehanna
41.6578
-76.0492
18820
58652
Roundtop Energy LLC
58715
Roundtop
IPP Non-CHP
PA
GEN3
4.4
4.2
Natural Gas Internal Combustion Engine
...
(OP) Operating
Susquehanna
41.6578
-76.0492
18821
58652
Roundtop Energy LLC
58715
Roundtop
IPP Non-CHP
PA
GEN4
4.4
4.2
Natural Gas Internal Combustion Engine
...
(OP) Operating
Susquehanna
41.6578
-76.0492
18822
58652
Roundtop Energy LLC
58715
Roundtop
IPP Non-CHP
PA
GEN5
4.4
4.2
Natural Gas Internal Combustion Engine
...
(OP) Operating
Susquehanna
41.6578
-76.0492
19003
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G01
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19004
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G02
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19005
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G03
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19006
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G04
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19007
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G05
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19008
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G06
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19009
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G07
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19010
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G08
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19011
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G09
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19012
58767
Temple University
58897
Temple SEGF
Commercial Non-CHP
PA
19G10
1.6
1.6
Natural Gas Internal Combustion Engine
...
(OP) Operating
Philadelphia
39.98
-75.1506
19202
57130
Janssen Pharmaceutical Co
59056
Spring House
IPP Non-CHP
PA
GEN 1
3.8
3.8
Natural Gas Internal Combustion Engine
...
(OP) Operating
Montgomery
40.1839
-75.2342
19435
59125
Marlboro Mushroom
59327
Marlboro Mushrooms Solar Field
Industrial Non-CHP
PA
GEN1
1.0
1
Solar Photovoltaic
...
(OP) Operating
Chester
39.8828
-75.8294
19636
59287
Laurel Capital Partners
59546
Beaver Solar LLC
IPP Non-CHP
PA
PV1
1.3
1.3
Solar Photovoltaic
...
(OP) Operating
Beaver
40.6914
-80.3239
19822
59526
State Correctnl Inst Laurel Highlands
59759
Glades Pike Generation Plant
Commercial Non-CHP
PA
CAT1
1.6
1.4
Landfill Gas
...
(OP) Operating
Somerset
40.0067
-79.041
19823
59526
State Correctnl Inst Laurel Highlands
59759
Glades Pike Generation Plant
Commercial Non-CHP
PA
CAT2
1.6
1.4
Landfill Gas
...
(OP) Operating
Somerset
40.0067
-79.041
19824
59526
State Correctnl Inst Laurel Highlands
59759
Glades Pike Generation Plant
Commercial Non-CHP
PA
SOLAR
3.3
1.5
Landfill Gas
...
(OP) Operating
Somerset
40.0067
-79.041
20180
60187
Granger Energy of Morgantown
60388
Granger Energy of Morgantown
IPP Non-CHP
PA
GEMT
1.6
1.6
Landfill Gas
...
(OP) Operating
Berks
40.1746
-75.9137
568 rows × 26 columns
In [46]:
PA_cap.describe()
Out[46]:
Plant ID
Nameplate Capacity (MW)
Operating Month
Operating Year
count
568.000000
568.000000
568.000000
568.000000
mean
35621.582746
81.443310
6.195423
1987.869718
std
25038.182319
203.914129
3.563807
23.276513
min
3096.000000
0.300000
1.000000
1905.000000
25%
3170.000000
1.600000
3.000000
1971.000000
50%
52149.000000
10.400000
6.000000
1995.000000
75%
56687.000000
57.600000
10.000000
2004.000000
max
60388.000000
1377.000000
12.000000
2016.000000
In [48]:
capacity.loc[(capacity['Plant State']=='PA') &
(capacity['Technology']=='Natural Gas Fired Combined Cycle'),:]
Out[48]:
Entity ID
Entity Name
Plant ID
Plant Name
Sector
Plant State
Generator ID
Nameplate Capacity (MW)
Net Summer Capacity (MW)
Technology
...
Status
Planned Derate Year
Planned Derate Month
Planned Derate of Summer Capacity (MW)
Planned Uprate Year
Planned Uprate Month
Planned Uprate of Summer Capacity (MW)
County
Latitude
Longitude
5224
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
2A
65.3
46
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5225
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
2B
65.3
48
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5226
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
3
65.3
49
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5227
14165
NRG Power Midwest LP
3096
Brunot Island
IPP Non-CHP
PA
ST4
144.0
101
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.4649
-80.0438
5381
19391
UGI Development Co
3176
Hunlock Power Station
IPP Non-CHP
PA
3
49.9
30.1
Natural Gas Fired Combined Cycle
...
(OP) Operating
Luzerne
41.2006
-76.07
5382
19391
UGI Development Co
3176
Hunlock Power Station
IPP Non-CHP
PA
5
48.0
48.7
Natural Gas Fired Combined Cycle
...
(OP) Operating
Luzerne
41.2006
-76.07
5383
19391
UGI Development Co
3176
Hunlock Power Station
IPP Non-CHP
PA
6
48.0
48.4
Natural Gas Fired Combined Cycle
...
(OP) Operating
Luzerne
41.2006
-76.07
11869
2468
Bucknell University
54333
Bucknell University
Commercial CHP
PA
G001
4.7
4.3
Natural Gas Fired Combined Cycle
...
(OP) Operating
Union
40.955
-76.8788
11870
2468
Bucknell University
54333
Bucknell University
Commercial CHP
PA
G502
1.2
0.5
Natural Gas Fired Combined Cycle
...
(OP) Operating
Union
40.955
-76.8788
12320
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
GT#1
8.3
46.2
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12321
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
GT#2
8.3
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12322
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
GT#5
8.3
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12323
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
GT#6
8.3
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12324
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
ST#1
9.5
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12325
56516
Morris Energy Operations Company, LLC
54693
York Generation Company LLC
IPP Non-CHP
PA
ST#2
9.5
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.9856
-76.6762
12528
7564
Grays Ferry Cogen Partnership
54785
Grays Ferry Cogeneration
IPP CHP
PA
GEN1
57.6
57
Natural Gas Fired Combined Cycle
...
(OP) Operating
Philadelphia
39.9422
-75.1881
12529
7564
Grays Ferry Cogen Partnership
54785
Grays Ferry Cogeneration
IPP CHP
PA
GEN2
135.0
113
Natural Gas Fired Combined Cycle
...
(OP) Operating
Philadelphia
39.9422
-75.1881
13268
55649
Ontelaunee Energy Center
55193
Ontelaunee Energy Center
IPP Non-CHP
PA
CTG1
250.0
180
Natural Gas Fired Combined Cycle
...
(OP) Operating
Berks
40.4219
-75.9356
13269
55649
Ontelaunee Energy Center
55193
Ontelaunee Energy Center
IPP Non-CHP
PA
CTG2
250.0
180
Natural Gas Fired Combined Cycle
...
(OP) Operating
Berks
40.4219
-75.9356
13270
55649
Ontelaunee Energy Center
55193
Ontelaunee Energy Center
IPP Non-CHP
PA
STG
228.0
180
Natural Gas Fired Combined Cycle
...
(OP) Operating
Berks
40.4219
-75.9356
13389
27031
Liberty Electric Power LLC
55231
Liberty Electric Power Plant
IPP Non-CHP
PA
GTG1
186.0
158.6
Natural Gas Fired Combined Cycle
...
(OP) Operating
2017
4
10.5
Delaware
39.8614
-75.3358
13390
27031
Liberty Electric Power LLC
55231
Liberty Electric Power Plant
IPP Non-CHP
PA
GTG2
186.0
158.6
Natural Gas Fired Combined Cycle
...
(OP) Operating
2016
10
10.5
Delaware
39.8614
-75.3358
13391
27031
Liberty Electric Power LLC
55231
Liberty Electric Power Plant
IPP Non-CHP
PA
STG
242.0
223.8
Natural Gas Fired Combined Cycle
...
(OP) Operating
Delaware
39.8614
-75.3358
13604
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
CT1A
198.9
189
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13605
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
CT1B
198.9
189
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13606
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
CT2A
198.9
189
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13607
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
CT2B
198.9
189
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13608
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
ST1
271.2
258
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13609
54817
Fairless Energy LLC
55298
Fairless Energy Center
IPP Non-CHP
PA
ST2
271.2
258
Natural Gas Fired Combined Cycle
...
(OP) Operating
Bucks
40.1475
-74.7411
13674
58199
TalenEnergy Ironwood LLC
55337
TalenEnergy Ironwood LLC
IPP Non-CHP
PA
CT1
259.2
218.7
Natural Gas Fired Combined Cycle
...
(OP) Operating
Lebanon
40.3509
-76.3658
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
13676
58199
TalenEnergy Ironwood LLC
55337
TalenEnergy Ironwood LLC
IPP Non-CHP
PA
ST4
259.2
219.7
Natural Gas Fired Combined Cycle
...
(OP) Operating
Lebanon
40.3509
-76.3658
13964
59923
Dynegy Fayette Energy Facility
55516
Fayette Energy Facility
IPP Non-CHP
PA
CTG1
163.5
174
Natural Gas Fired Combined Cycle
...
(OP) Operating
Fayette
39.8592
-79.9182
13965
59923
Dynegy Fayette Energy Facility
55516
Fayette Energy Facility
IPP Non-CHP
PA
CTG2
163.5
174
Natural Gas Fired Combined Cycle
...
(OP) Operating
Fayette
39.8592
-79.9182
13966
59923
Dynegy Fayette Energy Facility
55516
Fayette Energy Facility
IPP Non-CHP
PA
STG1
317.1
314
Natural Gas Fired Combined Cycle
...
(OP) Operating
Fayette
39.8592
-79.9182
13986
56608
Calpine Mid-Merit LLC
55524
York Energy Center
IPP Non-CHP
PA
CTG1
120.0
113
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.7375
-76.3067
13987
56608
Calpine Mid-Merit LLC
55524
York Energy Center
IPP Non-CHP
PA
CTG2
120.0
122
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.7375
-76.3067
13988
56608
Calpine Mid-Merit LLC
55524
York Energy Center
IPP Non-CHP
PA
CTG3
120.0
122
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.7375
-76.3067
13989
56608
Calpine Mid-Merit LLC
55524
York Energy Center
IPP Non-CHP
PA
STG1
200.0
188
Natural Gas Fired Combined Cycle
...
(OP) Operating
York
39.7375
-76.3067
14161
11275
Lower Mount Bethel Energy LLC
55667
Lower Mount Bethel Energy
IPP Non-CHP
PA
G1
211.5
160.9
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.8019
-75.1076
14162
11275
Lower Mount Bethel Energy LLC
55667
Lower Mount Bethel Energy
IPP Non-CHP
PA
G2
211.5
162.6
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.8019
-75.1076
14163
11275
Lower Mount Bethel Energy LLC
55667
Lower Mount Bethel Energy
IPP Non-CHP
PA
G3
228.6
214
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.8019
-75.1076
14180
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG1
127.0
118
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14181
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG2
127.0
127
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14182
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG3
127.0
127
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14183
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG5
127.0
118
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14184
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG6
127.0
127
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14185
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
CTG7
127.0
127
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14186
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
STG4
195.5
195
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14187
56607
Calpine Bethlehem LLC
55690
Bethlehem Power Plant
IPP Non-CHP
PA
STG8
195.5
195
Natural Gas Fired Combined Cycle
...
(OP) Operating
Northampton
40.6175
-75.3147
14204
23279
Allegheny Energy Supply Co LLC
55710
FirstEnergy Allegheny Energy Units 3 4 & 5
IPP Non-CHP
PA
UNT3
184.0
167
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.5453
-79.7686
14205
23279
Allegheny Energy Supply Co LLC
55710
FirstEnergy Allegheny Energy Units 3 4 & 5
IPP Non-CHP
PA
UNT4
184.0
167
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.5453
-79.7686
14206
23279
Allegheny Energy Supply Co LLC
55710
FirstEnergy Allegheny Energy Units 3 4 & 5
IPP Non-CHP
PA
UNT5
188.0
175
Natural Gas Fired Combined Cycle
...
(OP) Operating
Allegheny
40.5453
-79.7686
14290
6693
FPL Energy Marcus Hook LP
55801
FPL Energy Marcus Hook LP
IPP CHP
PA
CT13
188.2
172
Natural Gas Fired Combined Cycle
...
(OP) Operating
Delaware
39.807
-75.4216
14291
6693
FPL Energy Marcus Hook LP
55801
FPL Energy Marcus Hook LP
IPP CHP
PA
CT1A
188.2
178.7
Natural Gas Fired Combined Cycle
...
(OP) Operating
Delaware
39.807
-75.4216
14292
6693
FPL Energy Marcus Hook LP
55801
FPL Energy Marcus Hook LP
IPP CHP
PA
CTIB
188.2
172
Natural Gas Fired Combined Cycle
...
(OP) Operating
Delaware
39.807
-75.4216
14293
6693
FPL Energy Marcus Hook LP
55801
FPL Energy Marcus Hook LP
IPP CHP
PA
STG
271.5
263
Natural Gas Fired Combined Cycle
...
(OP) Operating
Delaware
39.807
-75.4216
14433
54885
NRG Wholesale Generation LP
55976
Hunterstown Power Plant
IPP Non-CHP
PA
101
179.0
153
Natural Gas Fired Combined Cycle
...
(OP) Operating
Adams
39.8725
-77.1672
14434
54885
NRG Wholesale Generation LP
55976
Hunterstown Power Plant
IPP Non-CHP
PA
201
179.0
153
Natural Gas Fired Combined Cycle
...
(OP) Operating
Adams
39.8725
-77.1672
14435
54885
NRG Wholesale Generation LP
55976
Hunterstown Power Plant
IPP Non-CHP
PA
301
179.0
153
Natural Gas Fired Combined Cycle
...
(OP) Operating
Adams
39.8725
-77.1672
14436
54885
NRG Wholesale Generation LP
55976
Hunterstown Power Plant
IPP Non-CHP
PA
401
361.0
299
Natural Gas Fired Combined Cycle
...
(OP) Operating
Adams
39.8725
-77.1672
61 rows × 26 columns
In [50]:
cols = ['Plant ID', 'Nameplate Capacity (MW)']
facility_cap = capacity.loc[:,cols].groupby('Plant ID').sum()
facility_cap.head()
Out[50]:
Nameplate Capacity (MW)
Plant ID
2
53.9
3
2569.5
4
225.0
7
138.0
8
1166.7
In [51]:
generation = pd.read_excel(fn3, header=5)
In [52]:
generation.head()
Out[52]:
Plant Id
Combined Heat And
Power Plant
Nuclear Unit Id
Plant Name
Operator Name
Operator Id
Plant State
Census Region
NERC Region
Reserved
...
Netgen
September
Netgen
October
Netgen
November
Netgen
December
Total Fuel Consumption
Quantity
Electric Fuel Consumption
Quantity
Total Fuel Consumption
MMBtu
Elec Fuel Consumption
MMBtu
Net Generation
(Megawatthours)
YEAR
0
3
N
NaN
Barry
Alabama Power Co
195
AL
ESC
SERC
NaN
...
251839
219263
.
.
4782719
4782719
4877614
4877614
2386339.000
2016
1
3
N
NaN
Barry
Alabama Power Co
195
AL
ESC
SERC
NaN
...
463646
401506
.
.
42413079
42413079
43208127
43208127
4486082.000
2016
2
3
N
NaN
Barry
Alabama Power Co
195
AL
ESC
SERC
NaN
...
473794
286736
.
.
1773704
1773704
37426485
37426485
3706973.300
2016
3
3
N
NaN
Barry
Alabama Power Co
195
AL
ESC
SERC
NaN
...
11246.3
14387.7
.
.
857741
857741
874585
874585
86059.704
2016
4
4
N
NaN
Walter Bouldin Dam
Alabama Power Co
195
AL
ESC
SERC
NaN
...
3380
884
.
.
0
0
4239930
4239930
454977.000
2016
5 rows × 97 columns
In [53]:
generation.dtypes
Out[53]:
Plant Id int64
Combined Heat And\nPower Plant object
Nuclear Unit Id float64
Plant Name object
Operator Name object
Operator Id int64
Plant State object
Census Region object
NERC Region object
Reserved float64
NAICS Code int64
EIA Sector Number int64
Sector Name object
Reported\nPrime Mover object
Reported\nFuel Type Code object
AER\nFuel Type Code object
Reserved.1 float64
Reserved.2 float64
Physical\nUnit Label object
Quantity\nJanuary object
Quantity\nFebruary object
Quantity\nMarch object
Quantity\nApril object
Quantity\nMay object
Quantity\nJune object
Quantity\nJuly object
Quantity\nAugust object
Quantity\nSeptember object
Quantity\nOctober object
Quantity\nNovember object
...
Elec_MMBtu\nJanuary object
Elec_MMBtu\nFebruary object
Elec_MMBtu\nMarch object
Elec_MMBtu\nApril object
Elec_MMBtu\nMay object
Elec_MMBtu\nJune object
Elec_MMBtu\nJuly object
Elec_MMBtu\nAugust object
Elec_MMBtu\nSeptember object
Elec_MMBtu\nOctober object
Elec_MMBtu\nNovember object
Elec_MMBtu\nDecember object
Netgen\nJanuary object
Netgen\nFebruary object
Netgen\nMarch object
Netgen\nApril object
Netgen\nMay object
Netgen\nJune object
Netgen\nJuly object
Netgen\nAugust object
Netgen\nSeptember object
Netgen\nOctober object
Netgen\nNovember object
Netgen\nDecember object
Total Fuel Consumption\nQuantity int64
Electric Fuel Consumption\nQuantity int64
Total Fuel Consumption\nMMBtu int64
Elec Fuel Consumption\nMMBtu int64
Net Generation\n(Megawatthours) float64
YEAR int64
dtype: object
In [54]:
generation.tail()
Out[54]:
Plant Id
Combined Heat And
Power Plant
Nuclear Unit Id
Plant Name
Operator Name
Operator Id
Plant State
Census Region
NERC Region
Reserved
...
Netgen
September
Netgen
October
Netgen
November
Netgen
December
Total Fuel Consumption
Quantity
Electric Fuel Consumption
Quantity
Total Fuel Consumption
MMBtu
Elec Fuel Consumption
MMBtu
Net Generation
(Megawatthours)
YEAR
6841
99999
Y
NaN
State-Fuel Level Increment
State-Fuel Level Increment
99999
WY
MTN
NaN
NaN
...
20.03
20.073
.
.
2471
197
14391
1144
225.055
2016
6842
99999
Y
NaN
State-Fuel Level Increment
State-Fuel Level Increment
99999
WY
MTN
NaN
NaN
...
8371.86
6890.44
.
.
5750996
817170
5662749
804426
84996.866
2016
6843
99999
Y
NaN
State-Fuel Level Increment
State-Fuel Level Increment
99999
WY
MTN
NaN
NaN
...
282.565
112.177
.
.
7449697
152028
1362700
27810
3212.683
2016
6844
99999
Y
NaN
State-Fuel Level Increment
State-Fuel Level Increment
99999
WY
MTN
NaN
NaN
...
0
0
.
.
0
0
0
0
0.000
2016
6845
99999
Y
NaN
State-Fuel Level Increment
State-Fuel Level Increment
99999
WY
MTN
NaN
NaN
...
10293
7668.97
.
.
406340
92966
7110741
1626327
133198.240
2016
5 rows × 97 columns
In [55]:
generation.replace('.', 0, inplace=True)
In [56]:
generation.dtypes
Out[56]:
Plant Id int64
Combined Heat And\nPower Plant object
Nuclear Unit Id float64
Plant Name object
Operator Name object
Operator Id int64
Plant State object
Census Region object
NERC Region object
Reserved float64
NAICS Code int64
EIA Sector Number int64
Sector Name object
Reported\nPrime Mover object
Reported\nFuel Type Code object
AER\nFuel Type Code object
Reserved.1 float64
Reserved.2 float64
Physical\nUnit Label object
Quantity\nJanuary int64
Quantity\nFebruary int64
Quantity\nMarch int64
Quantity\nApril int64
Quantity\nMay int64
Quantity\nJune int64
Quantity\nJuly int64
Quantity\nAugust int64
Quantity\nSeptember int64
Quantity\nOctober int64
Quantity\nNovember int64
...
Elec_MMBtu\nJanuary int64
Elec_MMBtu\nFebruary int64
Elec_MMBtu\nMarch int64
Elec_MMBtu\nApril int64
Elec_MMBtu\nMay int64
Elec_MMBtu\nJune int64
Elec_MMBtu\nJuly int64
Elec_MMBtu\nAugust int64
Elec_MMBtu\nSeptember int64
Elec_MMBtu\nOctober int64
Elec_MMBtu\nNovember int64
Elec_MMBtu\nDecember int64
Netgen\nJanuary float64
Netgen\nFebruary float64
Netgen\nMarch float64
Netgen\nApril float64
Netgen\nMay float64
Netgen\nJune float64
Netgen\nJuly float64
Netgen\nAugust float64
Netgen\nSeptember float64
Netgen\nOctober float64
Netgen\nNovember int64
Netgen\nDecember int64
Total Fuel Consumption\nQuantity int64
Electric Fuel Consumption\nQuantity int64
Total Fuel Consumption\nMMBtu int64
Elec Fuel Consumption\nMMBtu int64
Net Generation\n(Megawatthours) float64
YEAR int64
dtype: object
In [57]:
generation.columns
Out[57]:
Index([u'Plant Id', u'Combined Heat And\nPower Plant', u'Nuclear Unit Id',
u'Plant Name', u'Operator Name', u'Operator Id', u'Plant State',
u'Census Region', u'NERC Region', u'Reserved', u'NAICS Code',
u'EIA Sector Number', u'Sector Name', u'Reported\nPrime Mover',
u'Reported\nFuel Type Code', u'AER\nFuel Type Code', u'Reserved.1',
u'Reserved.2', u'Physical\nUnit Label', u'Quantity\nJanuary',
u'Quantity\nFebruary', u'Quantity\nMarch', u'Quantity\nApril',
u'Quantity\nMay', u'Quantity\nJune', u'Quantity\nJuly',
u'Quantity\nAugust', u'Quantity\nSeptember', u'Quantity\nOctober',
u'Quantity\nNovember', u'Quantity\nDecember', u'Elec_Quantity\nJanuary',
u'Elec_Quantity\nFebruary', u'Elec_Quantity\nMarch',
u'Elec_Quantity\nApril', u'Elec_Quantity\nMay', u'Elec_Quantity\nJune',
u'Elec_Quantity\nJuly', u'Elec_Quantity\nAugust',
u'Elec_Quantity\nSeptember', u'Elec_Quantity\nOctober',
u'Elec_Quantity\nNovember', u'Elec_Quantity\nDecember',
u'MMBtuPer_Unit\nJanuary', u'MMBtuPer_Unit\nFebruary',
u'MMBtuPer_Unit\nMarch', u'MMBtuPer_Unit\nApril', u'MMBtuPer_Unit\nMay',
u'MMBtuPer_Unit\nJune', u'MMBtuPer_Unit\nJuly',
u'MMBtuPer_Unit\nAugust', u'MMBtuPer_Unit\nSeptember',
u'MMBtuPer_Unit\nOctober', u'MMBtuPer_Unit\nNovember',
u'MMBtuPer_Unit\nDecember', u'Tot_MMBtu\nJanuary',
u'Tot_MMBtu\nFebruary', u'Tot_MMBtu\nMarch', u'Tot_MMBtu\nApril',
u'Tot_MMBtu\nMay', u'Tot_MMBtu\nJune', u'Tot_MMBtu\nJuly',
u'Tot_MMBtu\nAugust', u'Tot_MMBtu\nSeptember', u'Tot_MMBtu\nOctober',
u'Tot_MMBtu\nNovember', u'Tot_MMBtu\nDecember', u'Elec_MMBtu\nJanuary',
u'Elec_MMBtu\nFebruary', u'Elec_MMBtu\nMarch', u'Elec_MMBtu\nApril',
u'Elec_MMBtu\nMay', u'Elec_MMBtu\nJune', u'Elec_MMBtu\nJuly',
u'Elec_MMBtu\nAugust', u'Elec_MMBtu\nSeptember', u'Elec_MMBtu\nOctober',
u'Elec_MMBtu\nNovember', u'Elec_MMBtu\nDecember', u'Netgen\nJanuary',
u'Netgen\nFebruary', u'Netgen\nMarch', u'Netgen\nApril', u'Netgen\nMay',
u'Netgen\nJune', u'Netgen\nJuly', u'Netgen\nAugust',
u'Netgen\nSeptember', u'Netgen\nOctober', u'Netgen\nNovember',
u'Netgen\nDecember', u'Total Fuel Consumption\nQuantity',
u'Electric Fuel Consumption\nQuantity',
u'Total Fuel Consumption\nMMBtu', u'Elec Fuel Consumption\nMMBtu',
u'Net Generation\n(Megawatthours)', u'YEAR'],
dtype='object')
In [58]:
generation.columns = [name.strip().replace('\n', ' ') for name in generation.columns]
generation.columns
Out[58]:
Index([u'Plant Id', u'Combined Heat And Power Plant', u'Nuclear Unit Id',
u'Plant Name', u'Operator Name', u'Operator Id', u'Plant State',
u'Census Region', u'NERC Region', u'Reserved', u'NAICS Code',
u'EIA Sector Number', u'Sector Name', u'Reported Prime Mover',
u'Reported Fuel Type Code', u'AER Fuel Type Code', u'Reserved.1',
u'Reserved.2', u'Physical Unit Label', u'Quantity January',
u'Quantity February', u'Quantity March', u'Quantity April',
u'Quantity May', u'Quantity June', u'Quantity July', u'Quantity August',
u'Quantity September', u'Quantity October', u'Quantity November',
u'Quantity December', u'Elec_Quantity January',
u'Elec_Quantity February', u'Elec_Quantity March',
u'Elec_Quantity April', u'Elec_Quantity May', u'Elec_Quantity June',
u'Elec_Quantity July', u'Elec_Quantity August',
u'Elec_Quantity September', u'Elec_Quantity October',
u'Elec_Quantity November', u'Elec_Quantity December',
u'MMBtuPer_Unit January', u'MMBtuPer_Unit February',
u'MMBtuPer_Unit March', u'MMBtuPer_Unit April', u'MMBtuPer_Unit May',
u'MMBtuPer_Unit June', u'MMBtuPer_Unit July', u'MMBtuPer_Unit August',
u'MMBtuPer_Unit September', u'MMBtuPer_Unit October',
u'MMBtuPer_Unit November', u'MMBtuPer_Unit December',
u'Tot_MMBtu January', u'Tot_MMBtu February', u'Tot_MMBtu March',
u'Tot_MMBtu April', u'Tot_MMBtu May', u'Tot_MMBtu June',
u'Tot_MMBtu July', u'Tot_MMBtu August', u'Tot_MMBtu September',
u'Tot_MMBtu October', u'Tot_MMBtu November', u'Tot_MMBtu December',
u'Elec_MMBtu January', u'Elec_MMBtu February', u'Elec_MMBtu March',
u'Elec_MMBtu April', u'Elec_MMBtu May', u'Elec_MMBtu June',
u'Elec_MMBtu July', u'Elec_MMBtu August', u'Elec_MMBtu September',
u'Elec_MMBtu October', u'Elec_MMBtu November', u'Elec_MMBtu December',
u'Netgen January', u'Netgen February', u'Netgen March', u'Netgen April',
u'Netgen May', u'Netgen June', u'Netgen July', u'Netgen August',
u'Netgen September', u'Netgen October', u'Netgen November',
u'Netgen December', u'Total Fuel Consumption Quantity',
u'Electric Fuel Consumption Quantity', u'Total Fuel Consumption MMBtu',
u'Elec Fuel Consumption MMBtu', u'Net Generation (Megawatthours)',
u'YEAR'],
dtype='object')
In [59]:
'Quantity January'.split()
Out[59]:
['Quantity', 'January']
In [60]:
id_cols = ['Plant Id', 'NERC Region', 'AER Fuel Type Code']
monthly_cols = []
def find_col_names(cols):
for col in cols:
if 'January' in col:
monthly_cols.append(col.split()[0])
find_col_names(generation.columns)
id_cols + monthly_cols
Out[60]:
['Plant Id',
'NERC Region',
'AER Fuel Type Code',
u'Quantity',
u'Elec_Quantity',
u'MMBtuPer_Unit',
u'Tot_MMBtu',
u'Elec_MMBtu',
u'Netgen']
In [61]:
gen_may = pd.DataFrame(columns=id_cols + monthly_cols)
gen_may
Out[61]:
Plant Id
NERC Region
AER Fuel Type Code
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
In [62]:
gen_may[id_cols] = generation.loc[:,id_cols]
gen_may.head()
Out[62]:
Plant Id
NERC Region
AER Fuel Type Code
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
0
3
SERC
NG
NaN
NaN
NaN
NaN
NaN
NaN
1
3
SERC
NG
NaN
NaN
NaN
NaN
NaN
NaN
2
3
SERC
COL
NaN
NaN
NaN
NaN
NaN
NaN
3
3
SERC
NG
NaN
NaN
NaN
NaN
NaN
NaN
4
4
SERC
HYC
NaN
NaN
NaN
NaN
NaN
NaN
In [63]:
for col in monthly_cols:
gen_may[col] = generation.loc[:,col + ' May']
In [64]:
gen_may.head()
Out[64]:
Plant Id
NERC Region
AER Fuel Type Code
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
0
3
SERC
NG
64489
64489
1.017
65585
65585
223662.00
1
3
SERC
NG
4355654
4355654
1.017
4429700
4429700
421779.00
2
3
SERC
COL
178956
178956
21.425
3834132
3834132
376726.94
3
3
SERC
NG
63097
63097
1.017
64170
64170
6305.06
4
4
SERC
HYC
0
0
0.000
95361
95361
10233.00
In [65]:
facility_gen = gen_may.groupby(['Plant Id', 'NERC Region']).sum()
facility_gen.head()
Out[65]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Plant Id
NERC Region
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
SERC
0
0
0.000
95361
95361
10233.000
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
10
SERC
116191
116191
2.066
120025
120025
6734.000
14
SERC
0
0
0.000
132060
132060
14171.000
In [66]:
facility_gen.reset_index('NERC Region', inplace=True)
facility_gen.head()
Out[66]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Plant Id
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
SERC
0
0
0.000
95361
95361
10233.000
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
10
SERC
116191
116191
2.066
120025
120025
6734.000
14
SERC
0
0
0.000
132060
132060
14171.000
In [67]:
merged = facility_gen.merge(facility_cap, how='inner',
left_index=True, right_index=True)
In [68]:
merged.head()
Out[68]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Nameplate Capacity (MW)
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
2569.5
4
SERC
0
0
0.000
95361
95361
10233.000
225.0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
1166.7
10
SERC
116191
116191
2.066
120025
120025
6734.000
1288.4
14
SERC
0
0
0.000
132060
132060
14171.000
128.1
In [69]:
final = merged.merge(facility_emiss, how='outer',
left_index=True, right_index=True)
In [70]:
final
Out[70]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Nameplate Capacity (MW)
Operating Time
Gross Load (MW-h)
Steam Load (1000lb)
SO2 (tons)
Avg. NOx Rate (lb/MMBtu)
NOx (tons)
CO2 (short tons)
Heat Input (MMBtu)
EPA Region
3
SERC
4662196.0
4662196.0
24.476
8393587.0
8393587.0
1028473.000
2569.5
3844.00
1048450.25
NaN
595.911
0.5760
411.165
677078.976
8471790.125
4.0
4
SERC
0.0
0.0
0.000
95361.0
95361.0
10233.000
225.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
7
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
924.00
NaN
222159.75
0.990
0.2067
13.814
15771.300
266053.425
4.0
8
SERC
188233.0
188233.0
30.364
4588547.0
4588547.0
471836.004
1166.7
1837.25
505777.25
NaN
125.662
0.5375
440.348
531418.603
5179513.525
4.0
9
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
20.60
541.68
NaN
0.003
0.2751
1.504
NaN
10441.833
6.0
10
SERC
116191.0
116191.0
2.066
120025.0
120025.0
6734.000
1288.4
188.25
8897.75
NaN
48.579
0.8474
10.162
11340.124
144258.150
4.0
14
SERC
0.0
0.0
0.000
132060.0
132060.0
14171.000
128.1
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
15
SERC
0.0
0.0
0.000
219444.0
219444.0
23548.000
177.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
16
SERC
0.0
0.0
0.000
145265.0
145265.0
15588.000
210.6
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
17
SERC
0.0
0.0
0.000
193742.0
193742.0
20790.000
170.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
18
SERC
0.0
0.0
0.000
54386.0
54386.0
5836.000
181.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
26
SERC
197510.0
197510.0
30.196
4577634.0
4577634.0
453833.000
2034.0
744.00
495791.00
NaN
173.698
0.0646
165.370
524604.900
5113099.400
4.0
46
SERC
0.0
0.0
0.000
25805554.0
25805554.0
2467542.000
3494.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
47
SERC
58.0
58.0
5.800
336.0
336.0
62.000
1026.0
20.00
177.00
NaN
0.333
4.4379
0.722
192.400
2765.600
4.0
48
SERC
0.0
0.0
0.000
177946.0
177946.0
19095.000
115.2
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
49
SERC
0.0
0.0
0.000
261286.0
261286.0
28038.000
404.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
50
SERC
0.0
0.0
0.000
0.0
0.0
0.000
575.0
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4.0
51
SPP
241664.0
241664.0
14.902
2799148.0
2799148.0
238020.998
720.7
553.48
262392.63
NaN
1081.656
0.2080
241.863
270128.775
2481213.560
6.0
54
SERC
177832.0
177832.0
1.066
189569.0
189569.0
15037.000
1055.0
254.45
15652.65
NaN
0.057
0.3753
2.787
11101.898
186799.693
4.0
56
SERC
49159.0
49159.0
28.560
1098557.0
1098557.0
97056.000
538.0
759.23
112793.47
NaN
63.902
0.5125
167.878
129301.129
1260257.488
4.0
59
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
321.07
18822.00
NaN
16.264
0.2297
24.957
21946.742
209245.816
7.0
60
MRO
24713.0
24713.0
22.206
404792.0
404792.0
31448.000
324.3
753.95
36111.00
NaN
176.503
0.2169
39.165
44877.292
427905.242
7.0
64
ASCC
102.0
102.0
5.712
583.0
583.0
21.000
61.7
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
77
ASCC
0.0
0.0
0.000
84617.0
84617.0
9080.000
44.4
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
78
ASCC
0.0
0.0
0.000
157007.0
157007.0
16848.000
78.2
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
87
WECC
52499.0
52499.0
19.578
867759.0
867759.0
74925.000
257.0
744.00
82439.00
NaN
34.069
0.3811
161.168
88806.200
846735.000
6.0
96
ASCC
76562.0
76562.0
1.000
76562.0
76562.0
3455.000
312.4
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
104
WECC
106.0
106.0
0.000
0.0
0.0
44845.000
199.8
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
108
SPP
102821.0
102821.0
18.837
1607158.0
1607158.0
146461.005
348.7
660.18
160882.24
NaN
95.013
0.1813
151.412
173507.748
1654354.527
7.0
113
WECC
68608.0
68608.0
24.756
1253737.0
1253737.0
103305.000
839.9
992.93
120001.76
NaN
57.635
0.6462
149.985
135364.206
1319344.965
9.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
59654
TRE
0.0
0.0
0.000
243748.0
243748.0
26156.000
78.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59655
MRO
0.0
0.0
0.000
313100.0
313100.0
33598.000
97.8
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59702
WECC
0.0
0.0
0.000
141779.0
141779.0
15214.000
50.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59723
WECC
0.0
0.0
0.000
141024.0
141024.0
15133.000
30.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59732
TRE
0.0
0.0
0.000
408918.0
408918.0
43880.000
150.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59733
TRE
0.0
0.0
0.000
392479.0
392479.0
42116.000
150.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59734
TRE
0.0
0.0
0.000
405451.0
405451.0
43508.000
150.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59778
SERC
0.0
0.0
0.000
65186.0
65186.0
6995.000
47.7
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59784
WECC
1339613.0
1339613.0
1.024
1371764.0
1371764.0
187059.000
619.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59837
SPP
0.0
0.0
0.000
465577.0
465577.0
49960.000
150.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59838
SPP
0.0
0.0
0.000
181338.0
181338.0
19459.000
50.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59862
SERC
0.0
0.0
0.000
58831.0
58831.0
6313.000
30.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59894
SERC
0.0
0.0
0.000
66202.0
66202.0
7104.000
30.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59926
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
24.63
2794.92
NaN
0.015
0.0320
0.806
2972.500
50368.700
5.0
59943
TRE
0.0
0.0
0.000
757169.0
757169.0
81250.000
207.2
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59944
SERC
0.0
0.0
0.000
144920.0
144920.0
15551.000
80.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59965
WECC
0.0
0.0
0.000
89956.0
89956.0
9653.000
62.1
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59974
WECC
0.0
0.0
0.000
690044.0
690044.0
74047.000
250.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
59975
WECC
0.0
0.0
0.000
317582.0
317582.0
34079.000
150.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60008
WECC
0.0
0.0
0.000
127866.0
127866.0
13721.000
52.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60013
SPP
0.0
0.0
0.000
410055.0
410055.0
44002.000
151.8
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60049
WECC
0.0
0.0
0.000
306101.0
306101.0
32847.000
80.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60059
TRE
0.0
0.0
0.000
295142.0
295142.0
31671.000
110.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60069
SPP
0.0
0.0
0.000
612939.0
612939.0
65773.000
198.6
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60093
WECC
0.0
0.0
0.000
263569.0
263569.0
28283.000
109.8
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60104
TRE
0.0
0.0
0.000
796420.0
796420.0
85462.000
249.7
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60262
SPP
0.0
0.0
0.000
240132.0
240132.0
25768.000
73.4
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60314
SPP
0.0
0.0
0.000
103935.0
103935.0
11153.000
35.8
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
60419
WECC
0.0
0.0
0.000
142450.0
142450.0
15286.000
25.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
70454
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
62.78
7178.74
NaN
0.026
0.1711
3.388
5164.599
86899.023
4.0
2510 rows × 17 columns
In [71]:
final.to_csv('Final data.csv')
In [ ]:
In [75]:
final.plot.scatter(x='Nameplate Capacity (MW)',
y='Netgen')
Out[75]:
<matplotlib.axes._subplots.AxesSubplot at 0x11d524ed0>
In [76]:
import seaborn as sns
In [79]:
final.plot.scatter(x='Nameplate Capacity (MW)',
y='Netgen')
Out[79]:
<matplotlib.axes._subplots.AxesSubplot at 0x11d382290>
In [ ]:
Content source: gschivley/Teaching-python
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