In [1]:
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
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 [13]:
emissions.tail()
Out[13]:
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 [20]:
emissions.columns
Out[20]:
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')
Notice that most of the columns have a leading space?
In [26]:
columns_strip = [name.strip() for name in emissions.columns]
columns_strip
Out[26]:
['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)']
In [27]:
emissions.columns = columns_strip
emissions.columns
Out[27]:
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 [31]:
emissions.dtypes
Out[31]:
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
A single column from a dataframe is called a Series
In [32]:
type(emissions)
Out[32]:
pandas.core.frame.DataFrame
In [30]:
type(emissions['Operating Time'])
Out[30]:
pandas.core.series.Series
In [33]:
emissions['Operating Time']
Out[33]:
0 28.00
1 17.00
2 2.75
3 2.75
4 725.25
5 588.00
6 712.50
7 609.25
8 487.25
9 716.25
10 2.53
11 2.77
12 2.31
13 2.42
14 0.00
15 393.88
16 365.35
17 0.00
18 0.00
19 0.00
20 0.00
21 0.00
22 2.00
23 2.00
24 2.00
25 3.00
26 1.00
27 6.00
28 2.00
29 2.00
...
3894 21.05
3895 307.17
3896 394.15
3897 588.03
3898 281.58
3899 316.13
3900 398.32
3901 18.69
3902 673.51
3903 744.00
3904 743.30
3905 744.00
3906 650.58
3907 462.40
3908 744.00
3909 106.74
3910 743.70
3911 677.80
3912 55.90
3913 727.68
3914 744.00
3915 33.13
3916 735.62
3917 597.64
3918 15.25
3919 2.16
3920 662.62
3921 744.00
3922 581.64
3923 0.00
Name: Operating Time, dtype: float64
Index into a dataframe using .loc or .iloc with square brackets and row,column notation
In [34]:
emissions.loc[0:5,'Operating Time']
Out[34]:
0 28.00
1 17.00
2 2.75
3 2.75
4 725.25
5 588.00
Name: Operating Time, dtype: float64
In [36]:
emissions.iloc[0:5,:3]
Out[36]:
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 [47]:
emissions.groupby('Facility ID (ORISPL)')
Out[47]:
<pandas.core.groupby.DataFrameGroupBy object at 0x114709cd0>
Not all columns sum well
In [57]:
facility_emiss = emissions.groupby('Facility ID (ORISPL)').sum()
# facility_emiss = facility_emiss.iloc[:,2:]
facility_emiss
Out[57]:
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
If we want to keep the EPA Region, there are probably better ways to do it than this. We will write a little function that divides the month by 5 (May) and then divides the region by that result.
In [63]:
def correct_region(row):
num_units = row['Month'] / 5
region = row['EPA Region'] / num_units
return region
Not sure why this is returning a float. Go back to the function and return an int instead.
In [64]:
facility_emiss.apply(correct_region, axis=1)
Out[64]:
Facility ID (ORISPL)
3 4.0
7 4.0
8 4.0
9 6.0
10 4.0
26 4.0
47 4.0
50 4.0
51 6.0
54 4.0
56 4.0
59 7.0
60 7.0
87 6.0
108 7.0
113 9.0
116 9.0
117 9.0
118 9.0
120 9.0
124 9.0
126 9.0
127 6.0
130 4.0
136 4.0
141 9.0
147 9.0
160 9.0
165 6.0
170 6.0
...
57037 4.0
57073 9.0
57074 9.0
57075 9.0
57241 4.0
57267 9.0
57349 3.0
57482 9.0
57483 9.0
57515 9.0
57703 8.0
57839 2.0
57865 6.0
57881 8.0
57943 8.0
57978 9.0
58001 6.0
58005 6.0
58054 1.0
58079 2.0
58122 9.0
58235 2.0
58260 3.0
58284 6.0
58325 6.0
58471 6.0
58478 6.0
58562 6.0
59926 5.0
70454 4.0
dtype: float64
In [65]:
facility_emiss.loc[:,'EPA Region'] = facility_emiss.apply(correct_region, axis=1)
facility_emiss = facility_emiss.iloc[:,2:]
facility_emiss
Out[65]:
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
4.0
7
924.00
NaN
222159.75
0.990
0.2067
13.814
15771.300
266053.425
4.0
8
1837.25
505777.25
NaN
125.662
0.5375
440.348
531418.603
5179513.525
4.0
9
20.60
541.68
NaN
0.003
0.2751
1.504
NaN
10441.833
6.0
10
188.25
8897.75
NaN
48.579
0.8474
10.162
11340.124
144258.150
4.0
26
744.00
495791.00
NaN
173.698
0.0646
165.370
524604.900
5113099.400
4.0
47
20.00
177.00
NaN
0.333
4.4379
0.722
192.400
2765.600
4.0
50
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
4.0
51
553.48
262392.63
NaN
1081.656
0.2080
241.863
270128.775
2481213.560
6.0
54
254.45
15652.65
NaN
0.057
0.3753
2.787
11101.898
186799.693
4.0
56
759.23
112793.47
NaN
63.902
0.5125
167.878
129301.129
1260257.488
4.0
59
321.07
18822.00
NaN
16.264
0.2297
24.957
21946.742
209245.816
7.0
60
753.95
36111.00
NaN
176.503
0.2169
39.165
44877.292
427905.242
7.0
87
744.00
82439.00
NaN
34.069
0.3811
161.168
88806.200
846735.000
6.0
108
660.18
160882.24
NaN
95.013
0.1813
151.412
173507.748
1654354.527
7.0
113
992.93
120001.76
NaN
57.635
0.6462
149.985
135364.206
1319344.965
9.0
116
247.40
8653.84
NaN
0.032
0.2216
7.130
6368.940
107186.519
9.0
117
623.34
109950.39
NaN
0.257
0.0754
7.739
50958.633
857449.725
9.0
118
37.80
1491.10
NaN
0.007
0.0507
0.447
1333.907
22445.386
9.0
120
748.53
35334.66
NaN
0.120
0.1839
26.270
23956.571
403122.659
9.0
124
7.70
360.90
NaN
0.009
0.0468
0.081
298.620
5024.270
9.0
126
1101.56
76803.54
NaN
2.119
0.5604
69.574
50133.139
822072.677
9.0
127
401.58
129794.52
NaN
73.983
0.2907
203.293
145337.271
1385756.804
6.0
130
1518.03
822856.12
NaN
370.759
0.2077
289.871
860955.931
8391402.264
4.0
136
1451.60
689645.88
NaN
477.829
0.1152
187.062
682395.436
6651035.344
4.0
141
67.49
4296.61
NaN
0.014
0.5300
11.359
2730.960
45960.324
9.0
147
611.23
130042.08
NaN
0.277
0.0101
3.965
54824.884
922519.859
9.0
160
1970.04
185441.25
NaN
20.620
1.0331
359.939
185387.782
1905952.909
9.0
165
603.34
191538.88
NaN
506.266
0.2807
170.876
239423.987
2290169.297
6.0
170
638.74
139956.89
NaN
0.422
0.2584
239.594
83529.374
1405547.366
6.0
...
...
...
...
...
...
...
...
...
...
57037
1398.25
368847.25
NaN
0.850
0.0271
17.634
168415.150
2833845.350
4.0
57073
0.00
NaN
NaN
NaN
NaN
NaN
NaN
NaN
9.0
57074
407.00
NaN
NaN
0.015
0.0110
0.298
3187.900
54046.000
9.0
57075
248.00
NaN
NaN
0.007
0.0110
0.149
1602.200
27151.000
9.0
57241
42.34
2051.00
NaN
0.090
0.0555
1.154
3480.042
33986.189
4.0
57267
18.74
2488.57
NaN
0.008
0.0434
0.123
1712.645
28817.647
9.0
57349
393.61
100963.68
NaN
0.207
0.0089
2.387
40922.021
688589.496
3.0
57482
451.97
29914.41
NaN
0.088
0.1731
2.092
17568.752
295618.850
9.0
57483
345.92
10334.14
NaN
0.036
0.0692
0.712
6236.594
104945.273
9.0
57515
823.60
65754.07
NaN
0.174
0.1513
5.561
34605.858
582304.228
9.0
57703
733.14
19706.25
NaN
0.060
0.0540
0.859
12035.438
202527.156
8.0
57839
1101.30
357284.80
NaN
0.639
0.0110
5.838
126516.225
2128891.010
2.0
57865
81.55
10928.02
NaN
0.034
0.1633
2.704
6779.163
114074.151
6.0
57881
580.92
20205.20
NaN
0.062
0.0855
1.784
12307.201
207098.166
8.0
57943
1753.37
60954.81
NaN
0.183
0.0481
3.916
36090.079
607288.728
8.0
57978
16.81
2461.69
NaN
0.008
0.0201
0.119
1625.035
27347.542
9.0
58001
1485.62
417253.89
NaN
0.935
0.0426
12.778
185161.751
3115749.902
6.0
58005
994.60
292270.75
NaN
0.644
0.0166
7.113
127678.269
2148462.119
6.0
58054
743.03
53385.00
NaN
1.455
0.0584
19.356
69154.702
664207.371
1.0
58079
1268.00
385188.46
NaN
0.756
0.0128
7.746
149796.191
2520589.201
2.0
58122
2.57
61.56
NaN
0.000
0.0775
0.048
43.560
732.625
9.0
58235
119.55
7160.00
NaN
0.020
0.0191
0.384
3909.089
65770.895
2.0
58260
1824.11
705418.26
NaN
1.468
0.0321
17.479
290920.901
4895295.214
3.0
58284
16.62
483.78
NaN
0.001
0.0250
0.032
289.595
4874.589
6.0
58325
7.93
471.64
NaN
0.002
0.0457
0.147
390.728
6576.714
6.0
58471
81.30
8786.34
NaN
0.142
0.1028
1.593
6244.180
105066.232
6.0
58478
47.25
1823.50
NaN
0.005
0.0215
0.148
968.200
16293.125
6.0
58562
615.09
40172.06
NaN
0.111
0.0296
2.077
21929.390
368989.001
6.0
59926
24.63
2794.92
NaN
0.015
0.0320
0.806
2972.500
50368.700
5.0
70454
62.78
7178.74
NaN
0.026
0.1711
3.388
5164.599
86899.023
4.0
1208 rows × 9 columns
In [ ]:
In [9]:
capacity = pd.read_excel(fn2, sheetname='Operating', header=1)
In [10]:
capacity.head()
Out[10]:
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 [12]:
capacity.tail()
Out[12]:
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 [16]:
capacity.drop(20187, inplace=True)
In [18]:
capacity.loc[:,'Plant ID'] = capacity.loc[:,'Plant ID'].astype(int)
In [19]:
capacity.head()
Out[19]:
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 [70]:
capacity.columns
Out[70]:
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 [71]:
capacity.columns = [name.strip() for name in capacity.columns]
capacity.columns
Out[71]:
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 [45]:
PA_NGCC_cap = capacity.loc[(capacity['Plant State']=='PA') &
(capacity['Technology']=='Natural Gas Fired Combined Cycle'),:]
PA_NGCC_cap
Out[45]:
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 [72]:
cols = ['Plant ID', 'Nameplate Capacity (MW)']
facility_cap = capacity.loc[:,cols].groupby('Plant ID').sum()
facility_cap
Out[72]:
Nameplate Capacity (MW)
Plant ID
2
53.9
3
2569.5
4
225.0
7
138.0
8
1166.7
9
80.5
10
1288.4
11
72.9
12
46.9
13
100.0
14
128.1
15
177.0
16
210.6
17
170.0
18
181.0
19
91.0
20
87.6
21
50.5
26
2034.0
30
11.8
34
12.1
38
101.1
46
3494.0
47
1026.0
48
115.2
49
404.0
50
575.0
51
720.7
53
3.0
54
1055.0
...
...
60262
73.4
60263
1.6
60265
4.4
60267
2.7
60275
2.0
60276
4.1
60277
1.5
60278
1.9
60299
7.0
60312
4.9
60313
4.9
60314
35.8
60320
1.2
60321
16.1
60327
1.8
60341
1.0
60344
2.0
60349
2.3
60353
2.0
60365
1.6
60367
5.2
60369
5.2
60384
5.0
60388
1.6
60390
31.5
60401
5.2
60403
5.2
60411
2.2
60419
25.0
60425
2.0
7561 rows × 1 columns
In [73]:
generation = pd.read_excel(fn3, header=5)
Something weird is going on here. I know there are lots of rows with numeric data that are missing from this describe table.
In [108]:
generation.describe()
Out[108]:
Plant Id
Nuclear Unit Id
Operator Id
Reserved
NAICS Code
EIA Sector Number
Reserved.1
Reserved.2
Total Fuel Consumption Quantity
Electric Fuel Consumption Quantity
Total Fuel Consumption MMBtu
Elec Fuel Consumption MMBtu
Net Generation (Megawatthours)
YEAR
count
6846.000000
100.000000
6846.000000
0.0
6846.000000
6846.000000
0.0
0.0
6.846000e+03
6.846000e+03
6.846000e+03
6.846000e+03
6.846000e+03
6846.0
mean
47395.247590
1.590000
45494.412650
NaN
38365.911043
2.550394
NaN
NaN
1.735993e+06
1.523509e+06
5.046923e+06
4.736994e+06
5.018362e+05
2016.0
std
39659.073362
0.697687
38449.729044
NaN
68095.379303
2.051354
NaN
NaN
6.397009e+06
5.648343e+06
1.423608e+07
1.412352e+07
1.379263e+06
0.0
min
3.000000
1.000000
21.000000
NaN
22.000000
1.000000
NaN
NaN
0.000000e+00
0.000000e+00
0.000000e+00
0.000000e+00
-7.028300e+05
2016.0
25%
6001.250000
1.000000
12492.000000
NaN
22.000000
1.000000
NaN
NaN
0.000000e+00
0.000000e+00
6.762500e+02
4.747500e+02
1.809423e+02
2016.0
50%
55061.000000
1.000000
22129.000000
NaN
22.000000
2.000000
NaN
NaN
2.378500e+03
1.470500e+03
3.173170e+05
2.088960e+05
3.127657e+04
2016.0
75%
99999.000000
2.000000
99999.000000
NaN
99999.000000
3.000000
NaN
NaN
4.469722e+05
2.983672e+05
2.892082e+06
2.288356e+06
2.921652e+05
2016.0
max
99999.000000
4.000000
99999.000000
NaN
562213.000000
7.000000
NaN
NaN
1.533434e+08
1.208176e+08
1.505038e+08
1.505038e+08
1.544404e+07
2016.0
In [74]:
generation.head()
Out[74]:
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
Turns out that there are lots of dots (.) where it is no value. I'm going to replace these with zeros.
In [75]:
generation.tail()
Out[75]:
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 [110]:
generation.replace('.', 0, inplace=True)
There are line breaks in the middle of column names. I don't see any breaks or spaces at the beginning or end of names, but will still strip just to be safe.
In [76]:
generation.columns
Out[76]:
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 [79]:
generation.columns = [name.strip().replace('\n', ' ') for name in generation.columns]
generation.columns
Out[79]:
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')
I'm lazy and want to get a list of month names without typing them all
In [82]:
# could have done this as a list comprehension, but it would have been harder to read
months = []
for name in generation.columns:
if 'Netgen' in name:
month = name.split()[-1]
months.append(month)
months
Out[82]:
[u'January',
u'February',
u'March',
u'April',
u'May',
u'June',
u'July',
u'August',
u'September',
u'October',
u'November',
u'December']
In [90]:
id_cols = ['Plant Id', 'Plant State', '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[90]:
['Plant Id',
'Plant State',
'NERC Region',
'AER Fuel Type Code',
u'Quantity',
u'Elec_Quantity',
u'MMBtuPer_Unit',
u'Tot_MMBtu',
u'Elec_MMBtu',
u'Netgen']
In [91]:
pd.DataFrame(columns=id_cols + monthly_cols + ['Month'])
Out[91]:
Plant Id
Plant State
NERC Region
AER Fuel Type Code
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
In [113]:
gen_list = []
for month in months:
gen_df = pd.DataFrame(columns=id_cols + monthly_cols)
# Took me a few tries to figure out that I couldn't use .loc for gen_df
gen_df[id_cols] = generation.loc[:,id_cols]
gen_df['Month'] = month
for col in monthly_cols:
gen_df.loc[:,col] = generation.loc[:,col + ' ' + month]
gen_list.append(gen_df)
In [120]:
gen_stack = pd.concat(gen_list)
gen_stack.describe()
Out[120]:
Plant Id
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
count
82152.000000
8.215200e+04
8.215200e+04
82152.000000
8.215200e+04
8.215200e+04
8.215200e+04
mean
47395.247590
1.446661e+05
1.269590e+05
2.397057
4.205770e+05
3.947495e+05
4.181968e+04
std
39656.418102
6.144502e+05
5.480218e+05
5.409176
1.371208e+06
1.359613e+06
1.331186e+05
min
3.000000
0.000000e+00
0.000000e+00
0.000000
0.000000e+00
0.000000e+00
-1.164190e+05
25%
6001.000000
0.000000e+00
0.000000e+00
0.000000
0.000000e+00
0.000000e+00
0.000000e+00
50%
55061.000000
0.000000e+00
0.000000e+00
0.000000
2.546500e+03
1.822500e+03
3.217385e+02
75%
99999.000000
1.622225e+04
9.554750e+03
1.040000
1.742685e+05
1.271422e+05
1.720887e+04
max
99999.000000
1.650352e+07
1.356701e+07
34.180000
1.966522e+07
1.966522e+07
1.940054e+06
In [115]:
gen_stack['AER Fuel Type Code'].unique()
Out[115]:
array([u'NG', u'COL', u'HYC', u'DFO', u'NUC', u'WOO', u'HPS', u'SUN',
u'RFO', u'MLG', u'PC', u'ORW', u'GEO', u'OTH', u'OOG', u'WWW',
u'WOC', nan, u'WND'], dtype=object)
In [116]:
non_combust = ['HYC', 'NUC', 'SUN', 'GEO', 'WND'] # might be incomplete
In [117]:
def tag_combust(row):
if row['AER Fuel Type Code'] in non_combust:
return 0
else:
return 1
In [121]:
gen_stack['Combust'] = gen_stack.apply(tag_combust, axis=1)
In [122]:
gen_stack.head()
Out[122]:
Plant Id
Plant State
NERC Region
AER Fuel Type Code
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Month
Combust
0
3
AL
SERC
NG
57253
57253
1.017
58226
58226
268797.000
January
1
1
3
AL
SERC
NG
5248798
5248798
1.017
5338028
5338028
511773.000
January
1
2
3
AL
SERC
COL
159951
159951
20.589
3293231
3293231
329513.400
January
1
3
3
AL
SERC
NG
129803
129803
1.016
131880
131880
13195.605
January
1
4
4
AL
SERC
HYC
0
0
0.000
1301314
1301314
139641.000
January
0
In [149]:
test = gen_stack.loc[gen_stack['Month']=='May',:].groupby(['Plant Id', 'NERC Region']).sum()
test.head()
Out[149]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Plant Id
NERC Region
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
4
SERC
0
0
0.000
95361
95361
10233.000
0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
2
10
SERC
116191
116191
2.066
120025
120025
6734.000
7
14
SERC
0
0
0.000
132060
132060
14171.000
0
In [150]:
test.reset_index('NERC Region')
Out[150]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Plant Id
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
4
SERC
0
0
0.000
95361
95361
10233.000
0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
2
10
SERC
116191
116191
2.066
120025
120025
6734.000
7
14
SERC
0
0
0.000
132060
132060
14171.000
0
15
SERC
0
0
0.000
219444
219444
23548.000
0
16
SERC
0
0
0.000
145265
145265
15588.000
0
17
SERC
0
0
0.000
193742
193742
20790.000
0
18
SERC
0
0
0.000
54386
54386
5836.000
0
26
SERC
197510
197510
30.196
4577634
4577634
453833.000
4
46
SERC
0
0
0.000
25805554
25805554
2467542.000
0
47
SERC
58
58
5.800
336
336
62.000
7
48
SERC
0
0
0.000
177946
177946
19095.000
0
49
SERC
0
0
0.000
261286
261286
28038.000
0
50
SERC
0
0
0.000
0
0
0.000
2
51
SPP
241664
241664
14.902
2799148
2799148
238020.998
3
54
SERC
177832
177832
1.066
189569
189569
15037.000
2
56
SERC
49159
49159
28.560
1098557
1098557
97056.000
2
60
MRO
24713
24713
22.206
404792
404792
31448.000
2
64
ASCC
102
102
5.712
583
583
21.000
2
77
ASCC
0
0
0.000
84617
84617
9080.000
0
78
ASCC
0
0
0.000
157007
157007
16848.000
0
87
WECC
52499
52499
19.578
867759
867759
74925.000
2
96
ASCC
76562
76562
1.000
76562
76562
3455.000
1
104
WECC
106
106
0.000
0
0
44845.000
1
108
SPP
102821
102821
18.837
1607158
1607158
146461.005
2
113
WECC
68608
68608
24.756
1253737
1253737
103305.000
4
116
WECC
92596
92596
2.070
96601
96601
8323.000
4
117
WECC
1112689
1112689
3.117
1156084
1156084
130221.000
5
118
WECC
40761
40761
1.060
43207
43207
1618.000
3
...
...
...
...
...
...
...
...
...
59965
WECC
0
0
0.000
89956
89956
9653.000
0
59974
WECC
0
0
0.000
690044
690044
74047.000
0
59975
WECC
0
0
0.000
317582
317582
34079.000
0
60008
WECC
0
0
0.000
127866
127866
13721.000
0
60013
SPP
0
0
0.000
410055
410055
44002.000
0
60049
WECC
0
0
0.000
306101
306101
32847.000
0
60058
SERC
0
0
0.000
0
0
0.000
0
60059
TRE
0
0
0.000
295142
295142
31671.000
0
60064
SERC
0
0
0.000
0
0
0.000
0
60069
SPP
0
0
0.000
612939
612939
65773.000
0
60082
SERC
0
0
0.000
0
0
0.000
0
60087
TRE
0
0
0.000
0
0
0.000
0
60092
WECC
0
0
0.000
0
0
0.000
0
60093
WECC
0
0
0.000
263569
263569
28283.000
0
60104
TRE
0
0
0.000
796420
796420
85462.000
0
60127
RFC
0
0
0.000
0
0
0.000
0
60259
WECC
0
0
0.000
0
0
0.000
0
60262
SPP
0
0
0.000
240132
240132
25768.000
0
60285
WECC
0
0
0.000
0
0
0.000
0
60314
SPP
0
0
0.000
103935
103935
11153.000
0
60326
MRO
0
0
0.000
0
0
0.000
0
60339
TRE
0
0
0.000
0
0
0.000
0
60342
MRO
0
0
0.000
0
0
0.000
0
60351
WECC
0
0
0.000
0
0
0.000
0
60389
WECC
0
0
0.000
0
0
0.000
0
60405
SPP
0
0
0.000
0
0
0.000
0
60406
SPP
0
0
0.000
0
0
0.000
0
60419
WECC
0
0
0.000
142450
142450
15286.000
0
60445
WECC
0
0
0.000
0
0
0.000
0
60486
WECC
0
0
0.000
0
0
0.000
0
2260 rows × 8 columns
In [123]:
facility_gen = gen_stack.loc[gen_stack['Month']=='May',:].groupby('Plant Id').sum()
In [124]:
facility_gen.head()
Out[124]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Plant Id
3
4662196
4662196
24.476
8393587
8393587
1028473.000
4
4
0
0
0.000
95361
95361
10233.000
0
8
188233
188233
30.364
4588547
4588547
471836.004
2
10
116191
116191
2.066
120025
120025
6734.000
7
14
0
0
0.000
132060
132060
14171.000
0
If I want to keep the NERC Region, I can do that in the groupby
In [153]:
facility_gen = gen_stack.loc[gen_stack['Month']=='May',:].groupby(['Plant Id', 'NERC Region']).sum()
facility_gen.head()
Out[153]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Plant Id
NERC Region
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
4
SERC
0
0
0.000
95361
95361
10233.000
0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
2
10
SERC
116191
116191
2.066
120025
120025
6734.000
7
14
SERC
0
0
0.000
132060
132060
14171.000
0
In [154]:
facility_gen.reset_index('NERC Region', inplace=True)
facility_gen.head()
Out[154]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Plant Id
3
SERC
4662196
4662196
24.476
8393587
8393587
1028473.000
4
4
SERC
0
0
0.000
95361
95361
10233.000
0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
2
10
SERC
116191
116191
2.066
120025
120025
6734.000
7
14
SERC
0
0
0.000
132060
132060
14171.000
0
In [155]:
merged = facility_gen.merge(facility_cap, how='inner', left_index=True, right_index=True)
In [156]:
merged.describe()
Out[156]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Nameplate Capacity (MW)
count
2.204000e+03
2.204000e+03
2204.000000
2.204000e+03
2.204000e+03
2.204000e+03
2204.000000
2204.000000
mean
4.341980e+05
3.940222e+05
6.088443
1.324327e+06
1.269274e+06
1.349217e+05
1.699183
484.658122
std
1.116450e+06
1.004303e+06
12.526411
2.633492e+06
2.631382e+06
2.663379e+05
1.944481
615.046700
min
0.000000e+00
0.000000e+00
0.000000
0.000000e+00
0.000000e+00
-6.372300e+04
0.000000
1.800000
25%
0.000000e+00
0.000000e+00
0.000000
1.038605e+05
9.849125e+04
1.000100e+04
0.000000
94.575000
50%
3.765000e+02
2.850000e+02
0.924500
3.455885e+05
3.064225e+05
3.220900e+04
1.000000
233.900000
75%
2.545605e+05
2.182568e+05
3.066000
1.264416e+06
1.035504e+06
1.138057e+05
3.000000
650.000000
max
1.622117e+07
1.124778e+07
99.754000
2.580555e+07
2.580555e+07
2.467542e+06
11.000000
6809.000000
Save the non-combustion units, because I'm going to join the merged dataframe with the emissions dataframe and want to add back in the non-combustion
In [157]:
non_combust = merged.loc[merged['Combust']==0,:]
non_combust.describe()
Out[157]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Nameplate Capacity (MW)
count
933.0
933.0
933.0
9.330000e+02
9.330000e+02
9.330000e+02
933.0
933.000000
mean
0.0
0.0
0.0
1.090682e+06
1.090682e+06
1.088466e+05
0.0
248.966131
std
0.0
0.0
0.0
3.094933e+06
3.094933e+06
2.976431e+05
0.0
463.451807
min
0.0
0.0
0.0
0.000000e+00
0.000000e+00
-1.500000e+01
0.0
1.800000
25%
0.0
0.0
0.0
1.239710e+05
1.239710e+05
1.330300e+04
0.0
61.200000
50%
0.0
0.0
0.0
2.510540e+05
2.510540e+05
2.694000e+04
0.0
110.000000
75%
0.0
0.0
0.0
4.913350e+05
4.913350e+05
5.272400e+04
0.0
200.000000
max
0.0
0.0
0.0
2.580555e+07
2.580555e+07
2.467542e+06
0.0
4209.600000
In [158]:
non_combust.head()
Out[158]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
Nameplate Capacity (MW)
4
SERC
0
0
0.0
95361
95361
10233.0
0
225.0
14
SERC
0
0
0.0
132060
132060
14171.0
0
128.1
15
SERC
0
0
0.0
219444
219444
23548.0
0
177.0
16
SERC
0
0
0.0
145265
145265
15588.0
0
210.6
17
SERC
0
0
0.0
193742
193742
20790.0
0
170.0
In [159]:
merged = merged.merge(facility_emiss, how='inner', left_index=True, right_index=True)
merged.describe()
Out[159]:
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
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
count
9.020000e+02
9.020000e+02
902.000000
9.020000e+02
9.020000e+02
9.020000e+02
902.000000
902.000000
902.000000
7.870000e+02
2.800000e+01
804.000000
810.000000
810.000000
7.820000e+02
8.100000e+02
902.000000
mean
8.026699e+05
7.984092e+05
9.428446
1.737592e+06
1.732175e+06
1.880601e+05
2.871397
811.238581
825.084268
2.198615e+05
4.814780e+05
107.486081
0.493989
92.464405
1.687089e+05
1.937353e+06
5.252772
std
1.368488e+06
1.361816e+06
12.991945
2.442123e+06
2.440015e+06
2.605284e+05
1.620855
651.587349
892.628864
2.760750e+05
5.343461e+05
300.205896
1.316566
187.144645
2.448701e+05
2.477358e+06
2.177569
min
0.000000e+00
0.000000e+00
0.000000
0.000000e+00
0.000000e+00
-5.056000e+03
1.000000
27.000000
0.000000
0.000000e+00
5.103100e+02
0.000000
-7.815200
0.000000
0.000000e+00
3.400000e+00
1.000000
25%
3.070300e+04
3.037650e+04
1.031250
6.053450e+04
6.040225e+04
4.117250e+03
2.000000
349.050000
89.235000
1.383868e+04
7.159151e+04
0.086750
0.059175
3.705000
1.273775e+04
1.515684e+05
4.000000
50%
2.210980e+05
2.210980e+05
2.069500
7.232245e+05
7.149535e+05
7.754000e+04
2.000000
631.000000
579.315000
1.152687e+05
3.027295e+05
0.557500
0.179250
12.956000
8.169769e+04
1.027198e+06
5.000000
75%
9.083002e+05
9.083002e+05
18.052750
2.485223e+06
2.470881e+06
2.947978e+05
4.000000
1071.050000
1326.427500
3.320322e+05
6.532815e+05
44.930750
0.435050
73.797000
2.095907e+05
2.694102e+06
7.000000
max
1.124778e+07
1.124778e+07
75.589000
1.681146e+07
1.681146e+07
1.775310e+06
11.000000
4317.500000
5190.810000
1.652769e+06
1.783969e+06
3639.271000
17.099800
1722.928000
1.607836e+06
1.567091e+07
10.000000
In [160]:
merged.head()
Out[160]:
NERC Region
Quantity
Elec_Quantity
MMBtuPer_Unit
Tot_MMBtu
Elec_MMBtu
Netgen
Combust
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
4662196
24.476
8393587
8393587
1028473.000
4
2569.5
3844.00
1048450.25
NaN
595.911
0.5760
411.165
677078.976
8471790.125
4.0
8
SERC
188233
188233
30.364
4588547
4588547
471836.004
2
1166.7
1837.25
505777.25
NaN
125.662
0.5375
440.348
531418.603
5179513.525
4.0
10
SERC
116191
116191
2.066
120025
120025
6734.000
7
1288.4
188.25
8897.75
NaN
48.579
0.8474
10.162
11340.124
144258.150
4.0
26
SERC
197510
197510
30.196
4577634
4577634
453833.000
4
2034.0
744.00
495791.00
NaN
173.698
0.0646
165.370
524604.900
5113099.400
4.0
47
SERC
58
58
5.800
336
336
62.000
7
1026.0
20.00
177.00
NaN
0.333
4.4379
0.722
192.400
2765.600
4.0
Now concat the two dataframes
In [161]:
final = pd.concat([merged, non_combust])
final
Out[161]:
Avg. NOx Rate (lb/MMBtu)
CO2 (short tons)
Combust
EPA Region
Elec_MMBtu
Elec_Quantity
Gross Load (MW-h)
Heat Input (MMBtu)
MMBtuPer_Unit
NERC Region
NOx (tons)
Nameplate Capacity (MW)
Netgen
Operating Time
Quantity
SO2 (tons)
Steam Load (1000lb)
Tot_MMBtu
3
0.5760
677078.976
4
4.0
8393587
4662196
1048450.25
8471790.125
24.476
SERC
411.165
2569.5
1028473.000
3844.00
4662196
595.911
NaN
8393587
8
0.5375
531418.603
2
4.0
4588547
188233
505777.25
5179513.525
30.364
SERC
440.348
1166.7
471836.004
1837.25
188233
125.662
NaN
4588547
10
0.8474
11340.124
7
4.0
120025
116191
8897.75
144258.150
2.066
SERC
10.162
1288.4
6734.000
188.25
116191
48.579
NaN
120025
26
0.0646
524604.900
4
4.0
4577634
197510
495791.00
5113099.400
30.196
SERC
165.370
2034.0
453833.000
744.00
197510
173.698
NaN
4577634
47
4.4379
192.400
7
4.0
336
58
177.00
2765.600
5.800
SERC
0.722
1026.0
62.000
20.00
58
0.333
NaN
336
50
NaN
NaN
2
4.0
0
0
NaN
NaN
0.000
SERC
NaN
575.0
0.000
0.00
0
NaN
NaN
0
51
0.2080
270128.775
3
6.0
2799148
241664
262392.63
2481213.560
14.902
SPP
241.863
720.7
238020.998
553.48
241664
1081.656
NaN
2799148
54
0.3753
11101.898
2
4.0
189569
177832
15652.65
186799.693
1.066
SERC
2.787
1055.0
15037.000
254.45
177832
0.057
NaN
189569
56
0.5125
129301.129
2
4.0
1098557
49159
112793.47
1260257.488
28.560
SERC
167.878
538.0
97056.000
759.23
49159
63.902
NaN
1098557
60
0.2169
44877.292
2
7.0
404792
24713
36111.00
427905.242
22.206
MRO
39.165
324.3
31448.000
753.95
24713
176.503
NaN
404792
87
0.3811
88806.200
2
6.0
867759
52499
82439.00
846735.000
19.578
WECC
161.168
257.0
74925.000
744.00
52499
34.069
NaN
867759
108
0.1813
173507.748
2
7.0
1607158
102821
160882.24
1654354.527
18.837
SPP
151.412
348.7
146461.005
660.18
102821
95.013
NaN
1607158
113
0.6462
135364.206
4
9.0
1253737
68608
120001.76
1319344.965
24.756
WECC
149.985
839.9
103305.000
992.93
68608
57.635
NaN
1253737
116
0.2216
6368.940
4
9.0
96601
92596
8653.84
107186.519
2.070
WECC
7.130
334.0
8323.000
247.40
92596
0.032
NaN
96601
117
0.0754
50958.633
5
9.0
1156084
1112689
109950.39
857449.725
3.117
WECC
7.739
1207.4
130221.000
623.34
1112689
0.257
NaN
1156084
118
0.0507
1333.907
3
9.0
43207
40761
1491.10
22445.386
1.060
WECC
0.447
184.5
1618.000
37.80
40761
0.007
NaN
43207
120
0.1839
23956.571
4
9.0
361310
350394
35334.66
403122.659
7.834
WECC
26.270
425.0
33532.000
748.53
350394
0.120
NaN
361310
126
0.5604
50133.139
7
9.0
791633
768575
76803.54
822072.677
2.060
WECC
69.574
558.5
65563.000
1101.56
768575
2.119
NaN
791633
127
0.2907
145337.271
2
6.0
1372507
80710
129794.52
1385756.804
23.091
TRE
203.293
720.0
118269.996
401.58
80710
73.983
NaN
1372507
130
0.2077
860955.931
3
4.0
7687923
310705
822856.12
8391402.264
30.740
SERC
289.871
2390.1
761041.000
1518.03
310705
370.759
NaN
7687923
136
0.1152
682395.436
2
4.0
6351164
258794
689645.88
6651035.344
30.690
FRCC
187.062
1471.8
639443.003
1451.60
258794
477.829
NaN
6351164
141
0.5300
2730.960
4
9.0
47526
46042
4296.61
45960.324
7.844
WECC
11.359
613.5
3116.000
67.49
46042
0.014
NaN
47526
147
0.0101
54824.884
4
9.0
916075
888531
130042.08
922519.859
3.093
WECC
3.965
573.7
126819.000
611.23
888531
0.277
NaN
916075
160
1.0331
185387.782
5
9.0
1900767
295817
185441.25
1905952.909
45.247
WECC
359.939
660.7
163034.000
1970.04
295817
20.620
NaN
1900767
165
0.2807
239423.987
3
6.0
2191811
149290
191538.88
2290169.297
17.700
SPP
170.876
1134.0
174269.002
603.34
149290
506.266
NaN
2191811
170
0.2584
83529.374
1
6.0
1420943
1382240
139956.89
1405547.366
1.028
SERC
239.594
552.5
133914.000
638.74
1382240
0.422
NaN
1420943
201
NaN
NaN
4
6.0
0
0
NaN
NaN
0.000
SPP
NaN
185.0
0.000
0.00
0
NaN
NaN
0
202
NaN
NaN
2
6.0
0
0
NaN
NaN
0.000
SERC
NaN
120.0
0.000
0.00
0
NaN
NaN
0
203
NaN
NaN
2
6.0
0
0
NaN
NaN
0.000
SERC
NaN
136.0
0.000
0.00
0
NaN
NaN
0
207
0.7591
310498.110
3
4.0
3025091
163643
294306.40
3031531.603
23.227
FRCC
649.866
1358.0
266681.002
887.75
163643
182.552
NaN
3025091
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
59621
NaN
NaN
0
NaN
854636
0
NaN
NaN
0.000
TRE
NaN
299.7
91709.000
NaN
0
NaN
NaN
854636
59634
NaN
NaN
0
NaN
132022
0
NaN
NaN
0.000
WECC
NaN
45.0
14167.000
NaN
0
NaN
NaN
132022
59637
NaN
NaN
0
NaN
329035
0
NaN
NaN
0.000
MRO
NaN
151.4
35308.000
NaN
0
NaN
NaN
329035
59654
NaN
NaN
0
NaN
243748
0
NaN
NaN
0.000
TRE
NaN
78.0
26156.000
NaN
0
NaN
NaN
243748
59655
NaN
NaN
0
NaN
313100
0
NaN
NaN
0.000
MRO
NaN
97.8
33598.000
NaN
0
NaN
NaN
313100
59702
NaN
NaN
0
NaN
141779
0
NaN
NaN
0.000
WECC
NaN
50.0
15214.000
NaN
0
NaN
NaN
141779
59723
NaN
NaN
0
NaN
141024
0
NaN
NaN
0.000
WECC
NaN
30.0
15133.000
NaN
0
NaN
NaN
141024
59732
NaN
NaN
0
NaN
408918
0
NaN
NaN
0.000
TRE
NaN
150.0
43880.000
NaN
0
NaN
NaN
408918
59733
NaN
NaN
0
NaN
392479
0
NaN
NaN
0.000
TRE
NaN
150.0
42116.000
NaN
0
NaN
NaN
392479
59734
NaN
NaN
0
NaN
405451
0
NaN
NaN
0.000
TRE
NaN
150.0
43508.000
NaN
0
NaN
NaN
405451
59778
NaN
NaN
0
NaN
65186
0
NaN
NaN
0.000
SERC
NaN
47.7
6995.000
NaN
0
NaN
NaN
65186
59837
NaN
NaN
0
NaN
465577
0
NaN
NaN
0.000
SPP
NaN
150.0
49960.000
NaN
0
NaN
NaN
465577
59838
NaN
NaN
0
NaN
181338
0
NaN
NaN
0.000
SPP
NaN
50.0
19459.000
NaN
0
NaN
NaN
181338
59862
NaN
NaN
0
NaN
58831
0
NaN
NaN
0.000
SERC
NaN
30.0
6313.000
NaN
0
NaN
NaN
58831
59894
NaN
NaN
0
NaN
66202
0
NaN
NaN
0.000
SERC
NaN
30.0
7104.000
NaN
0
NaN
NaN
66202
59943
NaN
NaN
0
NaN
757169
0
NaN
NaN
0.000
TRE
NaN
207.2
81250.000
NaN
0
NaN
NaN
757169
59944
NaN
NaN
0
NaN
144920
0
NaN
NaN
0.000
SERC
NaN
80.0
15551.000
NaN
0
NaN
NaN
144920
59965
NaN
NaN
0
NaN
89956
0
NaN
NaN
0.000
WECC
NaN
62.1
9653.000
NaN
0
NaN
NaN
89956
59974
NaN
NaN
0
NaN
690044
0
NaN
NaN
0.000
WECC
NaN
250.0
74047.000
NaN
0
NaN
NaN
690044
59975
NaN
NaN
0
NaN
317582
0
NaN
NaN
0.000
WECC
NaN
150.0
34079.000
NaN
0
NaN
NaN
317582
60008
NaN
NaN
0
NaN
127866
0
NaN
NaN
0.000
WECC
NaN
52.0
13721.000
NaN
0
NaN
NaN
127866
60013
NaN
NaN
0
NaN
410055
0
NaN
NaN
0.000
SPP
NaN
151.8
44002.000
NaN
0
NaN
NaN
410055
60049
NaN
NaN
0
NaN
306101
0
NaN
NaN
0.000
WECC
NaN
80.0
32847.000
NaN
0
NaN
NaN
306101
60059
NaN
NaN
0
NaN
295142
0
NaN
NaN
0.000
TRE
NaN
110.0
31671.000
NaN
0
NaN
NaN
295142
60069
NaN
NaN
0
NaN
612939
0
NaN
NaN
0.000
SPP
NaN
198.6
65773.000
NaN
0
NaN
NaN
612939
60093
NaN
NaN
0
NaN
263569
0
NaN
NaN
0.000
WECC
NaN
109.8
28283.000
NaN
0
NaN
NaN
263569
60104
NaN
NaN
0
NaN
796420
0
NaN
NaN
0.000
TRE
NaN
249.7
85462.000
NaN
0
NaN
NaN
796420
60262
NaN
NaN
0
NaN
240132
0
NaN
NaN
0.000
SPP
NaN
73.4
25768.000
NaN
0
NaN
NaN
240132
60314
NaN
NaN
0
NaN
103935
0
NaN
NaN
0.000
SPP
NaN
35.8
11153.000
NaN
0
NaN
NaN
103935
60419
NaN
NaN
0
NaN
142450
0
NaN
NaN
0.000
WECC
NaN
25.0
15286.000
NaN
0
NaN
NaN
142450
1835 rows × 18 columns
In [162]:
final.index.rename('Plant ID', inplace=True)
In [163]:
final.describe()
Out[163]:
Avg. NOx Rate (lb/MMBtu)
CO2 (short tons)
Combust
EPA Region
Elec_MMBtu
Elec_Quantity
Gross Load (MW-h)
Heat Input (MMBtu)
MMBtuPer_Unit
NOx (tons)
Nameplate Capacity (MW)
Netgen
Operating Time
Quantity
SO2 (tons)
Steam Load (1000lb)
Tot_MMBtu
count
810.000000
7.820000e+02
1835.000000
902.000000
1.835000e+03
1.835000e+03
7.870000e+02
8.100000e+02
1835.000000
810.000000
1835.000000
1.835000e+03
902.000000
1.835000e+03
804.000000
2.800000e+01
1.835000e+03
mean
0.493989
1.687089e+05
1.411444
5.252772
1.406010e+06
3.924605e+05
2.198615e+05
1.937353e+06
4.634582
92.464405
525.352916
1.477843e+05
825.084268
3.945549e+05
107.486081
4.814780e+05
1.408673e+06
std
1.316566
2.448701e+05
1.830965
2.177569
2.809884e+06
1.034649e+06
2.760750e+05
2.477358e+06
10.254386
187.144645
629.910855
2.827274e+05
892.628864
1.039785e+06
300.205896
5.343461e+05
2.811094e+06
min
-7.815200
0.000000e+00
0.000000
1.000000
0.000000e+00
0.000000e+00
0.000000e+00
3.400000e+00
0.000000
0.000000
1.800000
-5.056000e+03
0.000000
0.000000e+00
0.000000
5.103100e+02
0.000000e+00
25%
0.059175
1.273775e+04
0.000000
4.000000
1.052255e+05
0.000000e+00
1.383868e+04
1.515684e+05
0.000000
3.705000
102.000000
1.072450e+04
89.235000
0.000000e+00
0.086750
7.159151e+04
1.056730e+05
50%
0.179250
8.169769e+04
0.000000
5.000000
3.320640e+05
0.000000e+00
1.152687e+05
1.027198e+06
0.000000
12.956000
257.000000
3.389400e+04
579.315000
0.000000e+00
0.557500
3.027295e+05
3.320640e+05
75%
0.435050
2.095907e+05
2.000000
7.000000
1.256538e+06
2.050515e+05
3.320322e+05
2.694102e+06
2.064000
73.797000
696.150000
1.284585e+05
1326.427500
2.050515e+05
44.930750
6.532815e+05
1.256538e+06
max
17.099800
1.607836e+06
11.000000
10.000000
2.580555e+07
1.124778e+07
1.652769e+06
1.567091e+07
75.589000
1722.928000
4317.500000
2.467542e+06
5190.810000
1.124778e+07
3639.271000
1.783969e+06
2.580555e+07
In [164]:
final['CO2 (short tons)'].sum() * 2000 * 2.2046 / final['Netgen'].sum()
Out[164]:
2145.06392182917
Content source: gschivley/Teaching-python
Similar notebooks: