In [1]:
import predictor as pr
import pandas as pd
from sklearn import linear_model
In [2]:
import os
In [3]:
names=[]
for file in os.listdir():
if file.endswith('.csv'):
names.append(file)
In [7]:
names[45]
Out[7]:
'UT.csv'
In [2]:
data=pd.read_csv('WA.csv')
In [3]:
data
Out[3]:
Year
HYTCP
WYTCP
SOEGP
NUETP
GDP
CLPRB
EMFDB
ENPRP
NGMPB
PAPRB
PCP
ZNDX
Nominal Price
Inflation Adjusted Price
0
1960.0
34349.0
0.0
0.0
0.0
NaN
3713.0
0.00000
0.00000
0.0
5.8
42.38
0.13
2.91
23.72
1
1961.0
37360.0
0.0
0.0
0.0
NaN
3111.0
0.00000
0.00000
0.0
0.0
48.75
5.43
2.85
22.96
2
1962.0
39553.0
0.0
0.0
0.0
NaN
3827.0
0.00000
0.00000
0.0
0.0
40.23
-0.71
2.85
22.69
3
1963.0
43117.0
0.0
0.0
0.0
11171.0
3094.0
0.00000
0.00000
0.0
0.0
39.34
-3.35
2.91
22.90
4
1964.0
47036.0
0.0
0.0
0.0
11392.0
1107.0
0.00000
0.00000
0.0
0.0
45.11
6.09
3.00
23.30
5
1965.0
49295.0
0.0
0.0
0.0
12330.0
895.0
0.00000
0.00000
0.0
0.0
36.53
-8.67
3.01
23.00
6
1966.0
52821.0
989.0
0.0
0.0
14270.0
960.0
0.00000
0.00000
0.0
0.0
41.66
-2.74
3.10
23.01
7
1967.0
58899.0
2015.0
0.0
0.0
15659.0
960.0
0.00000
0.00000
0.0
0.0
42.83
-4.42
3.12
22.53
8
1968.0
64336.0
3903.0
0.0
0.0
17014.0
2899.0
0.00000
0.00000
0.0
0.0
49.96
10.12
3.18
21.99
9
1969.0
67541.0
3667.0
0.0
0.0
18091.0
944.0
0.00000
0.00000
0.0
0.0
37.24
-6.95
3.32
21.81
10
1970.0
69525.0
2614.0
0.0
0.0
18112.0
602.0
0.00000
0.00000
0.0
0.0
42.24
-4.34
3.39
21.04
11
1971.0
71589.0
2553.0
0.0
0.0
18761.0
18470.0
0.00000
0.00000
0.0
0.0
48.87
9.64
3.60
21.42
12
1972.0
75883.0
2919.0
0.0
0.0
20483.0
42902.0
0.00000
0.00000
0.0
0.0
49.95
11.60
3.60
20.74
13
1973.0
69016.0
4432.0
0.0
0.0
23342.0
52974.0
0.00000
0.00000
0.0
0.0
41.42
-4.63
4.75
25.56
14
1974.0
82491.0
3889.0
0.0
0.0
26240.0
63390.0
0.00000
0.00000
0.0
0.0
47.30
4.63
9.35
45.60
15
1975.0
83708.0
3308.0
0.0
0.0
29282.0
60636.0
0.00000
0.00000
0.0
0.0
50.93
10.82
12.21
54.61
16
1976.0
94457.0
2405.0
0.0
0.0
32735.0
66565.0
0.00000
0.00000
0.0
0.0
33.18
-7.35
13.10
55.46
17
1977.0
66617.0
4315.0
0.0
0.0
36826.0
81923.0
0.00000
0.00000
0.0
0.0
41.56
0.12
14.40
57.20
18
1978.0
88906.0
4140.0
0.0
0.0
42668.0
76269.0
0.00000
0.00000
0.0
0.0
35.99
-3.08
14.95
55.24
19
1979.0
79511.0
3613.0
0.0
0.0
48713.0
82166.0
0.00000
0.00000
0.0
0.0
38.21
-6.98
25.10
82.51
20
1980.0
83111.0
2041.0
0.0
0.0
52682.0
83268.0
0.00000
0.00000
0.0
0.0
44.74
4.14
37.42
109.51
21
1981.0
93701.0
2042.0
0.0
0.0
58918.0
75087.0
87.95543
13.67742
0.0
0.0
43.80
6.59
35.75
94.83
22
1982.0
87705.0
3631.0
0.0
0.0
62933.0
67456.0
293.39871
45.78573
0.0
0.0
47.39
5.16
31.83
79.50
23
1983.0
85564.0
3494.0
0.0
0.0
67949.0
63034.0
551.20453
86.33290
0.0
0.0
51.84
12.22
29.08
70.34
24
1984.0
83431.0
5313.0
0.0
0.0
74170.0
62726.0
658.12346
103.44214
0.0
0.0
46.97
8.90
28.75
66.67
25
1985.0
77053.0
8038.0
0.0
0.0
77341.0
71895.0
705.63778
111.30457
0.0
0.0
29.99
-12.88
26.92
60.27
26
1986.0
78960.0
8439.0
0.0
0.0
84015.0
74536.0
745.81485
118.06341
0.0
0.0
41.63
-3.08
14.44
31.72
27
1987.0
69827.0
5528.0
0.0
0.0
90325.0
72073.0
815.28221
129.52203
0.0
0.0
34.39
-13.14
17.75
37.62
28
1988.0
68508.0
6000.0
0.0
0.0
99051.0
84209.0
817.79657
130.38967
0.0
0.0
39.95
-2.79
14.87
30.33
29
1989.0
71528.0
6118.0
0.0
0.0
108064.0
81690.0
770.70820
123.32352
0.0
0.0
36.56
-7.57
18.33
35.60
30
1990.0
87467.0
5742.0
0.0
0.0
118640.0
81056.0
645.53862
103.65362
0.0
0.0
50.93
9.27
23.19
42.62
31
1991.0
89342.0
4230.0
0.0
0.0
125895.0
82320.0
754.71555
121.62240
0.0
0.0
40.48
0.06
20.20
35.73
32
1992.0
68325.0
5692.0
0.0
0.0
134469.0
83183.0
671.61402
108.62593
0.0
0.0
37.13
-12.79
19.25
33.04
33
1993.0
67312.0
7135.0
0.0
0.0
142500.0
74932.0
730.51855
118.56642
0.0
0.0
33.68
-3.94
16.75
27.94
34
1994.0
65575.0
6740.0
0.0
0.0
150805.0
77210.0
697.87933
113.68483
0.0
0.0
42.51
-3.15
15.66
25.44
35
1995.0
82500.0
6942.0
0.0
0.0
155069.0
78433.0
598.81009
97.89021
0.0
0.0
51.09
7.43
16.75
26.48
36
1996.0
98518.0
5588.0
0.0
0.0
166540.0
72089.0
217.46874
35.67635
0.0
0.0
54.95
14.28
20.46
31.40
37
1997.0
104171.0
6244.0
0.0
0.0
179639.0
71253.0
336.25741
55.35976
0.0
0.0
51.07
15.06
18.64
27.98
38
1998.0
79815.0
6916.0
0.0
0.0
209988.0
72809.0
341.71558
56.45877
0.0
0.0
48.61
1.89
11.91
17.60
39
1999.0
96989.0
6086.0
0.0
0.0
230752.0
63996.0
268.28949
44.39189
0.0
0.0
49.30
7.73
16.56
23.89
40
2000.0
80263.0
8605.0
0.0
0.0
237381.0
66520.0
265.89493
44.05283
0.0
0.0
34.21
-7.78
27.39
38.29
41
2001.0
54734.0
8250.0
0.0
0.0
239397.0
72134.0
232.12790
38.50841
0.0
0.0
39.16
-2.94
23.00
31.30
42
2002.0
78167.0
9048.0
0.0
417.0
248879.0
91334.0
238.25380
39.57612
0.0
0.0
36.51
-8.24
22.81
30.52
43
2003.0
71757.0
7615.0
0.0
604.0
258388.0
97712.0
191.41752
31.93656
0.0
0.0
43.48
-5.87
27.69
36.26
44
2004.0
71576.0
8982.0
0.0
737.0
270385.0
89958.0
96.51025
16.17340
0.0
0.0
37.65
-7.25
37.66
47.98
45
2005.0
72075.0
8242.0
0.0
498.0
296403.0
82684.0
61.58859
10.36541
0.0
0.0
38.64
-3.40
50.04
61.65
46
2006.0
82008.0
9328.0
0.0
1038.0
316801.0
40267.0
0.00000
0.00000
0.0
0.0
50.26
3.08
58.30
69.64
47
2007.0
78829.0
8109.0
0.0
2438.0
345971.0
0.0
0.00000
0.00000
0.0
0.0
40.24
-4.88
64.20
74.44
48
2008.0
77637.0
9270.0
0.0
3657.0
354210.0
0.0
0.00000
0.00000
0.0
0.0
37.39
-6.55
91.48
102.00
49
2009.0
72933.0
6634.0
0.0
3572.0
348465.0
0.0
0.00000
0.00000
0.0
0.0
39.90
-4.19
53.48
59.93
50
2010.0
68288.0
9241.0
0.0
4745.0
359694.0
0.0
NaN
NaN
NaN
NaN
47.33
9.62
71.21
78.65
51
2011.0
91818.0
4806.0
1.0
6262.0
370149.0
0.0
NaN
NaN
NaN
NaN
43.82
4.94
87.04
93.21
52
2012.0
89464.0
9334.0
1.0
6600.0
388922.0
0.0
NaN
NaN
NaN
NaN
52.87
14.65
86.46
90.72
53
2013.0
78155.0
8461.0
1.0
7004.0
404540.0
0.0
NaN
NaN
NaN
NaN
35.68
-4.02
91.17
94.25
54
2014.0
79463.0
9497.0
1.0
7268.0
423795.0
0.0
NaN
NaN
NaN
NaN
48.81
5.31
85.60
87.05
55
2015.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
43.25
-9.98
41.85
42.53
56
2016.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
49.44
0.85
NaN
NaN
In [4]:
data=pr.future_df(data,range(2017,2021))
In [ ]:
Content source: uwkejia/Clean-Energy-Outlook
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