In [2]:
import csv
import pandas as pd
In [9]:
dict_reader = csv.DictReader(open('data/CAIT 2.0 Country GHG Emissions.csv'))
country_year = []
for row in dict_reader:
keys = row.keys()
clean_row = {}
for key in keys:
clean_row[key] = row[key].strip()
country_year.append(clean_row)
In [10]:
cy = pd.read_csv('data/CAIT 2.0 Country GHG Emissions.csv')
In [44]:
country_year
cy[cy["Country"].isin(["Germany", "Austria"])]
Out[44]:
Country
Year
Total GHG Emissions Excluding Land-Use Change and Forestry (MtCO2e)
Total GHG Emissions Including Land-Use Change and Forestry (MtCO2e)
Total CO? Excluding Land-Use Change and Forestry (MtCO2)
Total CH4 (MtCO2e)
Total N2O (MtCO2e)
Total F-Gas (MtCO2e)
Energy (MtCO2e)
Industrial Processes (MtCO2e)
Agriculture (MtCO2e)
Waste (MtCO2e)
LUCF (MtCO2)
Bunker Fuels (MtCO2)
Electricity/Heat (MtCO2)
Manufacturing/Construction (MtCO2)
Transportation (MtCO2)
Other Fuel Combustion (MtCO2e)
Fugitive Emissions (MtCO2e)
176
Austria
1990
74.9340
62.1559
58.8802
8.357588
6.260281
1.435894
57.655646
5.035472
8.676659
3.556211
-12.77809
0.86
19.78
10.08
13.64
13.946938
0.208707
177
Austria
1991
79.0045
66.2084
63.0588
8.215910
6.334795
1.394931
61.793464
5.042084
8.699697
3.459241
-12.79612
0.96
20.66
10.59
15.03
15.301357
0.212107
178
Austria
1992
74.0037
61.1715
58.1662
8.074233
6.409309
1.353969
56.921282
4.997399
8.722735
3.362271
-12.83217
1.04
17.38
9.67
15.04
14.615776
0.215506
179
Austria
1993
74.1716
61.3034
58.4422
7.932555
6.483823
1.313006
57.259100
4.901418
8.745773
3.265301
-12.86823
1.10
17.47
10.08
15.18
14.310195
0.218905
180
Austria
1994
74.1885
61.2842
58.5672
7.790877
6.558337
1.272043
57.466918
4.794445
8.768811
3.168330
-12.90428
1.14
18.85
9.84
15.24
13.314614
0.222304
181
Austria
1995
76.8094
63.8691
61.2963
7.649199
6.632851
1.231080
60.684736
4.251457
8.791849
3.071360
-12.94033
1.28
20.43
10.12
15.45
14.459033
0.225703
182
Austria
1996
80.2882
67.3119
65.0609
7.449835
6.564551
1.212927
64.444008
4.266895
8.628689
2.958649
-12.97639
1.42
20.94
10.36
17.07
15.850811
0.223197
183
Austria
1997
79.2814
66.2690
64.3399
7.250471
6.496251
1.194773
63.703280
4.256685
8.465528
2.845937
-13.01246
1.47
21.32
11.68
16.15
14.332590
0.220690
184
Austria
1998
79.4956
66.4471
64.8399
7.051106
6.427951
1.176619
64.202552
4.257468
8.302367
2.733225
-13.04851
1.54
20.46
10.90
18.25
14.374368
0.218184
185
Austria
1999
77.6415
64.5569
63.2716
6.851742
6.359651
1.158465
62.641824
4.239930
8.139207
2.620513
-13.08456
1.49
19.58
10.21
17.76
14.876146
0.215678
186
Austria
2000
77.6673
64.5467
63.5833
6.652378
6.291351
1.140311
62.961096
4.222393
7.976046
2.507801
-13.12062
1.63
19.21
11.30
18.50
13.737924
0.213171
187
Austria
2001
81.5971
64.3176
67.7643
6.543902
6.111568
1.177361
67.137434
4.123388
7.863260
2.463037
-17.27952
1.59
20.98
11.05
19.97
14.919769
0.217664
188
Austria
2002
82.9345
65.6155
69.3529
6.435426
5.931786
1.214410
68.693771
4.072016
7.750474
2.418272
-17.31901
1.49
21.44
10.96
21.84
14.231614
0.222157
189
Austria
2003
87.8550
70.4970
74.5246
6.326950
5.752004
1.251459
73.900109
3.943700
7.637688
2.373508
-17.35805
1.41
23.58
11.70
23.40
14.993459
0.226651
190
Austria
2004
88.9483
71.5509
75.8691
6.218474
5.572221
1.288508
75.036447
4.068200
7.524901
2.328744
-17.39736
1.67
24.16
12.38
23.73
14.535303
0.231144
191
Austria
2005
89.7697
72.3322
76.9417
6.109998
5.392439
1.325558
76.012785
4.060796
7.412115
2.283979
-17.43743
1.89
24.72
12.62
24.45
13.987148
0.235637
192
Austria
2006
87.7557
92.3648
74.9182
6.010376
5.455878
1.371213
73.828167
4.278990
7.449715
2.198834
4.60913
1.98
24.35
12.61
23.18
13.454362
0.233806
193
Austria
2007
85.4511
90.0768
72.6041
5.910754
5.519316
1.416869
71.323550
4.526497
7.487315
2.113689
4.62574
2.10
23.93
12.11
23.37
11.681576
0.231975
194
Austria
2008
86.1118
90.7534
73.2554
5.811132
5.582755
1.462525
71.908933
4.649428
7.524916
2.028544
4.64154
2.11
23.96
13.41
22.04
12.268789
0.230143
195
Austria
2009
79.3915
84.0492
66.5256
5.711510
5.646194
1.508180
65.504315
4.391303
7.562516
1.943399
4.65771
1.83
20.46
12.28
21.47
11.066003
0.228312
196
Austria
2010
85.0568
89.7307
72.1815
5.611888
5.709633
1.553836
71.339698
4.268746
7.600116
1.858254
4.67391
1.98
24.43
12.25
22.16
12.273217
0.226481
197
Austria
2011
83.6993
88.3732
70.6977
5.619402
5.760892
1.621245
69.767853
4.421615
7.651142
1.858660
4.67391
2.06
24.69
12.16
21.62
11.072787
0.225066
1430
Germany
1990
1170.6905
1107.4739
968.4600
105.603288
90.130354
6.496875
961.860808
52.680302
87.002776
43.111498
-63.21663
21.13
402.63
179.26
157.59
222.380808
NaN
1431
Germany
1991
1141.5211
1078.1033
942.9715
102.488747
89.260737
6.800162
962.310678
51.837333
84.919839
42.453248
-63.41781
19.25
399.98
153.93
160.28
222.184333
25.936346
1432
Germany
1992
1100.8033
1036.9831
905.9345
99.374206
88.391119
7.103450
921.956391
54.215020
82.836902
41.794998
-63.82017
19.08
383.75
145.92
162.79
204.977857
24.518533
1433
Germany
1993
1089.9713
1025.7488
898.7834
96.259665
87.521502
7.406737
913.488454
54.592163
80.753965
41.136747
-64.22252
21.25
375.97
137.48
167.36
209.321382
23.357071
1434
Germany
1994
1075.3534
1010.7285
887.8463
93.145123
86.651884
7.710024
901.051336
55.152506
78.671029
40.478497
-64.62488
22.09
376.24
139.31
164.81
197.754907
22.936429
1435
Germany
1995
1069.1875
1004.1603
885.3614
90.030582
85.782267
8.013311
898.230509
54.558689
76.588092
39.820247
-65.02723
22.19
370.37
140.07
166.68
199.668432
21.442077
1436
Germany
1996
1090.0924
1024.6622
913.1011
86.450525
82.057757
8.482937
926.072973
50.008721
76.738323
37.272343
-65.43018
22.49
381.12
132.30
167.78
224.124015
20.748958
1437
Germany
1997
1054.5583
988.7256
884.4020
82.870467
78.333246
8.952563
894.411467
48.543841
76.888555
34.724440
-65.83274
23.58
365.48
131.08
168.43
209.449598
19.971869
1438
Germany
1998
1041.1293
974.8944
877.8080
79.290410
74.608736
9.422189
886.717341
45.206658
77.038786
32.176536
-66.23491
23.87
365.47
129.90
171.33
200.715181
19.302160
1439
Germany
1999
1003.8816
937.2443
847.3952
75.710352
70.884225
9.891816
855.872295
41.191634
77.189018
29.628632
-66.63727
25.08
351.40
124.34
176.11
183.310764
20.711531
1440
Germany
2000
994.8552
927.8154
845.2038
72.130295
67.159715
10.361442
853.159689
37.275539
77.339249
27.080729
-67.03983
26.35
356.29
126.26
171.89
178.746347
19.973342
1441
Germany
2001
1008.8681
955.3679
861.7905
69.152698
66.779153
11.145801
870.599851
36.515479
76.577768
25.185046
-53.50029
25.62
368.40
120.29
167.96
194.815112
19.134739
1442
Germany
2002
993.1553
939.6549
848.6514
66.175102
66.398590
11.930160
857.206014
36.843627
75.816286
23.289363
-53.50036
26.11
370.17
120.31
165.55
182.893877
18.282137
1443
Germany
2003
984.4538
930.9535
842.5238
63.197506
66.018028
12.714519
849.401916
38.593408
75.054805
21.393680
-53.50034
27.32
374.06
117.61
160.62
179.772642
17.339274
1444
Germany
2004
985.5316
932.0327
846.1753
60.219909
65.637465
13.498879
852.722459
39.027812
74.293324
19.497997
-53.49885
29.26
382.16
120.82
162.64
170.771408
16.331051
1445
Germany
2005
954.5998
901.0995
817.8173
57.242313
65.256903
14.283238
823.977751
39.487864
73.531842
17.602314
-53.50032
30.39
367.47
120.26
154.89
165.820173
15.537578
1446
Germany
2006
965.5155
888.5549
832.3991
54.315077
64.181395
14.619890
837.012320
41.344575
71.007761
16.150842
-76.96061
31.80
373.75
122.05
152.48
173.699382
15.032938
1447
Germany
2007
935.4004
858.4334
805.9501
51.387841
63.105887
14.956542
810.448659
41.768661
68.483679
14.699371
-76.96702
34.23
386.52
123.57
148.23
137.688592
14.440068
1448
Germany
2008
938.7277
861.8190
812.9435
48.460606
62.030379
15.293193
817.113968
42.416251
65.959598
13.247899
-76.90871
34.27
366.85
123.36
147.02
166.027801
13.856167
1449
Germany
2009
876.1988
799.2322
754.0807
45.533370
60.954871
15.629845
759.576488
41.400385
63.435516
11.796427
-76.96658
32.86
338.75
106.06
147.16
154.307011
13.299477
1450
Germany
2010
903.9783
827.0112
785.5263
42.606134
59.879363
15.966497
791.053957
41.677912
60.911435
10.344956
-76.96702
32.70
353.46
115.57
147.01
162.466221
12.547736
1451
Germany
2011
882.9341
805.9671
765.9803
42.020623
58.041694
16.891529
769.594747
42.752812
60.640635
9.945956
-76.96702
31.71
350.51
114.15
148.74
143.711204
12.483543
In [18]:
scope1 = pd.read_pickle('data/scope1totals.pkl')
In [52]:
scope1.loc["Australia", "Energy"]
Out[52]:
Scope 1 30550166
Name: (Australia, Energy), dtype: float64
In [53]:
import newf
In [54]:
newf.favcolumn(scope1, 4)
hello
In [55]:
reload(newf)
Out[55]:
<module 'newf' from 'newf.pyc'>
In [ ]:
Content source: mirthbottle/datascience-class
Similar notebooks: