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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'Lower Confidence Interval', 'Upper Confidence Interval'}) and 1 missing columns ({'Standard Error'}).

This happened while the csv dataset builder was generating data using

hf://datasets/reach-vb/world-happiness-clone/2016.csv (at revision 80ee9c616208bf6ad969bf65751be61e54b6f0d0)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Country: string
              Region: string
              Happiness Rank: int64
              Happiness Score: double
              Lower Confidence Interval: double
              Upper Confidence Interval: double
              Economy (GDP per Capita): double
              Family: double
              Health (Life Expectancy): double
              Freedom: double
              Trust (Government Corruption): double
              Generosity: double
              Dystopia Residual: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1986
              to
              {'Country': Value(dtype='string', id=None), 'Region': Value(dtype='string', id=None), 'Happiness Rank': Value(dtype='int64', id=None), 'Happiness Score': Value(dtype='float64', id=None), 'Standard Error': Value(dtype='float64', id=None), 'Economy (GDP per Capita)': Value(dtype='float64', id=None), 'Family': Value(dtype='float64', id=None), 'Health (Life Expectancy)': Value(dtype='float64', id=None), 'Freedom': Value(dtype='float64', id=None), 'Trust (Government Corruption)': Value(dtype='float64', id=None), 'Generosity': Value(dtype='float64', id=None), 'Dystopia Residual': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'Lower Confidence Interval', 'Upper Confidence Interval'}) and 1 missing columns ({'Standard Error'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/reach-vb/world-happiness-clone/2016.csv (at revision 80ee9c616208bf6ad969bf65751be61e54b6f0d0)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Country
string
Region
string
Happiness Rank
int64
Happiness Score
float64
Standard Error
float64
Economy (GDP per Capita)
float64
Family
float64
Health (Life Expectancy)
float64
Freedom
float64
Trust (Government Corruption)
float64
Generosity
float64
Dystopia Residual
float64
Switzerland
Western Europe
1
7.587
0.03411
1.39651
1.34951
0.94143
0.66557
0.41978
0.29678
2.51738
Iceland
Western Europe
2
7.561
0.04884
1.30232
1.40223
0.94784
0.62877
0.14145
0.4363
2.70201
Denmark
Western Europe
3
7.527
0.03328
1.32548
1.36058
0.87464
0.64938
0.48357
0.34139
2.49204
Norway
Western Europe
4
7.522
0.0388
1.459
1.33095
0.88521
0.66973
0.36503
0.34699
2.46531
Canada
North America
5
7.427
0.03553
1.32629
1.32261
0.90563
0.63297
0.32957
0.45811
2.45176
Finland
Western Europe
6
7.406
0.0314
1.29025
1.31826
0.88911
0.64169
0.41372
0.23351
2.61955
Netherlands
Western Europe
7
7.378
0.02799
1.32944
1.28017
0.89284
0.61576
0.31814
0.4761
2.4657
Sweden
Western Europe
8
7.364
0.03157
1.33171
1.28907
0.91087
0.6598
0.43844
0.36262
2.37119
New Zealand
Australia and New Zealand
9
7.286
0.03371
1.25018
1.31967
0.90837
0.63938
0.42922
0.47501
2.26425
Australia
Australia and New Zealand
10
7.284
0.04083
1.33358
1.30923
0.93156
0.65124
0.35637
0.43562
2.26646
Israel
Middle East and Northern Africa
11
7.278
0.0347
1.22857
1.22393
0.91387
0.41319
0.07785
0.33172
3.08854
Costa Rica
Latin America and Caribbean
12
7.226
0.04454
0.95578
1.23788
0.86027
0.63376
0.10583
0.25497
3.17728
Austria
Western Europe
13
7.2
0.03751
1.33723
1.29704
0.89042
0.62433
0.18676
0.33088
2.5332
Mexico
Latin America and Caribbean
14
7.187
0.04176
1.02054
0.91451
0.81444
0.48181
0.21312
0.14074
3.60214
United States
North America
15
7.119
0.03839
1.39451
1.24711
0.86179
0.54604
0.1589
0.40105
2.51011
Brazil
Latin America and Caribbean
16
6.983
0.04076
0.98124
1.23287
0.69702
0.49049
0.17521
0.14574
3.26001
Luxembourg
Western Europe
17
6.946
0.03499
1.56391
1.21963
0.91894
0.61583
0.37798
0.28034
1.96961
Ireland
Western Europe
18
6.94
0.03676
1.33596
1.36948
0.89533
0.61777
0.28703
0.45901
1.9757
Belgium
Western Europe
19
6.937
0.03595
1.30782
1.28566
0.89667
0.5845
0.2254
0.2225
2.41484
United Arab Emirates
Middle East and Northern Africa
20
6.901
0.03729
1.42727
1.12575
0.80925
0.64157
0.38583
0.26428
2.24743
United Kingdom
Western Europe
21
6.867
0.01866
1.26637
1.28548
0.90943
0.59625
0.32067
0.51912
1.96994
Oman
Middle East and Northern Africa
22
6.853
0.05335
1.36011
1.08182
0.76276
0.63274
0.32524
0.21542
2.47489
Venezuela
Latin America and Caribbean
23
6.81
0.06476
1.04424
1.25596
0.72052
0.42908
0.11069
0.05841
3.19131
Singapore
Southeastern Asia
24
6.798
0.0378
1.52186
1.02
1.02525
0.54252
0.4921
0.31105
1.88501
Panama
Latin America and Caribbean
25
6.786
0.0491
1.06353
1.1985
0.79661
0.5421
0.0927
0.24434
2.84848
Germany
Western Europe
26
6.75
0.01848
1.32792
1.29937
0.89186
0.61477
0.21843
0.28214
2.11569
Chile
Latin America and Caribbean
27
6.67
0.058
1.10715
1.12447
0.85857
0.44132
0.12869
0.33363
2.67585
Qatar
Middle East and Northern Africa
28
6.611
0.06257
1.69042
1.0786
0.79733
0.6404
0.52208
0.32573
1.55674
France
Western Europe
29
6.575
0.03512
1.27778
1.26038
0.94579
0.55011
0.20646
0.12332
2.21126
Argentina
Latin America and Caribbean
30
6.574
0.04612
1.05351
1.24823
0.78723
0.44974
0.08484
0.11451
2.836
Czech Republic
Central and Eastern Europe
31
6.505
0.04168
1.17898
1.20643
0.84483
0.46364
0.02652
0.10686
2.67782
Uruguay
Latin America and Caribbean
32
6.485
0.04539
1.06166
1.2089
0.8116
0.60362
0.24558
0.2324
2.32142
Colombia
Latin America and Caribbean
33
6.477
0.05051
0.91861
1.24018
0.69077
0.53466
0.0512
0.18401
2.85737
Thailand
Southeastern Asia
34
6.455
0.03557
0.9669
1.26504
0.7385
0.55664
0.03187
0.5763
2.31945
Saudi Arabia
Middle East and Northern Africa
35
6.411
0.04633
1.39541
1.08393
0.72025
0.31048
0.32524
0.13706
2.43872
Spain
Western Europe
36
6.329
0.03468
1.23011
1.31379
0.95562
0.45951
0.06398
0.18227
2.12367
Malta
Western Europe
37
6.302
0.04206
1.2074
1.30203
0.88721
0.60365
0.13586
0.51752
1.6488
Taiwan
Eastern Asia
38
6.298
0.03868
1.29098
1.07617
0.8753
0.3974
0.08129
0.25376
2.32323
Kuwait
Middle East and Northern Africa
39
6.295
0.04456
1.55422
1.16594
0.72492
0.55499
0.25609
0.16228
1.87634
Suriname
Latin America and Caribbean
40
6.269
0.09811
0.99534
0.972
0.6082
0.59657
0.13633
0.16991
2.79094
Trinidad and Tobago
Latin America and Caribbean
41
6.168
0.10895
1.21183
1.18354
0.61483
0.55884
0.0114
0.31844
2.26882
El Salvador
Latin America and Caribbean
42
6.13
0.05618
0.76454
1.02507
0.67737
0.4035
0.11776
0.10692
3.035
Guatemala
Latin America and Caribbean
43
6.123
0.05224
0.74553
1.04356
0.64425
0.57733
0.09472
0.27489
2.74255
Uzbekistan
Central and Eastern Europe
44
6.003
0.04361
0.63244
1.34043
0.59772
0.65821
0.30826
0.22837
2.23741
Slovakia
Central and Eastern Europe
45
5.995
0.04267
1.16891
1.26999
0.78902
0.31751
0.03431
0.16893
2.24639
Japan
Eastern Asia
46
5.987
0.03581
1.27074
1.25712
0.99111
0.49615
0.1806
0.10705
1.68435
South Korea
Eastern Asia
47
5.984
0.04098
1.24461
0.95774
0.96538
0.33208
0.07857
0.18557
2.21978
Ecuador
Latin America and Caribbean
48
5.975
0.04528
0.86402
0.99903
0.79075
0.48574
0.1809
0.11541
2.53942
Bahrain
Middle East and Northern Africa
49
5.96
0.05412
1.32376
1.21624
0.74716
0.45492
0.306
0.17362
1.73797
Italy
Western Europe
50
5.948
0.03914
1.25114
1.19777
0.95446
0.26236
0.02901
0.22823
2.02518
Bolivia
Latin America and Caribbean
51
5.89
0.05642
0.68133
0.97841
0.5392
0.57414
0.088
0.20536
2.82334
Moldova
Central and Eastern Europe
52
5.889
0.03799
0.59448
1.01528
0.61826
0.32818
0.01615
0.20951
3.10712
Paraguay
Latin America and Caribbean
53
5.878
0.04563
0.75985
1.30477
0.66098
0.53899
0.08242
0.3424
2.18896
Kazakhstan
Central and Eastern Europe
54
5.855
0.04114
1.12254
1.12241
0.64368
0.51649
0.08454
0.11827
2.24729
Slovenia
Central and Eastern Europe
55
5.848
0.04251
1.18498
1.27385
0.87337
0.60855
0.03787
0.25328
1.61583
Lithuania
Central and Eastern Europe
56
5.833
0.03843
1.14723
1.25745
0.73128
0.21342
0.01031
0.02641
2.44649
Nicaragua
Latin America and Caribbean
57
5.828
0.05371
0.59325
1.14184
0.74314
0.55475
0.19317
0.27815
2.32407
Peru
Latin America and Caribbean
58
5.824
0.04615
0.90019
0.97459
0.73017
0.41496
0.05989
0.14982
2.5945
Belarus
Central and Eastern Europe
59
5.813
0.03938
1.03192
1.23289
0.73608
0.37938
0.1909
0.11046
2.1309
Poland
Central and Eastern Europe
60
5.791
0.04263
1.12555
1.27948
0.77903
0.53122
0.04212
0.16759
1.86565
Malaysia
Southeastern Asia
61
5.77
0.0433
1.12486
1.07023
0.72394
0.53024
0.10501
0.33075
1.88541
Croatia
Central and Eastern Europe
62
5.759
0.04394
1.08254
0.79624
0.78805
0.25883
0.0243
0.05444
2.75414
Libya
Middle East and Northern Africa
63
5.754
0.07832
1.13145
1.11862
0.7038
0.41668
0.11023
0.18295
2.09066
Russia
Central and Eastern Europe
64
5.716
0.03135
1.13764
1.23617
0.66926
0.36679
0.03005
0.00199
2.27394
Jamaica
Latin America and Caribbean
65
5.709
0.13693
0.81038
1.15102
0.68741
0.50442
0.02299
0.2123
2.32038
North Cyprus
Western Europe
66
5.695
0.05635
1.20806
1.07008
0.92356
0.49027
0.1428
0.26169
1.59888
Cyprus
Western Europe
67
5.689
0.0558
1.20813
0.89318
0.92356
0.40672
0.06146
0.30638
1.88931
Algeria
Middle East and Northern Africa
68
5.605
0.05099
0.93929
1.07772
0.61766
0.28579
0.17383
0.07822
2.43209
Kosovo
Central and Eastern Europe
69
5.589
0.05018
0.80148
0.81198
0.63132
0.24749
0.04741
0.2831
2.76579
Turkmenistan
Central and Eastern Europe
70
5.548
0.04175
0.95847
1.22668
0.53886
0.4761
0.30844
0.16979
1.86984
Mauritius
Sub-Saharan Africa
71
5.477
0.07197
1.00761
0.98521
0.7095
0.56066
0.07521
0.37744
1.76145
Hong Kong
Eastern Asia
72
5.474
0.05051
1.38604
1.05818
1.01328
0.59608
0.37124
0.39478
0.65429
Estonia
Central and Eastern Europe
73
5.429
0.04013
1.15174
1.22791
0.77361
0.44888
0.15184
0.0868
1.58782
Indonesia
Southeastern Asia
74
5.399
0.02596
0.82827
1.08708
0.63793
0.46611
0
0.51535
1.86399
Vietnam
Southeastern Asia
75
5.36
0.03107
0.63216
0.91226
0.74676
0.59444
0.10441
0.1686
2.20173
Turkey
Middle East and Northern Africa
76
5.332
0.03864
1.06098
0.94632
0.73172
0.22815
0.15746
0.12253
2.08528
Kyrgyzstan
Central and Eastern Europe
77
5.286
0.03823
0.47428
1.15115
0.65088
0.43477
0.04232
0.3003
2.2327
Nigeria
Sub-Saharan Africa
78
5.268
0.04192
0.65435
0.90432
0.16007
0.34334
0.0403
0.27233
2.89319
Bhutan
Southern Asia
79
5.253
0.03225
0.77042
1.10395
0.57407
0.53206
0.15445
0.47998
1.63794
Azerbaijan
Central and Eastern Europe
80
5.212
0.03363
1.02389
0.93793
0.64045
0.3703
0.16065
0.07799
2.00073
Pakistan
Southern Asia
81
5.194
0.03726
0.59543
0.41411
0.51466
0.12102
0.10464
0.33671
3.10709
Jordan
Middle East and Northern Africa
82
5.192
0.04524
0.90198
1.05392
0.69639
0.40661
0.14293
0.11053
1.87996
Montenegro
Central and Eastern Europe
82
5.192
0.05235
0.97438
0.90557
0.72521
0.1826
0.14296
0.1614
2.10017
China
Eastern Asia
84
5.14
0.02424
0.89012
0.94675
0.81658
0.51697
0.02781
0.08185
1.8604
Zambia
Sub-Saharan Africa
85
5.129
0.06988
0.47038
0.91612
0.29924
0.48827
0.12468
0.19591
2.6343
Romania
Central and Eastern Europe
86
5.124
0.06607
1.04345
0.88588
0.7689
0.35068
0.00649
0.13748
1.93129
Serbia
Central and Eastern Europe
87
5.123
0.04864
0.92053
1.00964
0.74836
0.20107
0.02617
0.19231
2.025
Portugal
Western Europe
88
5.102
0.04802
1.15991
1.13935
0.87519
0.51469
0.01078
0.13719
1.26462
Latvia
Central and Eastern Europe
89
5.098
0.0464
1.11312
1.09562
0.72437
0.29671
0.06332
0.18226
1.62215
Philippines
Southeastern Asia
90
5.073
0.04934
0.70532
1.03516
0.58114
0.62545
0.12279
0.24991
1.7536
Somaliland region
Sub-Saharan Africa
91
5.057
0.06161
0.18847
0.95152
0.43873
0.46582
0.39928
0.50318
2.11032
Morocco
Middle East and Northern Africa
92
5.013
0.0342
0.73479
0.64095
0.60954
0.41691
0.08546
0.07172
2.45373
Macedonia
Central and Eastern Europe
93
5.007
0.05376
0.91851
1.00232
0.73545
0.33457
0.05327
0.22359
1.73933
Mozambique
Sub-Saharan Africa
94
4.971
0.07896
0.08308
1.02626
0.09131
0.34037
0.15603
0.22269
3.05137
Albania
Central and Eastern Europe
95
4.959
0.05013
0.87867
0.80434
0.81325
0.35733
0.06413
0.14272
1.89894
Bosnia and Herzegovina
Central and Eastern Europe
96
4.949
0.06913
0.83223
0.91916
0.79081
0.09245
0.00227
0.24808
2.06367
Lesotho
Sub-Saharan Africa
97
4.898
0.09438
0.37545
1.04103
0.07612
0.31767
0.12504
0.16388
2.79832
Dominican Republic
Latin America and Caribbean
98
4.885
0.07446
0.89537
1.17202
0.66825
0.57672
0.14234
0.21684
1.21305
Laos
Southeastern Asia
99
4.876
0.06698
0.59066
0.73803
0.54909
0.59591
0.24249
0.42192
1.73799
Mongolia
Eastern Asia
100
4.874
0.03313
0.82819
1.3006
0.60268
0.43626
0.02666
0.3323
1.34759
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Dataset Card for World Happiness Report

Dataset Summary

Context

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

Content

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Inspiration

What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?

What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.

What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.

What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?

The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.

If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.

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This dataset was shared by @unsdsn

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The license for this dataset is cc0-1.0

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