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Datasets:
EduDevCommons
/
India-Population-Urban-and-Rural-Settlement-Dataset

Tasks:
Tabular Classification
License:
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
total_settlements: int64
urban_settlements: int64
rural_settlements: int64
total_population_est: int64
avg_population: double
max_population: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1061
to
{'Unnamed: 0': Value('int64'), 'Table Name': Value('string'), 'State Code': Value('int64'), 'District Code': Value('int64'), 'Area Name': Value('string'), 'Age Group': Value('string'), 'Total Persons': Value('int64'), 'Total Males': Value('int64'), 'Total Females': Value('int64'), 'Rural Persons': Value('int64'), 'Rural Males': Value('int64'), 'Rural Females': Value('int64'), 'Urban Persons': Value('int64'), 'Urban Males': Value('int64'), 'Urban Females': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 764, in write_table
                  self.write_rows_on_file()  # in case there are buffered rows to write first
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              total_settlements: int64
              urban_settlements: int64
              rural_settlements: int64
              total_population_est: int64
              avg_population: double
              max_population: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1061
              to
              {'Unnamed: 0': Value('int64'), 'Table Name': Value('string'), 'State Code': Value('int64'), 'District Code': Value('int64'), 'Area Name': Value('string'), 'Age Group': Value('string'), 'Total Persons': Value('int64'), 'Total Males': Value('int64'), 'Total Females': Value('int64'), 'Rural Persons': Value('int64'), 'Rural Males': Value('int64'), 'Rural Females': Value('int64'), 'Urban Persons': Value('int64'), 'Urban Males': Value('int64'), 'Urban Females': Value('int64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 6 new columns ({'urban_settlements', 'total_settlements', 'total_population_est', 'avg_population', 'rural_settlements', 'max_population'}) and 15 missing columns ({'Table Name', 'Urban Males', 'Rural Persons', 'Age Group', 'District Code', 'Unnamed: 0', 'Total Persons', 'Rural Males', 'Area Name', 'Urban Females', 'Total Males', 'Total Females', 'State Code', 'Rural Females', 'Urban Persons'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://india_population_dataset/historical_population_india.csv::hf://datasets/EduDevCommons/India-Population-Urban-and-Rural-Settlement-Dataset@d816b568431418305800427923a254ab8b9d78a9/India Population - Urban and Rural Settlement Dataset.zip, ['hf://datasets/EduDevCommons/India-Population-Urban-and-Rural-Settlement-Dataset@d816b568431418305800427923a254ab8b9d78a9/India Population - Urban and Rural Settlement Dataset.zip']
              
              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)
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              total_settlements: int64
              urban_settlements: int64
              rural_settlements: int64
              total_population_est: int64
              avg_population: double
              max_population: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1061
              to
              {'Unnamed: 0': Value('int64'), 'Table Name': Value('string'), 'State Code': Value('int64'), 'District Code': Value('int64'), 'Area Name': Value('string'), 'Age Group': Value('string'), 'Total Persons': Value('int64'), 'Total Males': Value('int64'), 'Total Females': Value('int64'), 'Rural Persons': Value('int64'), 'Rural Males': Value('int64'), 'Rural Females': Value('int64'), 'Urban Persons': Value('int64'), 'Urban Males': Value('int64'), 'Urban Females': Value('int64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Unnamed: 0
int64
Table Name
string
State Code
int64
District Code
int64
Area Name
string
Age Group
string
Total Persons
int64
Total Males
int64
Total Females
int64
Rural Persons
int64
Rural Males
int64
Rural Females
int64
Urban Persons
int64
Urban Males
int64
Urban Females
int64
0
C4114
0
0
India
All ages
1,210,854,977
623,270,258
587,584,719
833,748,852
427,781,058
405,967,794
377,106,125
195,489,200
181,616,925
1
C4114
0
0
India
0-4
112,806,778
58,632,074
54,174,704
82,986,660
43,036,377
39,950,283
29,820,118
15,595,697
14,224,421
2
C4114
0
0
India
5-9
126,928,126
66,300,466
60,627,660
93,807,612
48,825,259
44,982,353
33,120,514
17,475,207
15,645,307
3
C4114
0
0
India
10-14
132,709,212
69,418,835
63,290,377
96,804,494
50,488,158
46,316,336
35,904,718
18,930,677
16,974,041
4
C4114
0
0
India
15-19
120,526,449
63,982,396
56,544,053
83,902,472
44,570,557
39,331,915
36,623,977
19,411,839
17,212,138
5
C4114
0
0
India
20-24
111,424,222
57,584,693
53,839,529
73,835,046
38,138,662
35,696,384
37,589,176
19,446,031
18,143,145
6
C4114
0
0
India
25-29
101,413,965
51,344,208
50,069,757
66,068,270
33,375,989
32,692,281
35,345,695
17,968,219
17,377,476
7
C4114
0
0
India
30-34
88,594,951
44,660,674
43,934,277
57,911,779
28,934,192
28,977,587
30,683,172
15,726,482
14,956,690
8
C4114
0
0
India
35-39
85,140,684
42,919,381
42,221,303
56,062,707
28,125,561
27,937,146
29,077,977
14,793,820
14,284,157
9
C4114
0
0
India
40-44
72,438,112
37,545,386
34,892,726
47,581,008
24,565,235
23,015,773
24,857,104
12,980,151
11,876,953
10
C4114
0
0
India
45-49
62,318,327
32,138,114
30,180,213
40,688,228
20,864,270
19,823,958
21,630,099
11,273,844
10,356,255
11
C4114
0
0
India
50-54
49,069,254
25,843,266
23,225,988
32,031,788
16,789,547
15,242,241
17,037,466
9,053,719
7,983,747
12
C4114
0
0
India
55-59
39,146,055
19,456,012
19,690,043
25,861,514
12,536,529
13,324,985
13,284,541
6,919,483
6,365,058
13
C4114
0
0
India
60-64
37,663,707
18,701,749
18,961,958
26,291,245
12,931,592
13,359,653
11,372,462
5,770,157
5,602,305
14
C4114
0
0
India
65-69
26,454,983
12,944,326
13,510,657
18,916,270
9,208,311
9,707,959
7,538,713
3,736,015
3,802,698
15
C4114
0
0
India
70-74
19,208,842
9,651,499
9,557,343
13,807,600
6,978,179
6,829,421
5,401,242
2,673,320
2,727,922
16
C4114
0
0
India
75-79
9,232,503
4,490,603
4,741,900
6,383,717
3,113,424
3,270,293
2,848,786
1,377,179
1,471,607
17
C4114
0
0
India
80+
11,289,005
5,283,695
6,005,310
7,894,990
3,765,796
4,129,194
3,394,015
1,517,899
1,876,116
18
C4114
0
0
India
Age not stated
4,489,802
2,372,881
2,116,921
2,913,452
1,533,420
1,380,032
1,576,350
839,461
736,889
19
C4114
1
0
State - JAMMU & KASHMIR (01)
All ages
12,541,302
6,640,662
5,900,640
9,108,060
4,774,477
4,333,583
3,433,242
1,866,185
1,567,057
20
C4114
1
0
State - JAMMU & KASHMIR (01)
0-4
1,414,884
763,501
651,383
1,115,289
600,891
514,398
299,595
162,610
136,985
21
C4114
1
0
State - JAMMU & KASHMIR (01)
5-9
1,411,973
746,818
665,155
1,109,056
584,994
524,062
302,917
161,824
141,093
22
C4114
1
0
State - JAMMU & KASHMIR (01)
10-14
1,413,853
744,855
668,998
1,097,236
575,511
521,725
316,617
169,344
147,273
23
C4114
1
0
State - JAMMU & KASHMIR (01)
15-19
1,237,462
640,362
597,100
926,108
477,098
449,010
311,354
163,264
148,090
24
C4114
1
0
State - JAMMU & KASHMIR (01)
20-24
1,160,913
603,578
557,335
827,361
423,292
404,069
333,552
180,286
153,266
25
C4114
1
0
State - JAMMU & KASHMIR (01)
25-29
1,086,122
584,559
501,563
738,568
385,544
353,024
347,554
199,015
148,539
26
C4114
1
0
State - JAMMU & KASHMIR (01)
30-34
926,903
495,746
431,157
636,137
333,062
303,075
290,766
162,684
128,082
27
C4114
1
0
State - JAMMU & KASHMIR (01)
35-39
837,945
441,265
396,680
568,825
294,268
274,557
269,120
146,997
122,123
28
C4114
1
0
State - JAMMU & KASHMIR (01)
40-44
714,085
387,739
326,346
483,222
256,988
226,234
230,863
130,751
100,112
29
C4114
1
0
State - JAMMU & KASHMIR (01)
45-49
590,790
311,560
279,230
395,959
205,036
190,923
194,831
106,524
88,307
30
C4114
1
0
State - JAMMU & KASHMIR (01)
50-54
468,566
255,599
212,967
319,992
173,804
146,188
148,574
81,795
66,779
31
C4114
1
0
State - JAMMU & KASHMIR (01)
55-59
340,031
172,753
167,278
227,495
114,641
112,854
112,536
58,112
54,424
32
C4114
1
0
State - JAMMU & KASHMIR (01)
60-64
322,726
173,427
149,299
226,005
121,240
104,765
96,721
52,187
44,534
33
C4114
1
0
State - JAMMU & KASHMIR (01)
65-69
203,965
102,743
101,222
140,720
70,780
69,940
63,245
31,963
31,282
34
C4114
1
0
State - JAMMU & KASHMIR (01)
70-74
184,019
97,472
86,547
132,084
70,777
61,307
51,935
26,695
25,240
35
C4114
1
0
State - JAMMU & KASHMIR (01)
75-79
85,076
43,094
41,982
58,918
29,980
28,938
26,158
13,114
13,044
36
C4114
1
0
State - JAMMU & KASHMIR (01)
80+
126,870
65,844
61,026
94,242
49,712
44,530
32,628
16,132
16,496
37
C4114
1
0
State - JAMMU & KASHMIR (01)
Age not stated
15,119
9,747
5,372
10,843
6,859
3,984
4,276
2,888
1,388
38
C4114
2
0
State - HIMACHAL PRADESH (02)
All ages
6,864,602
3,481,873
3,382,729
6,176,050
3,110,345
3,065,705
688,552
371,528
317,024
39
C4114
2
0
State - HIMACHAL PRADESH (02)
0-4
544,984
285,011
259,973
500,931
261,743
239,188
44,053
23,268
20,785
40
C4114
2
0
State - HIMACHAL PRADESH (02)
5-9
591,177
311,457
279,720
537,559
282,441
255,118
53,618
29,016
24,602
41
C4114
2
0
State - HIMACHAL PRADESH (02)
10-14
639,224
338,240
300,984
580,265
305,500
274,765
58,959
32,740
26,219
42
C4114
2
0
State - HIMACHAL PRADESH (02)
15-19
640,461
336,729
303,732
575,991
300,554
275,437
64,470
36,175
28,295
43
C4114
2
0
State - HIMACHAL PRADESH (02)
20-24
643,866
323,720
320,146
573,439
285,167
288,272
70,427
38,553
31,874
44
C4114
2
0
State - HIMACHAL PRADESH (02)
25-29
589,160
290,125
299,035
523,605
255,016
268,589
65,555
35,109
30,446
45
C4114
2
0
State - HIMACHAL PRADESH (02)
30-34
546,357
271,253
275,104
485,375
239,272
246,103
60,982
31,981
29,001
46
C4114
2
0
State - HIMACHAL PRADESH (02)
35-39
517,966
258,230
259,736
459,275
227,906
231,369
58,691
30,324
28,367
47
C4114
2
0
State - HIMACHAL PRADESH (02)
40-44
445,560
222,269
223,291
394,635
195,137
199,498
50,925
27,132
23,793
48
C4114
2
0
State - HIMACHAL PRADESH (02)
45-49
395,128
197,642
197,486
350,793
173,489
177,304
44,335
24,153
20,182
49
C4114
2
0
State - HIMACHAL PRADESH (02)
50-54
330,687
165,724
164,963
295,411
145,792
149,619
35,276
19,932
15,344
50
C4114
2
0
State - HIMACHAL PRADESH (02)
55-59
266,860
135,245
131,615
240,093
119,998
120,095
26,767
15,247
11,520
51
C4114
2
0
State - HIMACHAL PRADESH (02)
60-64
231,372
115,464
115,908
212,263
105,331
106,932
19,109
10,133
8,976
52
C4114
2
0
State - HIMACHAL PRADESH (02)
65-69
158,607
78,387
80,220
146,096
71,950
74,146
12,511
6,437
6,074
53
C4114
2
0
State - HIMACHAL PRADESH (02)
70-74
130,587
62,119
68,468
120,932
57,250
63,682
9,655
4,869
4,786
54
C4114
2
0
State - HIMACHAL PRADESH (02)
75-79
75,706
36,440
39,266
69,958
33,561
36,397
5,748
2,879
2,869
55
C4114
2
0
State - HIMACHAL PRADESH (02)
80+
106,737
48,465
58,272
100,043
45,339
54,704
6,694
3,126
3,568
56
C4114
2
0
State - HIMACHAL PRADESH (02)
Age not stated
10,163
5,353
4,810
9,386
4,899
4,487
777
454
323
57
C4114
3
0
State - PUNJAB (03)
All ages
27,743,338
14,639,465
13,103,873
17,344,192
9,093,476
8,250,716
10,399,146
5,545,989
4,853,157
58
C4114
3
0
State - PUNJAB (03)
0-4
2,133,529
1,149,956
983,573
1,351,369
729,984
621,385
782,160
419,972
362,188
59
C4114
3
0
State - PUNJAB (03)
5-9
2,368,019
1,301,682
1,066,337
1,507,022
827,905
679,117
860,997
473,777
387,220
60
C4114
3
0
State - PUNJAB (03)
10-14
2,583,402
1,445,530
1,137,872
1,668,828
931,366
737,462
914,574
514,164
400,410
61
C4114
3
0
State - PUNJAB (03)
15-19
2,817,683
1,570,180
1,247,503
1,814,427
1,003,831
810,596
1,003,256
566,349
436,907
62
C4114
3
0
State - PUNJAB (03)
20-24
2,776,636
1,465,531
1,311,105
1,706,833
888,739
818,094
1,069,803
576,792
493,011
63
C4114
3
0
State - PUNJAB (03)
25-29
2,463,861
1,270,405
1,193,456
1,455,456
738,196
717,260
1,008,405
532,209
476,196
64
C4114
3
0
State - PUNJAB (03)
30-34
2,116,539
1,082,533
1,034,006
1,263,506
636,268
627,238
853,033
446,265
406,768
65
C4114
3
0
State - PUNJAB (03)
35-39
1,989,071
1,010,862
978,209
1,202,842
604,249
598,593
786,229
406,613
379,616
66
C4114
3
0
State - PUNJAB (03)
40-44
1,788,469
909,208
879,261
1,102,644
555,906
546,738
685,825
353,302
332,523
67
C4114
3
0
State - PUNJAB (03)
45-49
1,587,771
818,135
769,636
964,942
495,583
469,359
622,829
322,552
300,277
68
C4114
3
0
State - PUNJAB (03)
50-54
1,254,464
663,664
590,800
749,857
397,863
351,994
504,607
265,801
238,806
69
C4114
3
0
State - PUNJAB (03)
55-59
956,555
485,845
470,710
569,146
283,796
285,350
387,409
202,049
185,360
70
C4114
3
0
State - PUNJAB (03)
60-64
996,590
482,483
514,107
654,729
309,825
344,904
341,861
172,658
169,203
71
C4114
3
0
State - PUNJAB (03)
65-69
719,588
372,806
346,782
496,111
257,824
238,287
223,477
114,982
108,495
72
C4114
3
0
State - PUNJAB (03)
70-74
513,472
272,120
241,352
356,117
191,121
164,996
157,355
80,999
76,356
73
C4114
3
0
State - PUNJAB (03)
75-79
256,666
130,203
126,463
175,972
89,364
86,608
80,694
40,839
39,855
74
C4114
3
0
State - PUNJAB (03)
80+
379,501
186,050
193,451
274,781
135,823
138,958
104,720
50,227
54,493
75
C4114
3
0
State - PUNJAB (03)
Age not stated
41,522
22,272
19,250
29,610
15,833
13,777
11,912
6,439
5,473
76
C4114
4
0
State - CHANDIGARH (04)
All ages
1,055,450
580,663
474,787
28,991
17,150
11,841
1,026,459
563,513
462,946
77
C4114
4
0
State - CHANDIGARH (04)
0-4
81,854
43,347
38,507
3,021
1,611
1,410
78,833
41,736
37,097
78
C4114
4
0
State - CHANDIGARH (04)
5-9
91,194
49,255
41,939
2,921
1,572
1,349
88,273
47,683
40,590
79
C4114
4
0
State - CHANDIGARH (04)
10-14
93,464
52,140
41,324
2,537
1,421
1,116
90,927
50,719
40,208
80
C4114
4
0
State - CHANDIGARH (04)
15-19
104,418
60,521
43,897
2,765
1,787
978
101,653
58,734
42,919
81
C4114
4
0
State - CHANDIGARH (04)
20-24
121,244
68,467
52,777
3,593
2,255
1,338
117,651
66,212
51,439
82
C4114
4
0
State - CHANDIGARH (04)
25-29
110,237
60,694
49,543
3,806
2,260
1,546
106,431
58,434
47,997
83
C4114
4
0
State - CHANDIGARH (04)
30-34
90,803
49,625
41,178
3,101
1,902
1,199
87,702
47,723
39,979
84
C4114
4
0
State - CHANDIGARH (04)
35-39
83,672
45,566
38,106
2,321
1,455
866
81,351
44,111
37,240
85
C4114
4
0
State - CHANDIGARH (04)
40-44
68,921
37,268
31,653
1,547
947
600
67,374
36,321
31,053
86
C4114
4
0
State - CHANDIGARH (04)
45-49
60,352
32,893
27,459
1,049
638
411
59,303
32,255
27,048
87
C4114
4
0
State - CHANDIGARH (04)
50-54
46,942
26,108
20,834
723
415
308
46,219
25,693
20,526
88
C4114
4
0
State - CHANDIGARH (04)
55-59
34,925
19,745
15,180
503
272
231
34,422
19,473
14,949
89
C4114
4
0
State - CHANDIGARH (04)
60-64
25,801
13,530
12,271
481
277
204
25,320
13,253
12,067
90
C4114
4
0
State - CHANDIGARH (04)
65-69
15,839
8,250
7,589
255
140
115
15,584
8,110
7,474
91
C4114
4
0
State - CHANDIGARH (04)
70-74
11,385
5,998
5,387
181
96
85
11,204
5,902
5,302
92
C4114
4
0
State - CHANDIGARH (04)
75-79
6,422
3,255
3,167
75
43
32
6,347
3,212
3,135
93
C4114
4
0
State - CHANDIGARH (04)
80+
7,631
3,800
3,831
106
55
51
7,525
3,745
3,780
94
C4114
4
0
State - CHANDIGARH (04)
Age not stated
346
201
145
6
4
2
340
197
143
95
C4114
5
0
State - UTTARAKHAND (05)
All ages
10,086,292
5,137,773
4,948,519
7,036,954
3,519,042
3,517,912
3,049,338
1,618,731
1,430,607
96
C4114
5
0
State - UTTARAKHAND (05)
0-4
924,864
488,988
435,876
677,293
356,937
320,356
247,571
132,051
115,520
97
C4114
5
0
State - UTTARAKHAND (05)
5-9
1,058,801
560,179
498,622
770,582
404,255
366,327
288,219
155,924
132,295
98
C4114
5
0
State - UTTARAKHAND (05)
10-14
1,145,343
603,274
542,069
835,418
434,578
400,840
309,925
168,696
141,229
99
C4114
5
0
State - UTTARAKHAND (05)
15-19
1,124,110
588,483
535,627
792,898
407,069
385,829
331,212
181,414
149,798
End of preview.

India Population - Urban and Rural Settlement Dataset

This dataset was published on Kaggle by Samyakraj Bayar and mirrored here.

Download

The dataset is available as a ZIP archive: India Population - Urban and Rural Settlement Dataset.zip

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MIT
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