Datasets:

DBMoktan
/

crop-yield-prediction-in-nepal

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Float value 17.500000 was truncated converting to int64
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1852, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
                  return array_cast(
                         ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2006, in array_cast
                  return array.cast(pa_type)
                         ^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 1135, in pyarrow.lib.Array.cast
                File "/usr/local/lib/python3.12/site-packages/pyarrow/compute.py", line 412, in cast
                  return call_function("cast", [arr], options, memory_pool)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Float value 17.500000 was truncated converting to int64
              
              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 1348, 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 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 890, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 951, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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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Year int64	Districts string	Area int64	yield_kg/ha int64	crop_type string	avg_temp_C float64	max_temp_C float64	min_temp_C float64	avg_relative_humidity float64	avg_rainfall_mm_per_year float64	total_solar_radiation_kWh/m2 float64	total_PAR_MJ/m2 float64	avg_wind_speed_m/s float64	avg_pH_value float64	fertilizer_in_MT float64
2,067	Achham	9,450	2,400	Paddy	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Achham	6,351	1,550	Maize	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Achham	3,412	950	Millet	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Achham	0	0	Buckwheat	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Achham	16,139	1,450	Wheat	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Achham	514	1,298	Barley	13.058082	18.971014	8.11411	61.27663	1,245.93	6,522.01	2,827.55	1.555644	6.1	76
2,067	Arghakhanchi	8,768	2,600	Paddy	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Arghakhanchi	16,915	1,507	Maize	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Arghakhanchi	820	1,073	Millet	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Arghakhanchi	336	982	Buckwheat	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Arghakhanchi	7,340	1,836	Wheat	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Arghakhanchi	485	1,223	Barley	20.563014	26.143507	16.21063	55.356137	1,325.35	6,438.57	2,996.83	1.769233	5.9	612
2,067	Baglung	5,792	2,956	Paddy	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baglung	20,327	2,592	Maize	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baglung	17,700	1,301	Millet	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baglung	100	1,100	Buckwheat	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baglung	7,035	1,886	Wheat	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baglung	1,005	1,259	Barley	20.005452	25.423096	15.708904	57.221781	1,226.86	6,385.99	2,761.53	1.593342	5.7	439
2,067	Baitadi	5,300	1,890	Paddy	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Baitadi	9,500	1,800	Maize	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Baitadi	850	1,118	Millet	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Baitadi	0	0	Buckwheat	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Baitadi	12,000	2,058	Wheat	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Baitadi	600	1,000	Barley	11.780986	18.104329	6.420658	57.780959	905.5	6,708.83	2,953.94	1.683589	6	109
2,067	Bajhang	7,005	2,315	Paddy	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajhang	3,650	1,500	Maize	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajhang	2,285	1,007	Millet	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajhang	0	0	Buckwheat	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajhang	6,100	1,361	Wheat	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajhang	1,500	1,000	Barley	11.780986	18.104329	6.420658	57.780959	905.5	6,522.01	2,827.55	1.683589	5.6	147
2,067	Bajura	3,010	1,800	Paddy	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Bajura	790	2,214	Maize	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Bajura	2,610	1,000	Millet	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Bajura	7	1,500	Buckwheat	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Bajura	4,950	2,000	Wheat	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Bajura	1,072	1,650	Barley	9.549397	16.172247	4.111918	59.929671	937.29	6,522.01	2,827.55	1.642384	6	154
2,067	Banke	36,500	3,280	Paddy	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Banke	8,660	2,130	Maize	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Banke	0	0	Millet	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Banke	0	0	Buckwheat	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Banke	17,913	2,130	Wheat	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Banke	10	1,000	Barley	20.733534	26.487534	16.010603	55.956247	1,335.67	6,092.79	2,704.96	1.542274	6.4	5,913
2,067	Bara	52,725	3,200	Paddy	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bara	7,500	2,800	Maize	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bara	78	1,090	Millet	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bara	0	0	Buckwheat	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bara	29,000	3,341	Wheat	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bara	68	971	Barley	25.723781	32.056959	20.594767	54.83874	1,114.3	6,402.98	2,974.38	1.812137	6.7	7,732
2,067	Bardiya	42,550	3,500	Paddy	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bardiya	9,000	2,023	Maize	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bardiya	0	0	Millet	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bardiya	0	0	Buckwheat	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bardiya	18,890	2,795	Wheat	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bardiya	10	1,000	Barley	25.553233	31.691342	20.560356	52.890493	1,200.41	6,092.79	2,704.96	1.740685	6.8	7,655
2,067	Bhaktapur	4,300	5,400	Paddy	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhaktapur	2,000	3,000	Maize	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhaktapur	100	1,400	Millet	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhaktapur	0	0	Buckwheat	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhaktapur	3,200	3,089	Wheat	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhaktapur	50	1,000	Barley	19.624603	25.028575	15.524986	58.332767	1,044.95	6,421.67	2,868.84	1.509808	5.8	2,771
2,067	Bhojpur	16,103	2,337	Paddy	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Bhojpur	22,776	2,134	Maize	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Bhojpur	5,100	1,000	Millet	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Bhojpur	75	700	Buckwheat	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Bhojpur	2,510	1,657	Wheat	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Bhojpur	30	1,333	Barley	5.662411	11.093397	1.640932	71.618329	1,213.87	5,773.57	2,540.43	1.581178	5.4	15,732
2,067	Chitwan	32,770	3,386	Paddy	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Chitwan	20,660	2,539	Maize	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Chitwan	1,810	1,032	Millet	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Chitwan	700	1,107	Buckwheat	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Chitwan	8,728	3,040	Wheat	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Chitwan	61	1,000	Barley	25.180411	31.250849	20.217288	53.936904	1,182.25	6,402.98	2,974.38	1.488986	6.1	615
2,067	Dadeldhura	6,221	2,280	Paddy	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dadeldhura	3,744	1,707	Maize	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dadeldhura	302	1,291	Millet	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dadeldhura	0	0	Buckwheat	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dadeldhura	7,500	1,600	Wheat	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dadeldhura	218	1,032	Barley	16.60326	22.623178	11.563945	54.036	1,219.07	6,708.83	2,953.94	1.476658	5.9	10,437
2,067	Dailekh	8,507	3,130	Paddy	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dailekh	20,150	1,940	Maize	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dailekh	2,422	1,085	Millet	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dailekh	0	0	Buckwheat	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dailekh	7,300	1,352	Wheat	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dailekh	195	1,462	Barley	13.058082	18.971014	8.11411	61.27663	1,245.93	6,092.79	2,704.96	1.555644	5.9	18
2,067	Dang	38,500	3,012	Paddy	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Dang	25,200	2,185	Maize	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Dang	150	1,000	Millet	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Dang	0	0	Buckwheat	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Dang	12,750	2,275	Wheat	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Dang	30	833	Barley	22.614685	28.440192	17.948	54.894	1,278.14	6,452.09	2,802.61	1.861014	5.8	24
2,067	Darchula	4,290	2,500	Paddy	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Darchula	5,520	2,276	Maize	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Darchula	1,300	1,000	Millet	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Darchula	100	750	Buckwheat	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Darchula	5,265	1,140	Wheat	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Darchula	1,200	917	Barley	6.55189	12.700877	1.238822	59.174329	761.71	6,708.83	2,953.94	1.77937	6	11,824
2,067	Dhading	16,670	2,452	Paddy	15.642685	21.133562	11.254219	58.718	962.18	6,402.98	2,974.38	1.609397	5.5	16
2,067	Dhading	17,468	2,500	Maize	15.642685	21.133562	11.254219	58.718	962.18	6,402.98	2,974.38	1.609397	5.5	16
2,067	Dhading	7,140	1,003	Millet	15.642685	21.133562	11.254219	58.718	962.18	6,402.98	2,974.38	1.609397	5.5	16
2,067	Dhading	0	0	Buckwheat	15.642685	21.133562	11.254219	58.718	962.18	6,402.98	2,974.38	1.609397	5.5	16

End of preview.

Crop Yield Prediction Dataset for Nepal

Dataset Description

This dataset contains historical agricultural production features paired with multi-variable meteorological indicators and soil characteristics across various districts of Nepal (e.g., Achham, Terhathum). It is specifically engineered to build and evaluate predictive machine learning models for forecasting crop productivity based on environmental factors.

Primary Task: Tabular Regression / Predictive Modeling
Target Variable: yield_kg/ha
Geographic Coverage: Ecological zones and districts across Nepal
Temporal Scale: Historical data mapping (including records covering up to 2079 BS)

Dataset Structure

Data Fields

The dataset consists of 15 features tracking spatial, temporal, agricultural, and micro-climate parameters:

Column Name	Data Type	Description
`Year`	Integer	Year of data collection (Bikram Sambat / BS format).
`Districts`	String	The geographical district in Nepal where cultivation took place.
`Area`	Integer	Total land area under cultivation for the crop (in Hectares).
`yield_kg/ha`	Integer	Target: Crop yield output measured in kilograms per hectare.
`crop_type`	String	Type of crop grown (e.g., Paddy, Maize, Millet, Buckwheat).
`avg_temp_C`	Float	Average annual/seasonal ambient temperature in Celsius.
`max_temp_C`	Float	Maximum recorded temperature in Celsius.
`min_temp_C`	Float	Minimum recorded temperature in Celsius.
`avg_relative_humidity`	Float	Average relative humidity percentage.
`avg_rainfall_mm_per_year`	Float	Total accumulated precipitation per year in millimeters.
`total_solar_radiation_kWh/m2`	Float	Cumulative solar radiation intensity measured in kWh/m².
`total_PAR_MJ/m2`	Float	Total Photosynthetically Active Radiation in Megajoules per square meter.
`avg_wind_speed_m/s`	Float	Mean wind velocity in meters per second.
`avg_pH_value`	Float	Average soil pH level recorded in the cultivation region.
`fertilizer_in_MT`	Float	Total quantity of fertilizer applied in Metric Tons (MT).

How to Use

Loading via Hugging Face `datasets`

You can programmatically load this dataset directly into your Python workflow using the Hugging Face library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("YOUR_HF_USERNAME/YOUR_REPO_NAME")

# Convert the 'train' split to a Pandas DataFrame
df = dataset['train'].to_pandas()
print(df.head())

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