Datasets:

jeyanthiii
/

sample_ev_data_csv

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 1 new columns ({'Unnamed: 0'}) and 6 missing columns ({'user_profile', 'vehicle_id', 'charging_now', 'timestamp', 'district', 'grid_carbon_g_per_kwh'}).

This happened while the csv dataset builder was generating data using

hf://datasets/jeyanthiii/sample_ev_data_csv/raw/daily_user.csv (at revision 100c616865d492b1e4b700cd1eab20ad6057fbc7), ['hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/ingest_stream_2024.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/daily_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/heavy_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/moderate_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/rare_user.csv']

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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: string
              SOC: double
              SOH: double
              Charging_Cycles: int64
              Battery_Temp: double
              Motor_RPM: double
              Motor_Torque: double
              Motor_Temp: double
              Brake_Pad_Wear: double
              Charging_Voltage: int64
              Tire_Pressure: double
              DTC: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1723
              to
              {'timestamp': Value('string'), 'vehicle_id': Value('string'), 'user_profile': Value('string'), 'district': Value('string'), 'SOC': Value('float64'), 'SOH': Value('float64'), 'Charging_Cycles': Value('int64'), 'charging_now': Value('int64'), 'Battery_Temp': Value('float64'), 'Motor_RPM': Value('float64'), 'Motor_Torque': Value('float64'), 'Motor_Temp': Value('float64'), 'Brake_Pad_Wear': Value('float64'), 'Charging_Voltage': Value('int64'), 'Tire_Pressure': Value('float64'), 'DTC': Value('string'), 'grid_carbon_g_per_kwh': Value('float64')}
              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 1379, 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 956, 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 1702, 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 1858, 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 1 new columns ({'Unnamed: 0'}) and 6 missing columns ({'user_profile', 'vehicle_id', 'charging_now', 'timestamp', 'district', 'grid_carbon_g_per_kwh'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/jeyanthiii/sample_ev_data_csv/raw/daily_user.csv (at revision 100c616865d492b1e4b700cd1eab20ad6057fbc7), ['hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/ingest_stream_2024.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/daily_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/heavy_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/moderate_user.csv', 'hf://datasets/jeyanthiii/sample_ev_data_csv@100c616865d492b1e4b700cd1eab20ad6057fbc7/raw/rare_user.csv']
              
              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.

timestamp string	vehicle_id string	user_profile string	district string	SOC float64	SOH float64	Charging_Cycles int64	charging_now int64	Battery_Temp float64	Motor_RPM float64	Motor_Torque float64	Motor_Temp float64	Brake_Pad_Wear float64	Charging_Voltage int64	Tire_Pressure float64	grid_carbon_g_per_kwh float64
2024-01-28 21:00:00	EV-101	rare_user	University	65.936233	93.023739	151	0	30.130589	0	0	69.439794	41.944614	240	25.176506	339.6
2024-10-01 11:00:00	EV-102	moderate_user	Harbor	58.537448	87.578094	388	0	52.9674	3,023.071957	604.614391	87.34351	90.744275	400	25.332319	342.2
2024-11-07 23:00:00	EV-104	heavy_user	Industrial	38.251923	90.575159	14	0	30.045687	0	0	64.362927	64.591155	400	30.79185	576.8
2024-10-18 12:00:00	EV-101	rare_user	University	80	91.585665	347	1	36.347211	2,968.763473	593.752695	84.271041	90.106496	240	28.56772	327.1
2024-09-03 04:00:00	EV-104	heavy_user	Industrial	79.022028	91.156586	187	0	26.817945	0	0	66.557644	73.712616	400	29.594551	552.4
2024-06-07 17:00:00	EV-102	moderate_user	Downtown	23.934929	88.447503	334	0	49.647987	1,461.150558	292.230112	61.163739	30.309497	400	25.063491	416.1
2024-05-15 13:00:00	EV-104	heavy_user	Industrial	94.995015	92.129983	155	0	52.706672	1,651.343561	330.268712	76.932325	88.725009	400	27.82157	511.6
2024-11-17 02:00:00	EV-101	rare_user	University	80	91.424451	369	0	28.075626	0	0	68.834871	87.6555	240	25.018856	363.3
2024-07-05 09:00:00	EV-102	moderate_user	Harbor	48.630013	88.239947	347	0	51.553008	1,626.190093	325.238019	69.887851	98.064504	400	186.9	361.5
2024-04-15 03:00:00	EV-104	heavy_user	Industrial	24.500029	92.400985	145	0	32.582752	0	0	70.524956	93.004278	400	29.949198	572.1
2024-11-17 21:00:00	EV-102	moderate_user	Harbor	31.05633	87.222434	410	0	31.108069	0	0	75.493731	86.807825	400	30.804372	372.3
2024-09-20 00:00:00	EV-103	daily_user	Downtown	60.064798	98.03427	201	0	29.942182	0	0	70.414228	30.89259	240	28.96776	507.5
2024-11-02 14:00:00	EV-104	heavy_user	Industrial	83.171886	90.621758	13	0	31.04349	0	0	67.339826	65.324389	400	25.420843	514.3
2024-02-09 02:00:00	EV-104	heavy_user	Industrial	27.283924	92.980282	126	0	28.854303	0	0	70.631638	31.923035	400	28.677454	574.7
2024-01-07 17:00:00	EV-101	rare_user	University	65.514509	93.139605	135	0	35.553116	1,556.644816	311.328963	65.831707	43.712804	240	27.719085	316.7
2024-04-29 19:00:00	EV-104	heavy_user	Downtown	69.593576	92.269707	150	0	27.770294	0	0	72.691527	90.926019	400	25.540643	460.5
2024-04-15 06:00:00	EV-102	moderate_user	Industrial	92.775828	88.851849	309	0	31.407958	0	0	66.602977	34.93154	400	27.608035	531.6
2024-02-01 14:00:00	EV-102	moderate_user	Harbor	72.992591	91.739938	275	0	47.310716	2,971.446391	594.289278	84.052316	41.716901	400	28.005544	366.2
2024-01-23 20:00:00	EV-103	daily_user	University	80	99.712193	30	0	32.144634	0	0	68.865418	49.271284	240	28.52195	339.5
2024-07-24 13:00:00	EV-103	daily_user	University	57.615095	98.435513	160	0	29.932297	0	0	72.789212	35.26345	240	30.212515	324.7
2024-03-05 18:00:00	EV-102	moderate_user	Harbor	null	89.155158	290	0	24.874179	0	0	68.397365	38.245012	400	27.890259	374.6
2024-08-08 00:00:00	EV-101	rare_user	University	80	91.975311	294	0	31.299642	0	0	69.819247	96.004639	240	30.13625	384.8
2024-11-02 03:00:00	EV-104	heavy_user	Industrial	95.392317	90.625796	13	0	30.063726	0	0	66.130501	65.392776	400	25.560679	580.3
2024-03-25 10:00:00	EV-104	heavy_user	Industrial	100	92.581883	140	1	54.118527	2,990.731669	598.146334	85.266231	95.806462	400	29.160151	520.3
2024-12-28 15:00:00	EV-102	moderate_user	Harbor	79.382025	86.914118	430	0	30.71727	0	0	73.110658	83.30607	400	30.440833	348.4
2024-01-30 00:00:00	EV-101	rare_user	University	80	93.017558	152	0	28.737536	0	0	71.180785	41.853577	240	25.042076	386.9
2024-12-01 03:00:00	EV-103	daily_user	Downtown	25.172143	97.530439	252	0	33.902937	0	0	70.269435	95.343978	240	25.594124	473.2
2024-05-10 05:00:00	EV-101	rare_user	University	66.999525	92.465265	227	0	30.178491	0	0	69.730036	33.472203	240	26.912241	358
2024-05-09 10:00:00	EV-104	heavy_user	Downtown	78.983412	92.183948	153	0	55.367581	1,476.364869	295.272974	71.93403	89.569814	400	29.683144	433
2024-07-24 04:00:00	EV-102	moderate_user	Harbor	86.34679	88.098845	356	0	28.836284	0	0	69.850796	110.03	400	30.722475	353
2024-11-12 04:00:00	EV-101	rare_user	University	66.097882	91.451444	365	0	29.297996	0	0	66.656651	88.078782	240	25.612907	345.7
2024-11-23 18:00:00	EV-102	moderate_user	Harbor	23.835378	87.176978	413	0	31.054919	0	0	63.803946	86.251289	400	29.742314	371.8
2024-03-22 08:00:00	EV-101	rare_user	University	24.68964	92.731766	190	0	28.87312	0	0	67.279094	110.71	240	25.741934	326.8
2024-05-31 00:00:00	EV-102	moderate_user	Harbor	92.768649	88.505579	331	0	30.269995	0	0	71.010601	31.000966	400	26.444139	419
2024-03-19 13:00:00	EV-101	rare_user	Downtown	24.412539	92.746913	188	0	34.06249	1,448.710293	289.742059	69.74979	37.75036	240	26.083254	431.9
2024-07-30 22:00:00	EV-101	rare_user	Harbor	80	92.019453	288	0	34.042311	0	0	73.567247	96.675003	240	31.114872	377.7
2024-10-03 22:00:00	EV-101	rare_user	University	50.407195	91.664944	336	0	31.602241	0	0	69.389185	91.287946	240	30.32791	345.6
2024-03-06 02:00:00	EV-104	heavy_user	Industrial	69.776474	92.75193	134	0	34.814278	0	0	69.977719	98.421063	400	27.906244	603.2
2024-12-02 22:00:00	EV-101	rare_user	Downtown	24.171614	91.338351	380	0	27.498616	0	0	68.597176	86.352833	240	30.118679	461.1
2024-02-01 05:00:00	EV-103	daily_user	Downtown	37.329182	99.654262	35	0	26.753009	0	0	72.270572	48.658489	240	27.327186	453.2
2024-02-22 01:00:00	EV-103	daily_user	Industrial	35.499561	99.508381	50	0	30.472007	0	0	76.953852	47.042614	240	31.330077	595.7
2024-02-19 17:00:00	EV-102	moderate_user	Harbor	48.448536	91.604521	283	0	45.289206	1,562.218079	312.443616	69.516763	40.224804	400	31.744969	368.6
2024-12-11 12:00:00	EV-104	heavy_user	Industrial	62.23656	90.280001	24	0	37.349553	2,945.883077	589.176615	85.17806	60.027331	400	27.713732	497.2
2024-02-19 10:00:00	EV-101	rare_user	University	24.070737	92.906665	166	0	30.602596	0	0	64.860621	40.201696	240	null	321.7
2024-03-04 02:00:00	EV-101	rare_user	University	65.633827	92.831425	177	0	31.393706	0	0	71.187987	39.038218	240	27.946391	373.1
2024-10-20 05:00:00	EV-104	heavy_user	University	93.137425	90.741312	9	0	27.81216	0	0	67.660653	67.244142	400	29.440204	326.7
2024-08-11 16:00:00	EV-103	daily_user	Downtown	45.467232	98.308663	173	0	33.27686	3,037.472549	607.49451	87.305285	33.867667	240	27.613948	417.3
2024-10-10 10:00:00	EV-104	heavy_user	Industrial	null	90.826725	6	0	32.047916	2,917.22137	583.444274	88.487995	68.525884	400	25.379134	535.1
2024-11-10 09:00:00	EV-104	heavy_user	Industrial	76.291113	90.55505	15	0	33.875139	2,937.479584	587.495917	85.624027	64.34261	400	30.055464	519
2024-09-11 17:00:00	EV-101	rare_user	University	36.57237	91.786366	319	0	31.293502	0	0	68.502788	93.16654	240	25.988469	315.7
2024-08-16 17:00:00	EV-101	rare_user	University	54.036438	91.927853	300	0	31.184029	0	0	69.205584	95.294009	240	29.09148	295.7
2024-03-23 11:00:00	EV-102	moderate_user	Harbor	100	89.021169	299	1	47.498086	3,072.04547	614.409094	83.220783	36.746671	400	31.697685	353.6
2024-05-14 00:00:00	EV-104	heavy_user	Industrial	61.322939	92.145311	154	0	33.653697	0	0	65.054499	89.000892	400	28.290223	606.8
2024-10-25 18:00:00	EV-103	daily_user	Downtown	80	97.783891	227	1	37.695123	3,019.09092	603.818184	86.67264	98.12465	240	30.855608	432.8
2024-03-02 04:00:00	EV-104	heavy_user	University	67.166608	92.78493	133	0	28.362861	0	0	74.124691	98.890363	400	29.073966	350.2
2024-10-18 06:00:00	EV-104	heavy_user	Industrial	49.249123	90.758086	8	0	33.466536	1,461.861701	292.37234	72.747422	67.470457	400	30.040917	547.2
2024-09-06 23:00:00	EV-101	rare_user	Harbor	50.79395	91.811726	316	0	30.834933	0	0	66.824519	93.531572	240	26.549517	399.4
2024-02-08 18:00:00	EV-103	daily_user	Downtown	58.829582	99.601021	41	0	32.209837	1,643.367482	328.673496	78.689843	48.047562	240	26.24204	426.9
2024-05-16 22:00:00	EV-104	heavy_user	Industrial	56.606969	92.117709	155	0	31.422864	0	0	75.194436	88.510069	400	27.407259	582.1
2024-05-16 07:00:00	EV-101	rare_user	University	38.133401	92.432446	231	0	33.124149	0	0	68.995541	32.974818	240	26.184305	357.9
2024-11-03 05:00:00	EV-103	daily_user	Downtown	27.733214	97.725195	232	0	28.623026	0	0	69.699499	97.496097	240	29.629119	473.5
2024-10-08 13:00:00	EV-101	rare_user	University	39.563849	91.640074	339	0	35.061016	1,452.722981	290.544596	71.350504	90.919601	240	29.775182	330.8
2024-05-09 06:00:00	EV-101	rare_user	University	52.043581	92.470606	226	0	29.357494	0	0	69.059168	33.55887	240	27.021565	333.1
2024-05-04 00:00:00	EV-101	rare_user	University	27.354465	92.498993	222	0	30.741445	0	0	76.891032	33.967615	240	27.647431	385.7
2024-10-28 01:00:00	EV-101	rare_user	University	80	91.533714	354	0	29.857121	0	0	67.695871	89.322643	240	27.421453	369.6
2024-02-09 00:00:00	EV-104	heavy_user	University	27.283924	92.980772	126	0	31.896449	0	0	72.868167	31.923035	400	28.704096	397
2024-01-18 15:00:00	EV-104	heavy_user	Industrial	38.811502	93.168783	120	0	49.463271	1,566.345688	313.269138	69.47649	34.810253	400	28.05078	512.4
2024-07-03 21:00:00	EV-103	daily_user	Downtown	36.289291	98.580092	145	0	30.158153	0	0	70.081264	36.840872	240	26.183978	469.2
2024-11-22 08:00:00	EV-102	moderate_user	Harbor	85.621398	87.187261	413	0	29.859281	0	0	72.826016	86.391779	400	30.008236	340.5
2024-04-09 00:00:00	EV-101	rare_user	University	80	92.635317	204	0	31.390452	0	0	68.50104	36.071238	240	30.623426	407.1
2024-09-25 17:00:00	EV-104	heavy_user	Industrial	90.783502	90.955519	2	0	30.608562	1,617.998501	323.5997	74.337765	70.510045	400	29.80347	472.6
2024-04-26 08:00:00	EV-101	rare_user	University	27.168231	92.541157	216	0	30.008706	0	0	69.04732	34.645849	240	28.559711	334.7
2024-04-11 04:00:00	EV-104	heavy_user	Industrial	24.529919	92.434022	144	0	27.437413	0	0	72.897989	93.467936	400	31.129643	560
2024-05-29 05:00:00	EV-102	moderate_user	Harbor	26.643114	88.521175	329	0	33.28925	0	0	67.594873	31.210627	400	26.766022	390
2024-04-30 01:00:00	EV-102	moderate_user	Harbor	85.756668	88.74021	316	0	26.918392	0	0	67.287995	33.658578	400	31.964664	416.3
2024-07-28 09:00:00	EV-101	rare_user	University	24.751717	92.033886	285	0	30.402535	0	0	70.174558	96.922773	240	31.415006	332.1
2024-12-22 18:00:00	EV-103	daily_user	Downtown	46.612075	97.378686	268	0	29.9978	0	0	72.301915	93.645965	240	29.467684	431.9
2024-07-12 00:00:00	EV-102	moderate_user	Harbor	39.19949	88.18983	350	0	32.339806	0	0	65.414656	97.49745	400	25.907733	414.9
2024-01-11 08:00:00	EV-103	daily_user	Downtown	80	99.799892	21	0	29.962025	0	0	72.476028	50.226779	240	30.332939	414.4
2024-01-15 00:00:00	EV-102	moderate_user	Harbor	48.934082	91.874108	266	0	32.431694	0	0	73.266293	43.268455	400	31.188393	414.3
2024-11-01 03:00:00	EV-103	daily_user	Downtown	59.032547	97.739661	231	0	30.286302	0	0	70.739048	97.661108	240	29.930628	480
2024-01-18 06:00:00	EV-104	heavy_user	Industrial	60.283924	93.173312	120	0	49.803239	3,034.170299	606.83406	85.438495	34.932604	400	28.165846	552.8
2024-12-05 01:00:00	EV-103	daily_user	Downtown	34.217732	97.503088	255	0	33.851531	0	0	65.577408	95.042651	240	25.026737	471.4
2024-08-26 07:00:00	EV-104	heavy_user	Industrial	23.270996	91.227091	184	0	60.702216	1,604.441838	320.888368	71.31644	74.81208	400	31.978217	565.1
2024-03-06 06:00:00	EV-103	daily_user	Downtown	69.526104	99.415976	60	0	33.002531	1,420.63154	284.126308	72.899249	46.021866	240	29.425987	427.9
2024-06-19 14:00:00	EV-103	daily_user	Downtown	58.30266	98.679942	135	0	29.936927	0	0	66.746083	37.941269	240	28.242059	409.6
2024-04-21 20:00:00	EV-104	heavy_user	Industrial	20.035681	92.341162	147	0	29.599884	0	0	68.111704	92.048076	400	27.938855	558.3
2024-06-11 18:00:00	EV-101	rare_user	University	52.160685	92.288023	251	0	36.680882	2,967.559021	593.511804	86.114563	30.75506	240	30.036183	330.2
2024-02-05 09:00:00	EV-104	heavy_user	Industrial	48.11844	93.014713	125	0	50.150734	2,904.82988	580.965976	85.033881	32.522943	400	29.78869	508.1
2024-04-05 16:00:00	EV-104	heavy_user	Industrial	54.811822	92.479879	143	0	52.36634	2,939.836069	587.967214	85.758179	94.10626	400	null	492.7
2024-02-12 17:00:00	EV-104	heavy_user	Industrial	null	92.948747	127	0	50.003236	1,490.027002	298.0054	68.980071	31.458812	400	27.596879	519.6
2024-03-01 02:00:00	EV-102	moderate_user	Harbor	52.367292	91.52577	288	0	33.823049	0	0	70.947131	39.324644	400	29.892158	409
2024-10-25 07:00:00	EV-101	rare_user	University	80	91.548659	352	0	30.521797	0	0	71.333102	89.547597	240	27.758839	360.4
2024-06-28 07:00:00	EV-103	daily_user	Downtown	39.457125	98.619058	141	0	32.985574	1,502.816829	300.563366	67.436482	37.273659	240	27.001675	409.1
2024-07-11 17:00:00	EV-101	rare_user	University	38.852361	92.124753	273	0	26.802567	0	0	74.090565	98.281914	240	26.421605	313.2
2024-10-30 12:00:00	EV-103	daily_user	Industrial	58.393797	97.75078	230	0	28.986133	0	0	72.180272	97.769146	240	30.167487	519.8
2024-02-11 21:00:00	EV-102	moderate_user	Harbor	37.347964	91.664365	279	0	28.561091	0	0	65.971418	40.920452	400	26.153021	387.5
2024-08-12 22:00:00	EV-102	moderate_user	Harbor	64.39017	87.950235	365	0	27.248089	0	0	69.088899	94.899169	400	27.191625	384.2
2024-08-27 01:00:00	EV-103	daily_user	Downtown	57.924842	98.201497	184	0	29.05098	0	0	69.260284	32.710307	240	25.405019	492.8
2024-08-23 23:00:00	EV-104	heavy_user	Industrial	51.349303	91.244061	184	0	27.480633	0	0	68.116108	74.97914	400	25.671223	573.2

End of preview.

Attribution: original hourly telemetry derived from Kaggle dataset "EV Sensors: Driving Pattern Diagnostics (2020-24)" by Kunal Mehra (https://www.kaggle.com/datasets/kunalm95/ev-sensors-driving-pattern-diagnostics-2020-24), CC BY 4.0. Dimension enrichment and planted sensor faults are synthetic (deterministic, seed 7), also CC BY 4.0.

Bronze Layer — EV Sensor Arrival Stream (download this and work from here)

You are holding what a fleet operator's data engineer receives: raw sensor data, exactly as it arrives — and real arrival data is never clean. Everything you need for a data-cleaning exercise is in this folder. You do not need anything else.

file	what it is
`ingest_stream_2024.csv`	the demo/hands-on file: 35,363 hourly sensor rows, one calendar year (2024), four EVs, delivered shuffled in arrival order — with realistic sensor faults planted
`raw/daily_user.csv` … `raw/rare_user.csv`	the original Kaggle telemetry files (Kunal Mehra, CC BY 4.0), untouched — the "published clean" counterpart
`fault_census.json`	answer key: exact counts and ranges of every fault planted
`make_bronze_stream.py`	deterministic generator (seed 7) producing `ingest_stream_2024.csv` from the clean files — fully reproducible
`README.md`	this file

Schema of `ingest_stream_2024.csv` (17 columns)

column	meaning
`timestamp`	hourly, 2024
`vehicle_id`	EV-101 rare · EV-102 moderate · EV-103 daily · EV-104 heavy user
`user_profile`	usage profile of that vehicle
`district`	location that hour (Downtown · Harbor · University · Industrial), 82 % home district
`SOC`	state of charge %, valid range 0–100
`SOH`	state of health %, valid range 0–100
`Charging_Cycles`	lifetime full-charge cycles
`charging_now`	1 when SOC rises vs previous hour (derived)
`Battery_Temp`, `Motor_Temp`	°C
`Motor_RPM`, `Motor_Torque`	rpm · Nm
`Brake_Pad_Wear`	%, valid range 0–100
`Charging_Voltage`	V
`Tire_Pressure`	PSI by contract (valid ≈ 25–45)
`DTC`	0 = no fault, else diagnostic trouble code string
`grid_carbon_g_per_kwh`	district grid carbon intensity, gCO₂/kWh at that hour

The faults planted (what your cleaning demo will find)

fault	how to detect	count planted
missing readings	empty cell in `SOC`, `Battery_Temp`, `Tire_Pressure`	1,250 (434 + 398 + 418)
`SOC` out of bound	value > 100	276
`SOH` out of bound	value > 100	174
`Battery_Temp` out of bound	value < −25 °C	172
`Brake_Pad_Wear` out of bound	value > 100	209
`Tire_Pressure` zero (dead sensor)	value = 0	24
unit mix-up: pressure in kPa	value > 60 → divide by 6.895	517
duplicate rows (sensor retry/double-write)	identical full rows	419

After a correct cleaning pass you should have 34,944 rows — that number is in fault_census.json too, so participants can self-check.

No SOC outlier exceeds 145 and no SOH outlier exceeds 104.5 by design, so simple range rules are unambiguous. Cleaning policy questions to raise in class: fix, flag, or drop? for each fault type — a wrong indicator is worse than no indicator.

What the cleaned data enables (gold layer, built live)

battery SOH decline per vehicle/profile → fleet replacement decisions
DTC precursor patterns by usage profile → predictive maintenance
emissions per vehicle-day via grid_carbon_g_per_kwh × kWh charged → the "same EV, very different footprint by district" insight (Industrial ≈ 577 vs University ≈ 363 g/kWh on the full 2020–24 silver data)

Licensing / provenance

Original hourly telemetry: Kaggle "EV Sensors: Driving Pattern Diagnostics (2020-24)" by Kunal Mehra, CC BY 4.0. The arrival stream, dimension enrichment and the planted faults are synthetic additions under the same license (CC BY 4.0, attribution required). Absolute row-level values are simulated — treat as realistic-pattern teaching data, not ground truth.

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Total file size:

32.6 MB

Bronze Layer — EV Sensor Arrival Stream (download this and work from here)

Contents

Schema of ingest_stream_2024.csv (17 columns)

The faults planted (what your cleaning demo will find)

What the cleaned data enables (gold layer, built live)

Licensing / provenance

Schema of `ingest_stream_2024.csv` (17 columns)