Datasets:

jeyanthiii
/

sample_ev_data_csv

Unnamed: 0 stringdate 2020-01-01 00:00:00 2024-12-29 23:00:00	SOC float64 20 80	SOH float64 81.9 100	Charging_Cycles int64 0 575	Battery_Temp float64 21.8 51.2	Motor_RPM float64 0 4.73k	Motor_Torque float64 0 946	Motor_Temp float64 57.2 110	Brake_Pad_Wear float64 30 100	Charging_Voltage int64 240 240	Tire_Pressure float64 23.2 35
2020-01-01 00:00:00	20	99.999762	0	27.64196	0	0	71.031956	100	240	34.994742
2020-01-01 01:00:00	20	99.999513	0	31.252744	0	0	62.665014	100	240	34.989558
2020-01-01 02:00:00	20	99.999259	0	33.319184	0	0	69.339019	100	240	34.984385
2020-01-01 03:00:00	20	99.999009	0	30.226656	0	0	65.951953	100	240	34.977987
2020-01-01 04:00:00	20	99.998772	0	32.398368	0	0	73.412878	100	240	34.971496
2020-01-01 05:00:00	20	99.998541	0	31.826829	0	0	68.725799	100	240	34.96548
2020-01-01 06:00:00	80	99.997868	1	32.817731	3,061.327358	612.265472	85.466551	99.981949	240	34.959095
2020-01-01 07:00:00	70.048832	99.99739	1	31.144145	1,565.09169	313.018338	68.417758	99.962741	240	34.953818
2020-01-01 08:00:00	70.048832	99.997152	1	30.697869	0	0	70.370147	99.962741	240	34.948597
2020-01-01 09:00:00	70.048832	99.9969	1	25.190062	0	0	75.352445	99.962741	240	34.943708
2020-01-01 10:00:00	70.048832	99.996667	1	31.791154	0	0	65.65119	99.962741	240	34.937264
2020-01-01 11:00:00	70.048832	99.996436	1	29.746537	0	0	71.92564	99.962741	240	34.931731
2020-01-01 12:00:00	70.048832	99.996195	1	30.029676	0	0	65.708327	99.962741	240	34.926014
2020-01-01 13:00:00	70.048832	99.995961	1	30.847149	0	0	73.528422	99.962741	240	34.920295
2020-01-01 14:00:00	70.048832	99.99571	1	30.384648	0	0	65.806109	99.962741	240	34.913321
2020-01-01 15:00:00	70.048832	99.995456	1	32.445369	0	0	68.303614	99.962741	240	34.90705
2020-01-01 16:00:00	70.048832	99.995216	1	29.136962	0	0	67.962552	99.962741	240	34.900088
2020-01-01 17:00:00	58.851833	99.994714	1	30.771368	2,934.612738	586.922548	90.38458	99.9437	240	34.893281
2020-01-01 18:00:00	48.297747	99.994206	1	30.788072	3,029.577475	605.915495	81.783504	99.926028	240	34.887615
2020-01-01 19:00:00	48.297747	99.993959	1	28.735651	0	0	70.299899	99.926028	240	34.88261
2020-01-01 20:00:00	48.297747	99.993728	1	30.843208	0	0	68.475627	99.926028	240	34.877579
2020-01-01 21:00:00	48.297747	99.993474	1	32.914508	0	0	68.800997	99.926028	240	34.870709
2020-01-01 22:00:00	48.297747	99.993226	1	29.713472	0	0	66.235059	99.926028	240	34.864482
2020-01-01 23:00:00	48.297747	99.992988	1	29.591401	0	0	71.498327	99.926028	240	34.859355
2020-01-02 00:00:00	48.297747	99.992738	1	30.361993	0	0	66.716411	99.926028	240	34.853455
2020-01-02 01:00:00	48.297747	99.992498	1	29.960965	0	0	67.405997	99.926028	240	34.848434
2020-01-02 02:00:00	48.297747	99.992245	1	30.805779	0	0	71.415189	99.926028	240	34.843612
2020-01-02 03:00:00	48.297747	99.992004	1	29.663082	0	0	69.68106	99.926028	240	34.837402
2020-01-02 04:00:00	48.297747	99.991752	1	30.192304	0	0	72.073543	99.926028	240	34.831663
2020-01-02 05:00:00	48.297747	99.991516	1	27.238412	0	0	70.444481	99.926028	240	34.825243
2020-01-02 06:00:00	36.856157	99.991032	1	32.153647	3,008.177347	601.635469	92.245341	99.906504	240	34.818547
2020-01-02 07:00:00	26.707822	99.990572	1	32.230554	1,493.889592	298.777918	68.450204	99.88768	240	34.812896
2020-01-02 08:00:00	26.707822	99.990327	1	30.111421	0	0	76.41577	99.88768	240	34.805796
2020-01-02 09:00:00	26.707822	99.990083	1	29.950865	0	0	75.905261	99.88768	240	34.800542
2020-01-02 10:00:00	26.707822	99.989845	1	28.896642	0	0	69.186044	99.88768	240	34.794812
2020-01-02 11:00:00	26.707822	99.989612	1	31.677181	0	0	70.834043	99.88768	240	34.789724
2020-01-02 12:00:00	26.707822	99.989359	1	31.882098	0	0	69.076327	99.88768	240	34.78396
2020-01-02 13:00:00	26.707822	99.989105	1	29.382767	0	0	64.155844	99.88768	240	34.779111
2020-01-02 14:00:00	26.707822	99.988871	1	29.485142	0	0	66.54115	99.88768	240	34.772817
2020-01-02 15:00:00	26.707822	99.988638	1	33.474741	0	0	71.033171	99.88768	240	34.767436
2020-01-02 16:00:00	26.707822	99.988388	1	29.449857	0	0	65.722069	99.88768	240	34.761476
2020-01-02 17:00:00	80	99.987697	2	28.4743	3,001.21731	600.243462	84.537894	99.869728	240	34.755247
2020-01-02 18:00:00	69.073557	99.987199	2	30.265789	1,649.338193	329.867639	73.228002	99.853454	240	34.749149
2020-01-02 19:00:00	69.073557	99.986968	2	30.892475	0	0	69.292	99.853454	240	34.743455
2020-01-02 20:00:00	69.073557	99.986727	2	29.075893	0	0	68.975973	99.853454	240	34.737138
2020-01-02 21:00:00	69.073557	99.986495	2	30.832338	0	0	72.441918	99.853454	240	34.731092
2020-01-02 22:00:00	69.073557	99.986249	2	31.875406	0	0	71.716353	99.853454	240	34.724774
2020-01-02 23:00:00	69.073557	99.986001	2	27.29669	0	0	65.764462	99.853454	240	34.717965
2020-01-03 00:00:00	69.073557	99.985763	2	30.636546	0	0	70.121522	99.853454	240	34.712053
2020-01-03 01:00:00	69.073557	99.985519	2	31.733172	0	0	69.569328	99.853454	240	34.70672
2020-01-03 02:00:00	69.073557	99.985278	2	30.567075	0	0	73.846862	99.853454	240	34.699835
2020-01-03 03:00:00	69.073557	99.985035	2	30.127463	0	0	71.631592	99.853454	240	34.69428
2020-01-03 04:00:00	69.073557	99.984794	2	31.425138	0	0	72.699775	99.853454	240	34.687863
2020-01-03 05:00:00	69.073557	99.984553	2	27.584565	0	0	68.933735	99.853454	240	34.68254
2020-01-03 06:00:00	59.316078	99.984082	2	27.797612	2,993.481694	598.696339	84.583701	99.835576	240	34.675744
2020-01-03 07:00:00	49.121428	99.983591	2	25.314276	2,990.379293	598.075859	84.04611	99.817689	240	34.669435
2020-01-03 08:00:00	49.121428	99.983349	2	32.99828	0	0	69.791819	99.817689	240	34.664268
2020-01-03 09:00:00	49.121428	99.983113	2	29.304472	0	0	68.64374	99.817689	240	34.658906
2020-01-03 10:00:00	49.121428	99.982865	2	28.259694	0	0	67.053193	99.817689	240	34.653573
2020-01-03 11:00:00	49.121428	99.982629	2	30.76943	0	0	71.493347	99.817689	240	34.647365
2020-01-03 12:00:00	49.121428	99.982384	2	32.699434	0	0	72.350073	99.817689	240	34.641369
2020-01-03 13:00:00	49.121428	99.982151	2	30.015734	0	0	71.494019	99.817689	240	34.635697
2020-01-03 14:00:00	49.121428	99.981906	2	30.178586	0	0	63.534103	99.817689	240	34.628806
2020-01-03 15:00:00	49.121428	99.981665	2	30.450945	0	0	71.239649	99.817689	240	34.623784
2020-01-03 16:00:00	49.121428	99.981431	2	31.836094	0	0	70.671692	99.817689	240	34.617626
2020-01-03 17:00:00	38.419084	99.980956	2	29.575837	1,527.461095	305.492219	68.236457	99.801064	240	34.611865
2020-01-03 18:00:00	27.929115	99.980477	2	31.006854	1,401.478164	280.295633	69.033404	99.782268	240	34.605096
2020-01-03 19:00:00	27.929115	99.980223	2	31.708016	0	0	72.607881	99.782268	240	34.600118
2020-01-03 20:00:00	27.929115	99.979987	2	30.32353	0	0	70.801541	99.782268	240	34.594099
2020-01-03 21:00:00	27.929115	99.979747	2	32.17188	0	0	66.593868	99.782268	240	34.586976
2020-01-03 22:00:00	27.929115	99.979504	2	27.107839	0	0	66.039066	99.782268	240	34.580261
2020-01-03 23:00:00	27.929115	99.979264	2	27.748869	0	0	70.490824	99.782268	240	34.574194
2020-01-04 00:00:00	27.929115	99.979026	2	32.994085	0	0	66.907124	99.782268	240	34.567895
2020-01-04 01:00:00	27.929115	99.978774	2	28.969007	0	0	71.015506	99.782268	240	34.561692
2020-01-04 02:00:00	27.929115	99.978535	2	31.120109	0	0	73.504153	99.782268	240	34.556374
2020-01-04 03:00:00	27.929115	99.978283	2	30.575329	0	0	68.186244	99.782268	240	34.550368
2020-01-04 04:00:00	27.929115	99.978043	2	27.622083	0	0	72.143366	99.782268	240	34.544468
2020-01-04 05:00:00	27.929115	99.977804	2	31.048189	0	0	71.742582	99.782268	240	34.537269
2020-01-04 06:00:00	27.929115	99.977562	2	31.105263	0	0	65.698678	99.782268	240	34.53133
2020-01-04 07:00:00	27.929115	99.977325	2	32.628882	0	0	69.70821	99.782268	240	34.525841
2020-01-04 08:00:00	27.929115	99.977078	2	32.146028	0	0	68.589464	99.782268	240	34.520073
2020-01-04 09:00:00	27.929115	99.976842	2	28.395819	0	0	67.343663	99.782268	240	34.513416
2020-01-04 10:00:00	80	99.976159	3	28.838487	1,514.587962	302.917592	66.77915	99.76535	240	34.506448
2020-01-04 11:00:00	69.240256	99.975677	3	33.494493	2,930.405701	586.08114	82.410648	99.746369	240	34.501034
2020-01-04 12:00:00	69.240256	99.975442	3	29.87102	0	0	75.066546	99.746369	240	34.495434
2020-01-04 13:00:00	69.240256	99.975203	3	31.541275	0	0	68.369098	99.746369	240	34.490514
2020-01-04 14:00:00	69.240256	99.974966	3	31.97477	0	0	70.192544	99.746369	240	34.485237
2020-01-04 15:00:00	58.402782	99.974479	3	29.771778	1,497.593519	299.518704	68.018813	99.729204	240	34.479584
2020-01-04 16:00:00	47.905817	99.974007	3	27.159042	1,525.640676	305.128135	69.811856	99.711833	240	34.474745
2020-01-04 17:00:00	36.725353	99.973511	3	27.55846	2,951.051161	590.210232	84.348185	99.693925	240	34.467576
2020-01-04 18:00:00	36.725353	99.973267	3	31.188474	0	0	69.734071	99.693925	240	34.462117
2020-01-04 19:00:00	36.725353	99.973017	3	29.238343	0	0	70.035402	99.693925	240	34.455896
2020-01-04 20:00:00	36.725353	99.972767	3	30.173758	0	0	71.704446	99.693925	240	34.45048
2020-01-04 21:00:00	36.725353	99.972533	3	31.725747	0	0	69.165982	99.693925	240	34.444366
2020-01-04 22:00:00	36.725353	99.972297	3	30.344824	0	0	75.013064	99.693925	240	34.437574
2020-01-04 23:00:00	36.725353	99.972052	3	27.982392	0	0	66.229886	99.693925	240	34.432676
2020-01-05 00:00:00	36.725353	99.971818	3	28.665349	0	0	67.887551	99.693925	240	34.426864
2020-01-05 01:00:00	36.725353	99.971565	3	31.293345	0	0	60.863852	99.693925	240	34.42114
2020-01-05 02:00:00	36.725353	99.971327	3	28.586318	0	0	66.679047	99.693925	240	34.416016
2020-01-05 03:00:00	36.725353	99.971096	3	30.271599	0	0	70.14456	99.693925	240	34.408912

End of preview. Expand in Data Studio

⚡ EV Fleet Sensor Data — Databricks Medallion Case Study

Raw source CSVs for an end-to-end Databricks medallion architecture (Bronze → Silver → Gold) case study: a deliberately messy EV sensor stream is cleaned, quality-gated, and distilled into decision-grade insights — battery health ranking, DTC risk, charger siting, and a clean-charging window.

Question data: 1_car_telemetry_2024.csv — 35,363 rows of 2024 telemetry with planted faults
Answer key: the four archive_2020_2024_*.csv files — a clean ~175K-row sensor archive (2020–2024)
The messy 2024 stream was derived from the archive (faults planted, seed = 7), so every fault is verifiable. The two sources intentionally overlap in 2024 (stream = question, archive = ground truth).

Dataset Structure

🔴 Feed 1 — Car telemetry (the messy stream)

1_car_telemetry_2024.csv — hourly readings, 4 vehicles, calendar year 2024.

Column	Type	Description
`timestamp`	datetime	Hour of reading
`vehicle_id`	string	`EV-101` … `EV-104`
`user_profile`	string	`rare_user` / `daily_user` / `moderate_user` / `heavy_user`
`district`	string	Home district: University, Downtown, Harbor, Industrial
`SOC`	float	State of charge, %
`SOH`	float	State of health (battery), %
`Charging_Cycles`	int	Lifetime charge cycles
`charging_now`	int	0/1 — currently charging
`Battery_Temp`	float	Battery temperature, °C
`Motor_RPM`	int	Motor speed
`Motor_Torque`	float	Motor torque, Nm
`Motor_Temp`	float	Motor temperature, °C
`Brake_Pad_Wear`	float	Brake pad wear, %
`Charging_Voltage`	float	Charging voltage, V
`Tire_Pressure`	float	Tire pressure — ⚠️ planted unit faults (kPa vs PSI)
`DTC`	int	1 = diagnostic trouble code active
`grid_carbon_g_per_kwh`	float	Grid carbon intensity at that hour

Planted fault census: exact duplicates (419, to be dropped), kPa→PSI unit errors (fix), out-of-bounds values (fix + flag), missing readings (flag). Silver-layer policy: only duplicates die — the row always survives; the faulty value is nulled.

🔌 Feed 2 — Charging sessions

2_charging_sessions.csv — 484 completed sessions.

Column	Description
`session_id`, `station_id`	Identifiers
`district`	Where the session happened (drives charger-siting analysis)
`start_time`, `end_time`	Session window
`energy_kwh_source_analog`, `power_kw_analog`	Energy delivered / power draw
`soc_start_pct`, `soc_end_pct`	Battery % before → after
`battery_kwh`, `vehicle_model`	Vehicle pack size and model
`station_temp_c`	Station temperature
`status`	Session status

⚡ Feed 3 — Grid carbon intensity

3_grid_carbon_hourly.csv — 168 rows (7 days × 24 h): date, hour, carbon_g_per_kwh. Midnight peaks near ~~911 gCO₂/kWh; midday (~~12:00) drops to ~319.

🟢 The clean archive (ground truth)

Four files, one per user profile, each with 43,800 rows (24 h × 365 d × 5 yrs, 2020–2024):

archive_2020_2024_{rare,daily,moderate,heavy}_user.csv — same sensor columns as the stream (leading index column, no timestamps/district).

File	Vehicle	Profile
`archive_2020_2024_rare_user.csv`	Renault Zoe (52 kWh)	EV-101
`archive_2020_2024_daily_user.csv`	Hyundai Ioniq 5 (77 kWh)	EV-103
`archive_2020_2024_moderate_user.csv`	VW ID.4 (82 kWh)	EV-102
`archive_2020_2024_heavy_user.csv`	Ford F-150 Lightning (131 kWh)	EV-104

Example decisions this data supports

Transparent, verifiable arithmetic (no black-box model):

Risk score = soh_decay_pct_per_year × 20 + dtc_rate_pct × 10, thresholds 60 (act) / 30 (watch) → EV-102 scores 54.1 (decay 1.958 %/yr + 654 DTC events) → service first
Charger siting = total_co2_g ÷ vehicle_days → Industrial district worst at 43,311 gCO₂/vehicle-day across 86 sessions → site the next charger there
Charging window = cleanest vs dirtiest hour → shift charging to ~12:00: ~319 vs ~911 gCO₂/kWh (2.86× cleaner, ~592 g saved per kWh shifted)

How it was used

The files are ingested as-is into a Databricks Free Edition lakehouse:

Bronze (raw, immutable)  →  Silver (cleaned, quality gate: 35,363 − 419 dupes = 34,944 rows)
                         →  Gold (6 decision tables: soh_trend, dtc_risk_rank, co2_per_vehicle_day,
                                  grid_carbon_by_district_hour, fleet_worry_list, final_decision)

Usage

from huggingface_hub import hf_hub_download
import pandas as pd

path = hf_hub_download(repo_id="jeyanthiii/sample_ev_data_csv",
                       filename="1_car_telemetry_2024.csv",
                       repo_type="dataset")
df = pd.read_csv(path)

Notes

Faults are synthetic and intentional (teaching/demo dataset); the archive is the reference truth.
2024 rows exist in both stream and archive by design — that overlap is what makes the quality gate provable.
Local backup of this folder is kept at ~/Documents/work/temp/cl_demo/data/.

License

CC-BY-4.0 — free to use with attribution.

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