Upload dataset nigerian_energy_and_utilities_carbon_footprint
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README.md
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---
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license: gpl
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dataset_name: nigerian_energy_and_utilities_carbon_footprint
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pretty_name: Nigerian Energy & Utilities – Carbon Footprint
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size_categories: [10K<n<1M, 1M<n<10M]
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task_categories: [time-series-forecasting, tabular-regression, anomaly-detection]
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tags: [nigeria, energy, utilities, power, grid, smart-meter, renewables]
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language: [en]
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created: 2025-10-11
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---
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# Nigerian Energy & Utilities – Carbon Footprint
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Regional CO2 intensity (g/kWh) with generation mix shares (gas, hydro, solar, wind).
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- **[category]** Renewable & Environmental
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- **[rows]** ~100,000
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- **[formats]** CSV + Parquet (snappy)
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- **[geography]** Nigeria (DisCos, substations, plants)
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## Schema
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| column | dtype |
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|---|---|
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| timestamp | object |
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| region | object |
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| co2_g_per_kwh | float64 |
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| gas_share_pct | float64 |
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| hydro_share_pct | float64 |
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| solar_share_pct | float64 |
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| wind_share_pct | float64 |
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## Usage
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```python
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import pandas as pd
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df = pd.read_parquet('data/nigerian_energy_and_utilities_carbon_footprint/nigerian_energy_and_utilities_carbon_footprint.parquet')
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df.head()
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```
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```python
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from datasets import load_dataset
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ds = load_dataset('electricsheepafrica/nigerian_energy_and_utilities_carbon_footprint')
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ds
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```
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## Notes
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- Data generated with Nigeria-specific parameters (DisCos, tariff bands, 50 Hz grid)
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- Time-of-use shapes and seasonal/weather effects included where applicable
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- Values are internally consistent (e.g., kWh ~ kW*h; voltage/current ~ power)
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nigerian_energy_and_utilities_carbon_footprint.csv
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The diff for this file is too large to render.
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nigerian_energy_and_utilities_carbon_footprint.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:9633adfdec9904983a388e334db7661a28ab556e752b5e551d7c16cc2917bbf4
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size 1697818
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