electricsheepafrica/africa-chad-chad-environment-b0d4e8d7

Chad Environment | Africa (World Bank Group)

4,710 rows - 1 Africa country/area - 1960-2025 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 4,710 rows from World Bank Group, covering Chad Environment. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends.

Source-provided context: csv containing Environment indicators Indicators: Access to clean fuels and technologies for cooking, Access to electricity, Adjusted net savings, Adjusted savings, Agricultural land, Annual freshwater withdrawals, Aquaculture production, Arable land, Average precipitation in depth, Bird species, Capture fisheries production, Carbon dioxide, Carbon intensity of GDP, Coal rents, Droughts, Electricity production from oil, Electricity production from renewable sources, Energy intensity level of primary energy, F-gases emissions from Industrial Processes, Fish species, Forest area, Forest rents, Land area, Land area where elevation is below 5 meters, Level of water stress, Mammal species, Methane, Mineral rents, Mortality rate attributed to household and ambient air pollution, Mortality rate attributed to unintentional poisoning, Mortality rate attributed to unsafe water, Natural gas rents, Nitrous oxide, Oil rents, PM2.5 air pollution, PM2.5 pollution, People practicing open defecation.

How To Read This Dataset

• One row means: one indicator observation for one geography, time period, and optional source dimensions.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3, year, indicator_id.

Coverage

Dimension	Value
Rows	4,710
Countries/areas	1
First period	1960
Last period	2025
Indicators	1
Columns	22
Source format	CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
TCD	4,710	1960	2025	Chad

Indicators, Variables, Or Resource Contents

• chad-environment-68d9b5bf - Chad - Environment(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	chad-environment-68d9b5bf
indicator_name	string	Human-readable indicator name.	Chad - Environment
country_iso3	string	ISO3 country or area code.	TCD
country_name	string	Country or area name.	Chad
year	int64	Observation year.	2023
value	double	Numeric observation value.	39.9756194409149
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_country_name	string	Source dimension retained during long-form normalization.	Chad
dimension_country_iso3	string	Source dimension retained during long-form normalization.	TCD
dimension_indicator_name	string	Source dimension retained during long-form normalization.	Agricultural land (% of land area)
dimension_indicator_code	string	Source dimension retained during long-form normalization.	AG.LND.AGRI.ZS
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	World Bank Group
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Chad - Environment
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Environment Indicators for Chad
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	11ef69e6-ef63-4a3f-901f-23541feb59a3
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	d1742a17-567e-4126-ab93-9aa0a5d60465
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/11ef69e6-ef63-4a3f-901f-23541feb59a3...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T22:51:43Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-chad-chad-environment-b0d4e8d7")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "TCD"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: World Bank Group
• Publisher: World Bank Group
• Portal: https://data.humdata.org
• Resource: Environment Indicators for Chad
• License: CC BY 4.0
• Retrieved/generated: 2026-08-10T23:31:23Z
• Hugging Face repo: electricsheepafrica/africa-chad-chad-environment-b0d4e8d7

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Analyze seasonal or annual patterns
• Join with agriculture or health data
• Map geographic exposure
• Build time-series views and period-over-period comparisons
• Pivot to geography x period or indicator x period matrices
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_chad_chad_environment_b0d4e8d7_2025,
  title        = {Chad Environment | Africa (World Bank Group)},
  author       = {World Bank Group},
  year         = {2025},
  url          = {https://data.humdata.org/dataset/world-bank-environment-indicators-for-chad},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-chad-chad-environment-b0d4e8d7}}
}

License

Released under CC BY 4.0.

Original data is published by World Bank Group. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/world-bank-environment-indicators-for-chad