--- license: cc-by-4.0 language: - en task_categories: - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - csv - africa - niger - official-statistics - open-data configs: - config_name: default data_files: - split: train path: data/train-00000-of-00001.parquet pretty_name: "Future Displacement Forecasts | Africa (Niger official open data)" --- # Future Displacement Forecasts | Africa (Niger official open data) 241 rows - 1 Africa country - 2010-2023 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) ![rows](https://img.shields.io/badge/rows-241-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![years](https://img.shields.io/badge/years-2010-2023-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey) ## TL;DR This dataset packages one official `CSV` resource from **Niger** as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo. ## About the source - **Source:** [Future Displacement Forecasts](https://data.humdata.org/dataset/drc-displacement-forecasts) - **Publisher:** Danish Refugee Council - **Resource:** [Foresight historical forecasts](https://data.humdata.org/dataset/dd000cd0-5757-484f-9df8-4aee6c7362c5/resource/63418dc7-e3a9-41bc-980f-631b39365681/download/foresight-forecasts-historical.csv) - **Format:** `CSV` - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Packaging mode:** `indicator_long` ## Geographic coverage 1 Africa country: | Country | Rows | First year | Last year | Name | |---------|-----:|-----------:|----------:|------| | `NER` | 241 | 2010 | 2023 | `Niger` | ## Indicators or Resource Contents - `future-displacement-forecasts-e3b760f7` - Future Displacement Forecasts ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `string` | Stable indicator identifier. | `future-displacement-forecasts-e3b760f7` | | `indicator_name` | `string` | Human-readable indicator name. | `Future Displacement Forecasts` | | `country_iso3` | `string` | ISO3 country code. | `NER` | | `country_name` | `string` | Country name. | `Niger` | | `year` | `Int64` | Observation year. | `2010` | | `value` | `float64` | Numeric observation value. | `3133594.0` | | `unit` | `string` | Measurement unit, when available. | `source_units_unspecified` | | `dimension_country_name` | `string` | Source dimension. | `Afghanistan` | | `dimension_country_code` | `string` | Source dimension. | `AFG` | | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `` | | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `` | | `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `` | | `source_provider` | `category` | Publishing organization. | `Danish Refugee Council` | | `source_dataset` | `category` | Source package title. | `Future Displacement Forecasts` | | `source_resource` | `category` | Source resource title. | `Foresight historical forecasts` | | `source_package_id` | `category` | CKAN package UUID. | `dd000cd0-5757-484f-9df8-4aee6c7362c5` | | `source_resource_id` | `category` | CKAN resource UUID. | `63418dc7-e3a9-41bc-980f-631b39365681` | | `source_url` | `category` | Original source resource URL. | `https://data.humdata.org/dataset/dd000cd0-5757-484f-9df8-4aee6c7362c5/re` | | `license_id` | `category` | Source license identifier. | `cc-by` | | `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-14T00:35:21Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-niger-future-displacement-forecasts-e3b760f7") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python sample_country = df[df["country_iso3"] == "NER"] ``` ### Work with indicators ```python if "indicator_id" in df.columns: print(df["indicator_id"].value_counts().head()) sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns]) ``` ## Citation ```bibtex @misc{electric_sheep_africa_africa_niger_future_displacement_forecasts_e3b760f7_2023, title = {Future Displacement Forecasts | Africa (Niger official open data)}, author = {Danish Refugee Council}, year = {2023}, url = {https://data.humdata.org/dataset/drc-displacement-forecasts}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-niger-future-displacement-forecasts-e3b760f7}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Original data (c) Danish Refugee Council. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging. ## About Electric Sheep Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica) --- Provenance: ingested 2026-08-14 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/dd000cd0-5757-484f-9df8-4aee6c7362c5/resource/63418dc7-e3a9-41bc-980f-631b39365681/download/foresight-forecasts-historical.csv