Add README.md

README.md

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 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- displacement
+- internally-displaced-persons-idp
+- natural-disasters
+- tza
+pretty_name: "United Republic of Tanzania - Internal Displacements (New Displacements) – IDPs"
 dataset_info:
-  features:
-  - name: iso3
-    dtype: string
-  - name: country_name
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: start_date
-    dtype: timestamp[ns]
-  - name: start_date_accuracy
-    dtype: string
-  - name: end_date
-    dtype: timestamp[ns]
-  - name: end_date_accuracy
-    dtype: string
-  - name: event_name
-    dtype: string
-  - name: hazard_category
-    dtype: int64
-  - name: hazard_category_name
-    dtype: string
-  - name: hazard_sub_category
-    dtype: int64
-  - name: hazard_sub_category_name
-    dtype: string
-  - name: hazard_type
-    dtype: int64
-  - name: hazard_type_name
-    dtype: string
-  - name: hazard_sub_type
-    dtype: int64
-  - name: new_displacement
-    dtype: int64
-  - name: new_displacement_rounded
-    dtype: int64
-  - name: total_displacement
-    dtype: float64
-  - name: total_displacement_rounded
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 12392
-    num_examples: 48
-  - name: test
-    num_bytes: 2998
-    num_examples: 12
-  download_size: 23340
-  dataset_size: 15390
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 48
+    - name: test
+      num_examples: 12
 ---
+
+# United Republic of Tanzania - Internal Displacements (New Displacements) – IDPs
+
+**Publisher:** Internal Displacement Monitoring Centre (IDMC) · **Source:** [HDX](https://data.humdata.org/dataset/idmc-idp-data-tza) · **License:** `cc-by-igo` · **Updated:** 2026-03-18
+
+---
+
+## Abstract
+
+The [Global Internal Displacement Database (GIDD)](http://www.internal-displacement.org/database/displacement-data), maintained by the [Internal Displacement Monitoring Centre (IDMC)](https://www.internal-displacement.org/), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the [1998 Guiding Principles](https://www.internal-displacement.org/internal-displacement/guiding-principles-on-internal-displacement/), as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.
+
+
+The GIDD tracks two primary metrics: "People Displaced" or population "Stock" figures, which represent the total number of people living in displacement at year-end, and "New Displacement," which counts new displacement incidents (population Flows) rather than individual people, accounting for potential multiple displacements by the same person. This dataset serves as a crucial resource for understanding long-term trends and validated displacement figures globally. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.
+
+
+"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.
+
+
+"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.
+
+Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `start_date`, `end_date` column(s). Geographic scope: **TZA**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Conflict and security |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 60 |
+| **Columns** | 21 (9 numeric, 10 categorical, 2 datetime) |
+| **Train split** | 48 rows |
+| **Test split** | 12 rows |
+| **Geographic scope** | TZA |
+| **Publisher** | Internal Displacement Monitoring Centre (IDMC) |
+| **HDX last updated** | 2026-03-18 |
+
+---
+
+## Variables
+
+**Geographic** — `iso3` (TZA), `country_name` (United Republic of Tanzania), `year` (range 2008.0–2024.0), `start_date_accuracy` (Day, Week, Month), `end_date_accuracy` (Day, Week, Month) and 11 others.
+
+**Temporal** — `start_date`, `end_date`.
+
+**Identifier / Metadata** — `event_name` (Tanzania: Flood - 01/01/2011, Tanzania: Flood - 17/03/2008, Tanzania: Heavy Rains/Strong Winds - Tanga - December 2021), `esa_source` (HDX), `esa_processed` (2026-04-12).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-idmc-idp-data-tza")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `iso3` | object | 0.0% | TZA |
+| `country_name` | object | 0.0% | United Republic of Tanzania |
+| `year` | int64 | 0.0% | 2008.0 – 2024.0 (mean 2018.5) |
+| `start_date` | datetime64[ns] | 0.0% |  |
+| `start_date_accuracy` | object | 0.0% | Day, Week, Month |
+| `end_date` | datetime64[ns] | 6.7% |  |
+| `end_date_accuracy` | object | 6.7% | Day, Week, Month |
+| `event_name` | object | 0.0% | Tanzania: Flood - 01/01/2011, Tanzania: Flood - 17/03/2008, Tanzania: Heavy Rains/Strong Winds - Tanga - December 2021 |
+| `hazard_category` | int64 | 0.0% | 1.0 – 2.0 (mean 1.9667) |
+| `hazard_category_name` | object | 0.0% | Weather related, Geophysical |
+| `hazard_sub_category` | int64 | 0.0% | 1.0 – 4.0 (mean 3.2167) |
+| `hazard_sub_category_name` | object | 0.0% | Hydrological, Meteorological, Geophysical |
+| `hazard_type` | int64 | 0.0% | 1.0 – 14.0 (mean 10.85) |
+| `hazard_type_name` | object | 0.0% | Flood, Storm, Earthquake |
+| `hazard_sub_type` | int64 | 0.0% | 1.0 – 24.0 (mean 14.9667) |
+| `new_displacement` | int64 | 0.0% | 10.0 – 85010.0 (mean 6363.8167) |
+| `new_displacement_rounded` | int64 | 0.0% | 10.0 – 85000.0 (mean 6369.8667) |
+| `total_displacement` | float64 | 65.0% | 5.0 – 71693.0 (mean 7067.7143) |
+| `total_displacement_rounded` | float64 | 65.0% | 5.0 – 72000.0 (mean 7096.8571) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-12 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 2008.0 | 2024.0 | 2018.5 | 2019.0 |
+| `hazard_category` | 1.0 | 2.0 | 1.9667 | 2.0 |
+| `hazard_sub_category` | 1.0 | 4.0 | 3.2167 | 3.0 |
+| `hazard_type` | 1.0 | 14.0 | 10.85 | 10.0 |
+| `hazard_sub_type` | 1.0 | 24.0 | 14.9667 | 13.0 |
+| `new_displacement` | 10.0 | 85010.0 | 6363.8167 | 1153.0 |
+| `new_displacement_rounded` | 10.0 | 85000.0 | 6369.8667 | 1150.0 |
+| `total_displacement` | 5.0 | 71693.0 | 7067.7143 | 291.0 |
+| `total_displacement_rounded` | 5.0 | 72000.0 | 7096.8571 | 290.0 |
+
+---
+
+## Curation
+
+Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 2 column(s) with >80% missing values were removed: `hazard_subtype_name`, `event_codes`. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+
+---
+
+## Limitations
+
+- Data originates from Internal Displacement Monitoring Centre (IDMC) and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- The following columns have >20% missing values and should be treated with caution in modelling: `total_displacement`, `total_displacement_rounded`.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/idmc-idp-data-tza) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_idmc_idp_data_tza,
+  title     = {United Republic of Tanzania - Internal Displacements (New Displacements) – IDPs},
+  author    = {Internal Displacement Monitoring Centre (IDMC)},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/idmc-idp-data-tza},
+  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+}
+```
+
+---
+
+*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*