Add README.md

README.md

@@ -1,54 +1,196 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- tabular-regression
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- climate-weather
+- environment
+- tcd
+pretty_name: "Chad: Rainfall Indicators at Subnational Level"
 dataset_info:
-  features:
-  - name: date
-    dtype: timestamp[ns]
-  - name: adm_level
-    dtype: int64
-  - name: adm_id
-    dtype: int64
-  - name: pcode
-    dtype: string
-  - name: n_pixels
-    dtype: float64
-  - name: rfh
-    dtype: float64
-  - name: rfh_avg
-    dtype: float64
-  - name: r1h
-    dtype: float64
-  - name: r1h_avg
-    dtype: float64
-  - name: r3h
-    dtype: float64
-  - name: r3h_avg
-    dtype: float64
-  - name: rfq
-    dtype: float64
-  - name: r1q
-    dtype: float64
-  - name: r3q
-    dtype: float64
-  - name: version
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 17615573
-    num_examples: 121272
-  - name: test
-    num_bytes: 4404249
-    num_examples: 30318
-  download_size: 12203245
-  dataset_size: 22019822
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 121272
+    - name: test
+      num_examples: 30318
 ---
- 1st: Updated forecast, 1–10 of the same month  
+
+# Chad: Rainfall Indicators at Subnational Level
+
+**Publisher:** WFP - World Food Programme · **Source:** [HDX](https://data.humdata.org/dataset/tcd-rainfall-subnational) · **License:** `cc-by` · **Updated:** 2026-04-03
+
+---
+
+## Abstract
+
+This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units. 
+
+Included indicators are (for each dekad):
+
+- 10 day rainfall [mm] (`rfh`)  
+- rainfall 1-month rolling aggregation [mm] (`r1h`)
+- rainfall 3-month rolling aggregation [mm] (`r3h`)
+- rainfall long term average [mm] (`rfh_avg`)
+- rainfall 1-month rolling aggregation long term average [mm] (`r1h_avg`)
+- rainfall 3-month rolling aggregation long term average [mm] (`r3h_avg`)
+- rainfall anomaly [%] (`rfq`)
+- rainfall 1-month anomaly [%] (`r1q`)
+- rainfall 3-month anomaly [%] (`r3q`)
+
+The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the `n_pixels` column. Finally, the `type` column indicates if the value is based on a forecast, a preliminary or a final product.
+
+ Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. 
+Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below:
+
+Publication Day: Forecast type, Covers (Dekad) 
+
+
+- 6th: Initial forecast, 11–20 of the same month  
+- 11th: Updated forecast, 1–10 of the same month  
+- 16th: Initial forecast, 21–end of the same month  
+- 21st: Updated forecast, 11–20 of the same month  
+- 26th: Initial forecast, 1–10 of the following month
+
+For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs
+
+ For further details, please see the methodology section.
+
+Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `date` column(s). Geographic scope: **TCD**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Climate and environment |
+| **Unit of observation** | Time-series observations |
+| **Rows (total)** | 151,590 |
+| **Columns** | 17 (12 numeric, 4 categorical, 1 datetime) |
+| **Train split** | 121,272 rows |
+| **Test split** | 30,318 rows |
+| **Geographic scope** | TCD |
+| **Publisher** | WFP - World Food Programme |
+| **HDX last updated** | 2026-04-03 |
+
+---
+
+## Variables
+
+**Geographic** — `n_pixels` (range 13.0–8372.0).
+
+**Temporal** — `date`.
+
+**Identifier / Metadata** — `adm_id` (range 900172.0–1003195.0), `pcode` (TCD0703, TCD2201, TCD19), `esa_source` (HDX), `esa_processed` (2026-04-06).
+
+**Other** — `adm_level` (range 1.0–2.0), `rfh` (range 0.0–249.7955), `rfh_avg` (range 0.0–112.8859), `r1h` (range 0.0–569.2935), `r1h_avg` (range 0.0–322.5119) and 6 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-tcd-rainfall-subnational")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `date` | datetime64[ns] | 0.0% |  |
+| `adm_level` | int64 | 0.0% | 1.0 – 2.0 (mean 1.7527) |
+| `adm_id` | int64 | 0.0% | 900172.0 – 1003195.0 (mean 977692.9462) |
+| `pcode` | object | 0.0% | TCD0703, TCD2201, TCD19 |
+| `n_pixels` | float64 | 0.0% | 13.0 – 8372.0 (mean 910.4301) |
+| `rfh` | float64 | 0.0% | 0.0 – 249.7955 (mean 16.5197) |
+| `rfh_avg` | float64 | 0.0% | 0.0 – 112.8859 (mean 16.3901) |
+| `r1h` | float64 | 0.1% | 0.0 – 569.2935 (mean 49.6063) |
+| `r1h_avg` | float64 | 0.1% | 0.0 – 322.5119 (mean 49.2263) |
+| `r3h` | float64 | 0.5% | 0.0 – 1150.1631 (mean 149.3431) |
+| `r3h_avg` | float64 | 0.5% | 0.0 – 811.1459 (mean 148.2129) |
+| `rfq` | float64 | 0.0% | 13.9173 – 569.6287 (mean 100.3659) |
+| `r1q` | float64 | 0.1% | 13.8573 – 587.5419 (mean 100.4474) |
+| `r3q` | float64 | 0.5% | 19.1436 – 564.8682 (mean 100.5778) |
+| `version` | object | 0.0% | final, prelim, forecast |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-06 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `adm_level` | 1.0 | 2.0 | 1.7527 | 2.0 |
+| `adm_id` | 900172.0 | 1003195.0 | 977692.9462 | 1003149.0 |
+| `n_pixels` | 13.0 | 8372.0 | 910.4301 | 370.0 |
+| `rfh` | 0.0 | 249.7955 | 16.5197 | 1.2695 |
+| `rfh_avg` | 0.0 | 112.8859 | 16.3901 | 1.6532 |
+| `r1h` | 0.0 | 569.2935 | 49.6063 | 5.0804 |
+| `r1h_avg` | 0.0 | 322.5119 | 49.2263 | 5.9782 |
+| `r3h` | 0.0 | 1150.1631 | 149.3431 | 38.0284 |
+| `r3h_avg` | 0.0 | 811.1459 | 148.2129 | 40.9487 |
+| `rfq` | 13.9173 | 569.6287 | 100.3659 | 100.0 |
+| `r1q` | 13.8573 | 587.5419 | 100.4474 | 100.0 |
+| `r3q` | 19.1436 | 564.8682 | 100.5778 | 99.9499 |
+
+---
+
+## 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`. 1 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 WFP - World Food Programme and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/tcd-rainfall-subnational) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_tcd_rainfall_subnational,
+  title     = {Chad: Rainfall Indicators at Subnational Level},
+  author    = {WFP - World Food Programme},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/tcd-rainfall-subnational},
+  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.*