Add README.md

README.md

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 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-regression
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- climate-weather
+- el-nino-el-nina
+- cod
+pretty_name: "Daily Summaries of Precipitation Indicators for Congo, the Democratic Republic of the"
 dataset_info:
-  features:
-  - name: date
-    dtype: timestamp[ns]
-  - name: datatype
-    dtype: string
-  - name: station
-    dtype: string
-  - name: value
-    dtype: int64
-  - name: fl_miss
-    dtype: int64
-  - name: fl_cmiss
-    dtype: int64
-  - name: country
-    dtype: string
-  - name: indicator
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 17888
-    num_examples: 172
-  - name: test
-    num_bytes: 4576
-    num_examples: 44
-  download_size: 10428
-  dataset_size: 22464
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 172
+    - name: test
+      num_examples: 43
 ---
+
+# Daily Summaries of Precipitation Indicators for Congo, the Democratic Republic of the
+
+**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/daily-summaries-of-precipitation-indicators-for-congo--the-democratic-republic-of-the) · **License:** `other-pd-nr` · **Updated:** 2025-03-10
+
+---
+
+## Abstract
+
+This dataset contains the daily summaries on base stations across **Congo, the Democratic Republic of the**. The four indicators included are:   <br />
+ <br />
+* **TPCP**: Total precipitation <br />
+* **MXSD**: Maximum snow depth <br />
+* **TSNW**: Total snow fall <br />
+* **EMXP**: Extreme maximum daily precipitation <br />
+ <br />
+Indicators are compiled by the  National Centers for Environmental Information (NCEI), which is administrated by National Oceanic and Atmospheric Administration (NOAA) an organization part of the United States government. NOAA has access to data collected from thousands of base stations around the world, which collect data periodically on weather and climate conditions. <br />
+ <br />
+This dataset contains the latest **5 years of available data**.
+
+Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `date` column(s). Geographic scope: **COD**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Climate and environment |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 216 |
+| **Columns** | 10 (3 numeric, 6 categorical, 1 datetime) |
+| **Train split** | 172 rows |
+| **Test split** | 43 rows |
+| **Geographic scope** | COD |
+| **Publisher** | HDX |
+| **HDX last updated** | 2025-03-10 |
+
+---
+
+## Variables
+
+**Geographic** — `datatype` (TPCP, EMXP), `country` (Congo, DRC).
+
+**Temporal** — `date`.
+
+**Outcome / Measurement** — `value` (range 0.0–5258.0).
+
+**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07).
+
+**Other** — `station` (GHCND:CGM00064210, GHCND:CFM00064463, GHCND:CGM00064360), `fl_miss` (range 0.0–0.0), `fl_cmiss` (range 0.0–0.0), `indicator` (TPCP, EMXP).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-daily-summaries-of-precipitation-indicators-for-congo-the-democratic-republic-of-the")
+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% |  |
+| `datatype` | object | 0.0% | TPCP, EMXP |
+| `station` | object | 0.0% | GHCND:CGM00064210, GHCND:CFM00064463, GHCND:CGM00064360 |
+| `value` | int64 | 0.0% | 0.0 – 5258.0 (mean 420.7824) |
+| `fl_miss` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `fl_cmiss` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `country` | object | 0.0% | Congo, DRC |
+| `indicator` | object | 0.0% | TPCP, EMXP |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-07 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `value` | 0.0 | 5258.0 | 420.7824 | 180.0 |
+| `fl_miss` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `fl_cmiss` | 0.0 | 0.0 | 0.0 | 0.0 |
+
+---
+
+## 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`. 756 exact duplicate rows were removed. 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 HDX 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/daily-summaries-of-precipitation-indicators-for-congo--the-democratic-republic-of-the) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_daily_summaries_of_precipitation_indicators_for_congo_the_democratic_republic_of_the,
+  title     = {Daily Summaries of Precipitation Indicators for Congo, the Democratic Republic of the},
+  author    = {HDX},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/daily-summaries-of-precipitation-indicators-for-congo--the-democratic-republic-of-the},
+  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.*