Add README.md

README.md

@@ -1,82 +1,183 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- ports
+- trade
+- dji
+pretty_name: "Djibouti: Daily Port Activity Data and Shipment Estimates"
 dataset_info:
-  features:
-  - name: date
-    dtype: timestamp[ns, tz=UTC]
-  - name: year
-    dtype: int64
-  - name: month
-    dtype: int64
-  - name: day
-    dtype: int64
-  - name: portid
-    dtype: string
-  - name: portname
-    dtype: string
-  - name: country
-    dtype: string
-  - name: iso3
-    dtype: string
-  - name: portcalls_container
-    dtype: int64
-  - name: portcalls_dry_bulk
-    dtype: int64
-  - name: portcalls_general_cargo
-    dtype: int64
-  - name: portcalls_roro
-    dtype: int64
-  - name: portcalls_tanker
-    dtype: int64
-  - name: portcalls_cargo
-    dtype: int64
-  - name: portcalls
-    dtype: int64
-  - name: import_container
-    dtype: int64
-  - name: import_dry_bulk
-    dtype: int64
-  - name: import_general_cargo
-    dtype: int64
-  - name: import_roro
-    dtype: int64
-  - name: import_tanker
-    dtype: int64
-  - name: import_cargo
-    dtype: int64
-  - name: import
-    dtype: int64
-  - name: export_container
-    dtype: int64
-  - name: export_dry_bulk
-    dtype: int64
-  - name: export_general_cargo
-    dtype: int64
-  - name: export_roro
-    dtype: int64
-  - name: export_tanker
-    dtype: int64
-  - name: export_cargo
-    dtype: int64
-  - name: export
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 560453
-    num_examples: 2131
-  - name: test
-    num_bytes: 140179
-    num_examples: 533
-  download_size: 161172
-  dataset_size: 700632
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 2131
+    - name: test
+      num_examples: 532
 ---
+
+# Djibouti: Daily Port Activity Data and Shipment Estimates
+
+**Publisher:** PortWatch · **Source:** [HDX](https://data.humdata.org/dataset/djibouti-daily-port-activity-data-and-shipment-estimates) · **License:** `hdx-other` · **Updated:** 2026-04-21
+
+---
+
+## Abstract
+
+Daily count of port calls, estimates of incoming shipment volumes and outgoing shipment volumes (in metric tons) for ports in Djibouti.
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-21. Geographic scope: **DJI**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Humanitarian and development data |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 2,664 |
+| **Columns** | 31 (24 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 2,131 rows |
+| **Test split** | 532 rows |
+| **Geographic scope** | DJI |
+| **Publisher** | PortWatch |
+| **HDX last updated** | 2026-04-21 |
+
+---
+
+## Variables
+
+**Geographic** — `year` (range 2019.0–2026.0), `day` (range 1.0–31.0), `country` (Djibouti), `iso3` (DJI), `portcalls_dry_bulk` (range 0.0–4.0) and 8 others.
+
+**Temporal** — `date`, `month` (range 1.0–12.0).
+
+**Identifier / Metadata** — `portid` (port294), `portname` (Djibouti), `esa_source` (HDX), `esa_processed` (2026-04-24).
+
+**Other** — `portcalls_container` (range 0.0–6.0), `portcalls_general_cargo` (range 0.0–4.0), `portcalls_roro` (range 0.0–3.0), `portcalls_tanker` (range 0.0–4.0), `portcalls_cargo` (range 0.0–9.0) and 7 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-ports-djibouti")
+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, UTC] | 0.0% |  |
+| `year` | int64 | 0.0% | 2019.0 – 2026.0 (mean 2022.1607) |
+| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.3536) |
+| `day` | int64 | 0.0% | 1.0 – 31.0 (mean 15.6813) |
+| `portid` | object | 0.0% | port294 |
+| `portname` | object | 0.0% | Djibouti |
+| `country` | object | 0.0% | Djibouti |
+| `iso3` | object | 0.0% | DJI |
+| `portcalls_container` | int64 | 0.0% | 0.0 – 6.0 (mean 1.4692) |
+| `portcalls_dry_bulk` | int64 | 0.0% | 0.0 – 4.0 (mean 0.6077) |
+| `portcalls_general_cargo` | int64 | 0.0% | 0.0 – 4.0 (mean 0.5338) |
+| `portcalls_roro` | int64 | 0.0% | 0.0 – 3.0 (mean 0.2083) |
+| `portcalls_tanker` | int64 | 0.0% | 0.0 – 4.0 (mean 0.7316) |
+| `portcalls_cargo` | int64 | 0.0% | 0.0 – 9.0 (mean 2.8191) |
+| `portcalls` | int64 | 0.0% | 0.0 – 11.0 (mean 3.5507) |
+| `import_container` | int64 | 0.0% | 0.0 – 88612.0 (mean 9504.664) |
+| `import_dry_bulk` | int64 | 0.0% | 0.0 – 168340.0 (mean 13763.7252) |
+| `import_general_cargo` | int64 | 0.0% | 0.0 – 27112.0 (mean 1723.4546) |
+| `import_roro` | int64 | 0.0% | 0.0 – 5450.0 (mean 110.4703) |
+| `import_tanker` | int64 | 0.0% | 0.0 – 111380.0 (mean 14771.8408) |
+| `import_cargo` | int64 | 0.0% | 0.0 – 193610.0 (mean 25102.7117) |
+| `import` | int64 | 0.0% | 0.0 – 257073.0 (mean 39874.7898) |
+| `export_container` | int64 | 0.0% | 0.0 – 38191.0 (mean 1514.2842) |
+| `export_dry_bulk` | int64 | 0.0% | 0.0 – 54512.0 (mean 244.1517) |
+| `export_general_cargo` | int64 | 0.0% | 0.0 – 17384.0 (mean 346.5086) |
+| `export_roro` | int64 | 0.0% |  |
+| `export_tanker` | int64 | 0.0% |  |
+| `export_cargo` | int64 | 0.0% |  |
+| `export` | int64 | 0.0% |  |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-24 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 2019.0 | 2026.0 | 2022.1607 | 2022.0 |
+| `month` | 1.0 | 12.0 | 6.3536 | 6.0 |
+| `day` | 1.0 | 31.0 | 15.6813 | 16.0 |
+| `portcalls_container` | 0.0 | 6.0 | 1.4692 | 1.0 |
+| `portcalls_dry_bulk` | 0.0 | 4.0 | 0.6077 | 0.0 |
+| `portcalls_general_cargo` | 0.0 | 4.0 | 0.5338 | 0.0 |
+| `portcalls_roro` | 0.0 | 3.0 | 0.2083 | 0.0 |
+| `portcalls_tanker` | 0.0 | 4.0 | 0.7316 | 1.0 |
+| `portcalls_cargo` | 0.0 | 9.0 | 2.8191 | 3.0 |
+| `portcalls` | 0.0 | 11.0 | 3.5507 | 3.0 |
+| `import_container` | 0.0 | 88612.0 | 9504.664 | 6729.0 |
+| `import_dry_bulk` | 0.0 | 168340.0 | 13763.7252 | 0.0 |
+| `import_general_cargo` | 0.0 | 27112.0 | 1723.4546 | 0.0 |
+| `import_roro` | 0.0 | 5450.0 | 110.4703 | 0.0 |
+| `import_tanker` | 0.0 | 111380.0 | 14771.8408 | 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`. 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 PortWatch 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/djibouti-daily-port-activity-data-and-shipment-estimates) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_ports_djibouti,
+  title     = {Djibouti: Daily Port Activity Data and Shipment Estimates},
+  author    = {PortWatch},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/djibouti-daily-port-activity-data-and-shipment-estimates},
+  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.*