Standardize Electric Sheep Africa dataset card

README.md

@@ -1,183 +1,141 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
+license: other
 language:
 - en
-license: other
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-task_ids: []
+multilinguality: monolingual
+size_categories:
+- 10K<n<100K
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- ports
-- trade
-- mar
-pretty_name: "Morocco: Daily Port Activity Data and Shipment Estimates"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 23443
-    - name: test
-      num_examples: 5860
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "economics-finance"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "ports"
+- "trade"
+- "mar"
+pretty_name: "Morocco: Daily Port Activity Data and Shipment Estimates | Africa (original)"
 ---
 
-# Morocco: Daily Port Activity Data and Shipment Estimates
+# Morocco: Daily Port Activity Data and Shipment Estimates | Africa (original)
 
-**Publisher:** PortWatch · **Source:** [HDX](https://data.humdata.org/dataset/morocco-daily-port-activity-data-and-shipment-estimates) · **License:** `hdx-other` · **Updated:** 2026-04-21
+**Size category:** `10K<n<100K` - **Formats:** `parquet` - **Sector:** economics_finance - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
----
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-economics_finance-green)
+![downloads](https://img.shields.io/badge/HF_downloads-7-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
 
-## Abstract
+## TL;DR
 
-Daily count of port calls, estimates of incoming shipment volumes and outgoing shipment volumes (in metric tons) for ports in Morocco.
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
 
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-21. Geographic scope: **MAR**.
+## What This Dataset Covers
 
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
----
+Dataset context from the existing Hugging Face card: Morocco: Daily Port Activity Data and Shipment Estimates Publisher: PortWatch · Source: HDX · 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 Morocco. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-21. Geographic scope: MAR. Curated into ML-ready Parquet format by Electric Sheep Africa.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco.
 
-## Dataset Characteristics
+## Dataset Profile
 
-| | |
+| Field | Value |
 |---|---|
-| **Domain** | Humanitarian and development data |
-| **Unit of observation** | Country-level aggregates |
-| **Rows (total)** | 29,304 |
-| **Columns** | 31 (24 numeric, 6 categorical, 0 datetime) |
-| **Train split** | 23,443 rows |
-| **Test split** | 5,860 rows |
-| **Geographic scope** | MAR |
-| **Publisher** | PortWatch |
-| **HDX last updated** | 2026-04-21 |
-
----
-
-## Variables
-
-**Geographic** — `year` (range 2019.0–2026.0), `day` (range 1.0–31.0), `country` (Morocco), `iso3` (MAR), `portcalls_dry_bulk` (range 0.0–11.0) and 8 others.
-
-**Temporal** — `date`, `month` (range 1.0–12.0).
-
-**Identifier / Metadata** — `portid` (port10, port220, port319), `portname` (Agadir, Casablanca, Jorf Lasfar), `esa_source` (HDX), `esa_processed` (2026-04-23).
-
-**Other** — `portcalls_container` (range 0.0–17.0), `portcalls_general_cargo` (range 0.0–9.0), `portcalls_roro` (range 0.0–5.0), `portcalls_tanker` (range 0.0–7.0), `portcalls_cargo` (range 0.0–22.0) and 7 others.
-
----
-
-## Quick Start
+| Hugging Face repo | [`electricsheepafrica/africa-ports-morocco`](https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco) |
+| Sector | economics_finance |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, ports, trade, mar |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `10K<n<100K` |
+| Countries | Morocco |
+| ISO3 coverage | `MAR` |
+| Last modified on HF | `2026-04-23 12:05:49+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+
+## How To Read This Dataset
+
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+
+## Usage
 
 ```python
 from datasets import load_dataset
 
-ds    = load_dataset("electricsheepafrica/africa-ports-morocco")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
+ds = load_dataset("electricsheepafrica/africa-ports-morocco")
+print(ds)
 
-print(train.shape)
-train.head()
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
----
-
-## 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% | port10, port220, port319 |
-| `portname` | object | 0.0% | Agadir, Casablanca, Jorf Lasfar |
-| `country` | object | 0.0% | Morocco |
-| `iso3` | object | 0.0% | MAR |
-| `portcalls_container` | int64 | 0.0% | 0.0 – 17.0 (mean 1.1156) |
-| `portcalls_dry_bulk` | int64 | 0.0% | 0.0 – 11.0 (mean 0.3708) |
-| `portcalls_general_cargo` | int64 | 0.0% | 0.0 – 9.0 (mean 0.3578) |
-| `portcalls_roro` | int64 | 0.0% | 0.0 – 5.0 (mean 0.1249) |
-| `portcalls_tanker` | int64 | 0.0% | 0.0 – 7.0 (mean 0.4924) |
-| `portcalls_cargo` | int64 | 0.0% | 0.0 – 22.0 (mean 1.9692) |
-| `portcalls` | int64 | 0.0% | 0.0 – 24.0 (mean 2.4616) |
-| `import_container` | int64 | 0.0% | 0.0 – 692677.0 (mean 12900.4012) |
-| `import_dry_bulk` | int64 | 0.0% | 0.0 – 274938.0 (mean 6962.1928) |
-| `import_general_cargo` | int64 | 0.0% | 0.0 – 43366.0 (mean 615.9629) |
-| `import_roro` | int64 | 0.0% | 0.0 – 7196.0 (mean 32.5774) |
-| `import_tanker` | int64 | 0.0% | 0.0 – 158808.0 (mean 3757.8559) |
-| `import_cargo` | int64 | 0.0% | 0.0 – 692677.0 (mean 20511.2374) |
-| `import` | int64 | 0.0% | 0.0 – 694583.0 (mean 24269.1555) |
-| `export_container` | int64 | 0.0% | 0.0 – 585423.0 (mean 12130.4076) |
-| `export_dry_bulk` | int64 | 0.0% | 0.0 – 156407.0 (mean 3946.1661) |
-| `export_general_cargo` | int64 | 0.0% | 0.0 – 35535.0 (mean 371.5821) |
-| `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% | 2026-04-23 |
+### Convert To Pandas When Tabular
 
----
-
-## 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 | 17.0 | 1.1156 | 0.0 |
-| `portcalls_dry_bulk` | 0.0 | 11.0 | 0.3708 | 0.0 |
-| `portcalls_general_cargo` | 0.0 | 9.0 | 0.3578 | 0.0 |
-| `portcalls_roro` | 0.0 | 5.0 | 0.1249 | 0.0 |
-| `portcalls_tanker` | 0.0 | 7.0 | 0.4924 | 0.0 |
-| `portcalls_cargo` | 0.0 | 22.0 | 1.9692 | 0.0 |
-| `portcalls` | 0.0 | 24.0 | 2.4616 | 1.0 |
-| `import_container` | 0.0 | 692677.0 | 12900.4012 | 0.0 |
-| `import_dry_bulk` | 0.0 | 274938.0 | 6962.1928 | 0.0 |
-| `import_general_cargo` | 0.0 | 43366.0 | 615.9629 | 0.0 |
-| `import_roro` | 0.0 | 7196.0 | 32.5774 | 0.0 |
-| `import_tanker` | 0.0 | 158808.0 | 3757.8559 | 0.0 |
+```python
+from datasets import Dataset
 
----
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-## Curation
+## Data Quality Notes
 
-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.
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
 
----
+## Source And Provenance
 
-## Limitations
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco](https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-- 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/morocco-daily-port-activity-data-and-shipment-estimates) for the publisher's own methodology notes and caveats.
+## Suggested Analyses
 
----
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
 
 ## Citation
 
 ```bibtex
-@dataset{hdx_africa_ports_morocco,
-  title     = {Morocco: Daily Port Activity Data and Shipment Estimates},
-  author    = {PortWatch},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/morocco-daily-port-activity-data-and-shipment-estimates},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+@misc{electric_sheep_africa_africa_ports_morocco_2026,
+  title        = {Morocco: Daily Port Activity Data and Shipment Estimates | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ports-morocco}}
 }
 ```
 
+## License
+
+Released under Source-specific or other license.
+
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+
+## About Electric Sheep Africa
+
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
+
 ---
 
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.