electricsheepafrica/africa-monthly-cross-border-trade-for-mozambique-299

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
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
- eastern-africa
- trade
- moz
pretty_name: "Mozambique Monthly FEWS NET Cross Border Trade Data"
dataset_info:
splits:
- name: train
num_examples: 4584
- name: test
num_examples: 1146
---

# Mozambique Monthly FEWS NET Cross Border Trade Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/monthly_cross_border_trade_for_mozambique_299) · **License:** `cc-by` · **Updated:** 2026-03-30

---

## Abstract

Mozambique Monthly cross border trade data collected by FEWS NET since 2005.

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `start_date`, `period_date` column(s). Geographic scope: **MOZ**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 5,730 |
| **Columns** | 44 (11 numeric, 31 categorical, 2 datetime) |
| **Train split** | 4,584 rows |
| **Test split** | 1,146 rows |
| **Geographic scope** | MOZ |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-03-30 |

---

## Variables

**Geographic** — `reporting_country` (Malawi, Mozambique, Zambia), `reporting_country_code` (MW, MZ, ZM), `source_country_code` (MZ, MW, ZM), `destination_country_code` (MZ, MW, ZM), `flow_type` and 13 others.

**Temporal** — `start_date`, `period_date`, `value_one_month_ago` (range 0.027–13236.26), `pct_change_from_one_month_ago` (range -100.0–426963.3397).

**Outcome / Measurement** — `value` (range 0.0–235500.0).

**Identifier / Metadata** — `source` (Mozambique, Malawi, Zambia), `indicator_name` (TradeFlowQuantity), `source_organization`, `source_document`, `dataseries_name` and 5 others.

**Other** — `border_point` (Muloza, Nayuchi, Kolowiko), `destination` (Mozambique, Malawi, Zambia), `cpcv2` (R01122AC, P23161AA, R01701AA), `product` (Maize Grain (White), Rice (Milled), Beans (mixed)), `collection_status` and 6 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-monthly-cross-border-trade-for-mozambique-299")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `reporting_country` | object | 0.0% | Malawi, Mozambique, Zambia |
| `reporting_country_code` | object | 0.0% | MW, MZ, ZM |
| `border_point` | object | 45.0% | Muloza, Nayuchi, Kolowiko |
| `source` | object | 0.0% | Mozambique, Malawi, Zambia |
| `source_country_code` | object | 0.0% | MZ, MW, ZM |
| `destination` | object | 0.0% | Mozambique, Malawi, Zambia |
| `destination_country_code` | object | 0.0% | MZ, MW, ZM |
| `cpcv2` | object | 0.0% | R01122AC, P23161AA, R01701AA |
| `product` | object | 0.0% | Maize Grain (White), Rice (Milled), Beans (mixed) |
| `indicator_name` | object | 0.0% | TradeFlowQuantity |
| `start_date` | datetime64[ns] | 0.0% | |
| `period_date` | datetime64[ns] | 0.0% | |
| `value` | float64 | 0.0% | 0.0 – 235500.0 (mean 1055.6595) |
| `flow_type` | object | 0.0% | |
| `trade_type` | object | 0.0% | |
| `collection_status` | object | 0.0% | |
| `source_organization` | object | 0.0% | |
| `source_document` | object | 0.0% | |
| `dataseries_name` | object | 0.0% | |
| `dataseries` | int64 | 0.0% | 27987.0 – 7636337.0 (mean 3629942.7024) |
| `unit` | object | 0.0% | |
| `unit_type` | object | 0.0% | |
| `unit_name` | object | 0.0% | |
| `status` | object | 0.0% | |
| `common_unit` | object | 0.0% | |
| `common_unit_quantity` | float64 | 0.0% | 0.0 – 13236260.0 (mean 150868.8117) |
| `reporting_country_geographic_group` | object | 0.0% | |
| `reporting_country_fewsnet_region` | object | 0.0% | |
| `source_geographic_group` | object | 0.0% | |
| `source_fewsnet_region` | object | 0.0% | |
| `destination_geographic_group` | object | 0.0% | |
| `destination_fewsnet_region` | object | 0.0% | |
| `id` | float64 | 67.7% | 1158640.0 – 37980645.0 (mean 4092856.5551) |
| `value_one_month_ago` | float64 | 57.5% | 0.027 – 13236.26 (mean 390.3037) |
| `value_one_year_ago` | float64 | 66.6% | 0.027 – 13236.26 (mean 446.1606) |
| `value_two_years_ago` | float64 | 73.0% | 0.027 – 13236.26 (mean 498.5601) |
| `value_three_years_ago` | float64 | 78.1% | 0.027 – 13236.26 (mean 564.604) |
| `two_year_average` | float64 | 76.0% | 0.059 – 12214.5 (mean 524.5956) |
| `pct_change_from_one_month_ago` | float64 | 57.5% | -100.0 – 426963.3397 (mean 1305.9592) |
| `pct_change_from_one_year_ago` | float64 | 66.6% | -100.0 – 458967.2985 (mean 2163.3468) |
| `collection_schedule` | object | 0.0% | |
| `data_usage_policy` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `value` | 0.0 | 235500.0 | 1055.6595 | 0.0 |
| `dataseries` | 27987.0 | 7636337.0 | 3629942.7024 | 6554333.0 |
| `common_unit_quantity` | 0.0 | 13236260.0 | 150868.8117 | 0.0 |
| `id` | 1158640.0 | 37980645.0 | 4092856.5551 | 1161301.5 |
| `value_one_month_ago` | 0.027 | 13236.26 | 390.3037 | 55.6843 |
| `value_one_year_ago` | 0.027 | 13236.26 | 446.1606 | 61.0 |
| `value_two_years_ago` | 0.027 | 13236.26 | 498.5601 | 64.55 |
| `value_three_years_ago` | 0.027 | 13236.26 | 564.604 | 65.5882 |
| `two_year_average` | 0.059 | 12214.5 | 524.5956 | 88.1728 |
| `pct_change_from_one_month_ago` | -100.0 | 426963.3397 | 1305.9592 | 175.9035 |
| `pct_change_from_one_year_ago` | -100.0 | 458967.2985 | 2163.3468 | 182.6667 |

---

## 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`. 4 column(s) with >80% missing values were removed: `value_four_years_ago`, `value_five_years_ago`, `five_year_average`, `pct_change_from_five_year_average`. 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 FEWS NET 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: `border_point`, `id`, `value_one_month_ago`, `value_one_year_ago`, `value_two_years_ago`, `value_three_years_ago`, `two_year_average`, `pct_change_from_one_month_ago`....
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/monthly_cross_border_trade_for_mozambique_299) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_monthly_cross_border_trade_for_mozambique_299,
title = {Mozambique Monthly FEWS NET Cross Border Trade Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/monthly_cross_border_trade_for_mozambique_299},
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.*