---
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
- trade
- zmb
pretty_name: "Zambia Monthly FEWS NET Cross Border Trade Data"
dataset_info:
  splits:
    - name: train
      num_examples: 5940
    - name: test
      num_examples: 1485
---

# Zambia Monthly FEWS NET Cross Border Trade Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/monthly_cross_border_trade_for_zambia_299) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## Abstract

Zambia 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: **ZMB**.

*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)** | 7,425 |
| **Columns** | 44 (11 numeric, 31 categorical, 2 datetime) |
| **Train split** | 5,940 rows |
| **Test split** | 1,485 rows |
| **Geographic scope** | ZMB |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `reporting_country` (Zambia, Malawi, Tanzania, United Republic of), `reporting_country_code` (ZM, MW, TZ), `source_country_code` (ZM, MW, TZ), `destination_country_code` (ZM, CD, MW), `flow_type` and 13 others.

**Temporal** — `start_date`, `period_date`, `value_one_month_ago` (range 0.2–16824.0), `pct_change_from_one_month_ago` (range -100.0–147275.0004).

**Outcome / Measurement** — `value` (range 0.0–227830.0).

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

**Other** — `border_point` (Mqocha, Nakonde, Mpulungu), `destination` (Zambia, Congo, The Democratic Republic of the, Malawi), `cpcv2` (R01122AC, P23161AA, P23130AB), `product` (Maize Grain (White), Rice (Milled), Roller Maize Meal), `collection_status` and 6 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-monthly-cross-border-trade-for-zambia-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% | Zambia, Malawi, Tanzania, United Republic of |
| `reporting_country_code` | object | 0.0% | ZM, MW, TZ |
| `border_point` | object | 55.9% | Mqocha, Nakonde, Mpulungu |
| `source` | object | 0.0% | Zambia, Malawi, Tanzania, United Republic of |
| `source_country_code` | object | 0.0% | ZM, MW, TZ |
| `destination` | object | 0.0% | Zambia, Congo, The Democratic Republic of the, Malawi |
| `destination_country_code` | object | 0.0% | ZM, CD, MW |
| `cpcv2` | object | 0.0% | R01122AC, P23161AA, P23130AB |
| `product` | object | 0.0% | Maize Grain (White), Rice (Milled), Roller Maize Meal |
| `indicator_name` | object | 0.0% | TradeFlowQuantity |
| `start_date` | datetime64[ns] | 0.0% |  |
| `period_date` | datetime64[ns] | 0.0% |  |
| `value` | float64 | 0.0% | 0.0 – 227830.0 (mean 1172.3848) |
| `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% | 27990.0 – 7636338.0 (mean 2912286.4805) |
| `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 – 11391500.0 (mean 125883.6154) |
| `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 | 61.2% | 1158853.0 – 37980797.0 (mean 4125235.8966) |
| `value_one_month_ago` | float64 | 55.3% | 0.2 – 16824.0 (mean 338.675) |
| `value_one_year_ago` | float64 | 64.3% | 0.4 – 16824.0 (mean 347.472) |
| `value_two_years_ago` | float64 | 72.4% | 0.425 – 9440.0 (mean 320.7466) |
| `value_three_years_ago` | float64 | 79.3% | 0.425 – 6221.0 (mean 267.553) |
| `two_year_average` | float64 | 74.6% | 1.5 – 8705.5435 (mean 339.9495) |
| `pct_change_from_one_month_ago` | float64 | 55.3% | -100.0 – 147275.0004 (mean 835.5938) |
| `pct_change_from_one_year_ago` | float64 | 64.3% | -100.0 – 647662.5016 (mean 1669.0543) |
| `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 | 227830.0 | 1172.3848 | 0.6 |
| `dataseries` | 27990.0 | 7636338.0 | 2912286.4805 | 28047.0 |
| `common_unit_quantity` | 0.0 | 11391500.0 | 125883.6154 | 50.0 |
| `id` | 1158853.0 | 37980797.0 | 4125235.8966 | 1162001.5 |
| `value_one_month_ago` | 0.2 | 16824.0 | 338.675 | 88.7407 |
| `value_one_year_ago` | 0.4 | 16824.0 | 347.472 | 87.3025 |
| `value_two_years_ago` | 0.425 | 9440.0 | 320.7466 | 84.0625 |
| `value_three_years_ago` | 0.425 | 6221.0 | 267.553 | 71.11 |
| `two_year_average` | 1.5 | 8705.5435 | 339.9495 | 107.366 |
| `pct_change_from_one_month_ago` | -100.0 | 147275.0004 | 835.5938 | 100.0 |
| `pct_change_from_one_year_ago` | -100.0 | 647662.5016 | 1669.0543 | 137.3689 |

---

## 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_zambia_299) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_monthly_cross_border_trade_for_zambia_299,
  title     = {Zambia Monthly FEWS NET Cross Border Trade Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/monthly_cross_border_trade_for_zambia_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.*