---
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
- zwe
pretty_name: "Zimbabwe Daily FEWS NET Cross Border Trade Data"
dataset_info:
  splits:
    - name: train
      num_examples: 3202
    - name: test
      num_examples: 800
---

# Zimbabwe Daily FEWS NET Cross Border Trade Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/daily_cross_border_trade_for_zimbabwe_6819) · **License:** `cc-by` · **Updated:** 2026-03-30

---

## Abstract

Zimbabwe Daily cross border trade data collected by FEWS NET since 2018.

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: **ZWE**.

*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)** | 4,003 |
| **Columns** | 42 (9 numeric, 31 categorical, 2 datetime) |
| **Train split** | 3,202 rows |
| **Test split** | 800 rows |
| **Geographic scope** | ZWE |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-03-30 |

---

## Variables

**Geographic** — `reporting_country` (Zimbabwe, Zambia, Malawi), `reporting_country_code` (ZW, ZM, MW), `source_country_code` (ZA, ZM, ZW), `destination_country_code` (ZW, ZM, MZ), `flow_type` and 11 others.

**Temporal** — `start_date`, `period_date`, `value_one_month_ago` (range 0.027–10600.0), `pct_change_from_one_month_ago` (range -100.0–120589.6552).

**Outcome / Measurement** — `value` (range 0.0–31800.0).

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

**Other** — `border_point` (Beitbridge, Chirundu, Dedza), `destination` (Zimbabwe, Zambia, Mozambique), `cpcv2` (P23130AB, P23161AA, R01701AA), `product` (Roller Maize Meal, Rice (Milled), Beans (mixed)), `collection_status` and 6 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-daily-cross-border-trade-for-zimbabwe-6819")
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% | Zimbabwe, Zambia, Malawi |
| `reporting_country_code` | object | 0.0% | ZW, ZM, MW |
| `border_point` | object | 64.5% | Beitbridge, Chirundu, Dedza |
| `source` | object | 0.0% | South Africa, Zambia, Zimbabwe |
| `source_country_code` | object | 0.0% | ZA, ZM, ZW |
| `destination` | object | 0.0% | Zimbabwe, Zambia, Mozambique |
| `destination_country_code` | object | 0.0% | ZW, ZM, MZ |
| `cpcv2` | object | 0.0% | P23130AB, P23161AA, R01701AA |
| `product` | object | 0.0% | Roller Maize Meal, 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 – 31800.0 (mean 196.3835) |
| `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% | 27991.0 – 6960794.0 (mean 2355941.4642) |
| `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 – 6221000.0 (mean 24122.8569) |
| `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 | 62.5% | 1158959.0 – 37980943.0 (mean 2235463.8129) |
| `value_one_month_ago` | float64 | 60.6% | 0.027 – 10600.0 (mean 78.7851) |
| `value_one_year_ago` | float64 | 70.8% | 0.027 – 6221.0 (mean 77.5408) |
| `value_two_years_ago` | float64 | 79.7% | 0.027 – 6221.0 (mean 87.467) |
| `pct_change_from_one_month_ago` | float64 | 60.6% | -100.0 – 120589.6552 (mean 962.0421) |
| `pct_change_from_one_year_ago` | float64 | 70.8% | -100.0 – 40332.6123 (mean 866.9512) |
| `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 | 31800.0 | 196.3835 | 0.0 |
| `dataseries` | 27991.0 | 6960794.0 | 2355941.4642 | 28039.0 |
| `common_unit_quantity` | 0.0 | 6221000.0 | 24122.8569 | 0.0 |
| `id` | 1158959.0 | 37980943.0 | 2235463.8129 | 1161762.5 |
| `value_one_month_ago` | 0.027 | 10600.0 | 78.7851 | 16.92 |
| `value_one_year_ago` | 0.027 | 6221.0 | 77.5408 | 15.76 |
| `value_two_years_ago` | 0.027 | 6221.0 | 87.467 | 12.735 |
| `pct_change_from_one_month_ago` | -100.0 | 120589.6552 | 962.0421 | 50.0 |
| `pct_change_from_one_year_ago` | -100.0 | 40332.6123 | 866.9512 | 29.4389 |

---

## 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`. 6 column(s) with >80% missing values were removed: `value_three_years_ago`, `value_four_years_ago`, `value_five_years_ago`, `two_year_average`, `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`, `pct_change_from_one_month_ago`, `pct_change_from_one_year_ago`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/daily_cross_border_trade_for_zimbabwe_6819) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_daily_cross_border_trade_for_zimbabwe_6819,
  title     = {Zimbabwe Daily FEWS NET Cross Border Trade Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/daily_cross_border_trade_for_zimbabwe_6819},
  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.*