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toolathon123/south-america-logistics-quarterly

South America Logistics — Quarterly Dataset South America regional road / rail / city-delivery shipments, aggregated and published once per quarter. Covers Brazil (BR) and Argentina (AR). Current quarter: 2026-Q1 · Rows: 13,000 · Format: Parquet (snappy) Coverage Region Country Description BR Brazil Highway (road), rail and urban city-delivery legs, primarily from São Paulo (SP) hub AR Argentina Corridors coordinated with the Buenos Aires (CABA)… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/south-america-logistics-quarterly.

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Dataset Card

South America Logistics — Quarterly Dataset

South America regional road / rail / city-delivery shipments, aggregated and published once per quarter. Covers Brazil (BR) and Argentina (AR).

Current quarter: 2026-Q1 · Rows: 13,000 · Format: Parquet (snappy)

Coverage

Region	Country	Description
`BR`	Brazil	Highway (road), rail and urban city-delivery legs, primarily from São Paulo (SP) hub
`AR`	Argentina	Corridors coordinated with the Buenos Aires (CABA) partner team, plus cross-border BR↔AR

—Time window: shipments from 2026-01-01 to 2026-03-31 (Q1 2026).
—The dataset is sharded per country under data/:
—data/BR-2026-Q1.parquet (8,900 rows)
—data/AR-2026-Q1.parquet (4,100 rows)

Quick start

python

import pandas as pd

df = pd.read_parquet("hf://datasets/toolathon123/south-america-logistics-quarterly/data/BR-2026-Q1.parquet")
print(df.shape)

or with the datasets library:

python

from datasets import load_dataset

ds = load_dataset("toolathon123/south-america-logistics-quarterly")
print(ds["train"].num_rows)

Fields

Field	Type	Description
`order_id`	string	Unique shipment order identifier (primary key)
`country`	string	ISO-2 country: `BR` (Brazil) or `AR` (Argentina)
`region`	string	Derived region tag, `BR` or `AR` (matches `country`)
`origin_city`	string	Origin city
`destination_city`	string	Destination city
`shipment_date`	date	Date the shipment left the origin
`delivery_date`	date	Actual/expected delivery date
`weight_kg`	float	Cargo weight (kg)
`volume_m3`	float	Cargo volume (m³)
`carrier_name`	string	Transport carrier name
`vehicle_plate`	string	Vehicle licence plate (Mercosur format)
`distance_km`	float	Route distance (km)
`fuel_liters`	float	Estimated/measured fuel consumption (L)
`status`	string	`delivered`, `in_transit`, `delayed`, `pending`, `cancelled`
`delay_hours`	float	Delay in hours relative to plan (0 = on time)

Data sources

—PostgreSQL database — core shipments table export (Brazil hub + Argentina partner DB).
—Spreadsheet — manual cross-border / carrier reconciliation sheets maintained by the Buenos Aires team.

Data-quality pipeline (per quarter, automated)

1.Merge PostgreSQL export + spreadsheet export.
2.Standardise casing/whitespace, coerce numeric and date types.
3.Derive region from country.
4.Remove duplicate order_id (keep the most complete record).
5.Impute missing key fields (status → delivered, delay_hours → 0, weight_kg/volume_m3/fuel_liters → carrier/country medians or distance-based estimates).
6.Validate (no nulls in key fields, unique order_id, region↔country consistency, date order).
7.Export compressed Parquet shards per country (snappy).

Update frequency

—Quarterly. New Parquet shards replace the previous quarter's files under data/ (file name pattern {COUNTRY}-{YYYY-Qn}.parquet).
—Only the data/ files and this README.md need to be replaced/updated each quarter; the pipeline is fully scripted (scripts/generate_data.py, scripts/prepare_dataset.py, scripts/upload_dataset.py).

License

CC BY 4.0. Internal analytics dataset — share with permission of the São Paulo analytics team and the Buenos Aires partner.