Datasets:
File size: 2,462 Bytes
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license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- tsfile
- timeseries
- time-series
- format:tsfile
pretty_name: Commodity_Import
configs:
- config_name: default
data_files:
- split: train
path: commodity_import.tsfile
---
# Commodity_Import (TsFile)
Apache TsFile version of the `Commodity_Import` sub-dataset of [`Real-TSF/TIME-ProcessedCSV`](https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV).
## Overview
`Commodity_Import` is one of the processed time-series collections bundled in TIME-ProcessedCSV, a multi-domain repository of cleaned CSV series (energy, transport, weather, finance, health and more), each with a `timestamp` column and one or more measurement columns per file.
- **Source files:** 8 CSV file(s) under `https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV/tree/main/Commodity_Import`
- **Converted rows:** 5,578 (long format: one row per measurement)
- **Data files:** `['commodity_import.tsfile']`
## Schema (TsFile structure)
- **Time** (INT64, milliseconds) — the source `timestamp` column.
- **freq** (TAG) — source sampling-frequency directory (e.g. `H`, `15T`, `D`).
- **series** (TAG) — source file stem (e.g. `item0`, `NRSROT`).
- **channel** (TAG) — source measurement column name.
- **measurement** (FIELD, FLOAT) — the measurement value.
## Usage
Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
```python
from pathlib import Path
from tsfile import TsFileReader
path = Path("commodity_import.tsfile")
with TsFileReader(str(path)) as reader:
schemas = reader.get_all_table_schemas()
print("tables:", list(schemas))
table_name = next(iter(schemas))
table = schemas[table_name]
columns = [column.get_column_name() for column in table.get_columns()]
print("columns:", columns)
field_names = [
column.get_column_name()
for column in table.get_columns()
if column.get_column_name() not in {"Time", "time"}
]
if field_names:
with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
```
## Source & license
- Original dataset: https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV/tree/main/Commodity_Import
- Bundle: https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV
- License: apache-2.0
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