json-timeseries
JSON-TimeSeries (JTS specification) handling library
Decision gist · record as of 2026-08-14
Yes. The package is lightweight, actively maintained, permissively licensed, and fills a specific niche for JTS specification handling. Install friction is low, and there are no known security issues. Choose it if you need to work with time series data in JTS format; if you need general-purpose time series analysis (resampling, aggregation, statistical operations), verify whether this library or a complementary tool like pandas is the right fit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low friction installation with a single lightweight runtime dependency (python-dateutil).
- Repository is active with recent commits; no notable maintenance concerns.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.
last release 2024-09-04 (709 days) · last repo commit 2026-03-22
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,974 downloads/mo, #12,903 on PyPI
Alternatives
Verify before relying
pip install json-timeseries
from json_timeseries import TsRecord, TimeSeries, JtsDocument
from datetime import datetime
timeseries = TimeSeries(identifier='series_1', name='Series 1', data_type='NUMBER',
records=[TsRecord(timestamp=datetime.now(), value='1.23', quality=192, annotation='comment')])
jts_doc = JtsDocument([timeseries])
json_str = jts_doc.toJSONString()- Whether the package handles large time series datasets efficiently or has performance limits.
- Support for time series operations beyond basic construction (e.g., resampling, aggregation, filtering).
- Validation rules applied to record attributes (e.g., timestamp ordering, value type enforcement).
What it is and what it does
json-timeseries is a Python library for working with time series data in the JSON Time Series (JTS) specification format. It provides three main classes: TsRecord for individual timestamped data points (with optional value, quality code, and annotation fields), TimeSeries for grouping and managing related records with metadata like identifier, name, units, and data type, and JtsDocument for serializing one or more TimeSeries objects into JTS-compliant JSON output.
The library is designed for IoT and data logging scenarios where you need to construct, store, and exchange timestamped measurements. It depends only on python-dateutil for date handling, making it lightweight. The package targets Python 3.9+ and is actively maintained with no known vulnerabilities.
Use it for
- Construct and export IoT sensor readings with timestamps, quality indicators, and metadata in JTS format.
- Build time series datasets from measurement records and serialize them for downstream analysis or archival.
- Manipulate timestamped data by inserting or modifying records within a TimeSeries object before output.
- Aggregate multiple time series into a single JTS document for batch export or API submission.
- Parse and work with temporal data that includes quality codes and annotations alongside values.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is lightweight, actively maintained, permissively licensed, and fills a specific niche for JTS specification handling. Install friction is low, and there are no known security issues. Choose it if you need to work with time series data in JTS format; if you need general-purpose time series analysis (resampling, aggregation, statistical operations), verify whether this library or a complementary tool like pandas is the right fit.
Install
json-timeseries on PyPI
Before you install
Low friction installation with a single lightweight runtime dependency (python-dateutil). Repository is active with recent commits; no notable maintenance concerns.
Requires Python 3.9 or later.
License in practice
MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.
Quickstart
pip install json-timeseries
from json_timeseries import TsRecord, TimeSeries, JtsDocument
from datetime import datetime
timeseries = TimeSeries(identifier='series_1', name='Series 1', data_type='NUMBER',
records=[TsRecord(timestamp=datetime.now(), value='1.23', quality=192, annotation='comment')])
jts_doc = JtsDocument([timeseries])
json_str = jts_doc.toJSONString()Verify before relying
- Whether the package handles large time series datasets efficiently or has performance limits.
- Support for time series operations beyond basic construction (e.g., resampling, aggregation, filtering).
- Validation rules applied to record attributes (e.g., timestamp ordering, value type enforcement).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepython-dateutil |
| Maintenance | Actively maintained 709 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,974 / month, #12,903 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.9 |
Evidence: json_timeseries-0.1.7-py3-none-any.whl
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