timeseries-qc¶

The open source data quality-control layer for SCADA, DCS, IoT, and historian time-series data.

Add good / suspect / bad quality labels to every row of a pandas DataFrame in five lines. Then render a multi-tag horizontal status timeline — the chart that no other open-source library produces.

$ pip install timeseries-qc

Quickstart¶

import tsqc
import pandas as pd

df = pd.read_csv("sensor_data.csv")          # columns: timestamp, tag_name, value
result = tsqc.check(df, assume_tz="UTC")     # assume_tz required for tz-naive CSVs
result.plot().show()                          # renders the multi-tag quality timeline

That's the entire API. check() returns a QCResult with all downstream methods.

Get Started → Installation Guide → Traffic Dashboard → View on GitHub →

Features¶

Built-in Rules

Null, Flatline, Delta, Range, and Outlier rules cover the majority of real-world sensor faults. Custom rules accept any callable. Learn more about rules →

Timeline Chart

Plotly horizontal Gantt chart with one row per tag, color-coded by quality, interactive hover, and range selector. See visualization guide →

External Quality Column

Use a pre-existing historian quality column exclusively or merged with internal rules. Supports exclusive/combined/none modes. SCADA integration guide →

Timestamp Health

Detects gaps, duplicates, non-monotonic timestamps, frequency drift, and DST ambiguities. Timestamp validation docs →

YAML Configuration

Write rules in a plain text file. No Python required. Glob patterns supported for tag matching. View YAML guide →

Offline HTML Report

Self-contained export with embedded Plotly chart, summary tables, and per-issue breakdown. No CDN needed.

Pandas Native

Works with any DataFrame containing timestamp, tag_name, and value columns. Single-tag mode supported.

Quality Labels¶

● good ● suspect ● bad

When multiple rules fire, the worst level wins: bad > sus > good. Triggered rule names appear in a pipe-delimited quality_reasons column.

Example Output¶

Solar farm SCADA quality timeline — Solar farm — 3 tags, 1 week of hourly data. NaN bursts, flatlines, out-of-range values, and delta spikes flagged by all four rules.

Oil field SCADA quality timeline — Oil well pad — 3 tags, 1 month of hourly data. Pressure, flow, and temperature anomalies detected and classified.

Input & Output¶

Input¶

Column	Type	Notes
`timestamp`	datetime	UTC-aware or tz-naive (pass `assume_tz`)
`tag_name`	str	Sensor identifier. Omit column or pass `tag_col=None` for single-tag mode.
`value`	float	The measurement to check.

Output¶

result.df adds two columns to the original DataFrame:

Column	Values	Notes
`quality`	`"good"`, `"sus"`, `"bad"`	Worst-level rule wins
`quality_reasons`	e.g. `"flatline\\|range"`	Pipe-delimited triggered rule names

YAML Config¶

# tsqc_rules.yaml
default_rules:
  - check: null
    level: bad
  - check: flatline
    window: 1h
    min_delta: 0.001
    level: sus
  - check: delta
    max_delta: 50.0
    level: sus

tag_rules:
  "FOREBAY.LEVEL":
    - check: range
      min: 900
      max: 1100
      level: bad
  "GENERATOR.*":
    - check: range
      min: 0
      max: 200
      level: bad
    - check: flatline
      window: 30min
      min_delta: 0.5
      level: sus

result = tsqc.check(df, rules="tsqc_rules.yaml")
result.summary()           # DataFrame: pct_good/sus/bad per tag
result.issue_summary()     # DataFrame: per-issue runs (start, end, rows, duration, reasons)
result.check_timestamps()  # DataFrame: gap/duplicate/non_monotonic issues
result.export_report("report.html")  # Full HTML with chart + all tables

External Quality Column (v0.4.0)¶

Use a pre-existing quality/status column from your SCADA historian alongside or instead of internal rules:

Mode	Behavior
`exclusive`	External quality only; no internal rules run
`combined`	External + internal merged (worst-wins: bad > sus > good)
`none`	Internal only; ignores external column (escape hatch)

result = tsqc.check(df, external_quality_col="status", quality_mode="combined",
                     quality_map={0: "good", 1: "sus", 2: "bad"}, assume_tz="UTC")

See the User Guide for full details.

Comparison with Alternatives¶

	timeseries-qc	Pecos	SaQC	Great Expectations
Classification	Good / Sus / Bad	Pass / Fail	Flags	Pass / Fail
Timeline chart	Yes	No	No	No
YAML config	Yes	No	JSON	No
Time-series native	Yes	Yes	Yes	No
License	MIT	BSD-3	LGPL	Apache-2.0

Pecos (Sandia Labs) offers binary pass/fail and has been in maintenance mode since 2021 — no timeline chart and no YAML config.

SaQC (Helmholtz UFZ) is a rich flagging engine for environmental science but has an environmental-domain API, no timeline visualization, and an LGPL license.

Great Expectations is not timeseries-native and produces no visualization.

timeseries-qc is the only library that combines (1) Good/Sus/Bad classification, (2) the multi-tag horizontal status timeline, and (3) YAML-driven configuration in a single pip install.

Known Limitations (v0.5.0)¶

Pandas only. PySpark and Polars support are planned.
No YAML override of default rules. Tag-specific rules add to, not replace, default rules.
Visualization requires Plotly ≥ 5.0. Matplotlib output is not yet supported.

View Roadmap →