pyqicharts 2.0.0

Quality Improvement and Statistical Process Control charts for Python

pyqicharts

2.0.0 stable production release

pyqicharts is a Python package for Quality Improvement and Statistical Process Control charting. It is designed for analysts who need practical run charts, control charts, validation examples, sample data, reporting exports and education material.

The package is an independent open-source project by Assegai Studios Ltd. It is influenced by widely used quality-improvement and SPC methods, but it is not an official publication or endorsed implementation of any public body, vendor or external software project.

Install

python -m pip install pyqicharts

For reporting exports:

python -m pip install pyqicharts[reporting]

For interactive notebook outputs:

python -m pip install pyqicharts[interactive]

For development and test work:

python -m pip install -e .[dev]
python -m pytest --cov=pyqicharts

Quick Start

from pyqicharts import qic, sample_healthcare_qi_data

sample = sample_healthcare_qi_data()
chart = qic(sample, x="month", y="wait_time", chart="i")
chart.save_png("waiting_times.png")

Sample Data

from pyqicharts import sample_healthcare_qi_data, sample_subgroup_measurements, get_sample_data_path

healthcare = sample_healthcare_qi_data()
subgroups = sample_subgroup_measurements()
path = get_sample_data_path("sample_healthcare_qi_data.csv")

Main Public APIs

• qic(...) creates a matplotlib chart and returns a QicResult.
• qic_table(...) returns the calculation table as a pandas DataFrame.
• pareto_chart(...) and pareto_table(...) support Pareto workflows.
• export_png(...), export_excel(...), export_powerpoint(...) and create_report_bundle(...) support reporting.
• create_report_pack_from_config(...), load_chart_config(...) and run_chart_batch(...) support batch report workflows.
• powerbi_table(...), spc_summary_table(...), signal_table(...) and related helpers support dashboard imports.
• validation_manifest(...), validation_summary(...) and compare_to_expected(...) support reviewed validation workflows.
• qic_plotly(...), qic_altair(...) and qic_widget(...) provide optional interactive outputs.

Command Line

pyqicharts chart data.csv --x week --y value --chart i --output chart.png
pyqicharts report config.yml --output report
pyqicharts validate data.csv --config chart.yml

Validation Scope

The v2 stable production release distinguishes three kinds of evidence:

1. Internal regression tests: automated tests that guard package behaviour.
2. Reviewed examples: deterministic fixtures and expected-output tables reviewed for methodology consistency.
3. External certification: independently certified reference outputs. These are not bundled in this stable production release.

See VALIDATION_REPORT.md, PARITY_REPORT.md and docs/reports/statistical_review.md for details.

Governance

For high-stakes use, validate local workflows with local subject-matter experts, maintain a record of data definitions, review chart choice, and document any operational decisions made from chart outputs.