abspc 0.2.1

SPC chart visuals using the NHS Making Data Count methodology — Python, Looker & Looker Studio

abspc — Python SPC Charts

The abspc Python package provides publication-ready Statistical Process Control (SPC) charts following the NHS Making Data Count methodology.

Built on matplotlib, it produces high-quality static images suitable for board reports, dashboards, and quality-improvement publications.

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Installation

pip install abspc

Or install the development version from source:

git clone https://github.com/Aneurin-Bevan-University-Health-Board/biu_Making_Data_Count_CustomVisuals
cd biu_Making_Data_Count_CustomVisuals
pip install -e ".[dev]"

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Quick Start

import pandas as pd
from abspc import plot_spc_chart, plot_run_chart

# Minimal XmR chart
data = pd.DataFrame({"value": [48, 52, 49, 55, 47, 51, 53, 50, 48, 54]})
fig, ax = plot_spc_chart(data, chart_type="XmR")
fig.savefig("my_xmr_chart.png")

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Chart Types

XmR Chart

The XmR (individuals / moving-range) chart is the most common SPC chart type. It is suitable for individual measurements collected over time.

import numpy as np
import pandas as pd
from abspc import plot_spc_chart

data = pd.DataFrame({"value": np.random.normal(50, 3, 24)})

fig, ax = plot_spc_chart(
    data,
    chart_type="XmR",
    title="XmR Chart – Individual Measurements",
    xlabel="Month",
    ylabel="Value",
    shade_band=True,
    improvement_direction="high",
)

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p Chart

The p chart is for proportion data (e.g., percentage of patients waiting

4 hours). It requires a subgroup_size column containing the denominator.

data = pd.DataFrame({
    "value": [0.10, 0.12, 0.08, 0.15, 0.09, 0.11, 0.10, 0.13, 0.07, 0.12,
              0.09, 0.11, 0.10, 0.08, 0.14, 0.12, 0.09, 0.11, 0.08, 0.10,
              0.12, 0.09, 0.11, 0.10],
    "subgroup_size": [200] * 24,
})

fig, ax = plot_spc_chart(
    data,
    chart_type="p",
    title="p Chart – Proportion",
    xlabel="Month",
    ylabel="Proportion",
    improvement_direction="low",
)

You can also pass a numerator column and a denominator column:

data = pd.DataFrame({
    "events":     [20, 24, 16, 30, 18],
    "population": [200, 200, 200, 200, 200],
})
fig, ax = plot_spc_chart(
    data,
    chart_type="p",
    value_col="population",
    numerator_col="events",
    improvement_direction="low",
)

---

c Chart

The c chart is for counts of events in a fixed sample size (e.g., adverse events per ward per month).

data = pd.DataFrame({"value": [3, 5, 2, 6, 4, 3, 7, 5, 4, 6,
                                 5, 3, 4, 6, 5, 4, 3, 5, 6, 4,
                                 3, 5, 4, 6]})

fig, ax = plot_spc_chart(
    data,
    chart_type="c",
    title="c Chart – Count of Events",
    xlabel="Month",
    ylabel="Count",
    improvement_direction="low",
)

---

Run Chart

The run chart plots data against time with a median centre line and no control limits. It uses run-chart rules to detect signals (8-point shift and 6-point trend).

from abspc import plot_run_chart

data = pd.DataFrame({"value": np.random.normal(40, 4, 24)})

fig, ax = plot_run_chart(
    data,
    title="Run Chart – Median Centre Line",
    xlabel="Month",
    ylabel="Value",
    improvement_direction="high",
)

plot_spc_chart also accepts chart_type="run" and will automatically delegate to plot_run_chart:

fig, ax = plot_spc_chart(data, chart_type="run")

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Features

Logo Placement

Pass any image (PNG, JPEG, etc.) via logo_path to display your organisation's logo at the top-right of the chart, level with the title.

fig, ax = plot_spc_chart(
    data,
    chart_type="XmR",
    title="A&E 4-Hour Waits – Aneurin Bevan UHB",
    logo_path="path/to/logo.png",
    logo_zoom=0.08,
)

Note: logo_path places the logo in the title margin (top-right). The legacy nhs_logo_path parameter places an image inside the plot area.

---

Date Axis

All chart functions automatically detect datetime data on the x-axis and apply smart date tick formatting.

Option 1 — DatetimeIndex (auto-detected):

dates = pd.date_range("2022-01-01", periods=30, freq="MS")
data  = pd.DataFrame({"value": np.random.normal(75, 6, 30)}, index=dates)

fig, ax = plot_spc_chart(data, chart_type="XmR", title="Monthly Date Axis")

Option 2 — Explicit date column:

fig, ax = plot_run_chart(data, x_col="period", date_format="%b %Y")

date_format	Example output
"%b %Y"	Jan 2024
"%Y-%m"	2024-01
"%d/%m/%Y"	01/01/2024
None (default)	Auto (ConciseDateFormatter)

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Change-Point Annotations

Mark known process changes with vertical lines and labels:

fig, ax = plot_spc_chart(
    data,
    chart_type="XmR",
    change_points=[
        {"x": 9,  "label": "New protocol"},
        {"x": 20, "label": "Staff training"},
    ],
)

---

Auto-Rebase on Sustained Improvement

When ≥ 8 consecutive points show sustained improvement, control limits can be automatically recalculated for the new phase:

fig, ax = plot_spc_chart(
    data,
    chart_type="XmR",
    improvement_direction="high",
    auto_rebase=True,
)

Use rebase_control_limits for programmatic access without plotting:

from abspc import rebase_control_limits

result = rebase_control_limits(data, chart_type="XmR", improvement_direction="high")

Auto-rebase is supported for XmR, p, u, and c charts (not run charts).

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MDC Variation & Assurance Icons

Set show_icons=True to display the official Making Data Count variation and assurance icons at the top-left of the chart:

fig, ax = plot_spc_chart(
    data,
    chart_type="XmR",
    improvement_direction="high",
    target=60,
    show_target=True,
    show_icons=True,
)

Programmatic access:

from abspc import (
    calculate_control_limits,
    detect_special_causes,
    determine_variation_type,
    determine_assurance_type,
)

result = detect_special_causes(calculate_control_limits(data, chart_type="XmR"))
variation = determine_variation_type(result, value_col="value", improvement_direction="high")
assurance = determine_assurance_type(result, target=60, improvement_direction="high")

For run charts only the variation icon is shown (no control limits means assurance cannot be calculated).

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MDC Summary Table

plot_mdc_summary_table renders an NHS MDC-style summary table showing multiple measures at a glance:

from abspc import plot_mdc_summary_table

fig, ax = plot_mdc_summary_table(
    [
        {
            "data": df,
            "chart_type": "XmR",
            "measure": "A&E 4-Hour Waits",
            "description": "% patients seen within 4 hours",
            "value_col": "value",
            "improvement_direction": "high",
            "target": 95,
        },
        {
            "data": df_infections,
            "chart_type": "p",
            "measure": "Infection Rate",
            "description": "Proportion of infections per month",
            "value_col": "value",
            "improvement_direction": "low",
            "target": 0.05,
            "subgroup_col": "subgroup_size",
        },
    ],
    title="MDC Summary — Board Report",
)

---

Special-Cause Rules

Four NHS MDC rules (aligned with NHSRplotthedots):

Rule	Name	Description
1	Astronomical point	Single value outside 3σ limits
2	Shift	≥ 8 consecutive points above or below the mean
3	Trend	≥ 6 consecutive points all rising or all falling
4	Two-in-three	2 of 3 consecutive points in the warning zone

Use the detection functions directly:

from abspc import calculate_control_limits, detect_special_causes

result = calculate_control_limits(data, chart_type="XmR")
flags  = detect_special_causes(result)
print(flags[["value", "mean", "ucl", "lcl", "rule1", "rule2", "rule3", "rule4", "special_cause"]])

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API Reference

plot_spc_chart

fig, ax = plot_spc_chart(
    data,
    chart_type,                     # "XmR" | "p" | "u" | "c" | "run"
    value_col="value",
    subgroup_col="subgroup_size",
    numerator_col=None,
    x_col=None,
    title=None,
    xlabel="Observation",
    ylabel="Value",
    improvement_direction="high",
    target=None,
    show_target=False,
    shade_band=False,
    shade_color="#41B6E6",
    nhs_logo_path=None,
    ax=None,
    figsize=(12, 5),
    show_legend=True,
    change_points=None,
    auto_rebase=False,
    date_format=None,
    logo_path=None,
    logo_zoom=0.07,
    show_icons=False,
    icon_zoom=0.06,
)

Parameter	Type	Default	Description
data	pd.DataFrame	(required)	Input DataFrame with at least the value_col column.
chart_type	str	(required)	"XmR", "p", "u", "c", or "run" (case-insensitive).
value_col	str	"value"	Column containing the measured values.
subgroup_col	str | None	"subgroup_size"	Column with subgroup sizes. Required for "p" and "u".
numerator_col	str | None	None	For "p" charts: column with event counts when value_col holds the denominator.
x_col	str | None	None	Column for the x-axis. Auto-detects DatetimeIndex if None.
title	str | None	None	Chart title. Auto-generated if omitted.
xlabel	str	"Observation"	X-axis label.
ylabel	str	"Value"	Y-axis label.
improvement_direction	str	"high"	"high" or "low". Controls point colouring.
target	float | None	None	Optional target value for the target line and assurance calculation.
show_target	bool	False	Draw a dashed target line at target.
shade_band	bool	False	Fill between UCL and LCL with a translucent band.
shade_color	str	"#41B6E6"	Colour for tolerance-band shading.
nhs_logo_path	str | None	None	Logo inside the axes (legacy). Use logo_path instead.
ax	Axes | None	None	Existing axes to draw on. Creates a new figure when None.
figsize	tuple	(12, 5)	Figure size in inches. Ignored when ax is provided.
show_legend	bool	True	Add a colour legend.
change_points	list[dict] | None	None	Vertical annotation lines. Each dict needs "x" and "label".
auto_rebase	bool	False	Auto-detect sustained improvement and recalculate limits.
date_format	str | None	None	strftime-style format for datetime x-axis.
logo_path	str | None	None	Logo image at top-right of figure.
logo_zoom	float	0.07	Logo height as fraction of figure height.
show_icons	bool	False	Display MDC variation & assurance icons.
icon_zoom	float	0.06	Icon height as fraction of figure height.

Returns: (fig, ax) — matplotlib.figure.Figure and matplotlib.axes.Axes.

---

plot_run_chart

fig, ax = plot_run_chart(
    data,
    value_col="value",
    x_col=None,
    title=None,
    xlabel="Observation",
    ylabel="Value",
    improvement_direction="high",
    target=None,
    show_target=False,
    nhs_logo_path=None,
    ax=None,
    figsize=(12, 5),
    show_legend=True,
    change_points=None,
    date_format=None,
    logo_path=None,
    logo_zoom=0.07,
    show_icons=False,
    icon_zoom=0.06,
)

Same parameter semantics as plot_spc_chart (without chart_type, subgroup_col, numerator_col, shade_band, shade_color, auto_rebase).

Returns: (fig, ax).

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calculate_control_limits

Returns the input DataFrame extended with mean, ucl, lcl, uwl, lwl columns (or just mean for run charts).

detect_special_causes

Returns the DataFrame extended with boolean columns rule1, rule2, rule3, rule4, and special_cause.

detect_run_chart_signals

Returns the DataFrame extended with run_shift, run_trend, and run_signal.

rebase_control_limits

Returns the DataFrame with limits recalculated per improvement phase and a rebase_phase integer column.

show_summary

Generates a programmatic summary dictionary for a chart, including variation type, assurance status, descriptive statistics, triggered SPC rules, and a list of signal points. Pass show_summary=True to plot_spc_chart or plot_run_chart to render the summary as an additional figure.

from abspc import show_summary

summary = show_summary(data, chart_type="XmR", improvement_direction="high", target=60)
print(summary["variation"], summary["assurance"])

plot_mdc_summary_table

Renders an NHS MDC-style summary table for one or more measures (see the MDC Summary Table section above).

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Running Tests

pip install -e ".[dev]"
pytest

160 unit tests covering all chart types, SPC rules, run-chart signals, auto-rebase, change-point annotations, summary generation, and plotting.