Quickstart¶

This walks the whole flow on a small dataset: load a table, attach the spec limits, run an analysis, read the result. Every cell runs, so open it in Colab and change the numbers to your own.

Run in ColabView on GitHubDownload notebook

Install it first (skip this if mfgQC is already in your environment):

In [1]:

!pip install mfgqc

!pip install mfgqc

ERROR: Could not find a version that satisfies the requirement mfgqc (from versions: none)
ERROR: No matching distribution found for mfgqc

1. Load a table and attach the spec¶

mfgQC analyses take a QCData object. You build one by loading a tidy DataFrame, naming the measurement column, and attaching the engineering tolerances. Here is a made-up run of 100 widths in 20 subgroups of 5.

In [2]:

import numpy as np, pandas as pd, mfgqc

rng = np.random.default_rng(7)
df = pd.DataFrame({
    "width": np.round(rng.normal(1.50, 0.11, 100), 3),
    "lot":   np.repeat(np.arange(1, 21), 5),
})

qc = (mfgqc.load(df, measure="width", subgroup="lot", subgroup_size=5)
           .spec(lower=1.0, upper=2.0, target=1.5))
qc

import numpy as np, pandas as pd, mfgqc rng = np.random.default_rng(7) df = pd.DataFrame({ "width": np.round(rng.normal(1.50, 0.11, 100), 3), "lot": np.repeat(np.arange(1, 21), 5), }) qc = (mfgqc.load(df, measure="width", subgroup="lot", subgroup_size=5) .spec(lower=1.0, upper=2.0, target=1.5)) qc

Out[2]:

QCData(measure='width', n=100, roles={subgroup}, spec(lower=1.0, upper=2.0, target=1.5), history=2 steps)

measure is the value column, subgroup groups the rows into rational subgroups, and .spec(...) attaches the limits. Everything is immutable, so each call hands back a new object.

2. Capability¶

Run it and print the report.

In [3]:

print(qc.capability())

print(qc.capability())

Process Capability (method=normal)
==================================
n = 100   mean = 1.4809
sigma (within)  = 0.1013
sigma (overall) = 0.096767
Cp/Cpk sigma    = within (R-bar/d2)

Cp  = 1.645  95% CI (1.42, 1.87)
Cpk = 1.583  95% CI (1.35, 1.81)   (Cpu=1.708, Cpl=1.583)
Pp  = 1.722    Ppk = 1.657   (Ppu=1.788, Ppl=1.657)
Cpm = 1.69

Assumption checks:
  [PASS] normality (Anderson-Darling): AD=0.305, p=0.563; est. Cpk impact 4.9%; n=100
  [FAIL] subgroup_sufficiency (subgroup count >= 25): subgroup count 20; n=20

Recommendations:
  - Only 20 subgroups; >=25 recommended for a stable within-sigma estimate.

Three things to notice. Both sigma families are reported: within-subgroup (Cp, Cpk) and overall (Pp, Ppk), with the estimator named. Confidence intervals are shown because small-sample point estimates run optimistic. And the assumptions are checked and printed. Here normality passes but the subgroup count is flagged as low. mfgQC tells you and recommends a fix. It does not quietly change the math.

Every result also draws its own chart.

In [4]:

qc.capability().view()

qc.capability().view()

Out[4]:

3. Control chart¶

With no kind, mfgQC picks the chart from the subgroup size. Five per subgroup gives an X-bar and R chart.

In [5]:

print(qc.control_chart())

print(qc.control_chart())

Control Chart: xbar_r (inferred); rules=nelson
==============================================
Xbar: CL=1.4809  UCL=1.6169  LCL=1.345
R: CL=0.2356  UCL=0.49806  LCL=0

Out-of-control signals: none (process in control)

Assumption checks:
  [PASS] independence (lag-1 autocorrelation): r=0.193, p=0.388; n=20 [low power]

In [6]:

qc.control_chart().view()

qc.control_chart().view()

Out[6]:

4. One shape for everything¶

Every analysis returns the same surface, so once you know one you know them all.

In [7]:

cap = qc.capability()

cap.summary()        # a flat dict of the headline numbers

cap = qc.capability() cap.summary() # a flat dict of the headline numbers

Out[7]:

{'method': 'normal',
 'n': 100,
 'mean': 1.4809300000000005,
 'sigma_within': 0.10128976784178846,
 'sigma_overall': 0.09676685038448513,
 'Cp': 1.6454442558007925,
 'Cp_CI_low': 1.4164408239749233,
 'Cp_CI_high': 1.8740676845678936,
 'Cpk': 1.582687011884552,
 'Cpk_CI_low': 1.3527596612613806,
 'Cpk_CI_high': 1.8126143625077233,
 'Pp': 1.7223529132595263,
 'Ppk': 1.6566623731478098,
 'Cpm': np.float64(1.689850920191613),
 'confidence': 95,
 'normality_passed': True}

• cap.report() is the text above.
• cap.summary() is the flat dict you just saw.
• cap.to_dict() is the full JSON payload (numbers, assumption checks, provenance). Read this from code, never the text.
• cap.view() is the chart.

Next¶

• Reading the assumption report explains the guardrails.
• Gage R&R study walks a measurement-system study end to end.
• The Reference gives the formula, assumptions, and source standard behind every method.