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Pingouin

As explained in Effective Data Analysis

pingouin is a Python statistical package providing common statistical tests and comprehensive results, including internal-consistency analysis.

pingouin is a Python statistical package that provides common statistical tests, comprehensive results, and functions such as Cronbach’s alpha for internal-consistency analysis. section 6.3.1

A practical reliability workflow uses pingouin to:

  • Inspect correlations among candidate variables before forming a composite measure. section 6.3.1
  • Calculate Cronbach’s alpha and its confidence interval with cronbach_alpha. section 6.3.1
  • Refine the measure by removing unrelated variables or adding variables related to the same concept. section 6.3.1

Weather-variable reliability

Applying pg.cronbach_alpha to the numeric weather variables returns alpha = 0.545 with a confidence interval of approximately 0.501–0.586, indicating relatively low internal consistency.

How Effective Data Analysis covers it

How deep it goes
Calculates Cronbach’s alpha with a confidence interval after correlation inspection and interprets approximate reliability ranges.
Examples
Uses weather.csv, a numeric correlation matrix, and a Cronbach’s alpha calculation returning 0.545.
What it assumes
Assumes tabular variables and correlation inspection while demonstrating the reliability calculation with Python data.
What you can do after
Apply it via Python to calculate Cronbach’s alpha and assess whether variables coherently support a composite measure.
How it connects
Connects Cronbach’s alpha with correlation matrices, internal consistency, and composite measures.
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