KDnuggets Publishes 7-Step Python Guide to Automate Descriptive Statistics
Updated
Updated · KDnuggets · Jul 9
KDnuggets Publishes 7-Step Python Guide to Automate Descriptive Statistics
1 articles · Updated · KDnuggets · Jul 9
Summary
KDnuggets' new tutorial lays out a 7-step workflow for turning repetitive Python summary-statistics tasks into reusable, publication-ready reporting pipelines.
The guide starts with pandas basics on the 344-row Palmer Penguins dataset, then expands to include categorical summaries, missing-data percentages, skewness, kurtosis and grouped statistics.
It next highlights skimpy for one-call console summaries, fg-data-profiling for interactive HTML reports, and notes the profiling package was renamed again in April 2026 from ydata-profiling to fg-data-profiling.
For final outputs, the tutorial recommends tableone for research-style 'Table 1' summaries with p-values and SMDs, and Great Tables for polished HTML, image and manuscript-ready tables.
The broader pitch is to wrap those steps into a single reusable function so analysts spend less time rewriting mean() and std() calls and more time on interpretation.