source: kdnuggets: 7 steps to automating descriptive statistics with python
level: technical
every analysis starts with loading a dataset and checking its contents. most people copy and paste the same pandas snippets like df.describe() and df.isna().sum() over and over, then manually reformat the output for reports. this tutorial shows how to automate that process using the palmer penguins dataset. it covers seven steps, from basic pandas to specialized libraries, to create a pipeline that saves time and reduces errors.
the first steps use pandas alone. df.describe() gives a quick numeric summary but ignores categorical columns and distribution shape. adding include='all' brings in categorical columns. using .agg() lets you pick statistics like skewness and kurtosis, and you can add a missing data percentage. grouping by a column with groupby() before describe() gives stratified summaries. these built-in methods handle many everyday needs without extra dependencies.
for richer summaries, skimpy prints a console report with inline histograms and missing data counts for all column types. for a full interactive html report, fg-data-profiling generates overviews, correlations, and alerts. to produce a formal table one like in research papers, tableone creates stratified baseline tables with p-values and standardized mean differences, exportable to latex, html, or csv. each tool fits a different need, from quick exploration to final publication.
why it matters: automating descriptive statistics saves time, ensures consistency, and produces shareable results, letting data scientists focus on analysis rather than repetitive formatting.
source: kdnuggets: 7 steps to automating descriptive statistics with python