{"version":"1.0","type":"rich","title":"Gist 794352132f6e82f36c2cc959d6e10b17","description":"This example demonstrates a data-cleaning workflow for the Iris dataset, showing how column names are standardized before further analysis. It uses pandas DataFrames with a custom `pipe`-based pipeline that chains operations: first, a `csnap` utility snapshots the initial data head labeled \"Before\"; then, a `setcols` function applies a lambda to transform column names by lowercasing, removing \"(cm)\" units, stripping whitespace, and replacing spaces with underscores; finally, a second snapshot shows the cleaned head labeled \"After\". The code relies on `load_iris()` from scikit-learn and pandas' `pipe` method for functional composition, making the before-and-after effect visible in the console output.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-794352132f6e82f36c2cc959d6e10b17?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}