{"version":"1.0","type":"rich","title":"Gist ed8fd08aa5e827575c410b197825369e","description":"This example demonstrates how to filter the Iris dataset to retain only the sepal-related measurement columns. It defines a custom `cfilter` function that applies a lambda-based condition to either rows or columns, then pipes the dataset through a chain that first normalizes column names—converting them to lowercase, stripping units like \"(cm)\", and replacing spaces with underscores—before filtering columns. The visualization relies on pandas’ `pipe` method and string methods (`str.lower`, `str.replace`, `str.contains`) to transform and subset the data, showcasing a reusable pattern for column selection based on naming conventions.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-ed8fd08aa5e827575c410b197825369e?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}