{"version":"1.0","type":"rich","title":"Gist 740405b6405a6644fd16d3a50588de51","description":"This example shows a step-by-step transformation of the classic wine dataset, progressively filtering and selecting columns to isolate alcohol content, color intensity, and hue before randomly sampling five rows. The code chains pandas operations—including `rename`, `assign`, `query`, and `sort_values`—through a custom `csnap` helper that snapshots the intermediate state after each step. It uses NumPy’s `np.where` to create a binary `color_filter` column based on hue and color intensity thresholds, then resets the index and samples five rows with `sample(5)`. The visualization highlights how pipelined data wrangling can be inspected at each stage.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-740405b6405a6644fd16d3a50588de51?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}