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Gist 740405b6405a6644fd16d3a50588de51

✓ Published0🌍 Public
Aadiamaan92
Last edited Jan 27, 2019
Created on Jan 27, 2019

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.

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