Agent-Skills.md

Agent Skills: numpy-set-ops

Set-theoretic operations for finding unique elements, membership testing, and array intersections. Triggers: unique, isin, intersect1d, setdiff1d, union1d.

UncategorizedID: cuba6112/skillfactory/numpy-set-ops

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cuba6112/skillfactory
cuba6112

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skills/numpy-set-ops/SKILL.md

Skill Metadata

Name
numpy-set-ops
Description
Set-theoretic operations for finding unique elements, membership testing, and array intersections. Triggers: unique, isin, intersect1d, setdiff1d, union1d.

Overview

NumPy provides vectorized set operations for 1D arrays and multidimensional subarrays. These tools allow for deduplication, membership testing, and finding differences/intersections between datasets.

When to Use

  • Deduplicating rows in a large feature matrix.
  • Filtering a dataset to exclude a list of forbidden values.
  • Synchronizing two datasets by finding their intersection.
  • Compressing data by storing unique values and their index mappings.

Decision Tree

  1. Need to find non-duplicate elements?
    • Use np.unique.
  2. Need to reconstruct the original array from unique values?
    • Set return_inverse=True in np.unique.
  3. Checking if elements exist in another list?
    • Use np.isin(data, target_list).

Workflows

  1. Finding Unique Rows in a Dataset

    • Create a 2D array.
    • Call np.unique(arr, axis=0).
    • Inspect the result to see the deduplicated records.

  2. Reconstructing an Array from Sets

    • Call u, inv = np.unique(arr, return_inverse=True).
    • Store 'u' and 'inv' separately (useful for data compression).
    • Rebuild the original array using u[inv].

  3. Filtering by Membership

    • Define a 'forbidden' set of values.
    • Generate a boolean mask using ~np.isin(data, forbidden).
    • Filter the data: clean_data = data[mask].

Non-Obvious Insights

  • Flattening by Default: Set operations work on flattened 1D versions of input arrays unless an axis is explicitly specified.
  • NaN Handling: Like sorting, unique treats NaN as a value and sorts it to the end of the unique output.
  • Lexicographic Row Sort: When axis=0 is used in unique, the resulting unique rows are sorted lexicographically.

Evidence

  • "Returns the sorted unique elements of an array." Source
  • "isin(element, test_elements...)... broadcasting over element only." Source

Scripts

  • scripts/numpy-set-ops_tool.py: Routines for unique row detection and inverse reconstruction.
  • scripts/numpy-set-ops_tool.js: Simulated set intersection logic.

Dependencies

  • numpy (Python)

References