numpy-string-ops
Vectorized string manipulation using the char module and modern string alternatives, including cleaning and search operations. Triggers: string operations, numpy.char, text cleaning, substring search.
When & Why to Use This Skill
This Claude skill provides high-performance, vectorized string manipulation capabilities using NumPy's char and strings modules. It is designed for efficient text processing, enabling users to perform batch cleaning, substring searches, and complex string transformations on large datasets with optimized performance that significantly outperforms standard Python loops.
Use Cases
- Batch cleaning of large-scale text datasets by stripping whitespace and normalizing character casing across entire arrays simultaneously.
- Performing high-speed substring searches and filtering thousands of records using vectorized find operations and boolean indexing.
- Executing element-wise string concatenation and broadcasting operations to efficiently add prefixes or suffixes to columns of text data.
- Normalizing and preparing text data for machine learning pipelines by applying consistent transformations across multidimensional arrays.
| name | numpy-string-ops |
|---|---|
| description | Vectorized string manipulation using the char module and modern string alternatives, including cleaning and search operations. Triggers: string operations, numpy.char, text cleaning, substring search. |
Overview
NumPy's char submodule provides vectorized versions of standard Python string operations. It allows for efficient processing of arrays containing str_ or bytes_ types, though it is being transitioned to a newer strings module in recent versions.
When to Use
- Cleaning large text datasets (e.g., stripping whitespace, normalization).
- Performing batch substring searches across thousands of records.
- Concatenating columns of text data using broadcasting.
- Converting character casing for entire datasets simultaneously.
Decision Tree
- Starting new development?
- Use
numpy.stringsif available;numpy.charis legacy.
- Use
- Comparing strings with potential trailing spaces?
numpy.charcomparison operators automatically strip whitespace.
- Concatenating a constant prefix to an array of names?
- Use
np.char.add(prefix, name_array).
- Use
Workflows
Batch String Concatenation
- Create two arrays of strings, A and B.
- Use
np.char.add(A, B)to join them element-wise. - Broadcasting applies if one array is a single string and the other is multidimensional.
Cleaning Text Datasets
- Identify an array of messy text.
- Apply
np.char.strip(arr)to remove whitespace. - Use
np.char.lower(arr)to normalize casing across the entire dataset.
Finding Substrings in Arrays
- Use
np.char.find(text_array, 'target_word'). - Identify elements with non-negative indices (where the word was found).
- Filter the original array using boolean indexing based on the search result.
- Use
Non-Obvious Insights
- Legacy Status: The
charmodule is considered legacy; future-proof code should look towards thenumpy.stringsalternative. - Implicit Stripping: Unlike standard Python
==,charmodule comparison operators strip trailing whitespace before evaluating equality. - Vectorization Reality: While these operations are vectorized, string manipulation is inherently less performant than numeric math because strings have variable lengths and require more complex memory management.
Evidence
- "Unlike the standard numpy comparison operators, the ones in the char module strip trailing whitespace characters before performing the comparison." Source
- "The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_." Source
Scripts
scripts/numpy-string-ops_tool.py: Routines for batch text cleaning and search.scripts/numpy-string-ops_tool.js: Simulated string concatenation logic.
Dependencies
numpy(Python)