{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/7"},{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing/2"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Filters","url":"https://skillfed.io/packages/category/text-processing-filters"}],"enrichment":{"capability":"Converts strings and other inputs to numbers (int, float, or real) with fast, flexible error handling and type-checking functions that outperform Python's built-in int() and float().","skillfed_tags":["performance-optimization","numeric-parsing","type-validation"],"use_cases":["Parsing CSV or JSON data with numeric fields where conversion speed or robust error handling is critical","Validating user input or API payloads for numeric types before processing","Batch converting lists of strings to floats or ints with a single call and 2x speedup over list comprehensions","Handling unicode numerals (e.g., Roman numerals, circled digits) in text-to-number pipelines","Replacing Python's built-in int/float in performance-sensitive numeric parsing loops"],"what_it_does":"fastnumbers is a Python module that wraps and accelerates conversions from strings and other types to int, float, or real numbers. It provides three categories of functions: error-handling functions (try_float, try_int, try_real, try_forceint) that return a fallback value or original input on failure, checking functions (check_float, check_int, etc.) to validate whether input could be converted, and drop-in replacements for Python's built-in int() and float() that aim for speed parity or better. The module handles edge cases like unicode numerals, inf/nan substitution, and batch operations on iterables via a map option.\n\nThe package is built as a compiled extension (wheels for Python 3.9\u20133.14 on common platforms) with no runtime dependencies, making it lightweight to install. It targets use cases where numeric parsing is a bottleneck\u2014data processing pipelines, financial calculations, scientific computing, and bulk string-to-number conversions where the speed gains (claimed up to 5x for floats, 10x for error handling) justify the added dependency.","worth_installing":"Yes, if numeric parsing is a measurable bottleneck in your application. The package is stable (Production/Stable status, active maintenance, no known vulnerabilities), has zero runtime dependencies, and offers genuine performance gains for string-to-number conversions and batch operations. Install with caution if you rely on undocumented corner-case behavior of Python's int/float, as the documentation warns of specific differences; otherwise, it is a safe, low-friction upgrade."},"id":"fastnumbers","links":{"html":"https://skillfed.io/packages/fastnumbers","md":"https://skillfed.io/packages/fastnumbers.md","pypi":"https://pypi.org/project/fastnumbers/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-27","license_spdx":"MIT","license_treatment":"permissive","name":"fastnumbers","python_support":"supports_current","summary":"Super-fast and clean conversions to numbers."},"popularity":{"monthly_downloads":240745,"position":8900,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.2.0"}