{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Converts between calendar dates and numeric time values for non-standard and real-world calendars, with support for astronomical year numbering and multiple calendar systems.","skillfed_tags":["climate-data","time-handling","netcdf"],"use_cases":["Parse and convert NetCDF time coordinates in climate and weather datasets where non-standard calendars are used.","Convert between 'days since' or 'seconds since' reference dates and human-readable dates for scientific data analysis.","Handle astronomical year numbering and year-zero conventions in historical or extended-range climate simulations.","Index and subset time-series data by converting date ranges to numeric time indices.","Ensure consistent time representation across datasets with different calendar conventions in multi-model comparisons."],"what_it_does":"cftime is a time-handling library extracted from netcdf4-python that specializes in converting between calendar dates and numeric time values for scientific data. It handles non-standard calendars like 360-day calendars used in climate models and real-world calendars with support for astronomical year numbering, including year zero. The package is built around two core functions: num2date converts numeric time values into cftime.datetime objects, and date2num does the reverse. It depends only on numpy.\n\nThe library is commonly used in climate science, weather modeling, and geophysical data processing where time must be represented in non-Gregorian or extended calendars. It provides a cftime.datetime class that mimics Python's standard datetime but extends it with calendar awareness and methods like toordinal, fromordinal, and change_calendar for switching between calendar systems. The package has been maintained since its first release in 2018, with recent versions adding support for modern Python versions.","worth_installing":"Yes. cftime is a stable, actively maintained library with no known vulnerabilities, permissive MIT licensing, and wide adoption in scientific computing (top 5000 PyPI packages). Install friction is moderate but well-mitigated by prebuilt wheels for all major platforms and Python versions. It is essential for anyone working with climate data, NetCDF files, or non-Gregorian calendars."},"id":"cftime","links":{"html":"https://skillfed.io/packages/cftime","md":"https://skillfed.io/packages/cftime.md","pypi":"https://pypi.org/project/cftime/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-13","license_spdx":"MIT","license_treatment":"permissive","name":"cftime","python_support":"supports_current","summary":"Time-handling functionality from netcdf4-python"},"popularity":{"monthly_downloads":4849519,"position":2215,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.6.5"}