_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
python-pandas 2.2.3
Dependencies: which@2.21 xclip@0.13 xsel@1.2.0-1.062e6d3
Propagated dependencies: python-dateutil@2.9.0 python-jinja2@3.1.2 python-matplotlib@3.8.2 python-numpy@1.26.4 python-openpyxl@3.1.5 python-pytz@2025.1 python-tzdata@2025.1 python-xlrd@2.0.2 python-xlsxwriter@3.2.0
Channel: guix
Location: gnu/packages/python-science.scm (gnu packages python-science)
Home page: https://pandas.pydata.org
Licenses: Modified BSD
Synopsis: Data structures for data analysis, time series, and statistics
Description:

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

python-pandas 2.2.3
Dependencies: which@2.21 xclip@0.13 xsel@1.2.0-1.062e6d3
Propagated dependencies: python-dateutil@2.9.0 python-jinja2@3.1.2 python-matplotlib@3.8.2 python-numpy@1.26.4 python-openpyxl@3.1.5 python-pytz@2025.1 python-tzdata@2025.1 python-xlrd@2.0.2 python-xlsxwriter@3.2.0
Channel: guix
Location: gnu/packages/python-science.scm (gnu packages python-science)
Home page: https://pandas.pydata.org
Licenses: Modified BSD
Synopsis: Data structures for data analysis, time series, and statistics
Description:

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

Total results: 2