Source: intake Maintainer: Debian Med Packaging Team Uploaders: Shayan Doust , Étienne Mollier Section: science Priority: optional Build-Depends: debhelper-compat (= 13), python3-all, python3-setuptools, dh-python, python3-appdirs, python3-entrypoints, python3-dask, python3-jinja2, python3-numpy, python3-yaml, python3-requests, python3-msgpack, python3-fsspec, python3-pytest, python3-pandas, python3-tornado, python3-partd, python3-cloudpickle, python3-zarr, python3-msgpack-numpy, python3-sphinx, python3-sphinx-rtd-theme, python3-numpydoc, python3-aiohttp, python3-tqdm, libjs-bootstrap, libjs-html5shiv, libjs-jquery, node-html5shiv, debhelper Standards-Version: 4.6.1 Vcs-Browser: https://salsa.debian.org/med-team/intake Vcs-Git: https://salsa.debian.org/med-team/intake.git Homepage: https://github.com/intake/intake Rules-Requires-Root: no Package: python3-intake Architecture: any Section: python Depends: ${python3:Depends}, ${misc:Depends}, python3-appdirs, python3-entrypoints, python3-dask, python3-jinja2, python3-numpy, python3-yaml, python3-requests, python3-msgpack, python3-fsspec Description: lightweight package for finding and investigating data Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps you: . 1. Load data from a variety of formats into containers you already know, like Pandas dataframes, Python lists, NumPy arrays and more. 2. Convert boilerplate data loading code into reusable intake plugins. 3. Describe data sets in catalog files for easy reuse and sharing between projects and with others. 4. Share catalog information (and data sets) over the network with the Intake server. Package: python3-intake-doc Architecture: all Built-Using: ${sphinxdoc:Built-Using} Section: doc Depends: ${sphinxdoc:Depends}, ${misc:Depends}, libjs-bootstrap, libjs-html5shiv, libjs-jquery Recommends: python3-intake Description: documentation for the python3-intake package Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps you: . 1. Load data from a variety of formats into containers you already know, like Pandas dataframes, Python lists, NumPy arrays and more. 2. Convert boilerplate data loading code into reusable intake plugins. 3. Describe data sets in catalog files for easy reuse and sharing between projects and with others. 4. Share catalog information (and data sets) over the network with the Intake server. . This package contains documentation for python3-intake.