Data load and catalog system
Intake: A general interface for loading data
Intake is a lightweight set of tools for loading and sharing data in data science projects. Intake helps you:
- Load data from a variety of formats (see the current list of known plugins) into containers you already know, like Pandas dataframes, Python lists, NumPy arrays, and more.
- Convert boilerplate data loading code into reusable Intake plugins
- Describe data sets in catalog files for easy reuse and sharing between projects and with others.
- Share catalog information (and data sets) over the network with the Intake server
Documentation is available at Read the Docs.
Weekly news about this repo and other related projects can be found on the wiki
Recommended method using conda:
conda install -c conda-forge intake
You can also install using
pip, in which case you have a choice as to how many of the optional
dependencies you install, with the simplest having least requirements
pip install intake
and additional sections
[dataframe], or to include everything:
pip install intake[complete]
Note that you may well need specific drivers and other plugins, which usually have additional dependencies of their own.
- Create development Python environment with the required dependencies, ideally with
conda. The requirements can be found in the yml files in the
scripts/ci/directory of this repo.
conda env create -f scripts/ci/environment-py38.ymland then
conda activate test_env
- Install intake using
pip install -e .[complete]
pytestto run tests.
- Create a fork on github to be able to submit PRs.
- We respect, but do not enforce, pep8 standards; all new code should be covered by tests.
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