Installation#

h5col is not yet published to PyPI or conda-forge, so today it installs from its repository at github.com/HDFGroup/h5col. The package is pure Python; all of its binary needs are covered by its dependencies.

Requirements#

hdf5plugin is a regular runtime dependency, not an extra: importing it registers the widely used compression filters (Zstandard, Blosc2, and others) with HDF5. h5col imports it for you, so columns compressed with those filters read and write without any additional setup.

With pixi#

pixi is the environment manager the project itself uses. It creates the conda-based environment and installs h5col into it in editable mode:

git clone https://github.com/HDFGroup/h5col.git
cd h5col
pixi install
pixi run python -c "import h5col; print(h5col.__version__)"

With pip#

Inside a virtual environment, either install straight from the repository:

pip install git+https://github.com/HDFGroup/h5col.git

or from a clone, which is the better choice if you want the examples and tests:

git clone https://github.com/HDFGroup/h5col.git
cd h5col
pip install .

The dependencies install from PyPI; the h5py wheels bundle a suitable HDF5 library, so no system HDF5 is required.

With conda or mamba#

Install the compiled dependencies from conda-forge first, then the package itself with pip:

conda create -n h5col -c conda-forge "python>=3.11" "h5py>=3.11" "hdf5>=2.0" \
    "numpy>=1.26" "hdf5plugin>=4.0" "pydantic>=2.5"
conda activate h5col
pip install git+https://github.com/HDFGroup/h5col.git

For development#

Clone the repository and use the pixi environments described in Development; they pin the toolchain used by the test suite, the linters, and this documentation.