Installation

PyWheat package is published as a Python package and can be installed with pip (the Python package manager), ideally by using a virtual environment.

Supported Python versions

• Python 3.10 - 3.11
• pip version 19.0 or higher for Linux (requires manylinux2014 support) and Windows. pip version 20.3 or higher for macOS.

System requirements

• Ubuntu 16.04 or higher (64-bit)
• macOS 10.12.6 (Sierra) or higher (64-bit)
• Windows Native - Windows 7 or higher (64-bit)
• Windows WSL2 - Windows 10 19044 or higher (64-bit)

Environment optional

We recommend using a virtual environment, which is an isolated Python runtime. If you are in a virtual environment, any packages that you install or upgrade will be local to the environment. If you run into problems, you can just delete and recreate the environment. It's trivial to set up:

• Create a new virtual environment with:

  python3 -m venv venv
  

• Activate the environment with:

  macOS Windows Linux

  . venv/bin/activate
  

  . venv/Scripts/activate
  

  . venv/bin/activate
  

  Your terminal should now print (venv) before the prompt, which is how you know that you are inside the virtual environment that you just created.

• Exit the environment with:

  deactivate
  

with pip recommended

Open up a terminal and install PyWheat with:

Stable1.xLatest

pip install pywheat

This is the preferred method to install pywheat, as it will always install the most recent stable release.

pip install pywheat=="1.*"

or from sources:

pip install "git+https://github.com/egiron/pywheat@v1.0.0"

pip install git+https://github.com/egiron/pywheat

If you don't have pip installed, this Python installation guide can guide you through the process.

This will automatically install compatible versions of all dependencies: Numpy, Numba, Pandas, Scikit-learn, Scipy, Matplotlib, Seaborn, IPython, Shapely and Arrow. PyWheat library always strives to support the latest versions, so there's no need to install those packages separately.

Optional dependencies

• vaex, is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). Used to explore big combinations datasets saved in Parquet or HDF5 format.
• duckdb, is an in-process SQL OLAP database management system. It provides support for both reading and writing Parquet files in an efficient manner, as well as support for pushing filters and projections into the Parquet file scans.

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with git

PyWheat package can be directly used from GitHub by cloning the repository into a subfolder of your project root which might be useful if you want to use the very latest version.

Use Git to clone the pywheat repository:

git clone https://github.com/egiron/pywheat.git
cd pywheat

After cloning from git, you must install all required dependencies with:

pip install -e pywheat
# or
pip install -e .

Verify install

python3 -c "import pywheat; print(pywheat.__version__)"

If a pywheat version similar to pywheat version 1.0.0 is returned, you've installed PyWheat package successfully.

Success: PyWheat is now installed.

Support

If you're using PyWheat library in your organization and need assistance, e.g., to reduce processing times, improve performance or ensure compliance, get in touch to discuss our support offerings. We're happy to help!

Bugs may be reported at Issues.