Install

Woodwork is available for Python 3.8 - 3.11. It can be installed from PyPI, conda-forge, or from source.

To install Woodwork, run the following command:

PyPI

$ python -m pip install woodwork

Conda

$ conda install -c conda-forge woodwork

Add-ons

Woodwork allows users to install add-ons. Woodwork allows users to install add-ons individually or all at once:

Hint

Be sure to install Scala and Spark

PyPI

All Add-ons

$ python -m pip install "woodwork[complete]"

Dask

$ python -m pip install "woodwork[dask]"

Spark

$ python -m pip install "woodwork[spark]"

Update Checker

$ python -m pip install "woodwork[updater]"

Conda

All Add-ons

$ conda install -c conda-forge dask pyspark alteryx-open-src-update-checker

Dask

$ conda install -c conda-forge dask

Spark

$ conda install -c conda-forge pyspark

Update Checker

$ conda install -c conda-forge alteryx-open-src-update-checker

• Dask: Use Woodwork with Dask DataFrames

• Spark: Use Woodwork with Spark DataFrames

• Update Checker: Receive automatic notifications of new Woodwork releases

Source

To install Woodwork from source, clone the repository from Github, and install the dependencies.

Hint

Be sure to install Scala and Spark if you want to run all unit tests

git clone https://github.com/alteryx/woodwork.git
cd woodwork
python -m pip install .

Scala and Spark

macOS (Intel)

$ brew tap AdoptOpenJDK/openjdk
$ brew install --cask adoptopenjdk11
$ brew install scala apache-spark
$ echo 'export JAVA_HOME=$(/usr/libexec/java_home)' >> ~/.zshrc
$ echo 'export PATH="/usr/local/opt/openjdk@11/bin:$PATH"' >> ~/.zshrc

macOS (M1)

$ brew install openjdk@11 scala apache-spark pandoc
$ echo 'export PATH="/opt/homebrew/opt/openjdk@11/bin:$PATH"' >> ~/.zshrc
$ echo 'export CPPFLAGS="-I/opt/homebrew/opt/openjdk@11/include:$CPPFLAGS"' >> ~/.zprofile
$ sudo ln -sfn /opt/homebrew/opt/openjdk@11/libexec/openjdk.jdk /Library/Java/JavaVirtualMachines/openjdk-11.jdk

Ubuntu

$ sudo apt install openjdk-11-jre openjdk-11-jdk scala pandoc -y
$ echo "export SPARK_HOME=/opt/spark" >> ~/.profile
$ echo "export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin" >> ~/.profile
$ echo "export PYSPARK_PYTHON=/usr/bin/python3" >> ~/.profile

Amazon Linux

$ sudo amazon-linux-extras install java-openjdk11 scala -y
$ amazon-linux-extras enable java-openjdk11

Docker

It is also possible to run Woodwork inside a Docker container. You can do so by installing it as a package inside a container (following the normal install guide) or creating a new image with Woodwork pre-installed, using the following commands in your Dockerfile:

FROM --platform=linux/x86_64 python:3.8-slim-buster
RUN apt update && apt -y update
RUN apt install -y build-essential
RUN pip3 install --upgrade --quiet pip
RUN pip3 install woodwork

Optional Python Dependencies

Woodwork has several other Python dependencies that are used only for specific methods. Attempting to use one of these methods without having the necessary library installed will result in an ImportError with instructions on how to install the necessary dependency.

Dependency	Min Version	Notes
boto3	1.10.45	Required to read/write to URLs and S3
smart_open	5.0.0	Required to read/write to URLs and S3
pyarrow	4.0.1	Required to serialize to parquet
dask[distributed]	2021.10.0	Required to use with Dask DataFrames
pyspark	3.2.0	Required to use with Spark DataFrames

Development

To make contributions to the codebase, please follow the guidelines here.