DOC: Add woodwork to ecosystem docs (#41684)

Author: gsheni

doc/source/ecosystem.rst

@@ -75,12 +75,12 @@ Statsmodels leverages pandas objects as the underlying data container for comput
 Use pandas DataFrames in your `scikit-learn <https://scikit-learn.org/>`__
 ML pipeline.
 
-`Featuretools <https://github.com/featuretools/featuretools/>`__
+`Featuretools <https://github.com/alteryx/featuretools/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Featuretools is a Python library for automated feature engineering built on top of pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives". Users can contribute their own primitives in Python and share them with the rest of the community.
 
-`Compose <https://github.com/FeatureLabs/compose>`__
+`Compose <https://github.com/alteryx/compose>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Compose is a machine learning tool for labeling data and prediction engineering. It allows you to structure the labeling process by parameterizing prediction problems and transforming time-driven relational data into target values with cutoff times that can be used for supervised learning.
@@ -551,11 +551,12 @@ Library            Accessor     Classes                              Description
 ================== ============ ==================================== ===============================================================================
 `cyberpandas`_     ``ip``       ``Series``                           Provides common operations for working with IP addresses.
 `pdvega`_          ``vgplot``   ``Series``, ``DataFrame``            Provides plotting functions from the Altair_ library.
-`pandas-genomics`_ ``genomics`` ``Series``, ``DataFrame``            Provides common operations for quality control and analysis of genomics data
+`pandas-genomics`_ ``genomics`` ``Series``, ``DataFrame``            Provides common operations for quality control and analysis of genomics data.
 `pandas_path`_     ``path``     ``Index``, ``Series``                Provides `pathlib.Path`_ functions for Series.
 `pint-pandas`_     ``pint``     ``Series``, ``DataFrame``            Provides units support for numeric Series and DataFrames.
 `composeml`_       ``slice``    ``DataFrame``                        Provides a generator for enhanced data slicing.
 `datatest`_        ``validate`` ``Series``, ``DataFrame``, ``Index`` Provides validation, differences, and acceptance managers.
+`woodwork`_        ``ww``       ``Series``, ``DataFrame``            Provides physical, logical, and semantic data typing information for Series and DataFrames.
 ================== ============ ==================================== ===============================================================================
 
 .. _cyberpandas: https://cyberpandas.readthedocs.io/en/latest
@@ -565,5 +566,7 @@ Library            Accessor     Classes                              Description
 .. _pandas_path: https://github.com/drivendataorg/pandas-path/
 .. _pathlib.Path: https://docs.python.org/3/library/pathlib.html
 .. _pint-pandas: https://github.com/hgrecco/pint-pandas
-.. _composeml: https://github.com/FeatureLabs/compose
+.. _composeml: https://github.com/alteryx/compose
 .. _datatest: https://datatest.readthedocs.io/
+.. _woodwork: https://github.com/alteryx/woodwork
+

web/pandas/community/ecosystem.md

@@ -34,15 +34,15 @@ computation.
 Use pandas DataFrames in your [scikit-learn](https://scikit-learn.org/)
 ML pipeline.
 
-### [Featuretools](https://github.com/featuretools/featuretools/)
+### [Featuretools](https://github.com/alteryx/featuretools/)
 
 Featuretools is a Python library for automated feature engineering built
 on top of pandas. It excels at transforming temporal and relational
 datasets into feature matrices for machine learning using reusable
 feature engineering "primitives". Users can contribute their own
 primitives in Python and share them with the rest of the community.
 
-### [Compose](https://github.com/FeatureLabs/compose)
+### [Compose](https://github.com/alteryx/compose)
 
 Compose is a machine learning tool for labeling data and prediction engineering.
 It allows you to structure the labeling process by parameterizing
@@ -386,4 +386,5 @@ authors to coordinate on the namespace.
   | [pandas-genomics](https://pandas-genomics.readthedocs.io/en/latest/) | `genomics` | `Series`, `DataFrame` |
   | [pandas_path](https://github.com/drivendataorg/pandas-path/)         | `path`     | `Index`, `Series`     |
   | [pint-pandas](https://github.com/hgrecco/pint-pandas)                | `pint`     | `Series`, `DataFrame` |
-  | [composeml](https://github.com/FeatureLabs/compose)                  | `slice`    | `DataFrame`           |
+  | [composeml](https://github.com/alteryx/compose)                      | `slice`    | `DataFrame`           |
+  | [woodwork](https://github.com/alteryx/woodwork)                      | `slice`    | `Series`, `DataFrame` |