update readme and docs

Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>

README.md

@@ -32,9 +32,9 @@
 [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandera?style=for-the-badge)](https://anaconda.org/conda-forge/pandera)
 [![Discord](https://img.shields.io/badge/discord-chat-purple?color=%235765F2&label=discord&logo=discord&style=for-the-badge)](https://discord.gg/vyanhWuaKB)
 
-`pandera` provides a flexible and expressive API for performing data
-validation on dataframe-like objects to make data processing pipelines more
-readable and robust.
+`pandera` is a [Union.ai](https://union.ai/blog-post/pandera-joins-union-ai) open
+source project that provides a flexible and expressive API for performing data
+validation on dataframe-like objects to make data processing pipelines more readable and robust.
 
 Dataframes contain information that `pandera` explicitly validates at runtime.
 This is useful in production-critical or reproducible research settings. With
@@ -63,7 +63,7 @@ This is useful in production-critical or reproducible research settings. With
 
 ## Documentation
 
-The official documentation is hosted on ReadTheDocs: https://pandera.readthedocs.io
+The official documentation is hosted here: https://pandera.readthedocs.io
 
 
 ## Install
@@ -241,30 +241,6 @@ page or reach out to the maintainers and pandera community on
 - Use schemas as generative contracts to [synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html) for unit testing.
 - [Schema inference](https://pandera.readthedocs.io/en/stable/schema_inference.html) allows you to bootstrap schemas from data.
 
-## Alternative Data Validation Libraries
-
-Here are a few other alternatives for validating Python data structures.
-
-**Generic Python object data validation**
-
-- [voloptuous](https://github.com/alecthomas/voluptuous)
-- [schema](https://github.com/keleshev/schema)
-
-**`pandas`-specific data validation**
-
-- [opulent-pandas](https://github.com/danielvdende/opulent-pandas)
-- [PandasSchema](https://github.com/TMiguelT/PandasSchema)
-- [pandas-validator](https://github.com/c-data/pandas-validator)
-- [table_enforcer](https://github.com/xguse/table_enforcer)
-- [dataenforce](https://github.com/CedricFR/dataenforce)
-- [strictly typed pandas](https://github.com/nanne-aben/strictly_typed_pandas)
-- [marshmallow-dataframe](https://github.com/facultyai/marshmallow-dataframe)
-
-**Other tools for data validation**
-
-- [great_expectations](https://github.com/great-expectations/great_expectations)
-- [frictionless schema](https://framework.frictionlessdata.io/docs/guides/framework/schema-guide/)
-
 ## How to Cite
 
 If you use `pandera` in the context of academic or industry research, please

docs/source/index.rst

@@ -68,9 +68,10 @@ The Open-source Framework for Precision Data Testing
 
 |
 
-``pandera`` provides a flexible and expressive API for performing data
-validation on dataframe-like objects to make data processing pipelines more
-readable and robust.
+``pandera`` is a `Union.ai <https://union.ai/blog-post/pandera-joins-union-ai>`_
+open source project that provides a flexible and expressive API for performing data
+validation on dataframe-like objects to make data processing pipelines more readable
+and robust.
 
 Dataframes contain information that ``pandera`` explicitly validates at runtime.
 This is useful in production-critical data pipelines or reproducible research