[DOCS] Add GeoPandas API coverage reference

README.md

@@ -37,6 +37,7 @@
 [![R build](https://github.com/apache/sedona/actions/workflows/r.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/r.yml)
 [![Scala and Java build](https://github.com/apache/sedona/actions/workflows/java.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/java.yml)
 
+[![GeoPandas API coverage: 91.4% (including partial implementations)](https://img.shields.io/badge/GeoPandas_API_coverage-91.4%25-brightgreen)](docs/api/geopandas-coverage.md "GeoPandas 1.1.4 API availability in Sedona 2.0.0 development, including partial implementations")
 [![GitHub commit activity](https://img.shields.io/github/commit-activity/m/apache/sedona)](https://github.com/apache/sedona/graphs/commit-activity)
 [![GitHub Issues marked as good first issue](https://img.shields.io/github/issues/apache/sedona/good%20first%20issue?color=%237057ff)](https://github.com/apache/sedona/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22)
 
@@ -107,7 +108,7 @@ Some of the key features of Apache Sedona include:
 * Support for a wide range of geospatial data formats, including [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON), [WKT](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry), and [ESRI](https://www.esri.com) [Shapefile](https://en.wikipedia.org/wiki/Shapefile).
 * Scalable distributed processing of large vector and raster datasets.
 * Tools for spatial indexing, spatial querying, and spatial join operations.
-* Integration with popular geospatial Python tools such as [GeoPandas](https://geopandas.org).
+* GeoPandas API on Apache Spark. See the [coverage reference](docs/api/geopandas-coverage.md) for API availability and limitations in the 2.0.0 development version, compared with GeoPandas 1.1.4.
 * Integration with popular big data tools, such as Spark, [Hadoop](https://hadoop.apache.org/), [Hive](https://hive.apache.org/), and Flink for data storage and querying.
 * A user-friendly API for working with geospatial data in the [SQL](https://en.wikipedia.org/wiki/SQL), [Python](https://www.python.org/), [Scala](https://www.scala-lang.org/) and [Java](https://www.java.com) languages.
 * Flexible deployment options, including standalone, local, and cluster modes.

docs/sedonaspark.md

@@ -79,6 +79,7 @@ SedonaSpark extends Apache Spark with a rich set of out-of-the-box distributed S
 * **Blazing fast**: SedonaSpark executes computations in parallel on many nodes in a cluster so that large computations can run fast.
 * Supports **various file formats**, including GeoJSON, Shapefile, GeoParquet, STAC, JDBC, OSM PBF, CSV, and PostGIS.
 * Exposes several **language APIs,** including SQL, Python, Java, Scala, and R.
+* Provides a [GeoPandas-style API](tutorial/geopandas-api.md) on Spark. See the [API coverage reference](api/geopandas-coverage.md) for availability and limitations.
 * **Scalable**: Horizontally scale to tens, hundreds, or thousands of nodes depending on the size of your data.  You can process massive spatial datasets with SedonaSpark.
 * **Portable**: Easy to run in a custom environment, locally or in the cloud with AWS EMR, Microsoft Fabric, or Google DataProc.
 * **Extensible**: You can extend SedonaSpark with your custom logic that suits your specific geospatial data analysis needs.

docs/sedonaspark.zh.md

@@ -79,6 +79,7 @@ SedonaSpark 在 Apache Spark 之上扩展了一套丰富、开箱即用的分布
 * **极致性能**：SedonaSpark 在集群中的多个节点上并行执行计算，使大规模计算能够快速完成。
 * 支持**多种文件格式**，包括 GeoJSON、Shapefile、GeoParquet、STAC、JDBC、OSM PBF、CSV 和 PostGIS。
 * 提供多种**语言 API**，包括 SQL、Python、Java、Scala 和 R。
+* 在 Spark 上提供 [GeoPandas 风格的 API](tutorial/geopandas-api.md)。请参阅 [API 覆盖情况参考](api/geopandas-coverage.md)，了解可用功能和限制。
 * **可扩展**：可根据数据规模水平扩展到数十、数百乃至数千个节点，使用 SedonaSpark 处理海量空间数据集。
 * **可移植**：易于在自定义环境中运行，可在本地或云端（如 AWS EMR、Microsoft Fabric、Google DataProc）部署。
 * **可扩展定制**：可以使用自定义逻辑对 SedonaSpark 进行扩展，以满足特定的地理空间数据分析需求。

docs/tutorial/geopandas-api.md

@@ -21,6 +21,8 @@
 
 The GeoPandas API for Apache Sedona provides a familiar GeoPandas interface that scales your geospatial analysis beyond single-node limitations. This API combines the intuitive GeoPandas DataFrame syntax with the distributed processing power of Apache Sedona on Apache Spark, enabling you to work with planetary-scale datasets using the same code patterns you already know.
 
+See the [API coverage reference](../api/geopandas-coverage.md) for supported methods, parameter limitations, and the coverage percentage.
+
 ## Overview
 
 ### What is the GeoPandas API for Apache Sedona?

docs/tutorial/geopandas-api.zh.md

@@ -21,6 +21,8 @@
 
 Apache Sedona 上的 GeoPandas API 提供了与 GeoPandas 一致的接口，可以让您的地理空间分析突破单机的局限。该 API 把熟悉的 GeoPandas DataFrame 语法与 Apache Sedona 在 Apache Spark 上的分布式处理能力结合起来，让您能够使用同样的代码模式处理行星级（planetary-scale）的数据集。
 
+请参阅 [API 覆盖情况参考](../api/geopandas-coverage.md)，了解支持的方法、参数限制和 API 覆盖率。
+
 ## 概览
 
 ### 什么是 Apache Sedona 的 GeoPandas API？

mkdocs.yml

@@ -102,6 +102,7 @@ nav:
               - DataFrame/SQL: api/viz/sql.md
               - RDD: api/viz/java-api.md
           - Sedona R: api/rdocs
+          - GeoPandas API coverage: api/geopandas-coverage.md
           - Sedona Python: api/pydocs
       - Release notes: setup/release-notes.md
 
@@ -299,6 +300,7 @@ plugins:
             Raster DataFrame / SQL app: 栅格 DataFrame / SQL 应用
             Pure SQL environment: 纯 SQL 环境
             GeoPandas API on Sedona: Sedona 上的 GeoPandas API
+            GeoPandas API coverage: GeoPandas API 覆盖情况
             Spatial RDD app: 空间 RDD 应用
             Sedona R: Sedona R
             Work with GeoPandas and Shapely: 与 GeoPandas 和 Shapely 配合使用