Pre and post bug bash fixes

docs/geopandas-interop.ipynb

@@ -14,15 +14,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0434bead-2628-4844-a3f6-2f9c15a21899",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sedonadb\n",
+    "import sedona.db\n",
     "import geopandas as gpd\n",
     "\n",
-    "sd = sedonadb.connect()"
+    "sd = sedona.db.connect()"
    ]
   },
   {

docs/index.md

@@ -1,6 +1,8 @@
 ---
 hide:
   - navigation
+
+title: Introducing SedonaDB
 ---
 
 <!---
@@ -22,13 +24,30 @@ hide:
   under the License.
 -->
 
-# SedonaDB
+SedonaDB is a high-performance, dependency-free geospatial compute engine designed for single-node processing, making it ideal for smaller datasets on local machines or cloud instances.
+
+The initial `0.1` release supports a core set of vector operations, with comprehensive vector and raster computation capabilities planned for the near future.
+
+## Key features
+
+SedonaDB has several advantages:
+
+* **Exceptional Performance:** Built in Rust to process massive geospatial datasets with exceptional speed.
+* **Unified Geospatial Toolkit:** Access a comprehensive suite of functions for both vector and raster data in a single, powerful library.
+* **Seamless Ecosystem Integration:** Built on Apache Arrow for smooth interoperability with popular data science libraries like GeoPandas, DuckDB, and Polars.
+* **Flexible APIs:** Effortlessly switch between Python and SQL interfaces to match your preferred workflow and skill set.
+* **Guaranteed CRS Propagation:** Automatically manages coordinate reference systems (CRS) to ensure spatial accuracy and prevent common errors.
+* **Broad File Format Support:** Work with a wide range of both modern and legacy geospatial file formats like geoparquet.
+* **Highly Extensible:** Easily customize and extend the library's functionality to meet your project's unique requirements.
+
+## Run a query in SQL, Python, or Rust
 
-SedonaDB is a high-performance, dependency-free geospatial compute engine.
+SedonaDB offers a flexible query interface in SQL, Python, or Rust.
 
-You can easily run SedonaDB locally or in the cloud.  The first release supports a core set of vector operations, but the full-suite of common vector and raster computations will be supported soon.
+Engineered for speed, SedonaDB provides performant geospatial processing on a single machine. This makes it perfect for the rapid analysis of smaller datasets, whether you're working locally or on a cloud server. While the initial release focuses on core vector operations, a full suite of vector and raster computations is on the roadmap.
 
-SedonaDB only runs on a single machine, so it’s perfect for processing smaller datasets.  You can use SedonaSpark, SedonaFlink, or SedonaSnow for operations on larger datasets.
+For massive, distributed workloads, you can leverage the power of SedonaSpark,
+SedonaFlink, or SedonaSnow.
 
 === "SQL"
 
@@ -67,21 +86,9 @@ SedonaDB only runs on a single machine, so it’s perfect for processing smaller
         sd_sql("SELECT ST_Point(0, 1) as geom")
 	```
 
-## Key features
-
-SedonaDB has several advantages:
-
-* **Blazing-Fast Performance:** Built in Rust to process massive geospatial datasets with exceptional speed.
-* **Unified Geospatial Toolkit:** Access a comprehensive suite of functions for both vector and raster data in a single, powerful library.
-* **Seamless Ecosystem Integration:** Built on Apache Arrow for smooth interoperability with popular data science libraries like GeoPandas, DuckDB, and Polars.
-* **Flexible APIs:** Effortlessly switch between Python and SQL interfaces to match your preferred workflow and skillset.
-* **Guaranteed CRS Propagation:** Automatically manages coordinate reference systems (CRS) to ensure spatial accuracy and prevent common errors.
-* **Broad File Format Support:** Work with a wide range of both modern and legacy geospatial file formats like geoparquet.
-* **Highly Extensible:** Easily customize and extend the library's functionality to meet your project's unique requirements.
+## Install SedonaDB
 
-## Installation
-
-Here’s how to install SedonaDB with various build tools:
+Here's how to install SedonaDB with various build tools:
 
 === "pip"
 
@@ -95,12 +102,8 @@ Here’s how to install SedonaDB with various build tools:
 	install.packages("sedonadb", repos = "https://community.r-multiverse.org")
 	```
 
-## SedonaDB example with vector data
-
-TODO
-
 ## Have questions?
 
-Feel free to start a GitHub Discussion or join the Discord community to ask the developers any questions you may have.
+Start a [GitHub Discussion](https://github.com/apache/sedona-db/issues) or join the [Discord community](https://discord.com/invite/9A3k5dEBsY) and ask the developers any questions you may have.
 
 We look forward to collaborating with you!

docs/programming-guide.ipynb

@@ -11,11 +11,11 @@
     "\n",
     "You will learn how to create SedonaDB DataFrames, run spatial queries, and perform I/O operations with various types of files.\n",
     "\n",
-    "Let’s start by establishing a SedonaDB connection.\n",
+    "Let's start by establishing a SedonaDB connection.\n",
     "\n",
     "## Establish SedonaDB connection\n",
     "\n",
-    "Here’s how to create the SedonaDB connection:"
+    "Here's how to create the SedonaDB connection:"
    ]
   },
   {
@@ -25,23 +25,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sedonadb\n",
+    "import sedona.db\n",
     "\n",
-    "sd = sedonadb.connect()"
+    "sd = sedona.db.connect()"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "7aeaa60f-2325-418c-8e72-4344bd4a75fe",
    "metadata": {},
    "source": [
-    "Now let’s see how to create SedonaDB DataFrames.\n",
+    "Now, let's see how to create SedonaDB dataframes.\n",
     "\n",
     "## Create SedonaDB DataFrame\n",
     "\n",
     "**Manually creating SedonaDB DataFrame**\n",
     "\n",
-    "Here’s how to manually create a SedonaDB DataFrame:"
+    "Here's how to manually create a SedonaDB DataFrame:"
    ]
   },
   {
@@ -95,7 +95,7 @@
    "source": [
     "**Create SedonaDB DataFrame from files in S3**\n",
     "\n",
-    "For most production applications, you will create SedonaDB DataFrames by reading data from a file.  Let’s see how to read GeoParquet files in AWS S3 into a SedonaDB DataFrame."
+    "For most production applications, you will create SedonaDB DataFrames by reading data from a file.  Let's see how to read GeoParquet files in AWS S3 into a SedonaDB DataFrame."
    ]
   },
   {
@@ -116,7 +116,7 @@
    "id": "858fcc66-816d-4c71-8875-82b74169eccd",
    "metadata": {},
    "source": [
-    "Let’s now run some spatial queries.\n",
+    "Now, let's run some spatial queries.\n",
     "\n",
     "**Read from GeoPandas DataFrame**\n",
     "\n",
@@ -181,11 +181,11 @@
    "source": [
     "## Spatial queries\n",
     "\n",
-    "Let’s see how to run spatial operations like filtering, joins, and clustering algorithms.\n",
+    "Let's see how to run spatial operations like filtering, joins, and clustering algorithms.\n",
     "\n",
-    "***Spatial filtering***\n",
+    "**Spatial filtering**\n",
     "\n",
-    "Let’s run a spatial filtering operation to fetch all the objects in the following polygon:"
+    "Let's run a spatial filtering operation to fetch all the objects in the following polygon:"
    ]
   },
   {
@@ -232,21 +232,21 @@
    "source": [
     "You can see it only includes the divisions in the Nova Scotia area.  Skip to the visualization section to see how this data can be graphed on a map.\n",
     "\n",
-    "***K-nearest neighbors (KNN) joins***\n",
+    "**K-nearest neighbors (KNN) joins**\n",
     "\n",
     "Create `restaurants` and `customers` tables so we can demonstrate the KNN join functionality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "deaa36db-2fee-4ba2-ab79-1dc756cb1655",
    "metadata": {},
    "outputs": [],
    "source": [
     "df = sd.sql(\"\"\"\n",
     "SELECT name, ST_Point(lng, lat) AS location\n",
-    "FROM (VALUES \n",
+    "FROM (VALUES\n",
     "    (101, -74.0, 40.7, 'Pizza Palace'),\n",
     "    (102, -73.99, 40.69, 'Burger Barn'),\n",
     "    (103, -74.02, 40.72, 'Taco Town'),\n",
@@ -259,7 +259,7 @@
     "\n",
     "df = sd.sql(\"\"\"\n",
     "SELECT name, ST_Point(lng, lat) AS location\n",
-    "FROM (VALUES \n",
+    "FROM (VALUES\n",
     "    (1, -74.0, 40.7, 'Alice'),\n",
     "    (2, -73.9, 40.8, 'Bob'),\n",
     "    (3, -74.1, 40.6, 'Carol')\n",
@@ -349,17 +349,23 @@
    "id": "2e93fe6a-b0a7-4ec0-952c-dde9edcacdc4",
    "metadata": {},
    "source": [
-    "Notice how each customer has two rows - one for each of the two closest restaurants.\n",
-    "\n",
-    "## Files\n",
+    "Notice how each customer has two rows - one for each of the two closest restaurants."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cb1e53b",
+   "metadata": {},
+   "source": [
+    "## GeoParquet support\n",
     "\n",
-    "You can read GeoParquet files with SedonaDB, see the following example:\n",
+    "You can also read GeoParquet files with SedonaDB with `read_parquet()`\n",
     "\n",
     "```python\n",
-    "df = sd.read_parquet(\"some_file.parquet\")\n",
+    "df = sd.read_parquet(\"DATA_FILE.parquet\")\n",
     "```\n",
     "\n",
-    "Once you read the file, you can easily expose it as a view and query it with spatial SQL, as we demonstrated in the example above."
+    "Once you read the file, you can easily expose it as a view and query it with spatial SQL, as we demonstrated in the example above.\n"
    ]
   }
  ],