Copilot commented on code in PR #64:
URL: https://github.com/apache/sedona-db/pull/64#discussion_r2341835780
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+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.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
+```
+
+Read in the Overture divisions table into a SedonaDB DataFrame and create a
view:
+
+```python
+sd.read_parquet(
+
"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
+).to_view("division_area")
+```
+
+Let’s now run some spatial queries.
+
+**Read from GeoPandas DataFrame**
+
+This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.
+
+Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:
+
+```python
Review Comment:
The `gpd` module is used but never imported. Add `import geopandas as gpd`
to the code block or in the imports section.
```suggestion
```python
import geopandas as gpd
```
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+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.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
+```
+
+Read in the Overture divisions table into a SedonaDB DataFrame and create a
view:
+
+```python
+sd.read_parquet(
+
"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
+).to_view("division_area")
+```
+
+Let’s now run some spatial queries.
+
+**Read from GeoPandas DataFrame**
+
+This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.
+
+Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:
+
+```python
+path =
"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/natural-earth/files/natural-earth_cities.fgb";
+gdf = gpd.read_file(path)
+```
+
+Now convert the GeoPandas DataFrame to a SedonaDB DataFrame and view three
rows of content:
+
+```
Review Comment:
Missing language identifier for the code block. Should be ```python instead
of just ```.
```suggestion
```python
```
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+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.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
+```
+
+Read in the Overture divisions table into a SedonaDB DataFrame and create a
view:
+
+```python
+sd.read_parquet(
+
"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
+).to_view("division_area")
+```
+
+Let’s now run some spatial queries.
+
+**Read from GeoPandas DataFrame**
+
+This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.
+
+Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:
+
+```python
+path =
"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/natural-earth/files/natural-earth_cities.fgb";
+gdf = gpd.read_file(path)
+```
+
+Now convert the GeoPandas DataFrame to a SedonaDB DataFrame and view three
rows of content:
+
+```
+df = sd.create_data_frame(gdf)
+df.show(3)
+
+┌──────────────┬──────────────────────────────┐
+│ name ┆ geometry │
+│ utf8 ┆ geometry │
+╞══════════════╪══════════════════════════════╡
+│ Vatican City ┆ POINT(12.4533865 41.9032822) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ San Marino ┆ POINT(12.4417702 43.9360958) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ Vaduz ┆ POINT(9.5166695 47.1337238) │
+└──────────────┴──────────────────────────────┘
+```
+
+## Spatial queries
+
+Let’s see how to run spatial operations like filtering, joins, and clustering
algorithms.
+
+***Spatial filtering***
+
+Let’s run a spatial filtering operation to fetch all the objects in the
following polygon:
+
+```python
+nova_scotia_bbox_wkt = "POLYGON((-66.5 43.4, -66.5 47.1, -59.8 47.1, -59.8
43.4, -66.5 43.4))"
+
+ns = sd.sql(f"""
+SELECT country, region, names, geometry FROM division_area
+WHERE ST_Intersects(geometry,
ST_SetSRID(ST_GeomFromText('{nova_scotia_bbox_wkt}'), 4326))
+""")
+```
+
+Take a look at the data contained in the `ns` DataFrame:
+
+```
+ns.show(3)
+
+┌──────────┬──────────┬──────────────────────────────────────┬─────────────────────────────────────┐
+│ country ┆ region ┆ names ┆
geometry │
+│ utf8view ┆ utf8view ┆ struct(primary utf8, common map(fie… ┆
geometry │
+╞══════════╪══════════╪══════════════════════════════════════╪═════════════════════════════════════╡
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-117.8317675 49.000… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-59.7502166 44.2338… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ CA-NS ┆ {primary: Seal Island, common: , ru… ┆
POLYGON((-66.0528452 43.4531336,-6… │
+└──────────┴──────────┴──────────────────────────────────────┴─────────────────────────────────────┘
+```
+
+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.
+
+***K-nearest neighbors (KNN) joins***
+
+Create `restaurants` and `customers` tables so we can demonstrate the KNN join
functionality.
+
+```sql
+CREATE table restaurants AS (
+ SELECT * FROM (VALUES
+ (101, ST_Point(-74.01, 40.71), 'Pizza Palace'),
+ (102, ST_Point(-73.99, 40.69), 'Burger Barn'),
+ (103, ST_Point(-74.02, 40.72), 'Taco Town'),
+ (104, ST_Point(-73.98, 40.75), 'Sushi Spot'),
+ (105, ST_Point(-74.05, 40.68), 'Deli Direct')
+ ) AS t(id, location, name)
+)
+
+CREATE table customers AS (
+ SELECT * FROM (VALUES
+ (1, ST_Point(-74.0, 40.7), 'Alice'),
+ (2, ST_Point(-73.9, 40.8), 'Bob'),
+ (3, ST_Point(-74.1, 40.6), 'Carol')
+ ) AS t(id, location, name)
+)
+```
+
+Perform a KNN join to identify the two restaurants that are nearest to each
customer:
+
+```sql
+SELECT
+ c.name AS customer,
+ r.name AS restaurant
+FROM customers c, restaurants r
+WHERE ST_KNN(c.location, r.location, 2, false)
+ORDER BY c.name, r.name;
Review Comment:
[nitpick] Similar to the previous SQL block, this should be wrapped in a
Python code block with sd.sql() to maintain consistency with the programming
guide's Python-focused approach.
```suggestion
```python
sd.sql("""
SELECT
c.name AS customer,
r.name AS restaurant
FROM customers c, restaurants r
WHERE ST_KNN(c.location, r.location, 2, false)
ORDER BY c.name, r.name;
""")
```
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+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.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
+```
+
+Read in the Overture divisions table into a SedonaDB DataFrame and create a
view:
+
+```python
+sd.read_parquet(
+
"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
+).to_view("division_area")
+```
+
+Let’s now run some spatial queries.
+
+**Read from GeoPandas DataFrame**
+
+This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.
+
+Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:
+
+```python
+path =
"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/natural-earth/files/natural-earth_cities.fgb";
+gdf = gpd.read_file(path)
+```
+
+Now convert the GeoPandas DataFrame to a SedonaDB DataFrame and view three
rows of content:
+
+```
+df = sd.create_data_frame(gdf)
+df.show(3)
+
+┌──────────────┬──────────────────────────────┐
+│ name ┆ geometry │
+│ utf8 ┆ geometry │
+╞══════════════╪══════════════════════════════╡
+│ Vatican City ┆ POINT(12.4533865 41.9032822) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ San Marino ┆ POINT(12.4417702 43.9360958) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ Vaduz ┆ POINT(9.5166695 47.1337238) │
+└──────────────┴──────────────────────────────┘
+```
+
+## Spatial queries
+
+Let’s see how to run spatial operations like filtering, joins, and clustering
algorithms.
+
+***Spatial filtering***
+
+Let’s run a spatial filtering operation to fetch all the objects in the
following polygon:
+
+```python
+nova_scotia_bbox_wkt = "POLYGON((-66.5 43.4, -66.5 47.1, -59.8 47.1, -59.8
43.4, -66.5 43.4))"
+
+ns = sd.sql(f"""
+SELECT country, region, names, geometry FROM division_area
+WHERE ST_Intersects(geometry,
ST_SetSRID(ST_GeomFromText('{nova_scotia_bbox_wkt}'), 4326))
+""")
+```
+
+Take a look at the data contained in the `ns` DataFrame:
+
+```
+ns.show(3)
+
+┌──────────┬──────────┬──────────────────────────────────────┬─────────────────────────────────────┐
+│ country ┆ region ┆ names ┆
geometry │
+│ utf8view ┆ utf8view ┆ struct(primary utf8, common map(fie… ┆
geometry │
+╞══════════╪══════════╪══════════════════════════════════════╪═════════════════════════════════════╡
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-117.8317675 49.000… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-59.7502166 44.2338… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ CA-NS ┆ {primary: Seal Island, common: , ru… ┆
POLYGON((-66.0528452 43.4531336,-6… │
+└──────────┴──────────┴──────────────────────────────────────┴─────────────────────────────────────┘
+```
+
+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.
+
+***K-nearest neighbors (KNN) joins***
+
+Create `restaurants` and `customers` tables so we can demonstrate the KNN join
functionality.
+
+```sql
+CREATE table restaurants AS (
+ SELECT * FROM (VALUES
+ (101, ST_Point(-74.01, 40.71), 'Pizza Palace'),
+ (102, ST_Point(-73.99, 40.69), 'Burger Barn'),
+ (103, ST_Point(-74.02, 40.72), 'Taco Town'),
+ (104, ST_Point(-73.98, 40.75), 'Sushi Spot'),
+ (105, ST_Point(-74.05, 40.68), 'Deli Direct')
+ ) AS t(id, location, name)
+)
Review Comment:
[nitpick] The SQL code blocks show table creation statements but the context
suggests these should be executed through the SedonaDB Python interface
(sd.sql()). Consider wrapping these in Python code blocks with sd.sql() calls
for consistency with the rest of the guide.
```suggestion
```python
sd.sql("""
CREATE table restaurants AS (
SELECT * FROM (VALUES
(101, ST_Point(-74.01, 40.71), 'Pizza Palace'),
(102, ST_Point(-73.99, 40.69), 'Burger Barn'),
(103, ST_Point(-74.02, 40.72), 'Taco Town'),
(104, ST_Point(-73.98, 40.75), 'Sushi Spot'),
(105, ST_Point(-74.05, 40.68), 'Deli Direct')
) AS t(id, location, name)
)
""")
```
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