MrPowers commented on code in PR #64:
URL: https://github.com/apache/sedona-db/pull/64#discussion_r2345074538
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docs/programming-guide.ipynb:
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@@ -0,0 +1,387 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1932983e-1cd2-41d0-a5eb-0537b3ac3feb",
+ "metadata": {},
+ "source": [
+ "# SedonaDB Guide\n",
+ "\n",
+ "This page explains how to process vector data with SedonaDB.\n",
+ "\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",
+ "\n",
+ "## Establish SedonaDB connection\n",
+ "\n",
+ "Here’s how to create the SedonaDB connection:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "53c3b7a8-c42a-407a-a454-6ee1e943fbcc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sedonadb\n",
+ "\n",
+ "sd = sedonadb.connect()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7aeaa60f-2325-418c-8e72-4344bd4a75fe",
+ "metadata": {},
+ "source": [
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b3377767-d747-407c-92c0-8786c1998131",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = sd.sql(\"\"\"\n",
+ "SELECT * FROM (VALUES\n",
+ " ('one', ST_GeomFromWkt('POINT(1 2)')),\n",
+ " ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8,
-73.9 40.7, -74.0 40.7))')),\n",
+ " ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352
40.7306, -73.8561 40.8484)')))\n",
+ "AS t(val, point)\"\"\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f9e1319-2e7a-4d98-9df0-47a9a73cfff3",
+ "metadata": {},
+ "source": [
+ "Check the type of the DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e8be30ab-4818-4db8-bae2-83e973ad1b77",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sedonadb.dataframe.DataFrame"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8225ed1f-45a4-4915-a582-8ae191ec53ed",
+ "metadata": {},
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "151df287-4b2d-433e-9769-c3378df03b1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sd.read_parquet(\n",
+ "
\"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/\",\n",
+ " options={\"aws.skip_signature\": True, \"aws.region\":
\"us-west-2\"},\n",
+ ").to_view(\"division_area\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "858fcc66-816d-4c71-8875-82b74169eccd",
+ "metadata": {},
+ "source": [
+ "Let’s now run some spatial queries.\n",
+ "\n",
+ "**Read from GeoPandas DataFrame**\n",
+ "\n",
+ "This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.\n",
+ "\n",
+ "Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b81549f2-0f58-49e4-9011-8de6578c2b0e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import geopandas as gpd\n",
+ "\n",
+ "path =
\"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/natural-earth/files/natural-earth_cities.fgb\"\n";,
+ "gdf = gpd.read_file(path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2265f94b-ccb3-4634-8c52-a8799c68c76a",
+ "metadata": {},
+ "source": [
+ "Now convert the GeoPandas DataFrame to a SedonaDB DataFrame and view
three rows of content:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0e4819db-bf58-42d7-8b5b-f272d0f19266",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "┌──────────────┬──────────────────────────────┐\n",
+ "│ name ┆ geometry │\n",
+ "│ utf8 ┆ geometry │\n",
+ "╞══════════════╪══════════════════════════════╡\n",
+ "│ Vatican City ┆ POINT(12.4533865 41.9032822) │\n",
+ "├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ San Marino ┆ POINT(12.4417702 43.9360958) │\n",
+ "├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Vaduz ┆ POINT(9.5166695 47.1337238) │\n",
+ "└──────────────┴──────────────────────────────┘\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = sd.create_data_frame(gdf)\n",
+ "df.show(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6890bcc3-f3bd-4c47-bf86-2607bed5e480",
+ "metadata": {},
+ "source": [
+ "## Spatial queries\n",
+ "\n",
+ "Let’s see how to run spatial operations like filtering, joins, and
clustering algorithms.\n",
+ "\n",
+ "***Spatial filtering***\n",
+ "\n",
+ "Let’s run a spatial filtering operation to fetch all the objects in the
following polygon:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "8c8a4b48-8c4e-412e-900f-8c0f6f4ccc1d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+
"┌──────────┬──────────┬────────────────────────────────────────────────────────────────────────────┐\n",
+ "│ country ┆ region ┆ geometry
│\n",
+ "│ utf8view ┆ utf8view ┆ geometry
│\n",
+
"╞══════════╪══════════╪════════════════════════════════════════════════════════════════════════════╡\n",
+ "│ CA ┆ CA-NS ┆ POLYGON((-66.0528452 43.4531336,-66.0883401
43.3978188,-65.9647654 43.361… │\n",
+
"├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ CA ┆ CA-NS ┆ POLYGON((-66.0222822 43.5166842,-66.0252286
43.5100071,-66.0528452 43.453… │\n",
+
"├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ CA ┆ CA-NS ┆ POLYGON((-65.7451389 43.5336263,-65.7450818
43.5347004,-65.7449545 43.535… │\n",
+
"└──────────┴──────────┴────────────────────────────────────────────────────────────────────────────┘\n"
+ ]
+ }
+ ],
+ "source": [
+ "nova_scotia_bbox_wkt = (\n",
+ " \"POLYGON((-66.5 43.4, -66.5 47.1, -59.8 47.1, -59.8 43.4, -66.5
43.4))\"\n",
+ ")\n",
+ "\n",
+ "ns = sd.sql(f\"\"\"\n",
+ "SELECT country, region, geometry\n",
+ "FROM division_area\n",
+ "WHERE ST_Intersects(geometry,
ST_SetSRID(ST_GeomFromText('{nova_scotia_bbox_wkt}'), 4326))\n",
+ "\"\"\")\n",
+ "\n",
+ "ns.show(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32076e01-d807-40ed-8457-9d8c4244e89f",
+ "metadata": {},
+ "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",
+ "\n",
+ "Create `restaurants` and `customers` tables so we can demonstrate the KNN
join functionality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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",
+ " (101, -74.0, 40.7, 'Pizza Palace'),\n",
+ " (102, -73.99, 40.69, 'Burger Barn'),\n",
+ " (103, -74.02, 40.72, 'Taco Town'),\n",
+ " (104, -73.98, 40.75, 'Sushi Spot'),\n",
+ " (105, -74.05, 40.68, 'Deli Direct')\n",
+ ") AS t(id, lng, lat, name)\n",
+ "\"\"\")\n",
+ "sd.sql(\"drop view if exists restaurants\")\n",
+ "df.to_view(\"restaurants\")\n",
+ "\n",
+ "df = sd.sql(\"\"\"\n",
+ "SELECT name, ST_Point(lng, lat) AS location\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",
+ ") AS t(id, lng, lat, name)\n",
+ "\"\"\")\n",
+ "sd.sql(\"drop view if exists customers\")\n",
+ "df.to_view(\"customers\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e3bc4976-4245-432f-b265-7f6aa13f35b9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "┌───────┬───────────────────┐\n",
+ "│ name ┆ location │\n",
+ "│ utf8 ┆ geometry │\n",
+ "╞═══════╪═══════════════════╡\n",
+ "│ Alice ┆ POINT(-74 40.7) │\n",
+ "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Bob ┆ POINT(-73.9 40.8) │\n",
+ "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Carol ┆ POINT(-74.1 40.6) │\n",
+ "└───────┴───────────────────┘\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9df227d6-0972-457a-87e3-5a89802c460f",
+ "metadata": {},
+ "source": [
+ "Perform a KNN join to identify the two restaurants that are nearest to
each customer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "05565e15-ee18-431c-8fd2-673291d8d0ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "┌──────────┬──────────────┐\n",
+ "│ customer ┆ restaurant │\n",
+ "│ utf8 ┆ utf8 │\n",
+ "╞══════════╪══════════════╡\n",
+ "│ Alice ┆ Burger Barn │\n",
+ "├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Alice ┆ Pizza Palace │\n",
+ "├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Bob ┆ Pizza Palace │\n",
+ "├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Bob ┆ Sushi Spot │\n",
+ "├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Carol ┆ Deli Direct │\n",
+ "├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
+ "│ Carol ┆ Pizza Palace │\n",
+ "└──────────┴──────────────┘\n"
+ ]
+ }
+ ],
+ "source": [
+ "sd.sql(\"\"\"\n",
+ "SELECT\n",
+ " c.name AS customer,\n",
+ " r.name AS restaurant\n",
+ "FROM customers c, restaurants r\n",
+ "WHERE ST_KNN(c.location, r.location, 2, false)\n",
+ "ORDER BY c.name, r.name;\n",
+ "\"\"\").show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "\n",
+ "You can read GeoParquet files with SedonaDB, see the following
example:\n",
+ "\n",
+ "```python\n",
+ "df = sd.read_parquet(\"some_file.parquet\")\n",
+ "```\n",
+ "\n",
+ "Once you read the file, you can easily expose it as a view and query it
with spatial SQL."
Review Comment:
Good call, updated this language.
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