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Merge pull request #60 from neo4j/snowpark-example
Add Snowpark notebook example
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‎examples/snowpark-nvl-example.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0d3ffc27",
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"metadata": {},
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"source": [
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"# Visualizing Snowflake Tables"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6b83277d",
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"metadata": {},
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"source": [
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"\n",
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"This is a brief but complete example of how to visualize graphs represented by tables in Snowflake, using the Graph Visualization for Python library for Neo4j."
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]
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},
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{
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"cell_type": "markdown",
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"id": "168b2f0ec9520f4a",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"We will start by installing the necessary Python library requirements."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39e8a71b",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install snowflake-snowpark-python # Requires Python version <= 3.11\n",
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"%pip install neo4j-viz"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c91214441edff2d",
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"metadata": {},
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"source": [
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"We can now proceed to set up our connection to Snowflake by initializing a new session.\n",
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"Please not that you may need more or fewer connection parameters depending on your Snowflake configuration."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "801d0bed",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"from snowflake.snowpark import Session\n",
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"\n",
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"# Configure according to your own setup\n",
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"connection_parameters = {\n",
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" \"account\": os.environ.get(\"SNOWFLAKE_ACCOUNT\"),\n",
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" \"user\": os.environ.get(\"SNOWFLAKE_USER\"),\n",
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" \"password\": os.environ.get(\"SNOWFLAKE_PASSWORD\"),\n",
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" \"role\": os.environ.get(\"SNOWFLAKE_ROLE\"),\n",
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" \"warehouse\": os.environ.get(\"SNOWFLAKE_WAREHOUSE\"),\n",
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"}\n",
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"\n",
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"session = Session.builder.configs(connection_parameters).create()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ff57d28a917c569",
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"metadata": {},
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"source": [
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"Now can we create a new Snowflake database where we can put our little example tables.\n",
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"If you already have a database you want to use, you can skip this step."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "41ad4289420a9b36",
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"metadata": {},
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"outputs": [],
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"source": [
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"session.sql(\n",
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" \"CREATE DATABASE IF NOT EXISTS nvl_example DATA_RETENTION_TIME_IN_DAYS = 1\"\n",
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").collect()\n",
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"session.sql(\"USE DATABASE nvl_example\").collect()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "365a1c31",
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"metadata": {},
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"source": [
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"## Creating tables\n",
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"\n",
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"Next we will create a new table for the nodes in our graph, that will represent products of various categories."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d935b3d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"session.sql(\n",
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" \"CREATE OR REPLACE TABLE products (id INT, name VARCHAR, category INT)\"\n",
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").collect()\n",
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"\n",
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"session.sql(\"\"\"\n",
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"INSERT INTO products VALUES\n",
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"(1, 'Product 1', 1),\n",
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"(2, 'Product 1A', 1),\n",
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"(3, 'Product 1B', 1),\n",
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"(4, 'Product 2', 2),\n",
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"(5, 'Product 2A', 2),\n",
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"(6, 'Product 2B', 2),\n",
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"(7, 'Product 3', 3),\n",
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"(8, 'Product 3A', 3),\n",
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"(9, 'Product 3B', 3),\n",
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"(10, 'Product 4', 4),\n",
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"(11, 'Product 4A', 4),\n",
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"(12, 'Product 4B', 4)\n",
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"\"\"\").collect()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf08716eb4275659",
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"metadata": {},
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"source": [
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"Some of the products, are \"subproducts\" of certain parent products.\n",
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"We now create a table that encodes these \"PARENT\" relationships between the products."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be2ac16d3bd41e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"session.sql(\n",
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" \"CREATE OR REPLACE TABLE parents (source INT, target INT, type VARCHAR)\"\n",
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").collect()\n",
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"\n",
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"session.sql(\"\"\"\n",
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"INSERT INTO parents VALUES\n",
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"(2, 1, 'PARENT'),\n",
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"(3, 1, 'PARENT'),\n",
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"(5, 4, 'PARENT'),\n",
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"(6, 4, 'PARENT'),\n",
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"(8, 7, 'PARENT'),\n",
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"(9, 7, 'PARENT'),\n",
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"(11, 10, 'PARENT'),\n",
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"(12, 10, 'PARENT')\n",
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"\"\"\").collect()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a28bd5aa",
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"metadata": {},
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"source": [
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"## Fetching the data\n",
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"\n",
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"Next we fetch our tables from Snowflake and convert them to pandas DataFrames.\n",
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"Additionally, we rename the most of the table columns so that they are named according to the `neo4j-viz` API."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "deb6353193e2338b",
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"metadata": {},
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"outputs": [],
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"source": [
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"products_df = (\n",
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" session.table(\"products\")\n",
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" .to_pandas()\n",
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" .rename(columns={\"ID\": \"id\", \"NAME\": \"caption\"})\n",
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")\n",
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"parents_df = (\n",
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" session.table(\"parents\")\n",
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" .to_pandas()\n",
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" .rename(columns={\"SOURCE\": \"source\", \"TARGET\": \"target\", \"TYPE\": \"caption\"})\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "950e0e76cfcaf3d6",
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"metadata": {},
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"source": [
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"## Rendering the visualization\n",
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"With only one command we can now create a `VisualizationGraph` from these tables representing nodes and relationships.\n",
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"In order to enhance the visualization, we will also be utilizing the `color_nodes` function, which will assign a distinct color to each product category."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2322065c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from neo4j_viz.pandas import from_dfs\n",
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"\n",
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"VG = from_dfs(products_df, parents_df)\n",
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"\n",
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"# Using the default Neo4j color scheme\n",
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"VG.color_nodes(\"CATEGORY\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "da39f29deb1569e2",
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"metadata": {},
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"source": [
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"Let us now render our graph, using only default render options."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e8b0f4c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"VG.render()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac4c5e35a602ede2",
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"metadata": {},
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"source": [
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"You can scroll to zoom in and out in the visualization, and click-and-drag nodes to move them."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8aa5576fa3b25383",
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"metadata": {},
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"source": [
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"## Cleanup\n",
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"\n",
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"Lastly, we clean up the example database we created."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6eb218e892d420e",
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"metadata": {},
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"outputs": [],
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"source": [
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"session.sql(\"DROP DATABASE IF EXISTS nvl_example\").collect()\n",
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"session.close()"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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