Added notebooks for quickstart

Getting started with Snowpark using Snowflake Notebooks/Getting Started with Snowpark using Snowflake notebooks.ipynb

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+{
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Streamlit Notebook",
+   "name": "streamlit"
+  }
+ },
+ "nbformat_minor": 5,
+ "nbformat": 4,
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "61f3eed1-51a2-4810-bd35-4e116de35cb3",
+   "metadata": {
+    "name": "getting_started",
+    "collapsed": false
+   },
+   "source": "Welcome to Snowflake! This guide helps you get started with Snowpark for data exploration and analysis. In this exercise, you will:\n\n * Load data from Snowflake tables into Snowpark DataFrames\n * Perform exploratory data analysis on Snowpark DataFrames\n * Pivot and join data from multiple tables using Snowpark DataFrames\n * Save transformed data into a Snowflake table"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "754efa75-b08b-4134-bdf1-cd0ae8fe6fef",
+   "metadata": {
+    "name": "step_1",
+    "collapsed": false
+   },
+   "source": "## Import Snowpark and create Snowpark session"
+  },
+  {
+   "cell_type": "code",
+   "id": "cde5f575-ee0e-4761-9f6e-11936d520c09",
+   "metadata": {
+    "language": "python",
+    "name": "imports",
+    "collapsed": false,
+    "codeCollapsed": false
+   },
+   "outputs": [],
+   "source": "import snowflake.snowpark as snowpark\nfrom snowflake.snowpark.functions import month,year,col,sum",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "3775908f-ca36-4846-8f38-5adca39217f2",
+   "metadata": {
+    "language": "python",
+    "name": "snowpark_session",
+    "collapsed": false,
+    "codeCollapsed": false
+   },
+   "source": "from snowflake.snowpark.context import get_active_session\nsession = get_active_session()",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43ac9528-f167-4842-b4ca-dd7c3af2dc48",
+   "metadata": {
+    "name": "step_2",
+    "collapsed": false
+   },
+   "source": "## Load `campaign_spend` and  `monthly_revenue` tables into Snowpark dataframes"
+  },
+  {
+   "cell_type": "code",
+   "id": "8d50cbf4-0c8d-4950-86cb-114990437ac9",
+   "metadata": {
+    "language": "python",
+    "name": "read_from_table",
+    "collapsed": false
+   },
+   "source": "snow_df_spend = session.table('campaign_spend')\nsnow_df_revenue = session.table('monthly_revenue')",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6146fbe8-e88b-4d8f-8322-505f7e7a5c53",
+   "metadata": {
+    "name": "step_3",
+    "collapsed": false
+   },
+   "source": "## Total Spend per Year and Month For All Channels\nLet's transform the campaign spend data so we can see total cost per year/month per channel using `group_by()` and `agg()` Snowpark DataFrame functions."
+  },
+  {
+   "cell_type": "code",
+   "id": "c695373e-ac74-4b62-a1f1-08206cbd5c81",
+   "metadata": {
+    "language": "python",
+    "name": "spend_per_channel",
+    "collapsed": false
+   },
+   "source": "snow_df_spend_per_channel = snow_df_spend.group_by(year('DATE'), month('DATE'),'CHANNEL').\\\n                                            agg(sum('TOTAL_COST').as_('TOTAL_COST')).\\\n                                            with_column_renamed('\"YEAR(DATE)\"',\"YEAR\").\\\n                                            with_column_renamed('\"MONTH(DATE)\"',\"MONTH\").\\\n                                            sort('YEAR','MONTH')\nsnow_df_spend_per_channel.show()",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc9fe0a-e400-4c22-90dc-7dd6820ad0a4",
+   "metadata": {
+    "name": "step_4",
+    "collapsed": false
+   },
+   "source": "## Total Spend per Year and Month\nLet's further transform the campaign spend data by `pivoting` on the `channel` dimension. This should give us the campaign spend for every month across all channels on the same row."
+  },
+  {
+   "cell_type": "code",
+   "id": "30e5ae75-40a6-4d0f-a456-07c4e23a6eda",
+   "metadata": {
+    "language": "python",
+    "name": "spend_per_month",
+    "codeCollapsed": false,
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "snow_df_spend_per_month = snow_df_spend_per_channel.pivot('CHANNEL',['search_engine','social_media','video','email']).\\\n                                                    sum('TOTAL_COST').\\\n                                                    sort('YEAR','MONTH')\n\nsnow_df_spend_per_month = snow_df_spend_per_month.select(\n        col(\"YEAR\"),\n        col(\"MONTH\"),\n        col(\"'search_engine'\").as_(\"SEARCH_ENGINE\"),\n        col(\"'social_media'\").as_(\"SOCIAL_MEDIA\"),\n        col(\"'video'\").as_(\"VIDEO\"),\n        col(\"'email'\").as_(\"EMAIL\")\n    )\n\nsnow_df_spend_per_month.show()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35572f55-82f9-4b54-9622-304d03d5f7d7",
+   "metadata": {
+    "name": "step_5",
+    "collapsed": false
+   },
+   "source": "## Total Revenue per Year and Month\nNow let's transform the revenue data into revenue per year/month using `group_by()` and `agg()` functions."
+  },
+  {
+   "cell_type": "code",
+   "id": "459ce8e4-38e3-4a9c-aea5-d879ba3c7a2b",
+   "metadata": {
+    "language": "python",
+    "name": "revenue_per_month",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "snow_df_revenue_per_month = snow_df_revenue.group_by('YEAR','MONTH').\\\n                                            agg(sum('REVENUE')).\\\n                                            sort('YEAR','MONTH').\\\n                                            with_column_renamed('SUM(REVENUE)','REVENUE')\nsnow_df_revenue_per_month.show()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e7c9d6-22b4-4343-927a-6bb330b169cb",
+   "metadata": {
+    "name": "step_6",
+    "collapsed": false
+   },
+   "source": "## Join Total Spend and Total Revenue per Year and Month Across All Channels\nNext let's `join` this `revenue` data with the transformed `campaign spend` data so we can analyze the spend and revenue data side by side. "
+  },
+  {
+   "cell_type": "code",
+   "id": "471359bc-0ac7-4fe1-83c2-644a81f9d135",
+   "metadata": {
+    "language": "python",
+    "name": "join_spend_and_revenue_per_month",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "snow_df_spend_and_revenue_per_month = snow_df_spend_per_month.join(snow_df_revenue_per_month, [\"YEAR\",\"MONTH\"])\nsnow_df_spend_and_revenue_per_month.show()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc7f8bf0-a28a-4b33-bad3-c24bb2bab87a",
+   "metadata": {
+    "name": "step_7",
+    "collapsed": false
+   },
+   "source": "## Examine DataFrame Explain Plan\nSnowpark makes it really convenient to look at the DataFrame query and execution plan using `explain()` Snowpark DataFrame function."
+  },
+  {
+   "cell_type": "code",
+   "id": "04f05b39-dbc2-435f-9fc7-50e6cae31ebc",
+   "metadata": {
+    "language": "python",
+    "name": "dataframe_explain",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "snow_df_spend_and_revenue_per_month.explain()",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b78b1ff6-ca5f-41a1-8373-3665d7db50b9",
+   "metadata": {
+    "name": "step_8",
+    "collapsed": false
+   },
+   "source": "## Save Transformed Data into Snowflake Table\nLet's save the transformed data into a Snowflake table `SPEND_AND_REVENUE_PER_MONTH`"
+  },
+  {
+   "cell_type": "code",
+   "id": "2a75cfbc-be9e-4915-b2cc-03df0670583f",
+   "metadata": {
+    "language": "python",
+    "name": "write_to_table",
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": "snow_df_spend_and_revenue_per_month.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1f5ce2c-6c76-4222-8563-ad012a5ba0f3",
+   "metadata": {
+    "name": "additional_resources",
+    "collapsed": false
+   },
+   "source": "## Continue your learning!\n\nThis notebook is simply a `Hello World` of `Data Engineering with Snowpark`. To learn advanced data engineering with Snowflake, hop on to https://quickstarts.snowflake.com/guide/data_engineering_with_notebooks/index.html."
+  }
+ ]
+}

Getting started with Snowpark using Snowflake Notebooks/environment.yml

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+name: app_environment
+channels:
+  - snowflake
+dependencies:
+  - snowflake=0.8.0