Cordial and Snowflake Bidirectional Data Integration for Real-Time Customer Data

name: app_environment
channels:
  - snowflake
dependencies:
  - matplotlib=*
  - modin=0.28.1
  - seaborn=*
  - snowflake=*


git clone [email protected]:Snowflake-Labs/sfguide-data-engineering-pipelines-with-pandas-on-snowflake.git


{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "1dde02fa-0044-4b20-b7bb-10f1a5b3fabb",
      "metadata": {
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        "name": "cell1"
      },
      "source": [
        "### Data Engineering Pipelines with pandas on Snowflake\n",
        "\n",
        "This demo is using the [Snowflake Sample TPC-H dataset](https://docs.snowflake.com/en/user-guide/sample-data-tpch) that should be in a shared database named `SNOWFLAKE_SAMPLE_DATA`. You can run this notebook in a Snowflake Notebook. \n",
        "\n",
        "During this demo you will learn how to use [pandas on Snowflake](https://docs.snowflake.com/developer-guide/snowpark/python/snowpark-pandas) to:\n",
        "* Create datframe from a Snowflake table\n",
        "* Aggregate and transform data to create new features\n",
        "* Save the result into a Snowflake table\n",
        "* Create a serverless task to schedule the feature engineering\n",
        "\n",
        "pandas on Snowflake is delivered through the Snowpark pandas API as part of the Snowpark Python library (preinstalled with Snowflake Notebooks), which enables scalable data processing of Python code within the Snowflake platform. \n",
        "\n",
        "Start by adding neccessary libraries using the `Packages` dropdown, the additional libraries needed for this notebook is: \n",
        "* `modin` (select version 0.28.1)\n",
        "* `snowflake`\n",
        "* `matplotlib`\n",
        "* `seaborn`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4039104e-54fc-411e-972e-0f5a2d884595",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell2"
      },
      "outputs": [],
      "source": [
        "import streamlit as st\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d66adbc4-2b92-4d7d-86a5-217ee78e061f",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell3"
      },
      "outputs": [],
      "source": [
        "# Snowpark Pandas API\n",
        "import modin.pandas as spd\n",
        "# Import the Snowpark pandas plugin for modin\n",
        "import snowflake.snowpark.modin.plugin\n",
        "\n",
        "from snowflake.snowpark.context import get_active_session\n",
        "# Create a snowpark session\n",
        "session = get_active_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "811abc04-f6b8-4ec4-8ad4-34af28ff8c31",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell4"
      },
      "outputs": [],
      "source": [
        "# Name of the sample database and the schema to be used\n",
        "SOURCE_DATA_PATH = \"SNOWFLAKE_SAMPLE_DATA.TPCH_SF1\"\n",
        "SAVE_DATA_PATH = \"SNOW_PANDAS_DE_QS.DATA\"\n",
        "# Make sure we use the created database and schema for temp tables etc\n",
        "session.use_schema(SAVE_DATA_PATH)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0721a789-63a3-4c90-b763-50b8a1e69c92",
      "metadata": {
        "collapsed": false,
        "name": "cell5"
      },
      "source": [
        "We will start by creating a number of features based on the customer orders using the line items.\n",
        "\n",
        "Start with the `LINEITEM` table to create these features so we will start by creating a Snowpark Pandas Datframe aginst it, select the columns we are interested in and then show info about the dataframe, the shape and the first rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a091f1b-505f-4b61-9088-e7fd08e16f83",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell6"
      },
      "outputs": [],
      "source": [
        "lineitem_keep_cols = ['L_ORDERKEY', 'L_LINENUMBER', 'L_PARTKEY', 'L_RETURNFLAG', 'L_QUANTITY', 'L_DISCOUNT', 'L_EXTENDEDPRICE']\n",
        "lineitem_df = spd.read_snowflake(f\"{SOURCE_DATA_PATH}.LINEITEM\")[lineitem_keep_cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f360d4de-21f4-4723-9778-ceb8683c81c8",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell7"
      },
      "outputs": [],
      "source": [
        "st.dataframe(lineitem_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be5d37e2-e990-4e71-b762-41a64845955f",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell8"
      },
      "outputs": [],
      "source": [
        "# Get info about the dataframe\n",
        "lineitem_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "618f45b8-a2a8-4d08-967e-945d2329335e",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell9"
      },
      "outputs": [],
      "source": [
        "print(f\"DataFrame shape: {lineitem_df.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e53fea0b-2f36-4662-a382-98938a74f2c2",
      "metadata": {
        "collapsed": false,
        "name": "cell10"
      },
      "source": [
        "## Data Cleaning - Filtering and Aggregation\n",
        "\n",
        "Taking a look at different values for `L_RETURNFLAG` and include only line items that was delivered (`N`) or returned (`R`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "codeCollapsed": false,
        "collapsed": false,
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        "name": "cell11"
      },
      "outputs": [],
      "source": [
        "print(lineitem_df.L_RETURNFLAG.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "122cb06a-3a08-4d32-8864-4c8ff8c046b4",
      "metadata": {
        "collapsed": false,
        "name": "cell12"
      },
      "source": [
        "Add a filter to the dataframe"
      ]
    },
    {
      "cell_type": "code",
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        "name": "cell13"
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      "outputs": [],
      "source": [
        "print(f\"Before Filtering: {len(lineitem_df)} rows\")\n",
        "spd_lineitem = lineitem_df[lineitem_df['L_RETURNFLAG'] != 'A']\n",
        "print(f\"After Filtering: {len(spd_lineitem)} rows\")\n",
        "st.dataframe(spd_lineitem.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f802173-162f-4dff-8567-ade65b9f57f1",
      "metadata": {
        "collapsed": false,
        "name": "cell14"
      },
      "source": [
        "To track the actual discount a customer gets per order, we need to calculate that in a new column by taking the product of the amount of discount (`L_DISCOUNT`), numbers sold (`L_QUANTITY`), and the price of item (`L_EXTENDEDPRICE`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58f45f3d-3633-424e-b777-467a2ba0b22d",
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        "collapsed": false,
        "language": "python",
        "name": "cell15"
      },
      "outputs": [],
      "source": [
        "spd_lineitem['DISCOUNT_AMOUNT'] = spd_lineitem['L_DISCOUNT'] * spd_lineitem['L_QUANTITY'] * spd_lineitem['L_EXTENDEDPRICE']\n",
        "st.dataframe(spd_lineitem.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6ec9d862-e957-42b9-9d86-03f2ad3501f7",
      "metadata": {
        "collapsed": false,
        "name": "cell16"
      },
      "source": [
        "Now we want to compute the aggregate of items and discount amount, grouped by order key and return flag.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "578cbdf7-a655-416b-87da-417f7edd35bb",
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        "name": "cell17"
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      "outputs": [],
      "source": [
        "# Aggregations we want to do\n",
        "column_agg = {\n",
        "                'L_QUANTITY':['sum'], # Total Items Ordered  \n",
        "                'DISCOUNT_AMOUNT': ['sum'] # Total Discount Amount\n",
        "             }\n",
        "\n",
        "# Apply the aggregation\n",
        "spd_lineitem_agg = spd_lineitem.groupby(by=['L_ORDERKEY', 'L_RETURNFLAG'], as_index=False).agg(column_agg)\n",
        "\n",
        "# Rename the columns\n",
        "spd_lineitem_agg.columns = ['L_ORDERKEY', 'L_RETURNFLAG', 'NBR_OF_ITEMS', 'TOT_DISCOUNT_AMOUNT']\n",
        "st.dataframe(spd_lineitem_agg.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00dd1299-9bb2-4aba-9f37-b04ca3639892",
      "metadata": {
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        "name": "cell18"
      },
      "source": [
        "## Data Transformation - Pivot and reshape\n",
        "\n",
        "We want to separate the `NBR_OF_ITEMS` and `TOT_DISCOUNT_AMOUNT` by `L_RETURNFLAG` so we have one column for each uinique `L_RETURNFLAG` value.  \n",
        "Using the **pivot_table** method will give us one column for each unique value in `RETURN_FLAG`"
      ]
    },
    {
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      "execution_count": null,
      "id": "7f586e8a-017b-4672-80a1-bcc9430a87c3",
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        "name": "cell19"
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      "outputs": [],
      "source": [
        "# This will make L_ORDERKEY the index\n",
        "spd_lineitem_agg_pivot_df = spd_lineitem_agg.pivot_table(\n",
        "                                values=['NBR_OF_ITEMS', 'TOT_DISCOUNT_AMOUNT'], \n",
        "                                index=['L_ORDERKEY'],\n",
        "                                columns=['L_RETURNFLAG'], \n",
        "                                aggfunc=\"sum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "38dd144f-b18b-4673-b8c0-7db6d237ae59",
      "metadata": {
        "collapsed": false,
        "name": "cell20"
      },
      "source": [
        "The **pivot_table** method returns subcolumns and by renaming the columns we will get rid of those, and have one unique columns for each value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6166f8b0-fc8c-451e-9780-3e1f634ccbdd",
      "metadata": {
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        "name": "cell21"
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      "outputs": [],
      "source": [
        "spd_lineitem_agg_pivot_df.columns = ['NBR_OF_ITEMS_N', 'NBR_OF_ITEMS_R','TOT_DISCOUNT_AMOUNT_N','TOT_DISCOUNT_AMOUNT_R']\n",
        "# Move L_ORDERKEY back to column\n",
        "spd_lineitem_agg_pivot = spd_lineitem_agg_pivot_df.reset_index(names=['L_ORDERKEY'])\n",
        "st.dataframe(spd_lineitem_agg_pivot.head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1657bbc7-caf2-461c-9302-6f8d2187e0af",
      "metadata": {
        "collapsed": false,
        "name": "cell22"
      },
      "source": [
        "## Combine lineitem with orders information\n",
        "\n",
        "Load `ORDERS` table and join with dataframe with transformed lineitem information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c910ac10-38b3-4aa4-a7d2-6321243a4a60",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell23"
      },
      "outputs": [],
      "source": [
        "spd_order = spd.read_snowflake(f\"{SOURCE_DATA_PATH}.ORDERS\")\n",
        "# Drop unused columns \n",
        "spd_order = spd_order.drop(['O_ORDERPRIORITY', 'O_CLERK', 'O_SHIPPRIORITY', 'O_COMMENT'], axis=1)\n",
        "# Use streamlit to display the dataframe\n",
        "st.dataframe(spd_order.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97d52cd4-a71b-4c72-9137-accdf54b571b",
      "metadata": {
        "collapsed": false,
        "name": "cell24"
      },
      "source": [
        "Use **merge** to join the two dataframes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6aee6f94-f33b-4492-9f89-2808c05f07d4",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell25"
      },
      "outputs": [],
      "source": [
        "# Join dataframes\n",
        "spd_order_items = spd_lineitem_agg_pivot.merge(spd_order,\n",
        "                                               left_on='L_ORDERKEY', \n",
        "                                               right_on='O_ORDERKEY', \n",
        "                                               how='left')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3adc0331-1879-452f-9cc6-dd69f6824974",
      "metadata": {
        "collapsed": false,
        "name": "cell26"
      },
      "source": [
        "Drop the `L_ORDERKEY`column, it has the same values as `O_ORDERKEY`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8504a44d-d687-4c8d-af78-4b802901a168",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell27"
      },
      "outputs": [],
      "source": [
        "spd_order_items.drop('L_ORDERKEY', axis=1, inplace=True)\n",
        "st.write(f\"DataFrame shape: {spd_order_items.shape}\")\n",
        "st.dataframe(spd_order_items.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a8b050f9-77a9-460a-853b-888963e6a214",
      "metadata": {
        "collapsed": false,
        "name": "cell28"
      },
      "source": [
        "More aggregations grouped by customer (`O_CUSTKEY`)\n",
        "* Total items delivered by customer\n",
        "* Average items delivered by customer\n",
        "* Total items returned by customer\n",
        "* Average items returned by customer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36e32341-cc93-4b5d-a5f1-15a15d8ddf69",
      "metadata": {
        "codeCollapsed": false,
        "collapsed": false,
        "language": "python",
        "name": "cell29"
      },
      "outputs": [],
      "source": [
        "# Aggregations we want to do\n",
        "column_agg = {\n",
        "                'O_ORDERKEY':['count'], \n",
        "                'O_TOTALPRICE': ['sum' ,'mean', 'median'],\n",
        "                'NBR_OF_ITEMS_N': ['sum' ,'mean', 'median'],\n",
        "                'NBR_OF_ITEMS_R': ['sum' ,'mean', 'median'],\n",
        "                'TOT_DISCOUNT_AMOUNT_N': ['sum'],\n",
        "                'TOT_DISCOUNT_AMOUNT_R': ['sum']\n",
        "            }\n",
        "\n",
        "# Apply the aggregation\n",
        "spd_order_profile = spd_order_items.groupby(by='O_CUSTKEY', as_index=False).agg(column_agg)\n",
        "\n",
        "# Rename the columns\n",
        "spd_order_profile.columns = ['O_CUSTKEY', 'NUMBER_OF_ORDERS', 'TOT_ORDER_AMOUNT', 'AVG_ORDER_AMOUNT', 'MEDIAN_ORDER_AMOUNT', \n",
        "                             'TOT_ITEMS_DELIVERED', 'AVG_ITEMS_DELIVERED', 'MEDIAN_ITEMS_DELIVERED', \n",
        "                             'TOT_ITEMS_RETURNED', 'AVG_ITEMS_RETURNED', 'MEDIAN_ITEMS_RETURNED',\n",
        "                             'TOT_DISCOUNT_AMOUNT_N', 'TOT_DISCOUNT_AMOUNT_R']\n",
        "st.dataframe(spd_order_profile.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "daf0e441-43d1-4729-bc20-aea8f123befa",
      "metadata": {
        "collapsed": false,
        "name": "cell30"
      },
      "source": [
        "Calculate the total and average discount"
      ]
    }