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title Tutorial: Prepare data to perform clustering in Python
description In part two of this four-part tutorial series, you'll prepare the data from a SQL Server database to perform clustering in Python with SQL Server Machine Learning Services.
ms.prod sql
ms.technology machine-learning
ms.devlang python
ms.date 08/27/2019
ms.topic tutorial
author garyericson
ms.author garye
ms.reviewer davidph
monikerRange >=sql-server-ver15||=sqlallproducts-allversions

Tutorial: Prepare data to perform clustering in Python with SQL Server Machine Learning Services

In part two of this four-part tutorial series, you'll import and prepare the data from a SQL database using Python. Later in this series, you'll use this data to train and deploy a clustering model in Python with SQL Server Machine Learning Services.

In this article, you'll learn how to:

[!div class="checklist"]

  • Separate customers along different dimensions using R
  • Load the data from the Azure SQL database into an R data frame

In part one, you installed the prerequisites and imported the sample database.

In part three, you'll learn how to create and train a K-Means clustering model in Python.

In part four, you'll learn how to create a stored procedure in a SQL database that can perform clustering in Python based on new data.

Prerequisites

  • Part two of this tutorial assumes you have fulfilled the prerequisites of part one.

Separate customers

To prepare for clustering customers, you'll first separate customers along the following dimensions:

  • orderRatio = return order ratio (total number of orders partially or fully returned versus the total number of orders)
  • itemsRatio = return item ratio (total number of items returned versus the number of items purchased)
  • monetaryRatio = return amount ratio (total monetary amount of items returned versus the amount purchased)
  • frequency = return frequency

Open a new notebook in Azure Data Studio and enter the following script.

In the connection string, replace connection details as needed.

# Load packages.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import revoscalepy as revoscale
from scipy.spatial import distance as sci_distance
from sklearn import cluster as sk_cluster

def perform_clustering():
    ################################################################################################

    ## Connect to DB and select data

    ################################################################################################

    # Connection string to connect to SQL Server named instance.
    conn_str = 'Driver=SQL Server;Server=localhost;Database=tpcxbb_1gb;Trusted_Connection=True;'

    input_query = '''SELECT
    ss_customer_sk AS customer,
    ROUND(COALESCE(returns_count / NULLIF(1.0*orders_count, 0), 0), 7) AS orderRatio,
    ROUND(COALESCE(returns_items / NULLIF(1.0*orders_items, 0), 0), 7) AS itemsRatio,
    ROUND(COALESCE(returns_money / NULLIF(1.0*orders_money, 0), 0), 7) AS monetaryRatio,
    COALESCE(returns_count, 0) AS frequency
    FROM
    (
      SELECT
        ss_customer_sk,
        -- return order ratio
        COUNT(distinct(ss_ticket_number)) AS orders_count,
        -- return ss_item_sk ratio
        COUNT(ss_item_sk) AS orders_items,
        -- return monetary amount ratio
        SUM( ss_net_paid ) AS orders_money
      FROM store_sales s
      GROUP BY ss_customer_sk
    ) orders
    LEFT OUTER JOIN
    (
      SELECT
        sr_customer_sk,
        -- return order ratio
        count(distinct(sr_ticket_number)) as returns_count,
        -- return ss_item_sk ratio
        COUNT(sr_item_sk) as returns_items,
        -- return monetary amount ratio
        SUM( sr_return_amt ) AS returns_money
    FROM store_returns
    GROUP BY sr_customer_sk ) returned ON ss_customer_sk=sr_customer_sk'''


    # Define the columns we wish to import.
    column_info = {
        "customer": {"type": "integer"},
        "orderRatio": {"type": "integer"},
        "itemsRatio": {"type": "integer"},
        "frequency": {"type": "integer"}
    }

Load the data into a data frame

Results from the query are returned to Python using the revoscalepy RxSqlServerData function. As part of the process, you'll use the column information you defined in the previous script.

Append the following to the previous script.

    data_source = revoscale.RxSqlServerData(sql_query=input_query, column_Info=column_info,
                                            connection_string=conn_str)
    revoscale.RxInSqlServer(connection_string=conn_str, num_tasks=1, auto_cleanup=False)
    # import data source and convert to pandas dataframe.
    customer_data = pd.DataFrame(revoscale.rx_import(data_source))
    print("Data frame:", customer_data.head(n=5))

Run the function, and you should see results similar to the following.

perform_clustering()
Rows Read: 37336, Total Rows Processed: 37336, Total Chunk Time: 0.172 seconds
Data frame:     customer  orderRatio  itemsRatio  monetaryRatio  frequency
0    29727.0    0.000000    0.000000       0.000000          0
1    97643.0    0.068182    0.078176       0.037034          3
2    57247.0    0.000000    0.000000       0.000000          0
3    32549.0    0.086957    0.068657       0.031281          4
4     2040.0    0.000000    0.000000       0.000000          0

Clean up resources

If you're not going to continue with this tutorial, delete the tpcxbb_1gb database from your SQL Server.

Next steps

In part two of this tutorial series, you completed these steps:

  • Separate customers along different dimensions using R
  • Load the data from the Azure SQL database into an R data frame

To create a machine learning model that uses this customer data, follow part three of this tutorial series:

[!div class="nextstepaction"] Tutorial: Create a predictive model in Python with SQL Server Machine Learning Services