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titletitleSuffixdescriptionauthorms.authorms.datems.servicems.subservicems.topicms.devlangmonikerRange
Python tutorial: Prepare cluster data
SQL machine learning
In part two of this four-part tutorial series, you'll prepare SQL data to perform clustering in Python with SQL machine learning.
VanMSFT
vanto
04/17/2023
sql
machine-learning
tutorial
python
>=sql-server-2017||>=sql-server-linux-ver15||=azuresqldb-mi-current

Python tutorial: Prepare data to categorize customers with SQL machine learning

[!INCLUDE SQL Server 2017 SQL MI]

::: moniker range=">=sql-server-ver15||>=sql-server-linux-ver15" In part two of this four-part tutorial series, you'll restore and prepare the data from a 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 or on Big Data Clusters. ::: moniker-end ::: moniker range="=sql-server-2017" In part two of this four-part tutorial series, you'll restore and prepare the data from a 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. ::: moniker-end ::: moniker range="=azuresqldb-mi-current" In part two of this four-part tutorial series, you'll restore and prepare the data from a database using Python. Later in this series, you'll use this data to train and deploy a clustering model in Python with Azure SQL Managed Instance Machine Learning Services. ::: moniker-end

In this article, you'll learn how to:

[!div class="checklist"]

  • Separate customers along different dimensions using Python
  • Load the data from the database into a Python data frame

In part one, you installed the prerequisites and restored 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 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.importpyodbcimportmatplotlib.pyplotaspltimportnumpyasnpimportpandasaspdfromscipy.spatialimportdistanceassci_distancefromsklearnimportclusterassk_cluster################################################################################################## Connect to DB and select data################################################################################################# Connection string to connect to SQL Server named instance.conn_str=pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server}; SERVER=<server>; DATABASE=tpcxbb_1gb; UID=<username>; PWD=<password>') input_query='''SELECTss_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 frequencyFROM( 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) ordersLEFT 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_moneyFROM store_returnsGROUP 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 Pandas read_sql function. As part of the process, you'll use the column information you defined in the previous script.

customer_data=pd.read_sql(input_query, conn_str)

Now display the beginning of the data frame to verify it looks correct.

print("Data frame:", customer_data.head(n=5))
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.

Next steps

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

  • Separate customers along different dimensions using Python
  • Load the data from the database into a Python data frame

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

[!div class="nextstepaction"] Python tutorial: Create a predictive model

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