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

Applies to: SQL Server 2017 (14.x) and later Azure SQL Managed Instance

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.

In this article, you'll learn how to:

• 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.
import pyodbc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.spatial import distance as sci_distance
from sklearn import cluster as sk_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 = '''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 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:

Python tutorial: Create a predictive model