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

Článek
04/17/2023

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 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.

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

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