When combined with the connector, Pandas can be used to generate data frames that contain your Sage 300 data. Once created, a data frame can be passed to various other Python packages.

Connecting

Pandas relies on an SQLAlchemy engine to execute queries. Before you can use Pandas you must import it:

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("sage300:///?User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

Querying Data

In Pandas, SELECT queries are provided in a call to the read_sql() method, alongside a relevant connection object. Pandas executes the query on that connection, and returns the results in the form of a data frame, which can be used for a variety of purposes.

df = pd.read_sql("""
	SELECT
	   InvoiceUniquifier,
	   ApprovedLimit,
     $exNumericCol;
	FROM OEInvoices;""", engine)
print(df)