CData Python Connector for BigCommerce

Build 21.0.7930

From Pandas

When combined with the connector, Pandas can be used to generate data frames which contains your BigCommerce data. Once created, a data frame can be passed to various other python packages.

Connecting

Pandas will need to be imported before it can be used. Pandas will also rely on a SQLAlchemy engine when executing queries, as below:

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("bigcommerce:///?InitiateOAuth=GETANDREFRESH;OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;StoreId='YoUrSToREId';CallbackURL='http://localhost:33333'")

Querying Data

SELECT queries are provided in a call to the "read_sql()" method in pandas, alongside a relevant connection object. Pandas will execute the query on that connection, and return the results in the form of a data frame, which are used for a variety of purposes.

df = pd.read_sql("""
	SELECT
	   FirstName,
	   LastName,
     $exNumericCol;
	FROM Customers;""", engine)
print(df)

Modifying Data

To insert new records to a table, simply create a new data frame, and define its fields accordingly. From there, simply call "to_sql()" on the data frame to perform the INSERT operation with the connector, as in the below example. The "if _exists" argument must be set to "append" to prevent Pandas from attempting building the table from scratch, set index=False if needed to prevent Pandas from writing data frame index as a column:

df = pd.DataFrame({"FirstName": ["Jon Doe"], "LastName": ["John"]})
df.to_sql("Customers", con=engine, if_exists="append", index=False)

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Build 21.0.7930