From Pandas
When combined with the connector, Pandas can be used to generate data frames which contains your SuiteCRM 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("suitecrm:///?URL=http://mySuiteCRM.com;User=myUser;Password=myPassword;")
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 Name, Industry, $exNumericCol; FROM Accounts;""", 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({"Name": ["Manufacturing"], "Industry": ["Chemicals"]}) df.to_sql("Accounts", con=engine, if_exists="append", index=False)