From Pandas
When combined with the connector, Pandas can be used to generate data frames that contain your Azure DevOps 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("azuredevops:///?AuthScheme=Basic;Organization=MyAzureDevOpsOrganization;Catalog=Project_dev;Schema=Project;PersonalAccessToken=MyPAT;")
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 Id, BuildNumber, $exNumericCol; FROM WorkItems;""", engine) print(df)
Modifying Data
To insert new records into a table, create a new data frame, and define its fields accordingly. When that is done, call to_sql() on the data frame to perform the INSERT operation with the connector, as shown in the example below. You must set the "if _exists" argument to "append" to prevent Pandas from attempting building the table from scratch. To prevent Pandas from writing the data frame index as a column, set index=False.df = pd.DataFrame({"Id": ["Jon Doe"], "BuildNumber": ["John"]}) df.to_sql("WorkItems", con=engine, if_exists="append", index=False)