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Python Connector Libraries for Microsoft Project Data Connectivity. Integrate Microsoft Project with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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Use SQLAlchemy ORMs to Access Microsoft Project Data in Python


The CData Python Connector for MS Project enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Microsoft Project data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for MS Project and the SQLAlchemy toolkit, you can build Microsoft Project-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Microsoft Project data to query, update, delete, and insert Microsoft Project data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Microsoft Project data in Python. When you issue complex SQL queries from Microsoft Project, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Microsoft Project and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Microsoft Project Data

Connecting to Microsoft Project data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The User and Password properties, under the Authentication section, must be set to valid Microsoft Project user credentials. In addition, you will need to specify a URL to a valid Microsoft Project server organization root or Microsoft Project services file.

Follow the procedure below to install SQLAlchemy and start accessing Microsoft Project through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Microsoft Project Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Microsoft Project data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("microsoftproject:///?User=myuseraccount&Password=mypassword&URL=http://myserver/myOrgRoot")

Declare a Mapping Class for Microsoft Project Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Projects table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Projects(base): __tablename__ = "Projects" ProjectName = Column(String,primary_key=True) ProjectActualCost = Column(String) ...

Query Microsoft Project Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("microsoftproject:///?User=myuseraccount&Password=mypassword&URL=http://myserver/myOrgRoot") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Projects).filter_by(ProjectName="Tax Checker"): print("ProjectName: ", instance.ProjectName) print("ProjectActualCost: ", instance.ProjectActualCost) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Projects_table = Projects.metadata.tables["Projects"] for instance in session.execute(Projects_table.select().where(Projects_table.c.ProjectName == "Tax Checker")): print("ProjectName: ", instance.ProjectName) print("ProjectActualCost: ", instance.ProjectActualCost) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Microsoft Project Data

To insert Microsoft Project data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Microsoft Project.

new_rec = Projects(ProjectName="placeholder", ProjectName="Tax Checker") session.add(new_rec) session.commit()

Update Microsoft Project Data

To update Microsoft Project data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Microsoft Project.

updated_rec = session.query(Projects).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.ProjectName = "Tax Checker" session.commit()

Delete Microsoft Project Data

To delete Microsoft Project data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Projects).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for MS Project to start building Python apps and scripts with connectivity to Microsoft Project data. Reach out to our Support Team if you have any questions.