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Description:

pbipy 2.11.0

pbipy

pbipy is a Python Library for interacting with the Power BI Rest API. It aims to simplyify working with the Power BI Rest API and support programatic administration of Power BI in Python.
pbipy supports operations for Apps, Dataflows, Datasets, Gateways, Imports, Reports, and Workspaces (Groups), allowing users to perform actions on their PowerBI instance using Python.
Installation
pip install pbipy

Or to install the latest development code:
pip install git+https://github.com/andrewvillazon/pbipy

Getting Started: Authentication
To use pbipy you'll first need to acquire a bearer_token.
How do I get a bearer_token?
To acquire a bearer_token you'll need to authenticate against your Registered Azure Power BI App. Registering is the first step in turning on the Power BI Rest API, so from here on it's assumed your Power BI Rest API is up and running.
To authenticate against the Registered App, Microsoft provides the MSAL and azure-identity python libraries. These libraries support different ways of acquiring a bearer_token and which to use will depend on how your cloud/tenant is configured.
Because there are multiple ways to acquire the token, pbipy leaves it up to the user do this in the way that suits, rather than directly handling authentication (of course, this might change in future).
This README doesn't cover authentication in detail, however, these are some helpful resources that look at acquiring a bearer_token in the context of Power BI:

Power BI REST API with Python and MSAL. Part II.
Power BI REST API with Python Part III, azure-identity
Monitoring Power BI using REST APIs from Python

The example below uses the msal library to to get a bearer_token.
import msal

# msal auth setup
def acquire_bearer_token(username, password, azure_tenant_id, client_id, scopes):
app = msal.PublicClientApplication(client_id, authority=azure_tenant_id)
result = app.acquire_token_by_username_password(username, password, scopes)
return result["access_token"]

bearer_token = acquire_bearer_token(
username="your-username",
password="your-password",
azure_tenant_id="https://login.microsoftonline.com/your-azure-tenant-id",
client_id="your-pbi-client-id",
scopes=["https://analysis.windows.net/powerbi/api/.default"],
)

The code that follows assumes you've authenticated and acquired your bearer_token.
Useage
Start by creating the PowerBI() client. Interactions with the Power BI Rest API go through this object.
from pbipy import PowerBI

pbi = PowerBI(bearer_token)

To interact with the API, simply call the relevant method from the client.
# Grab the datasets from a workspace

pbi.datasets(group="f089354e-8366-4e18-aea3-4cb4a3a50b48")

pbipy converts API responses into regular Python objects, with snake case included! 🐍🐍
sales = pbi.dataset("cfafbeb1-8037-4d0c-896e-a46fb27ff229")

print(type(sales))
print(hasattr(sales, "configured_by"))

# <class 'pbipy.Dataset'>
# True

Most methods take in an object id...
dataset = pbi.dataset(
id="cfafbeb1-8037-4d0c-896e-a46fb27ff229",
group="a2f89923-421a-464e-bf4c-25eab39bb09f"
)

... or just pass in the object itself.
group = pbi.group("a2f89923-421a-464e-bf4c-25eab39bb09f")

dataset = pbi.dataset(
"cfafbeb1-8037-4d0c-896e-a46fb27ff229"
,group=group
)

If you need to access the raw json representation, this is supported to.
sales = pbi.dataset("cfafbeb1-8037-4d0c-896e-a46fb27ff229")

print(sales.raw)

# {
# "id": "cfafbeb1-8037-4d0c-896e-a46fb27ff229",
# "name": "SalesMarketing",
# "addRowsAPIEnabled": False,
# "configuredBy": "john@contoso.com",
# ...
# }

Example: Working with Datasets
Let's see how pbipy works by performing some operations on a Dataset.
First, we initialize our client.
from pbipy import PowerBI

pbi = PowerBI(bearer_token)

Now that we've got a client, we can load a Dataset from the API. To load a Dataset, we call the dataset() method with an id and group argument. In the Power BI Rest API, a Group and Workspace are synonymous and used interchangeably.
sales = pbi.dataset(
id="cfafbeb1-8037-4d0c-896e-a46fb27ff229",
group="f089354e-8366-4e18-aea3-4cb4a3a50b48",
)

print(sales)

# <Dataset id='cfafbeb1-8037-4d0c-896e-a46fb27ff229', name='SalesMarketing', ...>

Dataset not updating? Let's look at the Refresh History.
We call the refresh_history() method on our Dataset. Easy.
refresh_history = sales.refresh_history()

for entry in refresh_history:
print(entry)

# {"refreshType":"ViaApi", "startTime":"2017-06-13T09:25:43.153Z", "status": "Completed" ...}

Need to kick off a refresh? That's easy too.
sales.refresh()

How about adding some user permissions to our Dataset? Just call the add_user() method with the User's details and permissions.
# Give John 'Read' access on the dataset
sales.add_user("john@contoso.com", "User", "Read")

Lastly, if we're feeling adventurous, we can execute DAX against a Dataset and use the results in Python.
dxq_result = sales.execute_queries("EVALUATE VALUES(MyTable)")
print(dxq_result)

# {
# "results": [
# {
# "tables": [
# {
# "rows": [
# {
# "MyTable[Year]": 2010,
# "MyTable[Quarter]": "Q1"
# },
# ...
# }

Example: Working with the Admin object
pbypi also supports Administrator Operations, specialized operations available to users with Power BI Admin rights. Let's see how we can use these.
First, we need to initialize our client. Then we call the admin method and initialize an Admin object.
from pbipy import PowerBI

pbi = PowerBI(bearer_token)
admin = pbi.admin()

Need to review some access on some reports? We can call the report_users method.
users = admin.report_users("5b218778-e7a5-4d73-8187-f10824047715")
print(users[0])

# {"displayName": "John Nick", "emailAddress": "john@contoso.com", ...}

What about understanding User activity on your Power BI tenant?
from datetime import datetime

start_dtm = datetime(2019, 8, 31, 0, 0, 0)
end_dtm = datetime(2019, 8, 31, 23, 59, 59)

activity_events = admin.activity_events(start_dtm, end_dtm)

print(activity_events)

# [
# {
# "Id": "41ce06d1",
# "CreationTime": "2019-08-13T07:55:15",
# "Operation": "ViewReport",
# ...
# },
# {
# "Id": "c632aa64",
# "CreationTime": "2019-08-13T07:55:10",
# "Operation": "GetSnapshots",
# ...
# }
# ]

More examples
Datasets in a Workspace
datasets = pbi.datasets(group="f089354e-8366-4e18-aea3-4cb4a3a50b48")

for dataset in datasets:
print(dataset)

# <Dataset id='cfafbeb1-8037-4d0c-896e-a46fb27ff229', ...>
# <Dataset id='f7fc6510-e151-42a3-850b-d0805a391db0', ...>

List Workspaces
groups = pbi.groups()

for group in groups:
print(group)

# <Group id='a2f89923-421a-464e-bf4c-25eab39bb09f', name='contoso'>
# <Group id='3d9b93c6-7b6d-4801-a491-1738910904fd', name='marketing'>

Create a Workspace
group = pbi.create_group("contoso")
print(group)

# <Group id='a2f89923-421a-464e-bf4c-25eab39bb09f', name='contoso'>

Users and their access
group = pbi.group("a2f89923-421a-464e-bf4c-25eab39bb09f")
users = group.users()

for user in users:
print(user)

# {"identifier": "john@contoso.com", "groupUserAccessRight": "Admin", ... }
# {"identifier": "Adam@contoso.com", "groupUserAccessRight": "Member", ... }

Power BI Rest API Operations
pbipy methods wrap around the Operations described in the Power BI Rest API Reference:
Power BI REST APIs for embedded analytics and automation - Power BI REST API
What's implemented?
Most of the core operations on Datasets, Workspaces (Groups), Reports, Apps, and Dataflows are implemented. Given the many available endpoints, not everything is covered by pbipy, so expect a few features to be missing.
If an operation is missing and you think it'd be useful, feel free to suggest it on the Issues tab.

PowerBI Component
Progress
Notes

Datasets
Done

Groups (Workspaces)
Done

Reports
Done

Apps
Done

Dataflows
Done

Gateways
Done

Admin Operations
Done
Implements operations related to Datasets, Groups, Reports, Apps, and Dataflows only.

Imports
Done
Import from One Drive for Business not implemented.

Everything else
Backlog

Contributing
pbipy is an open source project. Contributions such as bug reports, fixes, documentation or docstrings, enhancements, and ideas are welcome. pbipy uses github to host code, track issues, record feature requests, and accept pull requests.
View CONTRIBUTING.md to learn more about contributing.
Acknowledgements
The design of this library was heavily inspired by (basically copied) the pycontribs/jira library. It also borrows elements of cmberryay's pypowerbi wrapper.
Thank You to all the contributors to these libraries for the great examples of what an API Wrapper can be.