![]() In order to run this code in Power BI, you just need to install the Pandas and Matplotlib libraries. Luckily, most of these packages are come built into to Python by default. Lines 1-4 simply import the packages necessary to run our code. R = requests.post(url=refresh_url, headers=header) Refresh_url = '' + group + '/datasets/' + dataset + '/refreshes' Groups = for d in groups_request]ĭatasets_request_url = '' + group + '/datasets'ĭatasets_request = json.loads(requests.get(url=datasets_request_url, headers=header).content)ĭatasets = for d in datasets_request] Groups_request = json.loads(requests.get(url=groups_request_url, headers=header).content) ![]() Token = context.acquire_token_with_username_password(resource=resource_url, Finally, check the boxes next to “Read all workspaces” and “Read and write all datasets” and click Register.Ĭontext = adal.AuthenticationContext(authority=authority_url, Give your application a name and select “Native” for the application type. Firstly, sign into the proper Power BI account. Navigate to and fill out the form like such. For example, if I want to retrieve a list of all of the Power BI datasets in “My Workspace,” there is an API endpoint for that information.īefore doing anything else, we need to register our future tool to use the Power BI API. In short, you can use it to perform a large number of functions programmatically using HTTP requests. If you’re wondering what the Power BI REST API is or what it can be used for, take a look at the documentation ( ). We’ll come back to this logic when we write the script, I just wanted to explain the general flow. Finally, we retrieve a list of datasets contained in each group and kick off a refresh for each dataset. Second, we retrieve a list of the workspaces available to us. Firstly, we must authenticate through the Power BI REST API to ensure we are who we say we are. The script will need to accomplish several tasks. The meat of the logic necessary to perform this technique will be contained in our Python script.
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