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Category: Python

Visualizing a Power BI Refresh with the semantic-link Library

Published 2023-12-06 by Kevin Feasel

Phil Seamark builds a notebook:

A few blogs back I shared a technique using Power BI Profiler (or VS Code) to run and capture a trace over a refresh of a Power BI semantic model (the object formally known as a dataset).

I’ve since received a lot of positive feedback from people saying how useful it was to visualize each internal step within a problematic Power BI refresh. Naturally, in the age of Fabric, I’m keen to share how the same approach works using a Microsoft Fabric Notebook.

Click through to see how you can do it.

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Scraping the Microsoft Fabric Road Map with Microsoft Fabric

Published 2023-11-28 by Kevin Feasel

Prathy Kamasani wants a report, not a webpage:

Like many I am also playing with Fabric, many of my clients are also excited about Fabric and want to know more about it. Being a solution architect in the consulting world one of the most common questions I get asked is: “When certain features will be available, Where are they in the roadmap?”. That’s what sparked the idea of scraping the Microsoft Fabric Roadmap and creating this Power BI report. It is based on a Direct Lake connection, so it has been a bit temperamental.

So, what did I do it? If you are not interested in the whole story. Here is Python code you can run to get a road map. If you are interested in my process carry on reading 

Click through for the process and explanation.

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Building an App with Streamlit

Published 2023-11-28 by Kevin Feasel

Riqo Chaar demonstrates Streamlit:

Off-the-shelf solutions for interactive data app development such as Microsoft Power BI are great – they allow users to easily develop data apps using a GUI. However, Power BI’s ease of use comes at the expense of reduced functionality. This is where programming languages such as Python, JavaScript or C# shine – you can practically code anything you like!

This blog will focus on Streamlit as a means of building interactive data apps. Streamlit is an open-source Python library that enables rapid creation of web apps (including, but not limited to, data apps) with minimal code. It acts as an intermediary between the easy-to-use, but functionally-limited characteristics of Power BI and the functionally-enhanced, but difficult-to-use characteristics of other programming tools such as JavaScript or C#.

I’ve grown to like Streamlit a lot. It’s really simple to put together a good-looking page, similar to Shiny in R.

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Formatting DAX Expressions with Python

Published 2023-11-01 by Kevin Feasel

Sandeep Pawar makes the code a bit more readable:

There is an old Italian saying “If it’s not formatted, it is not DAX”

When you get the list of measures from SemPy, it’s not formatted and is hard to read and understand. Thankfully, the SQLBI team has made the DAX parser and the formatter available via an API. I wrote a quick function to return the formatted DAX expression of a measure. You can either pass a DAX expression or the FabricDataFrame returned by fabric.list_measures()

Click through for the process, including the Python code to do the work.

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Loading Data from Sharepoint Lists into Microsoft Fabric

Published 2023-10-31 by Kevin Feasel

Stepan Resl loads some data:

In a time of Fabric, it’s worth pointing out our three options for data ingestion.

  • Data Pipelines with Copy Activity
  • Dataflows Gen 2
  • Notebooks

We must compare them to understand ​​what each can offer us from different perspectives. To be able to compare them thoroughly, there are some guardrails that we need to set so that everything goes the same way.

My biggest takeaway from this is, don’t load important business data into Sharepoint Lists to begin with.

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Enabling Python and R Support for VS Code Polyglot Notebooks

Published 2023-10-30 by Kevin Feasel

Joy George Kunjikkur enables a preview option:

Obviously, we should have Polyglot notebooks up and running. The first step to enable Python preview is that we need to install Jupyter on the machine and make sure the Python kernel spec is available. Run the below command to make sure it is there.

It looks like what the preview is doing is shelling out to Jupyter notebooks, so I’d imagine variables won’t cross over between languages.

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Analyzing Dependencies in Microsoft Fabric Measures

Published 2023-10-27 by Kevin Feasel

Sandeep Pawar has an interesting library for us:

In my previous blog post, I introduced Semantic-Link, discussing its use cases and explained how it enables us to create solutions that were either not possible or not easily achievable before. In this blog post, I would like to present another powerful use case that, although possible in the past, could not be created and used seamlessly in Power BI. Allow me to introduce the MeasureMaze Python library, which helps uncover insights from a complex maze of dependencies in a Power BI semantic model using Semantic-Link and the power of network analysis.

Sandeep dives into the approach and what you get out of this library. It’s very interesting.

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Working with Oracle Cloud Infrastructure from Python

Published 2023-10-26 by Kevin Feasel

Brendan Tierney peruses an SDK:

Having set up your Oracle Cloud account and perhaps initiated some services, such as an Autonomous Database or an AI Service, you have some options with how you’ll interact with those services. The first is to use the online interfaces and tools for them. Another is to access these OCI services from your local PC or laptop. This latter method gives you greater flexibility for accessing these services, automating various tasks and building out some applications. This post will step through how to install the necessary Python library, how to configure for connecting to OCI and will demo some of the simpler APIs for accessing the OCI services. This will help you verify your connection is working correctly.

Click through for the details.

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A Primer on A/B Testing for Engineers

Published 2023-10-18 by Kevin Feasel

John Mount performs some testing:

I’d like to discuss a simple variation of A/B testing in an engineering style.
By “an engineering style” I mean:

  • We will work a simulated example to see that the system works as claimed.
  • We will exhibit examples of problems before trying to fix them.
  • We will demonstrate all of the top level claims as calculations, and not delegate these to references.
  • We will leave fundamental math to the references, and not try to re-derive it.

In my opinion far too few A/B testing treatments check soundness, even on simulated data. This makes it easy for such articles to leave out important steps. If a relied on reference omits a step, the derived work may have to do the same.
We will implement the experiment design directly, instead of using a canned power calculator so we have a place to discuss some of the design issues in A/B test design.

This is an excellent dive into the topic and I highly recommend taking the time to read it.

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