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Category: Python

Using Jupyter as an External Tool for Power BI Desktop

Published 2020-08-18 by Kevin Feasel

David Eldersveld continues a series on Power BI external tools:

Many people use Python with notebooks, so let’s take a look at one possible way to enable a Jupyter external tool for Power BI Desktop. The following stepwise approach begins with simply opening Jupyter. It then progresses to creating and opening a notebook that includes Power BI’s server and database arguments. Finally, it works its way toward downloading a notebook definition contained in a GitHub gist and connects to Power BI’s tabular model to start to make this approach more useful.

This post continues a series of posts related to Python and Power BI. The first three parts of this blog series introduced some possible uses for Python connected to a Power BI model, how to setup a basic Python external tool, and how to both use it with a virtual environment and connect to the Tabular Object Model.

This was a cool usage of Power BI’s external tool functionality and starts to give you an idea of how powerful it can be.

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Getting Started with Jupyter Notebooks

Published 2020-08-17 by Kevin Feasel

Aveek Das takes us through the most popular name in notebooks:

In this article, I am going to explain what Jupyter Notebooks are and how to install the same on your machine. Further, I will demonstrate how to use these notebooks using Visual Studio Code and perform data analysis and other development activities. It is an open-source platform using which you can create and share documents that contain live code, equations, and visualizations along with the formatted text. Most importantly, these notebooks can be run on the web browser by just starting a server and using it. This open-source project is maintained by the team at Project Jupyter.

This is a fairly basic introduction to the topic, good if you have heard about notebooks but don’t know where to begin.

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Methods and Functions in Python

Published 2020-08-13 by Kevin Feasel

Sairam Uppugundla distinguishes methods from functions in Python:

Function and method both look similar as they perform in an almost similar way, but the key difference is the concept of ‘Class and its Object’. Functions can be called only by its name, as it is defined independently. But methods cannot be called by its name only we need to invoke the class by reference of that class in which it is defined, that is, the method is defined within a class and hence they are dependent on that class.

Read on to see how to create each, as well as more details on types of functions.

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New Features in Python 3.9

Published 2020-08-12 by Kevin Feasel

Harini Guptha walks us through some of what’s upcoming in Python 3.9:

The latest beta preview python 3.9.0b5 is released on July 20th, 2020. Yes, you heard it right. This is the last of the five planned beta release previews. This beta release gives an opportunity to the community to test the new features.

If you are planning to go for a python certification, make sure that you check out what functionalities are deprecated and what features are newly added in this release. Its always good to get yourself updated with the latest information. Without further ado, let’s go through the new features of Python 3.9.0b5.

Click through for the list.

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Using the Azure Architecture Icons

Published 2020-07-28 by Kevin Feasel

Steve Jones tries out some of the Azure Architecture Icons:

The icons are svg, so while they work in PowerPoint, adding them to something like this post in OpenLiveWriter doesn’t work. However, I could make a quick diagram and capture an image of it.

Not great, but it shows I can put icons on a page with arrows.

Going one step further, I’ve been digging into Diagrams by mingrammer lately. With it, you use Python to generate diagrams, and there are quite a few Azure icons in there, as well as AWS, on-prem, etc.

Here’s a quick example of what you can do, taken from an upcoming talk of mine:

There are some limitations based on the underlying library, such as how you can’t connect cluster to cluster—meaning I can’t draw a line from “Logging” to “Storage\Logs”; I have to draw it from a particular element (Loki) to a particular element (Elasticsearch). In a lot of traditional reference architecture diagrams, though, that isn’t a problem.

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Python and Power BI Desktop

Published 2020-07-24 by Kevin Feasel

David Eldersveld continues a series on Python as an external tool in Power BI. Part 2 is about defining a proper JSON formatted file:

To see a new tool in the Power BI Desktop ribbon, you need to define a JSON file and place it in a specific folder on your workstation. The featured external tools Tabular Editor, DAX Studio, and ALM Toolkit have installers that take care of this step. Since you do not have a dedicated installer to place this file in the required directory, you need to manually define your own.

As long as the “enhanced metadata format” for the data model is enabled, and the JSON in your file is accurate, you should see your new tool in your ribbon after you re-open Power BI Desktop.

Part 3 shows off virtual environments in Python as well as how to connect to the Tabular Object Model:

The Tabular Object Model (TOM) library for .NET opens Power BI’s data model to external tools. Two pieces are required to allow Python to interface with .NET:
1) Pythonnet package for .NET CLR (pip install pythonnet)
2) Python-SSAS module (ssas_api.py placed in the same folder as the main script you’d like to run)

The python-ssas (ssas_api.py) Python module that facilitates the TOM connection is all the work of Josh Dimarsky–originally for querying and processing Analysis Services. I simply repurposed it for use with Power BI Desktop or the XMLA endpoint in Power BI Premium and extended it with some relevant examples. Everything relies on Josh’s Python module, which has functions to connect to TOM, run DAX queries, etc.

This does look pretty handy.

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Python in Power BI Desktop

Published 2020-07-22 by Kevin Feasel

David Eldersveld dives into using Python as an external tool in Power BI:

Why use Python as an external “tool”? Even though Python isn’t a “tool” in the same sense as the “Big 3” community tools focused this month, I want to show how versatile the External Tools feature is. I also want to encourage people to use imagination and also explore how Power BI isn’t really as closed as some people think–at least the data model…

Some of these ideas are not exclusive to Python, but there’s enough variety in the Power BI and data science communities for people to possibly figure out if some of this might be useful within the context of their own environments, skills, and organizations.

David also follows up with a series of sample ideas.

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Comparing Gradient Descent to the Normal Equation for Small Data Sets

Published 2020-07-15 by Kevin Feasel

Pushkara Sharma compares two techniques for regression:

In this article, we will see the actual difference between gradient descent and the normal equation in a practical approach. Most of the newbie machine learning enthusiasts learn about gradient descent during the linear regression and move further without even knowing about the most underestimated Normal Equation that is far less complex and provides very good results for small to medium size datasets.

If you are new to machine learning, or not familiar with a normal equation or gradient descent, don’t worry I’ll try my best to explain these in layman’s terms. So, I will start by explaining a little about the regression problem.

I was surprised by the results.

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Comparing Koalas to PySpark

Published 2020-07-07 by Kevin Feasel

Tori Tompkins gives us an understanding of where Koalas fits in the Spark world:

One significant difference between Spark’s implementation of Dataframes and pandas is its immutability.

With Spark dataframes, you are unable to make changes to the existing object but rather create a brand new dataframe based on the old one. Pandas dataframes, however, allow you to edit the object in place. With Koalas, whilst still spark Dataframes under the hood, have kept the mutable syntax of pandas.

It does this by introducing this concept of an ‘Internal Frame’. This holds the spark immutable dataframe and manages the mapping between the Koalas column names and Spark column names. It also manages the Koalas index names to spark column name to replicate the index functionality in pandas (covered below). It acts as a bridge between Spark and Koalas by mimicking the pandas API with Spark. This Internal Frame replicates the mutable functionality of pandas by creating copies of the internal frame but appearing to be mutable.

Read the whole thing.

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Delaying Loops in Python

Published 2020-07-07 by Kevin Feasel

Jon Fletcher gives us three methods to delay a Python loop:

In this blog post, I will run through 3 different ways to execute a delayed Python loop. In these examples, we will aim to run the loop once every minute.
To show the delay, we will print out the current datetime using the datetime module.

I think I’d be a bit concerned about all three potentially failing—option 1 can get out of sync if it takes more than 1 second to process inside the loop, option 2 might miss a minute, and option 3 can also sometimes miss. But it’s interesting to think about.

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