Avi Singh

Avi Singh has personally experienced the transformation and empowerment that Power BI can bring - going from an Excel user to building large scale Power BI solutions. His mission now is to share the knowledge about Power Pivot and Power BI. 

RANKX With Ascending Order To Show Lowest Quotes By Vendors

RANKX with Ascending Order to Show Lowest Quotes by Vendors

By Avi Singh [Twitter]

This post is based on a query that I got in our monthly Q&A session held for our Online Class attendees.

Input = Multiple quotes for different Products from different Vendors
Desired Output = For each Product show the top three quotes, both price and the Vendor name

Go from a list of Price Quotes to showing the lowest Vendor Quotes for each Product

A bit more on the Q&A session before we dive in. All our Online Class attendees are invited to a monthly Q&A session, in order to support them in their Power BI journey. Often what you learn in class, you would only apply sometime later. With the Q&A session, if you run into issues or have any questions, you have the opportunity to bring it up and discuss with your instructor. You can sign up for our upcoming Online Live Class on August 3-4.

Step 1: Structuring the Tables

We would clean things up and import the data into separate data and lookup tables. This may seem superfluous for the sample data set, but a real data set could have a lot more rows in the data table and a lot more columns (attributes) for the lookup table. Hence separating the data table and lookup tables is always a good approach.

Our Vendor Quote data loaded as separate Data and Lookup Tables

Step 2: Brainstorm Approach to Writing Measure

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Cycle Plots Show You Data Insights using Power Pivot

By Avi Singh [Twitter]

If you want to look for trends based on weekday (Sunday…Saturday) or month-of-the-year seasonality (January…December), Cycle Plots can be a potent visualization tool. Some clever folks thought of this back in 1978, but my education on cycle plots has been from Naomi Robbins’ excellent paper. This question was asked during one of our Q&A sessions for the Online Class (Next Class scheduled for Aug 3-4). In this post I’ll discuss the Cycle Plot and then we would build it step by step using Power Pivot. Here is the end result we will achieve (animated gif):

Cycle Plot showing Weekday values for each Week Number and the Average. A slicer also lets the user select the weekday chart should start at

Looking for Periodic Trends

We’ll use sample data showing eight weeks worth of web traffic and we are looking for trends based on the weekday (Sunday…Saturday).

Try 1: We might plot Visitors by Weekday. While this does show the overall pattern of visitors across weekdays, we have no information of how visitor count is changing over those eight weeks.

Try1: No information on how visitors trend changes over the eight weeks

Try 2: We can plot visitors by date. This shows a trend over time and the cyclical pattern is apparent. However, it is hard to track the performance of a single weekday, say Monday over the eight weeks.

Try 2: Hard to track a single weekday (say Monday) over the eight weeks

Try 3: We plot by weekday but add Weeknum as a series. This has a lot of information coded in the graph, but that is also the problem. Viewer is overwhelmed and it is hard to look for the patterns we want to see.

Try 3: A little too much information

Cycle Plot

A Cycle Plot would show data for each weekday broken by the Weeknum. With the same data now rendered as Cycle Plot, you can see the trend for each weekday and see them in relation with other weekdays. Insights just start hitting you on the head!

Cycle Plot: Click to enlarge, see numbered insights below

1) Friday is the peak traffic day and Mon/Tue are the low traffic days.
2) Thu shows strong growth, especially in the recent weeks
3) Fri shows steady growth
4) Sunday shows a gradual decline (except for last week) and may need closer monitoring

Building a Cycle Plot Using Power Pivot

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Power BI Jun 2nd Webinar Recording Available


By Avi Singh [Twitter]

Thanks to all, who attended our Power BI Webinar on Jun 2nd. We had 400+ (well, 438 to be precise) attend, what ended up being a 2 hour webinar with all the questions being asked.

Be Notified of Future Webinars…

Click here to subscribe to our Newsletter, to be notified of future Webinars/Events.

Webinar Infographic

Here are some fun facts…

Webinar Recording: Build BI Solutions Using Power BI

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Red Nose Special: 24 Distinctive Ways to Die

By Avi Singh [Twitter]

Folks, May 21st is being celebrated as Red Nose Day across USA. Goal is to have some fun and raise money for children living in poverty. We at PowerPivotPro mix a bit of black humor in today’s post and promise to donate a portion of the proceeds from our upcoming PowerPivot Online Class (Jun 8-9) to Red Nose Day.

For today’s special, we were inspired by the CDC released graph showing distinctive causes of death by state. Not content to stare at a picture, we rebuilt the model from scratch using Power Query, Power Pivot and Power Map. Which lets us slice and dice and visualize the data various ways to our heart’s content.

Our results match closely with that of CDC, slight differences may exist since we use 10 year span from 2004-2013, whereas CDC researchers reportedly used 2001-2010.

Power Map Rendering of Distinctive Cause of Death by State

Distinctive cause of death

Distinctive Cause of Death by State (Click to see full list)

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