Often, when querying APIs it is required to enter date and time filters in ISO 8601 format . Today I show a quick way to convert DateTime to ISO 8601 string, based on an ordinary DateTime field according to the following pattern:
This represents the 11th October 3pm in UTC -1 timeszone.
Steps to convert DateTime to ISO 8601
If I enter:
into the formula bar, it will be converted to :
Comparing to the desired ISO format the year, month and days are in the wrong order. So using the universal Text.From function will not return the correct result.
Pivot operations in are a very handy feature in Power Query but they can slow down refresh performance. So with some bittersweet pleasure I can tell that I found a trick to speed them up. The sweetness comes from the fact that the performance improvement is very significant. But the bitterness comes from the fact that I could have used this for almost 4 years now, but was too blind to realize at the time when I first worked with the code.
Trick to speed up a slow pivot table
This might not work everywhere, but for my tests, it worked really well: Don’t use an aggregation function when you want fast pivoting:
Don’t aggregate when you want a fast pivot in Power Query
But if your data isn’t aggregated on the row- & column values already, you’ll get this error message:
Error when the values are not adequately aggregated
So to make this work, you have to aggregate the values on the axis’ values before.
Let’s walk through the steps:
Today I want to share quick tip on how to automatically detect and change all column types at once in Power Query.
Very often, when you expand a column in Power Query that contains a table or some records, the expanded columns will lose their types (like Chris Webb has described here for example). Or you might just have accidently deleted a “Changed Type”-step.
No types on columns
Did you know there is actually a superfast and easy way to do it?
- Click the mouse anywhere in the table
- Press Ctrl + a (check all)
Check the whole table with Ctrl + a
- Go to the Transform-tab ad choose: “Detect Data Type”
Transform with 1 click
Voila: All your columns should have types on them.
They have been automatically been detected by checking the first 100 rows of your table. So if you know that you’re having columns with inconsistent values in them, make sure to check the automatically assigned values.
Enjoy & stay queryious 😉
When downloading data from the web, it’s often best to grab the data from APIs that are designed for machine-to-machine communication than from the site that’s actually visible on the screen. Not only is the download usually faster, but you also often get more additional parameters that can be very useful. In this article I’m going to show you how to retrieve the relevant URLs for downloading files from webpages (without resorting to external tools like Fiddler) and how to tweak them to your needs.
Retrieving the URL to download files from webpages
Say I want to download historical stock prices from this webpage:
The screen will show a link to a download: Read more
This article was edited on 2nd Nov 2019. JoinKind.Local has been removed to avoid problems with merges on tables with primary keys:
In this article you’ll learn how to speed up the aggregation of joined/merged tables by orders of magnitude (I recorded up to 30 times faster execution times). This method works for merges where both table have multiple rows for each keys. If one of your tables has a primary key, the method Chris Webb describes here works just as good: Chris Webb’s article on how to improve performance on aggregations after joins using primary keys .
You can follow along the different methods in this file:
When you join a table to another table in Power Query, the UI gives you the option to either expand the columns (default) or aggregate the contents of the joint tables. That’s useful if multiple rows are returned for the rows of the table that has been joined to (left table):
If you’re working with large data or complex queries that take a long time refresh, cancelling one of those refreshes can even take longer time, especially, if the query has run for quite some time already.
Luckily, there is an easy trick to cancel refresh without loosing the work you’ve done already: