In this blog post I will show you how to join two 2D Python lists together.
The code is in the screenshot below.
Lines 1 – 2 are two lists that are going to be joined, line 3 is an empty list where the output will be appended to.
Lines 4 – 5 are two loops (one nested inside the other) which cycle through the records in both lists, line 6 checks whether the first items (index 0) in the two records from each list that are currently i & j match. If yes: the Key, Capital and Country are appended to our new list.
Lines 9 – 10 show the output record by record showing the join has worked successfully.
This code can be expanded for 3 lists, the code would have a 3rd for loop and an extra check in the if statement to find the correct record in the 3rd list to join.
Joining lists together in Python is useful when there is data in different lists and it would be beneficial if it were combined.
In Power BI Power Query there is an option to enable or disable whether a table is loaded into the report. The option ‘Enable load’ can be found by right clicking on the table. Typically, by default, the load is already enabled.
There is also an option ‘Include in report refresh’ which lets a user stop a table from refreshing when they refresh the entire report. This maybe useful for static tables or tables that are large which take a long time to refresh and a user wants to concentrate on how other tables are refreshing.
Once a user disables the ‘Enable load’ option, the table name turns italic which is an easy way for users to determine whether a table will be loaded or not.
After applying these changes, no data has been loaded into the report.
To re-enable the load, jump back into Power Query, right click on the table and ‘Enable load’.
Finally, some scenarios where it might be useful to disable loading a table:
– Disable loading tables in Power Query that were only ever stepping stones to create other tables
– See how removing a table effects your report before deleting it
– Removing a table that might be required again in the future
I have created a Power BI report which provides detail on the state of processing in a Tabular Cube.
The report uses the cube’s dynamic management views to provide information about the cube’s partitions.
To use the tabular cube processing report, you need to insert the following information:
- Database (Cube Name)
Once entered and assuming the connection is fine you need to accept some native queries. These statements are select statements and will not alter the cube. That should give you a report similar to the one below, I have used an adventure works tabular model as an example.
This report is inspired by one I found for general tabular cube documentation by Data Savvy, which I highly recommend. That report includes information on: tables, columns, relationships, measure and security:
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.
1 – Sleep
The sleep function from Python’s time module pauses the Python execution by the number of seconds inputted. The example below pauses the script for 60 seconds.
The above script has a 60 second delay between the end of a loop and the start of the next. However, what if we wanted to execute the next loop 60 seconds after the start of the previous loop.
In other words, how do we start the loop at the same time every minute.
2 – Calculate Sleep
To do this we calculate how long the loop should sleep for.
We will print the datetime every time the minutes changes. At the start of the loop we lookup the number of seconds that have passed so far this minute.
The number of seconds that have passed this minute is calculated from date_time[-2:]. Subtracting this from 60 gives the length of time in seconds for which the loop should sleep for, to execute when the next minute starts.
Once the loop has slept for the required number of seconds, we lookup the datetime again and print it out.
3 – Task Scheduler
The previous two options are good for executing a loop a few times, ten in our case. If we wanted to execute a python script continuously without expiring, we could use the above examples with an infinite appending loop.
However, if one loop errors the script will stop. Therefore, we want to execute the entire Python script once a minute using an external trigger. This is where we can use Task Scheduler.
Task Scheduler can execute a python script from source but it is often easier to use a batch file. The batch file includes the location of the python application (python.exe) and the location of the python script (.py). For more detail on using Task Scheduler and batch files to run Python scripts, please see the following datatofish post – https://datatofish.com/python-script-windows-scheduler
Our batch file is:
To demonstrate Task Scheduler, I’m going to run the following Python code every minute.
This code uses Pandas to produce a blank CSV file, but the name of CSV file is the datetime the script was run.
These following screenshots show the triggers and actions used.
This produced the following CSV files, we can see that the files takes 1 – 4 seconds to create.
In summary we have seen three different ways to delay a Python loop, two using loops inside Python and one using Task Scheduler. All can be used depending on what kind of delay is best.
A common request that is raised by clients is how to sort a table in Power BI by multiple columns, in the same way you can in Excel.
For a while, there was no way (at least no easy way) to do this until the Power BI March 2020 update.
I learnt this tip from the following YouTube video:
Full credit to Dhruvin Shah, check his video out.
Below I have a Power BI table displaying fruit sales, currently unsorted.
To sort the table by Fruit click on the column header Fruit.
The table is now sorted by Fruit in alphabetical order.
To add a secondary sort on Sales, hold the Shift key and click on the column header Sales.
The table is now sorted by
– Fruit name in alphabetical order
– Sales in descending order
Some extras to note:
– There is no limit on the number of columns that can be used to sort a table. Just hold the shift key and keep choosing columns.
– This feature is not available for matrices.
– To switch the sorting from ascending to descending or vice-versa continue to hold shift and click on the column header again.
Pi is 3.14159 to 5 decimal places.
To work out Pi, we will be using Leibniz’s formula:
X = 4 – 4/3 + 4/5 – 4/7 + 4/9 – …
This series converges to Pi, the more terms that are added to the series, the closer the value is to Pi.
For the proof on why this series converges to Pi – https://proofwiki.org/wiki/Leibniz’s_Formula_for_Pi
There are several points to note about the series:
- It’s infinite, we need to find a way to continue adding term after term.
- The denominator of the fraction increases by 2 every term.
- The terms alternate between positive and negative.
Firstly, let’s create a function called pi.
To continue adding terms, let’s use a for loop. Every time the loop executes another term is added.
range(1,10) will produce the numbers 1, 2, … 9, 10.
However, before the loops starts, our variables’ initial values need to be set.
The pi series will start from 0.
n represents the numerator of our fractions which is the constant 4.
d represents the denominator of our fractions which starts as 1.
d needs to increase by 2 every loop, let’s use sum equals to do this.
Pi will also use a sum equals, a denotes our positive/negative function which we will get on to:
The only problem left is how to get a to alternate between 1 and -1.
This is where we introduce modulo.
Modulo outputs the remainder of a division and is denoted by %.
This example shows from numbers 1 to 4, modulo 2 alternates between 0 and 1.
If we multiple by 2 and minus 1 it will alternate between 1 and -1 which is what we require.
Putting everything together gives:
This isn’t close to 3.14159 at all.
However, we are only executing the loop 10 times, hence only 10 terms are being used to calculate Pi.
Increasing the number of loops to a million will change this:
There it is! A function written from scratch to calculate Pi.
To get a value even closer to Pi just increase the number of loops.
Finally, if you do wish to use Pi in python the easiest way is to use the numpy library, which has a pi constant stored.
First question, why bother with layouts?
Using layouts in Power BI allows a user to make their visuals stand out better, the page looks professional and more appealing to its audience.
Second question, why PowerPoint?
The default page size in Power BI desktop is 16:9, (this trick doesn’t work for other Power BI page sizes), which is identical to a PowerPoint slide.
Therefore whatever is designed in PowerPoint will fit onto a Power BI page perfectly. Also PowerPoint is very easy to use; most people are familiar with it.
You’ll need a Power BI dashboard that you want to add a layout to. I’ll be using a dashboard displaying data on the Premier League top 50 goals scorers.
To start the creation of a layout, open PowerPoint with a blank side and add a rectangle.
Next, add glow to the rectangle, glow is found in the drawing ribbon under shape effects.
Repeat the process of adding rectangles with glow and lay them out so the visuals will fit.
If there is an important visual that needs to be a certain size you can find the size of the visual in Power BI in pixels and tailor the rectangle size to your visual.
Unfortunately PowerPoint displays sizes in cm so the numbers will need converting.
I use the following – https://www.unitconverters.net/typography/pixel-x-to-centimeter.htm
Once happy with the layout, save the slide as a PNG file. Upload this as the background in Power BI by going to format then page background, then selecting your created PNG file.
There you go!! A Power BI dashboard with its own custom layout.