Quick DAX : Word count

Posted by on Oct 13, 2018 in DAX, Power BI | 9 Comments
Facebooktwittergoogle_plusredditpinterestlinkedintumblrmailFacebooktwittergoogle_plusredditpinterestlinkedintumblrmail

Here is a technique you might consider if you need to split text down to individual words.  This could be used to help count, rank or otherwise aggregate the words in some longer text.  The approach detailed here uses spaces as a delimiter and will not be tripped up if multiple spaces are used between words.

There is no SPLIT function in DAX, so this approach uses the MID function to help find words.

The PBIX file used for the blog can be downloaded here.

[Updated 14th Oct, 2018]
A slightly updated version that uses UNICHAR/UNICODE to preserve the case (“A” versus “a”) of each letter can be downloaded here. The reason for this is DAX stores a dictionary of unique values for every column.  It is the first instance of any value that is added to the dictionary and assigned a new ID.  Subsequent values that are considered the same “A” and “a” are considered the same are assigned the same ID.  Using the UNICHAR/UNICODE version helps preserve the original case of each letter.

Output words to a Table

The first example takes a simple sentence and extracts each word in the sentence to its own row.

The first part of the DAX for the calculated table is as follows:

The initial variable called Sentence stores the text to be broken apart.  Notice I have deliberately injected multiple spaced between some of the words.  Feel free to replace this with your own text.

I then add a space to the start and end of the text to make sure the first/last words can be extracted.  The LengthOfSentence counts the number of individual characters in the text.  This is then used in the next step to create a pivoted version of the text that contains a row for every character.

The data stored in the PivotedSentence table expression will now look like this:

Next, I make use of the number in the [Value] column to add a column that will represent the upper and lower bounds of each word using the space as the delimiter.

This will add two columns that carry the character position for the start/end of each word.  You’ll notice I have highlighted the rows belonging to the word “quick”.  The number 6 in the [PrevSpace] column means this word begins at position 6 in the text, while the 10 in the [NextSpace] column represents the end of the word.   Every letter in the word carries the same value in these columns.

The TableOfWords variable creates a single column table with the unique values of the [PrevSpace] column – after filtering out any row with a space character.  The IN command could have additional items added to the filter.

For this example, the table expression in the TableOfWords variable will look like this:

The rows in this table expression could now be counted if the objective is simply to produce a word count.  I will show that code further down in a calculated column.

The next variable adds a column to the table expression that unpivots the letters in the Boundaries table expression using the CONCATENATEX function filtered down to only the letters that belong to the same word.

Now we have a table expression showing the split words as follows:

The RANKX function used the number in [Word Position] column to add a useful column that could be used to help extract the “first 5 words” or “Last N words” in some text.

The full calculation is here :

Add word count calculated column

In the same PBIX file, I have a ‘Dimension Stock Item’ table that has a text description for each item.  A slightly modified version of the above calculation is as follows:

The approach is the same and still pivots the text into a table expression with a single row per character.  Boundaries are established for each word, but there is no need to unpivot as a word count can be obtained by doing a COUNTROWS using the TableOfWords table expression.

 

 

 

Facebooktwittergoogle_plusredditpinterestlinkedintumblrmailFacebooktwittergoogle_plusredditpinterestlinkedintumblrmail
rssyoutuberssyoutube
Philip Seamark
Phil is Microsoft Data Platform MVP and an experienced database and business intelligence (BI) professional with a deep knowledge of the Microsoft B.I. stack along with extensive knowledge of data warehouse (DW) methodologies and enterprise data modelling. He has 25+ years experience in this field and an active member of Power BI community.

9 Comments

Leave a Reply

Your email address will not be published. Required fields are marked *