You might be inclined to skip learning about list and record and jump directly to table. After all, as far as types that hold other values go, you mostly work with table. A table’s data is made up of simple values, so it makes since that you learned about them (parts 6, 7, 8 and 9). However, list and record might feel like types you won’t normally use, so why not skip them?

Turns out, a table behaves in ways like both a list and a record. If you want to leverage all that table offers, learning about these types is foundational. Besides, as you work with M, you just might find that list and record are more useful in their own right than you first thought.

Oh, did I mention that there’s a surprising relationship between let expressions and type record? All the more reason not to skip over it!

Time to finish up our exploration of primitive, single-value holding types! We’ll learn about handling true and false, explore the oxymoron of null (a value that represents the absence of a value) and touch on binary.

Temporal—something related to time (not to be confused with the word temporary). M offers several temporal types: date, time, datetime, datetimezone and duration.

This group of types has a lot in common, so we’ll explore it a little differently than the types we’ve encountered previously. First, we’ll introduce each type and look at its unique facets. Then, we’ll examine how the various types in this family play together (like common functionality they share).

In our exploration of Power Query’s types, number is next!

You might think that working with numbers would be so simple we’d hardly need to talk about them. However, there’s a got-ya that can bite: if you’re not careful, you can end up with arithmetic not producing the results you expect! After we go over M’s syntax for numeric literals, we’ll talk about this potential pain-point and how to not let it cause unexpected complications.

Also, in M, columns can be tagged to identify the specific kind of numbers they contain. Properly setting this subtype can improve performance and storage as well as enhance the default formatting used for the column’s values. We’ll learn how to do this (it’s easy!).

Examine an executing Power Query mashup under a microscope and what to you see? Data pulsing down pathways between expressions. Increase your microscope’s magnifying power. As you zoom in on one of the flows, what you’re viewing transforms from a blurred stream of data into the individual data items that make up that flow.

As you study what you see, you notice that the data items flowing by fit into groupings based on the kind of value they contain: some hold text, others are made up of a date, a time or a datetime; yet others are true/false values, then there are numbers…and it looks like there are even more categories beyond these!

In this post, we’ll begin exploring these categories—we’ll begin exploring the kinds of values supported by the M language. In programming parlance, these kinds of values” are called types.

Types can have special behaviors associated with them. For example, date and time have a special rule around addition: add a date and a time together and you get back a datetime! We’ll investigate these special rules.

Also, we’ll discuss the literal syntax associated with each type. In this context, ”literal” implies that you literally want to write out a value in code. You might, for example, literally want to set variable rate to the hand-selected value of 100. The syntax you use to code up the expression that produces the literal value you want is called literal syntax. Different types have different literal syntax rules. We’ll talk about these rules, as well.

There’s a lot to cover. In this post, we’ll explore the specifics of text (strings). If we explored all of the types in this post, it would get mighty long, so we’ll save the others for later.

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