Tag Archives: Microsoft Excel

Lazy, Streamed, Immutable: Try Building a Table

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Lazy evaluation, streaming and immutability are key Power Query concepts which must be understood to truly grasp how Power Query “thinks.” Want to test your understanding in these foundational areas—and try to grow it—by tackling an assignment?!

Your Task

Code up a single row table containing the following columns:

  • A column of hard-coded data.
  • A couple columns whose data is fetched from an API.
  • A couple columns whose data is fetched from another API.
Example of expected table

Where:

  • Neither API is called if the table’s rows are simply counted.
  • Only the first API is called if the columns containing data from the second API are removed from the table, and vice versa.
  • Each API is called at most once, even when multiple columns that contain data from that API are output.

Without:

  • Using any standard library table functions to build the table (i.e. no “Table.*” functions may be used).
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Value.ReplaceType & Table Column Renames (Bug Warning!)

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If you work in the advanced realm of ascribing types, there are a couple interesting behaviors to be aware of related to table column renames (including a bug!).

Positional, Not Name-Based

Imagine you want to set column type claims on a table, so you create a query that uses Value.ReplaceType to ascribe an appropriate new table type.

// BaseDataSet
let 
  // in real life, comes from a database
  Data = #table(
          {"Amount", "TransactionID"}, 
          {
              {100.25, 1},
              {32.99, 2}
          }
        )
in
        Data

// MyNicerTable
let
  Source = BaseDataSet, 
  Result = Value.ReplaceType(Source, type table [Amount = Currency.Type, TransactionID = Int64.Type])
in
  Result
Query output: | Amount | TransactionID | | 100.25 | 1 | | 32.99 | 2 |

So far, so good.

Later on, someone decides that the ID column should be moved to be leftmost, so they reorder columns by editing BaseDataSet. However, they don’t touch your MyNicerTable query with its Value.ReplaceType code. Look closely at what that expression now outputs:

Query output showing column names/types swapped

The column that contains transaction IDs is now named “Amount” and typed Currency.Type. Similarly, “Amount” values now show up under the column name “TransactionID” which is typed as whole number. Ouch!

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The Flair of a Culture (or Two)

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Numbers and dates are formatted differently in different parts of the world. How are these cultural differences handled in the realm of Power Query? Turns out, arguably, there can be not just one—but two—sets of rules in play.

In the M language, numbers and date/time-based values are natively stored in culture-agnostic formats. It doesn’t matter what part of the world you’re in or how it formats values, when #date(2023, 1, 23) is evaluated, Power Query understands that the referenced year is 2023, the month is 1 and the day is the 23rd—and it maintains this understanding throughout the value’s lifetime. Similar holds true with the other date/time types, as well as with numbers.

On the other hand, when converting values of these types to or from text, culture does come into play—but which culture?

Number.ToText(123456.78, "n")
// outputs:
// 123,456.78 (if the culture is en-US)
// 123.456,78 (if the culture is es-ES)
// 123 456,78 (if the culture is se-SE)
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The Oldest Is Not Always the Minimum: Power Query Date-Based Values May Not Compare the Way You’d Expect

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The task seemed simple enough: Determine the earliest date between two columns, so that this value could be used when generating a date dimension.

A little M code that took the minimum of each column, then the minimum of those minimums seemed to meet the need until—ouch—I discovered it wasn’t finding the earliest date. Why?

let
  OrderCreatedMin = List.Min(SomeTable[OrderCreated]),
  TransactionReceivedMin = List.Min(SomeTable[TransactionReceived]),
  OverallMin = Date.From(List.Min({ OrderCreatedMin, TransactionReceivedMin }))
in
  OverallMin

What do you think? Do you see any problems with the above?

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M Language Specification in Review: 2022

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During 2022, the Power Query M Language Specification received seven substantiative revisions (beyond typo fixes, formatting tweaks, and the such). Each brought clarification to ambiguous points or corrected cases where the specification did not align with actual mashup engine behavior. None of the revisions added new language functionality or otherwise resulted in the mashup engine changing.

Interestingly, while the M language gained two new features last year (try‘s catch and structured error messages), neither of these has yet to make it into the language spec.

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Power Query M Primer (Part 25): Extending the Global Environment

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To the average Power Query user, how the standard library and data connectors end up in the global environment may be irrelevant. What matters is that, however they get there, they’re there and they work! But in the world of advanced M development, how identifiers come to be injected directly into the global environment becomes interesting. Of particular pertinence is the extension/module system that plays a pivotal role in part of this process.

Welcome to a new world: extending the global environment, here we come!

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Power Query PostgreSQL Data Connector Feature Request: Support “Complex” Columns

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A challenge with the current Power Query PostgreSQL data connector is that it does not understand how to work with PostgreSQL’s complex typed columns.

Background

Some examples of PostgreSQL’s complex typed columns:

Scenario: PostgreSQL allows a single column to be defined as containing a specific complex type.
Example: Column phone_number could be defined to as being of type telephone_number (which has fields “areacode”, “number” and “extension”).
M Equivalent: Column contains a record.

Scenario: A single column can also be defined to contain an array of values of a specific scalar type.
Example: Column visit_dates could be defined as an array of date values.
M Equivalent: Column contains a list.

Scenario: A column can be configured to contain an array of a given complex type.
Example: Column order_lines could be defined as an array of order_line values.
M Equivalent: Column contains a nested table.

Challenge

Power Query does not understand any of these more advanced column set ups. Depending on the exact scenario, when PQ encounters one of the above, it either renders out the raw textual equivalent of the entire column (like “{}” in column project_contingency_items, below) or an error (such as the below complaint that “We don’t support CLR type ‘System.Dynamic.ExpandoObject’.”).

Query Editor output showcasing a lack of support for PostgreSQL's complex data types

The net effect is that, for a table/view where these more complex structures are used, hand-written SQL (e.g. Value.NativeQuery) may be necessary as Power Query may be unable to make “sense” out of the relevant columns on its own. This makes the level of effort involved with ingesting this data into PQ much higher.

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MySQL’s Invalid Dates + Power Query = No Effect Query Folding?!

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Results display showing 0001-01-01 value

You’re authoring in Power Query. You decide that rows with 0001-01-01 in a certain column should be removed, so you filter on the column, excluding 0001-01-01 values. After you apply your filter, nothing changes: the 0001-01-01 rows are still present. What is going on?

This issue bit me recently. Turns out, it’s due to a translation loss when bridging between the worlds of MySQL and Power Query.

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No Built-In Power Query Connector: Am I Stuck?

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Uh oh! You’re using Microsoft Power BI or Excel and you’ve discovered that Power Query does not have a built-in connector for the data source you’re interested in consuming.

Question: What does this mean for you?

Answer:
(pick which of the below you think is most likely correct)

  • No go. You cannot access this data source from Power Query.
  • Custom connector. “Built-in connector” and “custom connector” sound like opposites. Either you may need to fork out a bit of $$ to get a custom connector created, or give up.
  • Something else. Hmm…what are the other options?

I’d suggest starting with “something else.” When Power Query doesn’t ship with a connector for your source of interest, there are a number of other options to consider.

Let’s take a journey through the main other options for directly connecting to a source from Power Query. We’ll order these into “stages” based on an approximation of the level of effort involved. Then, we’ll expand our scope and touch on several possibilities that involve using external tools or languages to provide the needed data to Power Query.

But first, the problem scenario….

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