Gist 67bf315eb5b9cafa9a5899b88f252dab

Gist f3a61a41e6832de955bdf750f4c54ba3

This example demonstrates a custom Power Query function that multiplies its input by a metadata value retrieved from a previously invoked function. The visualization shows how dynamic metadata can influence computed results within a data transformation pipeline. The code uses the `Expression.Evaluate` API to access the `Value.Metadata` function, pulling the `[a]` field from shared context, then multiplies that value by the parameter `x`. The rendering approach is not visual; instead, it highlights programmatic data shaping in Power Query, with the function `fn.pq` serving as the core logic. The source is a single gist file authored by Hugoberry.

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[Power Query] Closures, partial application and cascading parameters

This example shows how Power Query applies functional techniques to build a cascading parameter system, where a selection in one step narrows the choices in the next. It demonstrates closures and partial application by defining a function `fn.ts` that takes an initial parameter `x` and returns a second function awaiting `y`, ultimately computing their product. The visualization uses this pattern to drive linked dropdowns or sliders, where the output of one control feeds directly into the next. The code relies on Power Query’s native M language functions for data shaping, with no external libraries, and the rendering approach is not specified in the provided snippet.

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Power Query incremental load example for querypower.com

This example demonstrates a Power Query incremental load setup for querypower.com, showing how a simple list source is transformed step by step. The code defines a query named `Query1` that generates a numeric range from 0 to 9, serving as the foundational data source. It uses the Power Query M language’s `let` and `in` syntax to structure the transformation pipeline, with `Source` assigned the output of `{0..9}`. The visualization highlights the initial stage of an incremental refresh pattern, where only new or changed rows would be processed in subsequent runs. The example focuses on the query definition itself, not on external libraries or rendering, as the M code contains no visual output or advanced APIs.

[Power Query] Closures, partial application and cascading parameters

This example demonstrates how Power Query’s M language implements closures and partial application through nested functions. The code defines `addTo` as a function that returns another function, capturing the outer variable `x`. By calling `addTo(5)`, it creates `addToFive`, a closure that retains the value 5 in its scope. When `addToFive(3)` is invoked, the closure evaluates to 8, showing how cascading parameters flow through each layer. The rendering is purely textual, with no external libraries or data sources; the logic itself is the visualization.

Gist 10da47bd381918d303c5db903afe5886

This example demonstrates how to generate all non-empty combinations of a set of strings using bitmask arithmetic. It shows three separate Power Query (M) implementations, each taking a hardcoded list like {"B", "F", "T"} and producing every possible subset as concatenated text. The primary approach uses `Number.BitwiseAnd` and `Number.Power` to iterate through binary masks, while `List.Transform`, `List.Select`, and `List.Positions` filter and combine elements. A recursive alternative using `@fnCombinations` is also included. The visualizations highlight different algorithmic strategies—iterative bitwise versus recursive decomposition—for the same combinatorial output, with no external libraries beyond Power Query’s built-in list functions.

Gist 47d998760fa7271ac3157ce7d9555612

This example demonstrates how to query and load data from external sources using Power Query's OleDb.Query function, specifically connecting to a local CSV file and an XML catalog. The code shows two approaches: one using Microsoft's ACE OLEDB provider to read a delimited text file, and another using the MSDAOSP provider with MSXML2.DSOControl to access XML data. The visualization highlights the raw query definitions rather than rendering graphical output, focusing on the data-acquisition layer. Both queries reference file paths on the local system, with the CSV query specifying the file directory and the XML query pointing to a catalog file.

Searializing a table into and XML string in Power Query

This example demonstrates how to flatten a tabular dataset into a single XML string using Power Query’s M language. It shows the transformation of a table’s records into nested `<row>` elements wrapped in a top-level `<table>` tag. The code relies on `Table.ToRecords`, `List.Accumulate`, and `Record.FieldNames` to iterate over rows and columns, dynamically generating tags with `Text.Format`. Field names and values are inserted into `<#0>` and `</#0>` placeholders, producing a structured XML output. The approach is purely functional, avoiding loops, and directly serializes the table’s content into a text-based hierarchical format.

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Gist faec7eb6c03589ff608bab991699407b

This example demonstrates how to generate and execute a dynamic SQL script in Power Query (M language) to bulk-insert data into a SQL Server table using a table-valued parameter (TVP). It builds a textual TVP declaration and populates it by chunking source rows into sets of 1,000 entries, then constructs a multi-statement script that inserts each chunk and calls a stored procedure. The code relies on M functions like `Table.TransformRows`, `List.Generate`, `Text.Combine`, and `Value.NativeQuery` to programmatically assemble and run the query. The visualization highlights the data flow from a source table to a SQL stored procedure, emphasizing the chunking logic and the use of native query execution.