Coming from Power Query

If you're used to Power Query (in Excel or Power BI), you understand the value of repeatable data transformations. Sigilweaver brings similar power with a visual canvas and more flexibility.

Terminology Mapping

Power Query	Sigilweaver	Notes
Query	Workflow	Saved transformation pipeline
Step	Tool	Individual transformation operation
Applied Steps	Workflow canvas	Visual representation of all steps
Source	Input tool	Load data from files
Table output	Output tool	Save transformed data
Filter Rows	Filter tool	Keep/remove rows by condition
Choose Columns	Select tool	Select, rename, reorder columns
Group By	Summarize tool	Aggregate by groups
Merge Queries	Join tool	Combine two tables
Append Queries	Union tool	Stack tables vertically
Add Column (Custom)	Formula tool	Create calculated columns
Sort	Sort tool	Order rows

Common Tasks

Loading Data

Power Query:
Data → Get Data → From File → Select file → Transform Data

Sigilweaver:
Drag Input tool → Pick your file → Preview data

Filtering Rows

Power Query:
Click column header dropdown → Filter values OR Home → Remove Rows → Custom Filter

Sigilweaver:
Drag Filter tool → Write expression like pl.col("sales") > 1000 → Use T (true) output

Key difference: Power Query uses clickable filters. Sigilweaver uses Polars expressions - more powerful but requires learning syntax.

Selecting/Removing Columns

Power Query:
Right-click column → Remove OR Choose Columns button

Sigilweaver:
Drag Select tool → Check columns to keep → Configure rename/reorder/casting

Creating Calculated Columns

Power Query:
Add Column → Custom Column → Enter M formula like [Price] * [Quantity]

Sigilweaver:
Drag Formula tool → Expression: pl.col("price") * pl.col("quantity") → Name output column

Grouping and Aggregating

Power Query:
Group By → Choose group columns → Add aggregations

Sigilweaver:
Drag Summarize tool → Set columns to "Group By" → Choose aggregation (Sum, Mean, Count, etc.) for each metric

Merging Queries (Joins)

Power Query:
Home → Merge Queries → Select second table → Choose columns → Select join kind

Sigilweaver:
Drag Join tool → Wire left (L) and right (R) inputs → Configure join keys → Outputs: J (matched), L (left only), R (right only)

Appending Queries (Union)

Power Query:
Home → Append Queries → Select tables to stack

Sigilweaver:
Drag Union tool → Wire multiple inputs → Stack vertically

Key Differences

1. M Language vs. Polars Expressions

Power Query uses M language for custom operations:

= [Sales] * 1.1
= if [Status] = "Active" then [Price] else 0

Sigilweaver uses Polars expressions (Python-like syntax):

pl.col("sales") * 1.1

pl.when(pl.col("status") == "Active")
  .then(pl.col("price"))
  .otherwise(0)

The Expressions Guide has everything you need to learn Polars syntax.

2. Visual Canvas vs. Applied Steps List

Power Query shows steps as a linear list in the right panel. You edit by clicking steps.

Sigilweaver shows your workflow as a visual graph on the canvas:

• See branches and merges spatially
• Understand complex pipelines at a glance
• Wire tools together like a flowchart

3. No Excel Dependency

Power Query lives inside Excel (or Power BI). Sigilweaver is a standalone desktop application:

• Runs on Windows, macOS, and Linux
• No Excel license required
• Processes data without loading into spreadsheet cells
• Handles datasets far larger than Excel's row limits

4. Lazy Evaluation

Power Query evaluates steps when you click "Close & Load." Sigilweaver uses lazy evaluation:

• Workflow is optimized before execution
• Preview data without executing entire pipeline
• Efficient handling of large datasets

Your First Workflow

Let's replicate a common Power Query task: load sales data, filter for current year, calculate profit margin, and group by region.

Steps:

1. Load data:

   • Drag Input tool to canvas
   • Pick your CSV file
   • Click tool to preview

2. Filter for current year:

   • Drag Filter tool, wire from Input
   • Expression: pl.col("year") == 2024
   • Use the T (true) output

3. Calculate profit margin:

   • Drag Formula tool, wire from Filter's T output
   • Expression: (pl.col("revenue") - pl.col("cost")) / pl.col("revenue") * 100
   • Output column name: profit_margin

4. Group by region:

   • Drag Summarize tool, wire from Formula
   • Set region to Group By
   • Set revenue to Sum
   • Set profit_margin to Mean

5. Save results:

   • Drag Output tool, wire from Summarize
   • Choose format (Excel, CSV, Parquet)
   • Pick output location and execute

Why Switch from Power Query?

• Handle larger datasets - No row limits, processes data in streaming fashion
• Faster performance - Polars is built in Rust, optimized for speed
• Visual complexity - See branching workflows, multiple outputs, complex joins spatially
• Cross-platform - Works on any OS, no Excel needed
• Open source - Free, AGPL-licensed, community-driven

Next Steps

• Interface Guide - Learn the canvas, tool palette, and wiring
• Expressions Basics - Master Polars expressions (like M language, but Python-based)
• Tools Reference - Detailed documentation for every tool

If you're comfortable with Power Query's step-by-step transformations, you'll adapt quickly. The visual canvas makes complex workflows easier to understand than scrolling through Applied Steps.