Power Query Problems

Extract Machine-Specific Data Blocks in Excel Using Power Query

Nimra Malik Sep 28, 2026 870 views

Question details

The user needs to extract and analyze daily drilled data stored in non-standard, machine-specific blocks within an Excel worksheet.

How to Extract Machine-Specific Data Blocks in Excel

Product: Excel
Device & OS: not provided
Scenario: Attempting to analyze daily drilled data where machines move between blocks, rendering standard pivot table extraction ineffective.
Observed behavior: A standard Pivot Table cannot extract the required information because the source data is scattered in machine-specific blocks rather than a normalized tabular format.

Before you start

Ensure your Excel version supports Power Query (Get & Transform Data) and verify that you have clear headers or markers identifying the machine blocks before restructuring your data.

Solution 1Recommended

Restructure and Normalize Data Using Power Query

Use Power Query to import the scattered daily data, identify block boundaries, and transform it into a flat table with one row per record.

Standard Pivot Tables require data to be in a tabular format (one row per record). When data is grouped into visual blocks, Power Query is the most efficient tool to clean and restructure it without manual copying and pasting.

By defining transformation rules, Power Query can automatically assign the correct machine and block to each row and easily refresh when new daily data arrives.

Load Data into Power Query

Navigate to the 'Data' tab on the Excel ribbon. Click 'From Table/Range' or 'Get Data' > 'From File' > 'From Workbook' to load your daily drilled data worksheet into the Power Query Editor.

Identify Block Boundaries

In the Power Query Editor, locate the column containing your machine or block markers. Go to the 'Add Column' tab and use 'Conditional Column' to flag rows where a new block starts.

Fill Down Machine Assignments

Select your newly created marker column, navigate to the 'Transform' tab, click 'Fill', and select 'Down'. This assigns the correct machine and block marker to every corresponding data row below it.

Filter and Unpivot

Filter out null rows or subheadings using the drop-down arrows on your column headers. If your dates or measured values span multiple columns, select them, right-click, and choose 'Unpivot Columns' to normalize the values.

Load to a New Worksheet

Once the data is transformed into distinct columns for Date, Machine, Block, and Measured Values, click 'Close & Load' on the 'Home' tab to output the normalized table into a new worksheet.

Restructure and Normalize Data Using Power Query

Sample Data Required for Dynamic Layouts: If your daily layout changes regularly, you must establish a consistent rule for headers and block markers so Power Query can process boundaries dynamically.

Efficient Data Analysis

Restructure and Analyze Data in WPS Spreadsheet

WPS Spreadsheet offers powerful data management tools, including advanced Pivot Tables, formulas, and data consolidation features, to help you extract, restructure, and analyze machine-specific records with ease.

1. Open your data file: Launch WPS Spreadsheet and open the workbook containing your unnormalized daily drilled data blocks.
2. Normalize the data layout: Create a new sheet with standard headers (Date, Machine, Block, Value). Use lookup formulas (like INDEX/MATCH) or manual consolidation to align the block data into this flat tabular format.
3. Insert a Pivot Table: Select the newly normalized data range, navigate to the 'Insert' tab, and click 'PivotTable' to dynamically summarize your machine data.

Seamless compatibility with Microsoft Excel formats (.xlsx, .xls, .csv).Robust Pivot Table functionality for analyzing large, structured data sets.Built-in Data tools like Text to Columns and Remove Duplicates for quick manual normalization.Lightweight application that handles complex calculations quickly and efficiently.

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Frequently Asked Questions

Why doesn't a standard Pivot Table work on my machine-specific data blocks?

Pivot Tables require tabular, normalized data where each row represents a single, complete record. If your data is grouped into distinct visual blocks with isolated headers, the Pivot Table cannot correctly cross-reference the machines, dates, and measured values.

How do I handle daily Excel data layouts that change regularly?

For regularly changing layouts, it is best to establish consistent block markers or keywords within the file. You can then use Power Query's 'Conditional Column' and 'Fill Down' features to dynamically scan for these markers and restructure the data regardless of where the blocks move.

What does 'normalizing' the data mean in Excel?

Normalizing means restructuring scattered or visually grouped data blocks into a single, continuous flat table. In this context, it involves creating dedicated columns for Date, Machine, Block, and Value, ensuring every single row contains all the dimensions necessary for data analysis.

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