[solved] Parse JSON response list arrays as columns instead of rows using Power BI / Power Query / M Code
# How to Transform JSON Response List Arrays into Columns Using Power BI and M code: A Step-by-Step Guide
In the realm of data analysis and BI tools, efficiently parsing and transforming data structures is crucial for insightful analysis. Power BI, accompanied by its powerful Power Query Editor and M code, offer robust solutions for such tasks. This guide specifically addresses a common but tricky scenario: transforming JSON response list arrays into columns instead of rows. This approach, while less intuitive, significantly enhances data readability and allows for better analytical outcomes.
## Understanding the Problem
JSON (JavaScript Object Notation) is widely used for data interchange due to its text-based, lightweight, and easy-to-read structure. However, when dealing with JSON data in Power BI, especially list arrays, analysts often encounter challenges in structuring this data in a way that's conducive to analysis. The default behavior is to present these list arrays as rows, which can be cumbersome when the objective is to analyze specific elements across arrays. The solution involves a sequence of steps in Power Query Editor using M code.
## Step-by-Step Solution
The following detailed walkthrough demonstrates how to transform JSON list arrays into columns using M code within Power BI:
### Step 1: Import and Parse the JSON Document
Begin by importing your JSON document into Power BI:
```m
let
Source = Json.Document(File.Contents("document.json")),
#"Converted to Table" = Record.ToTable(Source)
in
#"Converted to Table"
This initial step converts the JSON document into a table format that Power Query can work with.
Step 2: Expand the List Arrays
Next, expand the list arrays that are nested within the table:
#"Expanded Value" = Table.ExpandListColumn(#"Converted to Table", "Value"),
#"Expanded Value1" = Table.ExpandRecordColumn(#"Expanded Value", "Value", {"ReportID", "ReportName", "ReportType", "ReportTitles", "ReportDate", "UpdatedDateUTC", "Rows"}, {"ReportID", "ReportName", "ReportType", "ReportTitles", "ReportDate", "UpdatedDateUTC", "Rows"})
This expansion lays out all elements of the list arrays into separate rows, preparing them for further manipulation.
Step 3: Prepare Headers for Column Transformation
To ensure proper column naming during the pivot operation, obtain the headers from your data and prepare them for renaming:
#"HeaderRaw" = #"Expanded Value1"{0}[Cells],
#"Converted to Table1" = Table.FromList(HeaderRaw, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
#"Added Index" = Table.AddIndexColumn(#"Converted to Table1", "Index", 1, 1, Int64.Type)
This step creates a foundation for correctly naming the columns post-pivot.
Step 4: Pivot the Table Based on the Index
Pivoting the table is crucial for transforming list items into columns:
#"Pivoted Column" = Table.Pivot(Table.TransformColumnTypes(#"Removed Columns1", {{"Custom.Index", type text}}, "en-GB"), List.Distinct(Table.TransformColumnTypes(#"Removed Columns1", {{"Custom.Index", type text}}, "en-GB")[Custom.Index]), "Custom.Index", "Custom.Rows.1.Cells.Value"),
#"Final" = Table.RenameColumns(#"Pivoted Column", #"RenameRecords")
This operation rearranges the table so that each list item occupies its column, significantly improving the table's readability and analytical utility.
Why This Approach is Beneficial
The method outlined here, though intricate, provides clear benefits:
- Enhanced Data Readability: Converting list arrays into columns allows for more intuitive data analysis and visualization.
- Streamlined Analysis: Analyzing data by attributes laid out as columns is more straightforward and efficient.
- Flexibility in Data Modeling: This transformation supports more sophisticated data modeling techniques in Power BI.
Conclusion
Parsing JSON response list arrays into columns in Power BI, though ostensibly challenging, can be effectively managed with a detailed understanding of Power Query and M code. The process, as described, not only makes the data more accessible but also significantly enriches the analytical capabilities of Power BI users.
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