Csv To Json Power Automate

To convert CSV to JSON in Power Automate, here are the detailed steps, making it easy and fast to integrate your data:

First, understand that Power Automate doesn’t have a direct “CSV to JSON” action. You’ll need to leverage a combination of actions to achieve this transformation. The most common and robust method involves reading the CSV content, splitting it into rows, and then processing each row to form JSON objects. This approach is highly effective for converting a CSV file to JSON in Power Automate or turning a CSV string to JSON Power Automate.

Here’s a quick guide to convert CSV to JSON using Power Automate:

Get your CSV Content:
- If your CSV is in a SharePoint file, use “Get file content” from SharePoint.
- If it’s in an email attachment, use the “Get attachment” action.
- If it’s directly available as text, use a variable.
- You can also leverage tools like the one above this content to convert your CSV data into a JSON array directly, then simply paste that JSON into a “Compose” action in Power Automate. This bypasses much of the manual parsing.

Initialize Variables (if parsing manually):

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Initialize a variable named JSONOutputArray of type “Array”. This will store your final JSON array.
Initialize a variable named CSVRows of type “Array”.

Split CSV into Rows (if parsing manually):
- Use the “Compose” action to get the CSV content.
- Use the split() expression to break the CSV content into rows. For example, split(outputs('Get_file_content')?['body'], decodeUriComponent('%0A')) for newlines.
- Skip the header row if your CSV has one. Use skip() expression, e.g., skip(split(outputs('Compose_CSV_Content'), decodeUriComponent('%0A')), 1).
Process Each Row (if parsing manually):
- Use an “Apply to each” control on the output of your row splitting.
- Inside the loop, for each row, split it by the delimiter (e.g., comma ,) to get individual column values.
- Use a “Compose” action to construct a JSON object for the current row, mapping column headers to values. This is where you essentially turn a CSV table to JSON Power Automate.
Append to Array (if parsing manually):
- Use the “Append to array variable” action to add the newly formed JSON object (from step 4) to your JSONOutputArray.
Final JSON Output:
- After the loop, your JSONOutputArray variable will contain the complete JSON array. You can then use this for further actions, such as saving it to a file, sending it in an HTTP request, or using the “Parse JSON” action if you need to access specific properties. This is crucial if you need to convert CSV to JSON array Power Automate.
Leveraging “Parse JSON” (Highly Recommended):
- Once you have your JSON output, either from your manual parsing or from a direct conversion tool (like the one provided above), you can copy a sample of that JSON.
- In Power Automate, add a “Parse JSON” action.
- Click “Generate from sample” and paste your JSON sample. This will automatically create the schema, allowing you to easily access properties using dynamic content in subsequent actions. This is key for working with the converted CSV to JSON array in Power Automate.

This structured approach ensures you can effectively convert CSV string to JSON Power Automate, providing a robust solution for your data transformation needs. Remember, understanding your data’s structure (delimiters, headers) is paramount for a smooth conversion.

Table of Contents

Demystifying CSV to JSON Transformation in Power Automate

Transforming data is a cornerstone of effective automation, and one of the most frequent requirements is converting Comma Separated Values (CSV) to JavaScript Object Notation (JSON). While both formats are widely used for data exchange, JSON’s structured, hierarchical nature often makes it more suitable for integration with web services, APIs, and many modern applications. Power Automate, Microsoft’s robust automation platform, provides the tools to achieve this, even without a direct “CSV to JSON” action. This section will dive deep into the methodologies, best practices, and potential pitfalls when you convert CSV to JSON using Power Automate.

The Nuances of CSV Data Structure

CSV files, at their core, are simple plain text files that use a specific character (typically a comma, but sometimes a semicolon or tab) to delimit data fields. Each line usually represents a data record, and the first line often contains headers that define the meaning of each column. For example:

Name,Age,City
Ali,30,Dubai
Fatima,25,Cairo

The simplicity of CSV can also be its complexity when dealing with special characters, commas within data fields (requiring quotation marks), or inconsistent delimiters. Understanding these nuances is crucial before attempting any conversion in Power Automate. A common issue is unescaped commas within data, which can break the split() function. Always ensure your CSV is clean or that you implement robust parsing logic.

Why Convert CSV to JSON?

The drive to convert CSV to JSON array Power Automate stems from several practical benefits:

API Compatibility: Many APIs exclusively communicate using JSON. Converting your CSV data enables seamless interaction with these services, whether you’re sending data to a third-party application or consuming data from a web service.
Data Structure and Hierarchy: JSON supports nested objects and arrays, allowing for more complex and hierarchical data representations than the flat structure of CSV. This is particularly useful when dealing with related data sets.
Ease of Consumption: For developers and many modern applications, JSON is often easier to parse and manipulate programmatically. Dynamic content in Power Automate, especially after a “Parse JSON” action, leverages this structure efficiently.
Database Integration: Many NoSQL databases (like Azure Cosmos DB or MongoDB) are document-oriented and store data primarily in JSON or BSON (Binary JSON) format. Converting CSV to JSON simplifies direct data ingestion into such databases.
Reporting and Analytics: While CSV can be used for basic reports, JSON can facilitate richer, structured reports by allowing for complex data relationships that might be cumbersome to represent in a flat CSV.

The Power Automate Toolkit for CSV to JSON

Power Automate, true to its low-code nature, offers several actions that, when combined, can effectively convert CSV data. You’ll primarily rely on: Csv to json in excel

Get file content: To retrieve the raw CSV data from sources like SharePoint, OneDrive, or local drives (via Power Automate Desktop).
Compose: An incredibly versatile action for string manipulation, including split(), replace(), and general text processing.
Initialize variable: To create containers for your processed data, specifically an array variable for the final JSON output.
Apply to each: To iterate through rows of data.
Append to array variable: To build your JSON array iteratively.
Parse JSON: The cornerstone for defining the schema of your JSON, allowing Power Automate to understand its structure and provide dynamic content.

Method 1: Manual Parsing of CSV String to JSON Array

This method is the most common and robust approach when you have the CSV content as a string within Power Automate. It provides fine-grained control over the parsing process, especially useful when dealing with variations in CSV formatting.

Step-by-Step Breakdown: Manual CSV Parsing

Let’s break down the process of how to convert CSV string to JSON Power Automate.

1. Obtaining the CSV String

Your first step is to get the raw CSV data into a string variable or a “Compose” action.

From a file: If your CSV is stored in SharePoint, OneDrive, or a local network drive, use actions like “Get file content” (SharePoint), “Get file content (V2)” (OneDrive for Business), or “Read text file” (Power Automate Desktop). The output of these actions will be the binary content of the file. You might need to convert this to a string using base64ToString() expression if the content type isn’t text. For most cloud connectors, the output is directly accessible as a string.
From an email attachment: Use the “Get attachment” action after an email trigger. The content will be base64 encoded, so you’ll need to use base64ToString() to get the raw CSV string.
From a database query: If a database query returns a CSV string, simply assign it to a “Compose” action or a string variable.
From a form input: If a user submits CSV data via a form (e.g., Microsoft Forms), assign that input directly.

Let’s assume you have the CSV string in a “Compose” action named CSV_Content.

2. Initializing Variables

You need two key variables for this process: Dec to bin ip

JSONOutputArray: Type: Array. This will hold your final JSON array of objects.
CSVHeaders: Type: Array. This will store the header names from your CSV, which will become the keys in your JSON objects.

3. Splitting CSV into Rows and Headers

This is where the magic of string manipulation begins.

Get all rows: Use a “Compose” action (let’s call it All_CSV_Rows) and the expression split(outputs('CSV_Content'), decodeUriComponent('%0A')). The decodeUriComponent('%0A') represents a newline character, which is the standard row delimiter. If your CSV uses \r\n (CRLF), you might need split(outputs('CSV_Content'), decodeUriComponent('%0D%0A')).
Extract Headers: Use another “Compose” action (e.g., CSV_Headers_Raw) and the expression split(first(outputs('All_CSV_Rows')), ','). This takes the first row and splits it by the comma delimiter.
- Pro Tip: If your headers might contain spaces or need trimming, refine this with a “Select” action or an Apply to each to trim each header value. For example, split(replace(first(outputs('All_CSV_Rows')),' ',''), ',') or map trim(item()) in a “Select” action.
Store Headers in Variable: Set CSVHeaders variable to the output of CSV_Headers_Raw.
Get Data Rows (excluding header): Use a “Compose” action (e.g., Data_Rows) and the expression skip(outputs('All_CSV_Rows'), 1). This removes the first row (headers) from the list of rows.

4. Iterating Through Data Rows and Constructing JSON Objects

Now, for each data row, you’ll convert it into a JSON object.

“Apply to each” loop: Add an “Apply to each” control and select the output of your Data_Rows compose action. This will iterate over each data row string.
Split Current Row: Inside the loop, add a “Compose” action (e.g., Current_Row_Values) and use the expression split(item(), ','). This splits the current row string into an array of values based on the comma delimiter.
- Important: Be mindful of quoted fields (e.g., "John Doe, Jr."). Simple split(',') won’t handle these correctly. For robust parsing of complex CSVs, consider using Azure Functions or Azure Logic Apps’ built-in CSV connector if available, or a specialized connector from the Power Automate marketplace, as native expressions have limitations. However, for well-formed CSVs, this approach works.
Construct JSON Object for Current Row: This is the most critical part. Add a “Compose” action (e.g., Current_JSON_Object). Here, you’ll manually construct the JSON object using expressions that combine headers and values. Ip address to hex
- You’ll typically use the json() function to create an object and dynamic content to pick values.
- The structure will look like this:
```
{
  "@{variables('CSVHeaders')[0]}": "@{outputs('Current_Row_Values')[0]}",
  "@{variables('CSVHeaders')[1]}": "@{outputs('Current_Row_Values')[1]}",
  // ... and so on for all your columns
}
```
- This can become cumbersome if you have many columns. A more dynamic approach involves a nested “Apply to each” or a “Select” action, but the manual approach is clearer for beginners.
- Advanced Dynamic Object Creation (for many columns):
  - Inside the “Apply to each” loop for Data_Rows, add another “Compose” action (Row_Values_Array) with split(item(), ',').
  - Then, add a “Select” action.
    - From: range(0, length(variables('CSVHeaders')))
    - Map:
```
{
  "Key": "@{variables('CSVHeaders')[item()]}",
  "Value": "@{outputs('Row_Values_Array')[item()]}"
}
```
  - This “Select” action will output an array of objects, where each object has a “Key” (header) and “Value” (corresponding data).
  - Now, you need to turn this array of key-value pairs into a single JSON object. This is typically done with a custom expression or by passing it to an Azure Function. For simpler cases, the manual approach above is sufficient. However, for a true csv table to json power automate conversion with many columns, the Select action prepares the data for a more advanced transformation (e.g., using a custom code snippet or an external service).

5. Appending to the JSON Array

Inside the “Apply to each” loop, after Current_JSON_Object (or your dynamically built object), add an “Append to array variable” action.
Name: JSONOutputArray
Value: outputs('Current_JSON_Object')

6. Final JSON Output

After the “Apply to each” loop completes, your JSONOutputArray variable will contain the complete JSON array representing your CSV data. You can then use this array for various purposes:

Saving to a file: Use create file actions (SharePoint, OneDrive) and set the content to string(variables('JSONOutputArray')).
Sending via HTTP request: Use the “HTTP” action with the JSONOutputArray as the body.
Parsing the JSON for dynamic content: This is crucial.

Method 2: Leveraging the “Parse JSON” Action for Schema Generation

The “Parse JSON” action is your best friend when working with JSON in Power Automate. It allows you to define the schema of your JSON data, which then enables Power Automate to provide dynamic content for specific properties within that JSON. This greatly simplifies accessing data fields like items('Apply_to_each')?['HeaderName'].

How to Use “Parse JSON” Effectively

Get a Sample JSON: The most straightforward way to use “Parse JSON” is to provide a sample payload.
- If you’ve manually parsed your CSV and generated a JSONOutputArray, you can simply copy its content after a test run.
- Alternatively, use an external tool (like the one above this article) to convert a sample of your CSV data into JSON. This will give you a clean JSON output that you can use as a sample.
- Consider a small sample CSV like:
```
Product,Price,Quantity
Laptop,1200.50,1
Mouse,25.00,5
```
  The expected JSON output would be: Decimal to ip
```
[
  {
    "Product": "Laptop",
    "Price": "1200.50",
    "Quantity": "1"
  },
  {
    "Product": "Mouse",
    "Price": "25.00",
    "Quantity": "5"
  }
]
```
Add “Parse JSON” Action: In your Power Automate flow, after the JSONOutputArray variable is populated (or wherever your JSON string originates), add a “Parse JSON” action.
Content: Set the “Content” field to your JSONOutputArray variable (or the string variable/Compose action holding your JSON). Make sure to convert it to a string using string(variables('JSONOutputArray')).
Generate Schema: Click the “Generate from sample” button. Paste your sample JSON (copied from step 1) into the pop-up window and click “Done”. Power Automate will automatically infer the schema.
- Note on Data Types: The “Parse JSON” action infers data types based on the sample. If a field is 1200.50 in the sample, it might infer it as a number. If it’s "1200.50", it’s a string. Be aware of this, especially if you need to perform calculations later. You might need to manually edit the schema to explicitly define types (e.g., type: ["string", "number"] for flexible parsing, or just number if you expect numbers). If your CSV values are always strings, that’s fine; you can convert them to numbers later if needed.
Accessing Dynamic Content: After configuring “Parse JSON”, any action downstream will be able to access the properties of your JSON objects using dynamic content. For example, if you iterate through the parsed JSON, you can directly refer to Body_Product or Body_Price, depending on the schema generated. This is critical for processes that need to convert json output to csv power automate or convert JSON to Excel Power Automate.

Handling Specific Scenarios: CSV Table to JSON & CSV File to JSON

While the manual parsing method covers the general case, specific scenarios require tailored considerations.

CSV Table to JSON Power Automate (Complex Delimiters & Quoting)

When dealing with a csv table to json power automate where the CSV contains commas within fields (e.g., "City, State"), or uses non-standard delimiters like semicolons (;) or tabs (\t), the simple split(',') approach will fail.

Option 1: Pre-processing: If the CSV is consistently structured with quotes, you might need to use more advanced string manipulation to parse correctly. Often, replacing the problematic delimiter with a unique placeholder before splitting, and then re-replacing after parsing, can work.
Option 2: External Tools/Connectors: For truly complex CSV parsing (e.g., respecting RFC 4180 CSV standard), Power Automate’s native expressions are limited.
- Azure Function: A powerful alternative is to deploy a simple Azure Function (written in C# or Python) that takes the CSV string as input and returns a JSON string. Libraries like CsvHelper (.NET) or csv module (Python) handle quoting, delimiters, and other CSV complexities gracefully. This is often the most robust solution for enterprise-grade CSV parsing.
- Custom Connector: If you repeatedly parse complex CSVs from a specific source, consider building a custom connector that wraps an API capable of CSV to JSON conversion.
- Power Automate Desktop: For on-premise files, Power Automate Desktop flows can leverage scripting languages (like PowerShell or Python) to perform the conversion locally before passing the JSON string back to the cloud flow.

CSV File to JSON Power Automate (Large Files)

When you convert csv file to json power automate, especially large files (e.g., hundreds of thousands of rows or MBs), performance and governor limits become critical.

Streaming (Limited in Cloud Flows): Power Automate cloud flows don’t natively support true streaming for large file processing within the Apply to each loop without hitting memory limits for the input.
Consider Dataverse/Azure Blob Storage + Azure Functions:
1. Store CSV: Upload the large CSV file to Azure Blob Storage or Dataverse File column.
2. Trigger Azure Function: Set up an Azure Function that triggers when a new file is uploaded to the blob storage.
3. Process in Function: The Azure Function reads the CSV file in chunks, processes it into JSON, and then writes the JSON output to another blob, a database, or sends it to another service. This offloads the heavy lifting from Power Automate and leverages scalable Azure compute.
4. Power Automate Interacts with Output: Power Automate can then monitor the output location (e.g., check for the JSON file in another blob container) and proceed with its workflow.
Power Automate Desktop for Large Files: For local files, Power Automate Desktop can handle larger files more efficiently by processing them directly on the machine. You can use scripting actions within Desktop flows to read and transform CSVs into JSON.

Convert CSV to Excel Power Automate

While the focus here is CSV to JSON, it’s worth noting that Power Automate also excels at converting CSV to Excel Power Automate. This usually involves:

Getting CSV Content: Same as before.
Creating a CSV Table: Use the “Create CSV table” action. This is useful for data that needs to be structured before going into Excel.
Creating an Excel Workbook: Use the “Create file” action in OneDrive or SharePoint and give it an .xlsx extension.
Adding Rows to a Table: Use the “Add a row into a table” action for Excel. You’ll need to define a table within your Excel file first, and then map the columns from your parsed CSV to the Excel table columns. This is often an “Apply to each” loop, where each row from your CSV is added to the Excel table.

This transformation is often requested alongside CSV to JSON, as different downstream systems might require different data formats. Octal to ip address converter

Optimizing Your CSV to JSON Power Automate Flows

Performance and reliability are key for any automation. Here’s how to optimize your CSV to JSON flows:

Error Handling and Resilience

Try-Catch-Finally: Power Automate allows for scoped actions (similar to try-catch blocks). Encapsulate your parsing logic within a “Scope” action, then configure a parallel “Scope” to run “After” the primary scope has failed, allowing you to log errors or send notifications.
Empty CSVs/Rows: Implement checks for empty CSV content (empty()) or empty rows within your Apply to each loop to prevent errors. Use a Condition action to skip empty rows.
Malformed Data: Consider how your flow will react if a CSV row has fewer columns than headers. The outputs('Current_Row_Values')[index] expression will fail if the index is out of bounds. You might need to use if() expressions to provide default values or skip malformed rows. For example, if(greaterOrEquals(length(outputs('Current_Row_Values')), 2), outputs('Current_Row_Values')[1], '').

Performance Considerations

Batching (for large CSVs): If you’re using HTTP actions to send JSON data, and the resulting JSON array is very large, consider breaking it into smaller chunks (batches) and sending them in separate HTTP requests.
Concurrency Control: For Apply to each loops, you can enable concurrency control in the settings. This allows multiple iterations to run in parallel, significantly speeding up processing for large datasets. However, be mindful of API rate limits if your loop makes external calls.
Minimize Actions: Each action adds overhead. If possible, consolidate string manipulation into fewer “Compose” actions using more complex expressions.
Power Automate Premium: If you have high volume requirements, consider a Power Automate Premium license, which offers higher API call limits and throughput.

Security Best Practices

Sensitive Data: If your CSV contains sensitive information, ensure that your flow handles it securely. Avoid logging sensitive data unnecessarily. Use secure inputs/outputs for actions dealing with credentials or confidential information.
Least Privilege: When connecting to data sources (e.g., SharePoint), ensure the service principal or user account running the flow has only the necessary permissions.
Data Masking: For data that doesn’t need to be fully visible, consider masking it (e.g., last 4 digits of a card number) before converting to JSON if the JSON will be stored or transmitted to less secure systems.

Beyond Basic Conversion: Advanced Scenarios

The power of Power Automate lies in its flexibility. Here are some advanced scenarios related to power automate csv to json array and convert json output to csv power automate:

Converting JSON Output to CSV Power Automate

Yes, you can convert json output to csv power automate! This is essentially the reverse of the process discussed.

Parse JSON: Start with your JSON array (or object) and use “Parse JSON” to get the schema.
Initialize String Variable: Create an empty string variable, let’s call it CSVOutputString.
Extract Headers: If your JSON objects have consistent keys, manually list them in the order you want for your CSV header. Append these to CSVOutputString followed by a newline.
“Apply to each” on JSON Array: Loop through your parsed JSON array.
Construct CSV Row: Inside the loop, for each JSON object, use a “Compose” action to build a CSV row string. For example: @{items('Apply_to_each')?['Product']},@{items('Apply_to_each')?['Price']},@{items('Apply_to_each')?['Quantity']}. Ensure you handle commas within values by wrapping them in double quotes and escaping existing quotes (e.g., replace(item(), '"', '""')).
Append to String Variable: Append this constructed CSV row string (followed by a newline) to your CSVOutputString variable.
Create CSV File: After the loop, CSVOutputString will hold your complete CSV content. Use “Create file” to save it with a .csv extension.

This reverse transformation is particularly useful for reporting or feeding data to legacy systems that still rely on CSVs.

JSON to CSV Power Automate Desktop

Power Automate Desktop offers more direct ways to handle file transformations, as it can leverage local scripting engines (PowerShell, Python) or even .NET assemblies. Oct ipl

Read JSON File: Use a “Read text from file” action to get the JSON content.
Convert JSON to Custom Object: Use a “Convert JSON to custom object” action. This makes the JSON data directly accessible within the Desktop flow.
Loop and Write to CSV: Iterate through the list of custom objects. For each object, construct a CSV line by accessing its properties and then use “Write text to file” (in append mode) to build your CSV file.
- PowerShell/Python Script: Alternatively, pass the JSON string to a PowerShell or Python script action. These languages have excellent JSON and CSV parsing libraries (ConvertFrom-Json / ConvertTo-Csv in PowerShell; json / csv modules in Python) that can perform the conversion in a single, robust step.

Integrating with External Services for Conversion

For highly specialized or large-scale conversions, consider dedicated cloud services:

Azure Data Factory: A fully managed data integration service that supports complex data transformations, including CSV to JSON, at scale. You can trigger ADF pipelines from Power Automate.
Azure Logic Apps: Similar to Power Automate but designed more for enterprise integration and serverless workflows. Logic Apps have a built-in “Data Operations” connector that can perform advanced JSON manipulations. While it doesn’t have a direct CSV-to-JSON, its extensibility makes it a strong contender for complex scenarios.
API-based Converters: Some third-party APIs offer CSV to JSON conversion as a service. You can call these APIs using the “HTTP” connector in Power Automate. Just ensure the service is reliable, secure, and adheres to your data privacy requirements.

By mastering these techniques and understanding the underlying data structures, you can build powerful and efficient Power Automate flows that handle virtually any CSV to JSON transformation challenge. Remember, the journey is one of continuous learning and adaptation, much like mastering any new skill to improve your daily workflow.

FAQ

What is the easiest way to convert CSV to JSON in Power Automate?

The easiest way is to use an external tool (like the one provided above this article) to convert your CSV data into a JSON array, then paste that JSON into a “Compose” action in Power Automate. After that, use the “Parse JSON” action to automatically generate a schema, making the data easily accessible.

Can Power Automate directly convert a CSV file to JSON?

No, Power Automate does not have a single, direct “CSV to JSON” action. You need to combine several actions like “Get file content”, “Compose”, “Apply to each”, and “Append to array variable” to manually parse the CSV string and construct the JSON array.

How do I convert a CSV string to JSON Power Automate?

To convert a CSV string to JSON in Power Automate, first get the CSV content into a string. Then, split the string into rows, extract headers, and iterate through each data row. Inside the loop, split each row into values and construct a JSON object using these values and the extracted headers. Finally, append each constructed JSON object to an array variable to form the complete JSON array. Bin to ipynb converter

What is the “Parse JSON” action used for in Power Automate?

The “Parse JSON” action is used to understand the structure (schema) of a JSON string. By providing a sample JSON payload, Power Automate automatically generates a schema, which then allows you to easily access specific properties within the JSON using dynamic content in subsequent actions.

How do I handle CSV files with headers when converting to JSON in Power Automate?

When converting CSV files with headers, you should first split the CSV content into rows. Then, take the first row as your headers and store them in an array variable. When iterating through the subsequent data rows, use these header names as keys for your JSON objects and map them to the corresponding values from each data row.

Is it possible to convert CSV table to JSON Power Automate if the CSV has complex structures like quoted commas?

Native Power Automate expressions for splitting (split()) might struggle with complex CSV structures that include quoted commas (e.g., "City, State"). For such cases, consider using an Azure Function (Python or C#) with specialized CSV parsing libraries, or Power Automate Desktop with scripting capabilities, to handle the robust parsing.

How do I convert CSV to JSON array Power Automate for multiple rows?

After splitting your CSV content into an array of rows, use an “Apply to each” control to iterate over each data row. Inside this loop, you’ll construct a JSON object for the current row and then use “Append to array variable” to add this object to your final JSON array.

Can I convert JSON output to CSV Power Automate?

Yes, you can convert JSON output back to CSV in Power Automate. This involves parsing the JSON, iterating through the JSON array, and for each JSON object, constructing a CSV-formatted string (handling delimiters and quotes), and then appending these strings to a larger variable which ultimately forms the CSV content. Bin ipswich

What are the limitations of converting large CSV files to JSON in Power Automate cloud flows?

Power Automate cloud flows have limitations on memory and runtime for single actions, which can be hit with very large CSV files. For large files (e.g., over 100MB or hundreds of thousands of rows), it’s often more efficient to use Azure Functions, Azure Data Factory, or Power Automate Desktop to handle the processing at scale.

How does Power Automate Desktop help with CSV to JSON conversion?

Power Automate Desktop can be more effective for large or complex CSV files stored locally. It can leverage local scripting engines (like PowerShell or Python) that have robust libraries for CSV and JSON manipulation, allowing for efficient in-memory processing before passing the JSON result back to a cloud flow.

Can I specify data types (e.g., number, boolean) when converting CSV to JSON in Power Automate?

When manually constructing JSON objects, all values from CSV are initially treated as strings. If you use “Parse JSON”, it infers data types from your sample. You can manually edit the schema in “Parse JSON” to specify type: "number" or type: "boolean" for specific fields, which helps Power Automate correctly interpret the data downstream.

What if my CSV uses a semicolon (`;`) instead of a comma as a delimiter?

If your CSV uses a semicolon as a delimiter, simply replace the comma (,) in your split() expressions with a semicolon (;). For example, split(item(), ';').

How can I make my CSV to JSON Power Automate flow more robust against errors?

To make your flow robust, implement error handling using “Scope” actions with run after configurations (e.g., “has failed”). Add conditional checks for empty values or malformed rows. For critical data, consider logging errors to a SharePoint list or sending notification emails. Bin ip checker

Can I use Power Automate to convert JSON to Excel Power Automate?

Yes, after parsing your JSON array, you can use an “Apply to each” loop to iterate through each JSON object. Inside the loop, use the “Add a row into a table” action (Excel Online Business connector) to append data to an Excel table, effectively converting JSON to Excel.

How do I use the generated JSON output in another Power Automate action?

Once your CSV is converted to a JSON array (stored in a variable), you can use this variable as the “Content” for a “Parse JSON” action. After parsing, the individual properties of your JSON objects will become available as dynamic content for subsequent actions, such as sending data via HTTP or updating a database.

What is the best practice for handling dynamic CSV headers?

If your CSV headers can change, the manual parsing method is more challenging. A better approach is to use a “Select” action or an Azure Function to dynamically map column positions to new header names. In Power Automate, you could try to build a dynamic object using the item() and itemIndex() from nested Apply to each loops, combining the header array with the value array.

Can I skip rows in the CSV during conversion?

Yes, you can skip rows. After splitting the CSV into all rows, use the skip() expression. For example, skip(outputs('All_CSV_Rows'), 1) skips the first row (typically the header). You can also add conditions within your “Apply to each” loop to skip specific rows based on content.

What is the role of `decodeUriComponent('%0A')` in splitting CSV rows?

decodeUriComponent('%0A') represents a newline character (line feed, ASCII 10). It’s used in the split() expression to correctly identify and separate individual rows in the CSV string. If your CSV uses CRLF (\r\n), you would use decodeUriComponent('%0D%0A'). Css minifier tool

How do I ensure numerical values are treated as numbers in the JSON output?

When manually constructing the JSON, values from CSV are typically strings. If you use “Parse JSON” on the resulting JSON, it infers types. To ensure numerical values are always treated as numbers, you might need to:

Manually edit the generated schema in the “Parse JSON” action to specify type: "number".
Use expressions like int() or float() on the values before constructing the JSON object if you are building the JSON manually and intend to use it directly without a Parse JSON step.

What should I do if my CSV contains empty lines?

Empty lines in a CSV can cause errors during parsing. When you split the CSV content into rows, add a Condition action inside your “Apply to each” loop that checks if the current item() (row) is empty using the empty() expression. If it’s empty, terminate the current iteration of the loop to skip that row.

Csv to json power automate