Yaml to csv bash

To solve the problem of converting YAML data to CSV format using bash, here are the detailed steps and methods, primarily leveraging command-line tools like yq and jq for robust parsing, and traditional bash scripting for manipulation:

1. Install Necessary Tools:

   • yq (YAML processor): This tool is invaluable for parsing and manipulating YAML files directly from the command line. It’s like jq but for YAML.
     • Linux (Debian/Ubuntu): sudo snap install yq or sudo apt-get update && sudo apt-get install yq (ensure you get the yq by mikefarah, not python-yq).
     • macOS (Homebrew): brew install yq
   • jq (JSON processor): Since YAML is often converted to JSON as an intermediate step, jq is crucial for processing JSON data and extracting fields.
     • Linux (Debian/Ubuntu): sudo apt-get install jq
     • macOS (Homebrew): brew install jq
   • csvkit (Optional, but powerful): For advanced CSV manipulation once data is in CSV format.
     • Python/pip: pip install csvkit

2. Basic Conversion Strategy: The most effective approach usually involves:

   • YAML to JSON: Use yq to convert your YAML input into JSON. This is often the most reliable way to handle nested structures.
   • JSON to CSV: Use jq to extract the desired fields from the JSON, format them, and output as CSV.

3. Step-by-Step for Simple YAML (Array of Objects):
   Let’s say you have data.yaml:

   - name: Alice
     age: 30
     city: New York
   - name: Bob
     age: 24
     city: London
   - name: Charlie
     age: 35
     city: Paris
   

   To convert this yaml to csv bash:

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   • Command: yq -o=json '.' data.yaml | jq -r '(map(keys) | add | unique) as $cols | map(. as $row | $cols | map($row[.])) as $rows | $cols, $rows[] | @csv'
   • Explanation:
     • yq -o=json '.' data.yaml: Converts data.yaml to JSON. The . refers to the root of the document.
     • jq -r '(map(keys) | add | unique) as $cols | ... | @csv': This is the jq magic.
       • (map(keys) | add | unique) as $cols: Dynamically extracts all unique keys (headers) from all objects in the array and stores them in the $cols variable. This handles cases where not all objects have the same keys.
       • map(. as $row | $cols | map($row[.])) as $rows: For each object (.) in the input array, it iterates through the $cols (headers) and retrieves the corresponding value from the current object ($row[.]). If a key doesn’t exist, jq will return null, which @csv handles as an empty string. This creates an array of arrays representing the data rows.
       • $cols, $rows[]: Outputs the headers first, then iterates through each data row.
       • | @csv: Formats the output as a CSV string, handling quoting and escaping automatically.
   • Output (to output.csv):

     age,city,name
     30,New York,Alice
     24,London,Bob
     35,Paris,Charlie
     

     (Note: jq sorts keys alphabetically by default for the header, which is standard behavior.)

This setup gives you a powerful and flexible way to convert yaml to csv bash, ensuring your yaml to csv linux workflows are efficient and robust.

Mastering YAML to CSV Conversion in Bash

Converting YAML data to CSV format directly within a bash environment is a common requirement for data processing, scripting, and automation tasks. While YAML (Yet Another Markup Language) is excellent for human-readable configuration and data serialization, CSV (Comma Separated Values) remains the universal lingua franca for spreadsheet applications and many data ingestion systems. The journey from the hierarchical, nested nature of YAML to the flat, tabular structure of CSV can seem daunting, but with the right tools and bash scripting prowess, it becomes a streamlined process. This section will delve deep into the methodologies, best practices, and advanced techniques to efficiently yaml to csv bash, ensuring data integrity and flexibility.

Understanding the Core Challenge: YAML’s Structure vs. CSV’s Flatness

The fundamental difference between YAML and CSV lies in their data structures. YAML supports complex, nested hierarchies, lists, and associative arrays, making it ideal for representing intricate configurations or structured documents. In contrast, CSV is inherently flat, representing data as a series of rows and columns, where each row is a record and each column a field.

• YAML’s Versatility:

  • Hierarchical data: Think of parent: { child: value }. This nesting is a core YAML strength.
  • Lists/Arrays: - item1, - item2.
  • Dictionaries/Maps: key: value.
  • Data Types: Explicit support for strings, numbers, booleans, nulls.
  • Comments: Human-readable comments.

• CSV’s Simplicity:

  • Tabular data: Rows and columns.
  • No inherent nesting: All data must be represented in a flat structure. Nested YAML structures need to be “flattened” for CSV.
  • String-centric: While numbers and dates can be represented, CSV typically treats all values as strings.
  • Delimiter-based: Commas are standard, but other delimiters (tabs, semicolons) are possible.

The challenge in yaml to csv bash is primarily about mapping the potentially complex, multi-level YAML data into a single-level, delimited CSV format, often involving dynamic header generation, handling missing keys, and proper value escaping. Successfully navigating this requires tools that can interpret YAML’s structure and then flatten it into a format suitable for CSV. Liquibase xml to yaml

Essential Tools for Conversion: yq, jq, and Beyond

To effectively convert yaml to csv bash, you’ll need specialized command-line utilities. These tools act as the bedrock of your conversion pipeline, providing robust parsing and transformation capabilities.

yq – The YAML Processor

yq (by Mike Farah) is a portable command-line YAML processor, often referred to as “jq for YAML.” It allows you to query, update, and transform YAML documents. Its ability to convert YAML to JSON is particularly crucial for our yaml to csv bash goals.

• Key Features for Conversion:

  • YAML to JSON Conversion: yq -o=json '.' input.yaml is the primary command. This converts the entire YAML document into a JSON equivalent, preserving nesting.
  • Path Expressions: Similar to jq, yq uses path expressions (e.g., .data[0].name) to extract specific values or sub-documents.
  • Streaming Output: Can process large files efficiently.
  • Cross-platform: Available on Linux, macOS, and Windows.

• Installation:

  • Snap (Linux): sudo snap install yq (recommended for easy updates)
  • Homebrew (macOS): brew install yq
  • Direct Download: Binaries are available on the GitHub releases page for various systems.

jq – The JSON Processor

Once YAML is converted to JSON, jq becomes the workhorse. jq is a lightweight and flexible command-line JSON processor. It’s perfect for slicing, dicing, filtering, mapping, and transforming structured data. Its powerful querying language makes it indispensable for extracting and formatting data for CSV. Xml to yaml cantera

• Key Features for Conversion:

  • JSON Path Expressions: Highly flexible syntax for navigating JSON objects and arrays.
  • Filtering and Mapping: map(), select(), keys(), values().
  • String Manipulation: tostring, string interpolation ("\(.key)").
  • CSV Output: The @csv operator is a game-changer, automatically handling quoting and escaping of values.
  • Dynamic Header Generation: Ability to dynamically determine column names based on the input data.

• Installation:

  • APT (Debian/Ubuntu): sudo apt-get install jq
  • Homebrew (macOS): brew install jq
  • Direct Download: Binaries available on stedolan/jq GitHub releases.

Other Tools (Optional but Useful)

• csvkit: A suite of utilities for converting to and working with CSVs, including in2csv for converting other formats (like JSON) to CSV. While jq can often do the heavy lifting for JSON to CSV, csvkit offers additional features for analysis and manipulation of CSV files once created.
• awk / sed: For simpler, fixed-format data, awk or sed can perform basic text manipulation, but they lack the structural awareness of yq and jq for nested data. Their use is generally discouraged for yaml to csv bash if yq and jq can be applied, as they can lead to brittle scripts.

Basic Conversion Scenarios and Solutions

Let’s explore common yaml to csv bash conversion scenarios, starting with the simplest and moving towards more complex structures.

Scenario 1: Flat List of Key-Value Pairs (Array of Objects)

This is the most common and straightforward scenario. Each item in the YAML list represents a row, and the keys within each item represent the columns.

data.yaml: Xml format to text

- id: 101
  name: Ali Khan
  email: [email protected]
  status: active
- id: 102
  name: Fatima Zahra
  email: [email protected]
  status: inactive
- id: 103
  name: Omar Sharif
  email: [email protected]
  status: active

Bash Command (yaml to csv linux):

yq -o=json '.' data.yaml | jq -r '(map(keys) | add | unique) as $cols | map(. as $row | $cols | map($row[.])) as $rows | $cols, $rows[] | @csv' > output.csv

Explanation:

• yq -o=json '.' data.yaml: Converts the YAML array into a JSON array of objects.
• jq -r '...':
  • (map(keys) | add | unique) as $cols: This is critical. It iterates through each object (map(keys)), collects all keys from all objects (add), and then gets only the unique keys (unique). These unique keys form our dynamic header, stored in $cols.
  • map(. as $row | $cols | map($row[.])) as $rows: For each original object (. as $row), it creates a new array ($cols | map(...)) by mapping each header (.) to its corresponding value in the current object ($row[.]). If a key is missing in an object, jq returns null, which @csv renders as an empty string. This creates an array of arrays, representing the CSV rows.
  • $cols, $rows[]: Outputs the header row first, followed by each data row.
  • | @csv: Takes the arrays (header and data rows) and formats them correctly as CSV, adding quotes where necessary (e.g., if a value contains a comma).
• > output.csv: Redirects the output to a file named output.csv.

output.csv:

email,id,name,status
[email protected],101,Ali Khan,active
[email protected],102,Fatima Zahra,inactive
[email protected],103,Omar Sharif,active

(Note: jq typically sorts keys alphabetically for the header when using keys unless explicitly ordered.)

Scenario 2: Single YAML Object (Not an Array)

Sometimes, you might have a single YAML object that you want to convert into a one-row CSV, or perhaps extract specific fields. Xml to txt conversion

config.yaml:

application:
  name: MyWebApp
  version: 1.0.0
  environment: production
database:
  type: postgres
  host: db.example.com
  port: 5432

Bash Command (convert yaml to csv bash – single row from top-level fields):
To get application_name, application_version, database_type etc. as columns:

yq -o=json '.' config.yaml | \
jq -r '[
    .application.name,
    .application.version,
    .application.environment,
    .database.type,
    .database.host,
    .database.port
] | @csv' | \
(echo "app_name,app_version,environment,db_type,db_host,db_port"; cat -) > output_config.csv

Explanation:

• yq -o=json '.' config.yaml: Converts YAML to JSON.
• jq -r '[...] | @csv': Manually selects specific fields by their paths and puts them into an array, which is then converted to a single CSV row.
• (echo ...; cat -): This pattern is used to prepend a static header. echo prints the header, and cat - reads the jq output from standard input.

output_config.csv:

app_name,app_version,environment,db_type,db_host,db_port
MyWebApp,1.0.0,production,postgres,db.example.com,5432

Scenario 3: Flattening Nested Data

This is where the power of yq and jq really shines. You often need to take nested YAML data and flatten it into new, composite column names for CSV. Xml to json schema

users.yaml:

users:
  - id: U001
    profile:
      firstName: Sarah
      lastName: Ahmad
      contact:
        email: [email protected]
        phone: "+123456789"
    roles: [admin, editor]
  - id: U002
    profile:
      firstName: Yusuf
      lastName: Malik
      contact:
        email: [email protected]
    roles: [viewer]

Bash Command (yaml to csv bash – flattening):
This requires a bit more jq artistry to flatten the nested structure. We’ll use jq‘s paths and reduce functions, or a custom flattening function.

yq -o=json '.users[]' users.yaml | \
jq -r '
  def flatten(prefix):
    . as $in | reduce paths(scalars) as $path ({};
      .[ ($path | map(tostring) | join("_")) ] = $in | getpath($path)
    );

  ( (.[0] | flatten("") | keys_unsorted) as $header | $header ),
  ( . | map(flatten("") | .[$header[]]) )
| @csv' > output_flattened.csv

Explanation:

• yq -o=json '.users[]' users.yaml: This is a crucial yq step. .users[] extracts each item from the users list and outputs them as separate JSON objects, one per line. This is a common pattern for processing lists.
• jq -r 'def flatten(prefix): ...' defines a flatten function. This function takes a prefix (though we’re using an empty prefix here for simplicity, the original source often uses it for prepending to keys). It uses paths(scalars) to find all paths to scalar (non-object/array) values, and then reduce to build a new object where keys are the joined paths (e.g., profile_firstName, contact_email).
• ( (.[0] | flatten("") | keys_unsorted) as $header | $header ): Gets the first JSON object (.[0]), flattens it, gets its keys, and uses those as the header. keys_unsorted is used to maintain insertion order if possible, though jq‘s default behavior is often alphabetical for keys().
• ( . | map(flatten("") | .[$header[]]) ): Flattens each object in the input stream, then for each flattened object, it extracts values corresponding to the $header keys.
• | @csv: Formats the header and data rows as CSV.

output_flattened.csv:

id,profile_firstName,profile_lastName,profile_contact_email,profile_contact_phone,roles_0,roles_1
U001,Sarah,Ahmad,[email protected],+123456789,admin,editor
U002,Yusuf,Malik,[email protected],,viewer,

(Note: The roles array is also flattened here to roles_0, roles_1. For more advanced array handling, you might need to concatenate array elements into a single cell, perhaps comma-separated, before conversion.) Xml to text online

This approach to convert yaml to csv bash for nested data is powerful, though it requires a deeper understanding of jq‘s capabilities.

Advanced Techniques and Considerations

Beyond basic conversions, several advanced techniques can enhance your yaml to csv bash scripts.

Handling Missing Keys and Default Values

When converting YAML to CSV, not all objects in a list might have the same set of keys. The dynamic header generation (map(keys) | add | unique) handles this by including all possible columns. When a key is missing for a particular row, jq‘s . operator (e.g., $row[.]) returns null, which @csv correctly translates to an empty string. This is generally the desired behavior.

If you need a default value instead of an empty string for missing keys, you can use // (the “alternative operator”) in jq:

# Example: Use "N/A" if 'email' is missing
yq -o=json '.' data.yaml | jq -r '
  (map(keys) | add | unique) as $cols |
  map(. as $row | $cols | map($row[.] // "N/A")) as $rows |
  $cols, $rows[] | @csv'

Custom Delimiters and Quoting

While comma is standard, CSV can use other delimiters (e.g., tab-separated values, TSV, or semicolon-separated values). Xml to csv linux

• Custom Delimiter with jq:
  The @csv operator defaults to comma and double-quote for quoting. If you need a different delimiter, you’d typically have to build the CSV string manually, joining fields with your chosen delimiter and manually handling quoting:

  # Example: Semicolon-separated values
  yq -o=json '.' data.yaml | jq -r '
    . as $data |
    ( ($data | map(keys) | add | unique) as $cols |
      $cols | map(.) | join(";") ),
    ( $data | map( . as $row | $cols | map($row[.] // "") | join(";") ) )
    | .[]'
  

  This manually joins fields with a semicolon. You would need to implement a more robust quoting mechanism if your values themselves contain semicolons or double quotes. For this reason, sticking to @csv and comma is often best, unless absolutely necessary.

• Pre-existing CSV Standards: Remember that RFC 4180 defines standard CSV, recommending commas and double quotes. Deviating from this can cause issues with other tools.

Large File Processing (Streaming)

For very large YAML files, memory consumption can become an issue if the entire file is loaded into memory by yq or jq. Both tools are designed to stream data when possible.

• yq for streaming: When using yq .[] or yq .key[], yq will often output each matched document as a separate JSON object on its own line, which jq can then process line by line. This is efficient. Yaml to json schema

  # Process items one by one
  yq -o=json '.items[]' large_data.yaml | \
  jq -c '.id, .name' # -c for compact output, one JSON object per line
  

  This approach minimizes memory usage as jq processes each JSON object as it arrives.

Scripting for Reusability

For complex or repetitive conversions, encapsulate your yaml to csv bash logic into a reusable bash script.

convert_yaml.sh:

#!/bin/bash

# A robust script to convert YAML files to CSV using yq and jq.
# Handles dynamic headers and basic flattening.

if [ "$#" -ne 2 ]; then
    echo "Usage: $0 <input_yaml_file> <output_csv_file>"
    echo "Example: $0 data.yaml output.csv"
    exit 1
fi

INPUT_YAML="$1"
OUTPUT_CSV="$2"

if [ ! -f "$INPUT_YAML" ]; then
    echo "Error: Input YAML file '$INPUT_YAML' not found."
    exit 1
fi

# Determine if the root is an array or a single object
# We assume if the first char after trimming whitespace is '-' it's an array
IS_ARRAY=$(head -n 5 "$INPUT_YAML" | grep -q '^-' && echo "true" || echo "false")

if [ "$IS_ARRAY" = "true" ]; then
    # Process as an array of objects
    yq -o=json '.' "$INPUT_YAML" | \
    jq -r '
        (map(keys) | add | unique) as $cols |
        map(. as $row | $cols | map($row[.] // "")) as $rows |
        $cols, $rows[] | @csv
    ' > "$OUTPUT_CSV"
else
    # Process as a single object (flat keys)
    # This might need adjustment for deeply nested single objects.
    # For now, it will output a single row with top-level keys.
    # To handle nested single objects, you'd need the flatten function from before.
    yq -o=json '.' "$INPUT_YAML" | \
    jq -r '
        . as $data |
        (keys_unsorted) as $cols |
        $cols, ($cols | map($data[.] // "")) | @csv
    ' > "$OUTPUT_CSV"
    echo "Warning: Input YAML is a single object. Only top-level keys are converted to columns."
    echo "For nested single objects, modify the script to use a flattening function."
fi

if [ $? -eq 0 ]; then
    echo "Successfully converted '$INPUT_YAML' to '$OUTPUT_CSV'"
else
    echo "Error: Conversion failed. Check your YAML format and tool installations (yq, jq)."
fi

Usage: bash convert_yaml.sh mydata.yaml mydata.csv

This script provides a basic yaml to csv bash wrapper, checking for file existence and handling both array and single-object root YAMLs (with a caveat for deeply nested single objects). Tsv requirements

Potential Pitfalls and Troubleshooting

While yq and jq are powerful, you might encounter issues.

• YAML Syntax Errors: yq is strict. Even minor indentation issues or missing colons will cause it to fail.
  • Solution: Use a YAML linter (e.g., yamllint) or an online YAML validator to check your file before processing.
• Missing Tools: Ensure yq and jq are installed and in your system’s PATH.
  • Solution: Run yq --version and jq --version to verify.
• Complex Nested Structures: The simple flattening logic (jq -r 'def flatten(...)) might not be sufficient for all nested YAML structures, especially if you have lists of lists or deeply embedded arrays.
  • Solution: You’ll need more sophisticated jq logic to iterate through nested arrays, perhaps joining elements with a separator within a single CSV cell, or creating multiple rows from a single YAML entry if the nested list should become separate rows. This is a common challenge and requires careful mapping.
• Data Types: jq handles data types (numbers, booleans, strings) correctly. However, CSV is fundamentally text-based. If you have specific numerical or date formatting needs, you might need to apply string formatting in jq or post-process the CSV.
• Header Order: jq‘s keys function often returns keys in alphabetical order. If you need a specific column order, you must explicitly list them in your jq query:

  yq -o=json '.' data.yaml | \
  jq -r '
      ["id", "name", "email", "status"], # Explicit header order
      (.[] | [.id, .name, .email, .status]) # Match order for data rows
      | @csv
  '
  

Practical Applications and Use Cases

The ability to convert yaml to csv bash opens up a multitude of practical applications:

• Configuration Management: Convert YAML-based application configurations into a readable CSV for auditing, comparison, or integration with spreadsheet-based tools. For instance, converting Ansible inventory or Kubernetes deployment manifests (after kubectl get -o yaml) to CSV can help identify discrepancies or quickly survey resource allocations.
• Data Migration and Reporting: Extract specific datasets stored in YAML format and transform them into CSV for import into databases, business intelligence tools, or for generating reports.
• Log Analysis: If structured logs are captured in YAML (or can be converted), yaml to csv linux can help flatten them for easier analysis in spreadsheet software.
• API Data Processing: When consuming APIs that return YAML, or converting curl output, bash scripts can quickly parse and transform the data into CSV for local processing or storage.
• DevOps and Automation: Automate the conversion of data files for CI/CD pipelines, where one step might output YAML and a subsequent step requires CSV. For example, converting test results or deployment metadata from YAML to CSV for reporting dashboards.
• Inventory Management: Convert YAML inventory files (e.g., for assets, software licenses) into a tabular format that can be easily shared or updated by non-technical stakeholders using standard spreadsheet applications. This can be particularly useful for small businesses managing their assets efficiently without relying on complex database systems.

By mastering yaml to csv bash conversions, you empower yourself to bridge the gap between human-readable structured data and universally consumable tabular data, making your scripting and data handling workflows significantly more efficient. This skill is invaluable for anyone working with modern configuration and data formats in a command-line environment.

FAQ

What is YAML?

YAML (YAML Ain’t Markup Language) is a human-friendly data serialization standard often used for configuration files, data exchange between languages, and storing structured data. It emphasizes readability and uses indentation to define structure, similar to Python.

What is CSV?

CSV (Comma Separated Values) is a plain text file format used for storing tabular data, where each line in the file is a data record and each record consists of one or more fields, separated by commas. It’s widely compatible with spreadsheets and data analysis tools. Json to text dataweave

Why would I convert YAML to CSV using bash?

Converting YAML to CSV using bash is useful for automating data transformation, especially in scripting, DevOps, and data processing workflows. It allows you to flatten hierarchical YAML data into a tabular format easily consumable by spreadsheets, databases, or reporting tools, all from the command line on Linux or macOS.

What are the main tools needed for YAML to CSV conversion in bash?

The primary tools you’ll need are yq (a command-line YAML processor) and jq (a command-line JSON processor). yq converts YAML to JSON, and jq then transforms the JSON into CSV format.

How do I install yq and jq on Linux?

On Debian/Ubuntu, you can install jq with sudo apt-get install jq. For yq, it’s recommended to install via Snap: sudo snap install yq. Ensure you’re getting the yq by Mike Farah.

How do I install yq and jq on macOS?

Using Homebrew, you can install both with brew install yq and brew install jq.

Can I convert nested YAML data to CSV?

Yes, you can convert nested YAML data to CSV. This typically involves flattening the nested structure by creating composite column names (e.g., parent_child_key) using jq‘s path expressions and functions like flatten. Json to yaml swagger

What if my YAML file has inconsistent keys across different items in a list?

jq can dynamically generate CSV headers by identifying all unique keys across all objects in a YAML array. This ensures that all possible columns are included, and missing values for a particular row will appear as empty cells in the CSV.

How does jq handle missing values when converting to CSV?

When a key is present in the header but missing in a specific data object, jq‘s @csv operator will output an empty string for that field, which is the standard CSV behavior for missing data.

Can I specify a custom delimiter for the CSV output?

While jq‘s @csv operator defaults to a comma and double-quote for quoting, you can manually construct the CSV string using join(";") or another delimiter. However, implementing robust quoting for custom delimiters becomes more complex and often requires a custom function within jq or external tools.

What is the @csv operator in jq?

The @csv operator in jq is a powerful built-in function that takes an array and formats it into a CSV string. It automatically handles adding double quotes around fields that contain commas, newlines, or double quotes, and it escapes internal double quotes by doubling them.

How can I add a header row to my CSV output?

You can generate the header dynamically using jq‘s map(keys) | add | unique logic and then output it before the data rows within the same jq command. Alternatively, for a static header, you can use echo "header1,header2..." and pipe the jq output after it using cat -. Json to text postgres

Can I convert a single YAML object (not an array) to a single CSV row?

Yes, you can. You would typically select the specific fields from the single YAML object and present them as a jq array, then use @csv to convert this array into a single CSV row. You might need to manually specify the header row using echo.

Is it possible to convert only specific fields from YAML to CSV?

Absolutely. You can use jq‘s path expressions to select only the desired fields from your YAML (after yq converts it to JSON). For example, jq -r '.[] | [.name, .email] | @csv' would only extract the name and email fields.

What are some common errors when converting YAML to CSV?

Common errors include YAML syntax errors (indentation, typos), missing yq or jq installations, incorrect jq path expressions for complex YAML structures, or issues with file paths.

How can I debug my YAML to CSV conversion script?

Break down the pipeline:

1. Run yq -o=json . your_file.yaml first to see the JSON output.
2. Then, pipe that JSON to jq step-by-step, building your jq query incrementally to identify where the transformation goes wrong.

Can I convert large YAML files to CSV using this method?

Yes, yq and jq are designed to be stream-oriented when possible, meaning they can process large files without loading the entire content into memory. Using yq -o=json '.items[]' to output individual JSON objects per line helps jq process data in a streaming fashion. Json to text file python

How can I handle lists within a YAML object when converting to CSV?

If a YAML object has a list (e.g., roles: [admin, viewer]), you have a few options for CSV:

1. Flatten: Create columns like roles_0, roles_1 (as shown in the advanced example).
2. Join: Join the list elements into a single string within one CSV cell (e.g., admin,viewer) using jq‘s join(",") operator.
3. Multiple Rows: If each item in the list should generate a separate CSV row, you’d need more complex jq logic, possibly involving flatten or map to duplicate the parent data for each list item.

Are there any GUI tools for YAML to CSV conversion?

Yes, there are online converters and desktop applications that offer GUI-based YAML to CSV conversion. However, for automation and command-line workflows, yq and jq in bash are generally more flexible and efficient.

Can I use this method to convert other formats like XML to CSV?

The specific yq command is for YAML. For XML, you would use a dedicated XML parsing tool like xmlstarlet or xq (another tool by Mike Farah, similar to yq but for XML) to convert XML to JSON first, and then proceed with jq to CSV. The core principle of converting to JSON then to CSV remains valid.

Convert utc to unix timestamp javascript