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The data export module provides functions to export scraped data to JSON, CSV, and XML file formats.

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Functions

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export_to_json

export_to_json(data: List[Dict[str, Any]], filename: str) -> None
Export data to a JSON file with proper formatting and UTF-8 encoding.
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data
List[Dict[str, Any]]
required
List of dictionaries containing the data to export. Each dictionary represents a record.
​
filename
str
required
Name of the file to save the JSON data. Can include a path (e.g., “output/data.json”).

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Example

from scrapegraphai.utils import export_to_json
from scrapegraphai.graphs import SmartScraperGraph

# Configure and run your scraper
graph_config = {
    "llm": {"model": "openai/gpt-4o-mini"},
}

smart_scraper = SmartScraperGraph(
    prompt="Extract product names and prices",
    source="https://example.com/products",
    config=graph_config,
)

result = smart_scraper.run()

# Export to JSON
export_to_json(result, "products.json")
# Output: Data exported to products.json

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Output Format

The JSON file is formatted with 4-space indentation: 
[
    {
        "product_name": "Wireless Mouse",
        "price": "$29.99"
    },
    {
        "product_name": "Mechanical Keyboard",
        "price": "$89.99"
    }
]

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export_to_csv

export_to_csv(data: List[Dict[str, Any]], filename: str) -> None
Export data to a CSV file. The CSV headers are automatically generated from the keys of the first dictionary.
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data
List[Dict[str, Any]]
required
List of dictionaries containing the data to export. All dictionaries should have the same keys for consistent CSV structure.
​
filename
str
required
Name of the file to save the CSV data. Can include a path (e.g., “output/data.csv”).

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Example

from scrapegraphai.utils import export_to_csv
from scrapegraphai.graphs import SmartScraperGraph

# Configure and run your scraper
graph_config = {
    "llm": {"model": "openai/gpt-4o-mini"},
}

smart_scraper = SmartScraperGraph(
    prompt="Extract article titles and authors",
    source="https://example.com/blog",
    config=graph_config,
)

result = smart_scraper.run()

# Export to CSV
export_to_csv(result, "articles.csv")
# Output: Data exported to articles.csv

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Output Format

The CSV file includes headers and properly escaped values: 
title,author,date
"Getting Started with Web Scraping","John Doe","2024-01-15"
"Advanced Python Techniques","Jane Smith","2024-01-20"

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Handling Empty Data

from scrapegraphai.utils import export_to_csv

# If data is empty, the function will print a message
empty_data = []
export_to_csv(empty_data, "output.csv")
# Output: No data to export

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export_to_xml

export_to_xml(
    data: List[Dict[str, Any]],
    filename: str,
    root_element: str = "data"
) -> None
Export data to an XML file with customizable root element name.
​
data
List[Dict[str, Any]]
required
List of dictionaries containing the data to export. Each dictionary is converted to an XML <item> element.
​
filename
str
required
Name of the file to save the XML data. Can include a path (e.g., “output/data.xml”).
​
root_element
str
default:"data"
Name of the root element in the XML structure. Defaults to “data”.

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Example

from scrapegraphai.utils import export_to_xml
from scrapegraphai.graphs import SmartScraperGraph

# Configure and run your scraper
graph_config = {
    "llm": {"model": "openai/gpt-4o-mini"},
}

smart_scraper = SmartScraperGraph(
    prompt="Extract company names and locations",
    source="https://example.com/companies",
    config=graph_config,
)

result = smart_scraper.run()

# Export to XML with default root element
export_to_xml(result, "companies.xml")
# Output: Data exported to companies.xml

# Export with custom root element
export_to_xml(result, "companies_custom.xml", root_element="companies")

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Output Format

The XML file includes proper declaration and structure: 
<?xml version='1.0' encoding='utf-8'?>
<data>
    <item>
        <company_name>Tech Corp</company_name>
        <location>San Francisco</location>
    </item>
    <item>
        <company_name>Data Inc</company_name>
        <location>New York</location>
    </item>
</data>

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Complete Example

from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.utils import export_to_json, export_to_csv, export_to_xml

# Configure the scraper
graph_config = {
    "llm": {"model": "openai/gpt-4o-mini"},
}

smart_scraper = SmartScraperGraph(
    prompt="Extract all products with their names, prices, and ratings",
    source="https://example.com/products",
    config=graph_config,
)

# Run the scraper
result = smart_scraper.run()
print(f"Extracted {len(result)} products")

# Export to multiple formats
export_to_json(result, "products.json")
export_to_csv(result, "products.csv")
export_to_xml(result, "products.xml", root_element="products")

print("Data exported to all formats successfully!")

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Best Practices

  1. Consistent Data Structure: Ensure all dictionaries in your data list have the same keys for clean CSV exports.
  2. File Paths: Use absolute or relative paths for better organization: 
    export_to_json(data, "output/2024/january/products.json")
  3. Error Handling: Wrap exports in try-except blocks for production code: 
    try:
    export_to_csv(result, "output.csv")
except Exception as e:
    print(f"Export failed: {e}")
  4. Format Selection:
    • Use JSON for structured data and API integration
    • Use CSV for spreadsheet compatibility and data analysis
    • Use XML for legacy system integration or specific format requirements