What file formats can Kanna export scraped data to?

Kanna is not a standard tool or library that I'm aware of in the context of web scraping as of my last update in early 2023. It's possible that you might be referring to Nokogiri, which is a popular Ruby library for parsing HTML, XML, and other documents, or you might be thinking of Scrapy, which is a widely-used Python framework for web scraping.

However, since Kanna is not recognized as a specific web scraping tool, I will instead provide general information on the file formats that web scraping tools can typically export data to.

Web scraping tools can usually export data to a variety of file formats, depending on the capabilities of the tool and the requirements of the user. Here are some common file formats to which scraped data can be exported:

1. CSV (Comma-Separated Values): CSV is a popular plain-text format that is used to store tabular data. Many web scraping tools allow exporting data into CSV format because it is simple and widely supported by spreadsheet applications like Microsoft Excel and Google Sheets.

   import csv
   
   data = [['Name', 'Price'], ['Product 1', '9.99'], ['Product 2', '19.99']]
   with open('output.csv', 'w', newline='') as file:
       writer = csv.writer(file)
       writer.writerows(data)
   

2. JSON (JavaScript Object Notation): JSON is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. It's a common format for web APIs and is also used to store structured data.

   import json
   
   data = {
       "products": [
           {"name": "Product 1", "price": "9.99"},
           {"name": "Product 2", "price": "19.99"}
       ]
   }
   with open('output.json', 'w') as file:
       json.dump(data, file)
   

3. XML (eXtensible Markup Language): XML is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. It is particularly useful when you want to maintain document structure and metadata.

   import xml.etree.ElementTree as ET
   
   products = ET.Element("products")
   product1 = ET.SubElement(products, "product", name="Product 1")
   price1 = ET.SubElement(product1, "price")
   price1.text = "9.99"
   
   product2 = ET.SubElement(products, "product", name="Product 2")
   price2 = ET.SubElement(product2, "price")
   price2.text = "19.99"
   
   tree = ET.ElementTree(products)
   tree.write("output.xml", encoding='utf-8', xml_declaration=True)
   

4. Excel (.xlsx): Some scraping tools support exporting data directly to Excel formats, which can be useful for users who wish to manipulate the data using Excel's features.

   import pandas as pd
   
   data = {
       'Name': ['Product 1', 'Product 2'],
       'Price': [9.99, 19.99]
   }
   df = pd.DataFrame(data)
   df.to_excel('output.xlsx', index=False)
   

5. SQL databases: Often, the scraped data needs to be stored in a database rather than a file. Web scraping tools may provide features to export scraped data directly into SQL databases.

   import sqlite3
   
   conn = sqlite3.connect('scraped_data.db')
   c = conn.cursor()
   
   # Create table
   c.execute('''CREATE TABLE products
                (name text, price real)''')
   
   # Insert a row of data
   c.execute("INSERT INTO products VALUES ('Product 1', 9.99)")
   c.execute("INSERT INTO products VALUES ('Product 2', 19.99)")
   
   # Save (commit) the changes
   conn.commit()
   conn.close()
   

The above examples are written in Python, which is commonly used for web scraping. If you're using JavaScript (Node.js environment) for web scraping, similar functionalities exist with packages like csv-writer, jsonfile, xmlbuilder, exceljs, and database drivers for SQL databases.

If you were referring to a specific tool named Kanna that I'm not aware of, please provide more context or check the tool's documentation for the supported export file formats.