How to Convert an XML File to a Pandas DataFrame with Six Specific Columns?

Converting XML to Pandas DataFrame with Ease

Problem:

Given an XML file with a specific structure, the task is to convert it into a clean and organized pandas DataFrame with six columns: 'key,' 'type,' 'language,' 'feature,' 'web,' and 'data.'

Solution:

The most efficient method to accomplish this conversion is to utilize Python's standard 'xml' library. This library provides a straightforward way to parse and manipulate XML data. Here's how to proceed:

1. Parse the XML: Using the 'ElementTree' class from the 'xml' library, parse the XML file into an ElementTree object.
2. Iterate over Authors: From the parsed XML, iterate over each 'author' tag.
3. Extract Document Data: For each 'author,' iterate over its child 'document' elements and extract the desired data.
4. Create a Dictionary for Each Document: Create a dictionary for each 'document' containing the relevant data, including the text content.
5. Convert Dictionaries to DataFrame: Finally, convert the list of dictionaries into a pandas DataFrame.

Code Snippet:

import pandas as pd
import xml.etree.ElementTree as ET

xml_data = "<author..>..." # Replace with your XML string

etree = ET.parse(xml_data)

def iter_docs(author):
    for doc in author.iter('document'):
        doc_dict = author.attrib.copy()
        doc_dict.update(doc.attrib)
        doc_dict['data'] = doc.text
        yield doc_dict

doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

print(doc_df)</author..>

This method ensures a systematic and efficient conversion of the XML data into a DataFrame that meets the desired format.

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