How can I convert XML to a Pandas DataFrame efficiently?

Converting XML to a Pandas DataFrame Efficiently

XML files can often contain valuable data that can be analyzed using tools such as Pandas. To convert an XML file to a DataFrame, an effective approach can be found below:

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

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

xml_data = io.StringIO(u'''YOUR XML STRING HERE''')

etree = ET.parse(xml_data) #create an ElementTree object
doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

Explanation:

• The iter_docs generator function iterates over the XML document, extracting author attributes, document attributes, and the content of the document's text node into a dictionary.
• The Pandas DataFrame is then constructed from a list of dictionaries generated by the iter_docs function.

Additional Notes:

The example XML provided in the question assumes a single author. If there are multiple authors, an additional generator function, iter_author, can be used to iterate over each author and yield all their respective document dictionaries. This would require modifying the last line of the example code to:

doc_df = pd.DataFrame(list(iter_author(etree)))

For further guidance on working with XML in Python, refer to the ElementTree tutorial in the xml library documentation.

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