How can I efficiently convert XML data into a Pandas DataFrame in Python?

Converting XML Documents to Pandas DataFrames

When dealing with XML data, it often becomes necessary to process and analyze the information within them. Pandas offers a convenient tool for this purpose, allowing you to convert XML into structured DataFrames.

Let's consider an XML structure like the one given:

<author type="XXX" language="EN" gender="xx" feature="xx" web="foobar.com">
    <documents count="N">
        <document KEY="e95a9a6c790ecb95e46cf15bee517651">![CDATA[A large text with lots of strings and punctuations symbols [...]
]]></document>
        <!-- more documents -->
    </documents>
</author>

The goal is to convert this XML into a DataFrame with columns for key, type, language, feature, web, and data.

Efficient Approach with ElementTree

To accomplish this, we can leverage Python's xml.etree.ElementTree, which provides an efficient way to parse XML. Here's an improved approach:

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

xml_data = "file_path"
etree = ET.parse(xml_data) #create an ElementTree object

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

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

In this approach, we use the iter_docs generator function to iterate over the author and document elements, extracting relevant attributes and data. Then, we create a DataFrame from the generated list of dictionaries.

Additional notes:

• If there are multiple author elements in your XML, you can use another generator function to loop over them and iterate over the documents within each.
• If the root element of your XML is not author, adjust the etree.getroot() call accordingly.

Benefits of using ElementTree:

• Efficient parsing and memory usage.
• Provides a hierarchical representation of the XML structure, making it easy to navigate.
• Supports various XML features such as namespaces and comments.

By following this approach, you can efficiently convert complex XML data into a tabular format using pandas DataFrames, enabling further data manipulation and analysis.

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