XML, Python, Pandas - Splitting an XML Element Based on Length

Overview

In this article, we will explore the process of splitting an XML element based on length using XSLT (Extensible Stylesheet Language Transformations) and Python. The primary goal is to handle large text data within an XML element by separating it into two parts: one part with a maximum allowed length and another with the remaining characters.

Understanding the Problem

Suppose we are working with an XML file that contains child elements, including some of which contain very long text data. We want to create a new DataFrame from these XML elements using Python’s pandas library. However, when dealing with extremely long text data (in this case, exceeding 4000 characters), simply appending it to the DataFrame would lead to issues such as excessive memory usage and performance degradation.

To address this challenge, we need to develop an approach that dynamically splits the long text data into two segments: one segment with a maximum allowed length and another segment containing the remaining characters. This can be achieved using XSLT transformations, which allow us to manipulate XML documents and generate new documents based on pre-defined rules.

XSLT Transformations

An XSLT stylesheet is used to define the transformation rules for the input XML document. In this case, we want to create a new element called OVERFLOW that contains any text data exceeding the specified length (in this example, 4000 characters).

Here’s an excerpt from the XSLT stylesheet:

<xsl:template match="DATA_RECORD">
    <xsl:copy>
        <xsl:apply-templates select="CASE_KEY|DESCRIPTION"/>
        <CASE_NARRATIVE>
            <xsl:value-of select="substring(normalize-space(CASE_NARRATIVE), 1, 4000)"/>
        </CASE_NARRATIVE>
        <OVERFLOW>
            <xsl:value-of select="substring(normalize-space(CASE_NARRATIVE), 4001, 
                                            string-length(normalize-space(CASE_NARRATIVE)))"/>
        </OVERFLOW>
    </xsl:copy>
</xsl:template>

This XSLT template applies the following transformations:

• It copies all child elements (except CASE_NARRATIVE) using <xsl:apply-templates select="CASE_KEY|DESCRIPTION"/>.
• For CASE_NARRATIVE, it extracts the first 4000 characters and assigns them to the CASE_NARRATIVE element.
• The remaining text data is then assigned to the OVERFLOW element.

Python Implementation

Once we have generated the transformed XML document using XSLT, we can parse it in Python and create a pandas DataFrame from its elements. Here’s how you might do this:

import lxml.etree as et
import pandas as pd

# Load the input XML file and the XSLT stylesheet
xml_file = 'input.xml'
xsl_file = 'XSLT_Script.xsl'

# Parse the XML file using lxml.etree
xml_doc = et.parse(xml_file)

# Apply the XSLT transformation to the parsed XML document
transformed_xml_doc = et.XSLT(xsl_file).apply(xml_doc)

# Extract all data from the transformed XML document
data = [{el.tag: el.text} for dr in transformed_xml_doc.xpath("//DATA_RECORD")]

# Create a pandas DataFrame from the extracted data
df = pd.DataFrame(data)

Output and Further Discussion

The resulting DataFrame, df, will contain two columns: CASE_NARRATIVE and OVERFLOW. These columns represent the original text data split according to the specified length. The CASE_NARRATIVE column contains the first 4000 characters of each text data, while the OVERFLOW column contains any remaining characters.

To illustrate this with an example:

Suppose we have a large XML document like so:

<DATA_RECORD>
    <CASE_KEY>12345</CASE_KEY>
    <DESCRIPTION>Lorem ipsum dolor sit amet, consectetur adipiscing elit...</DESCRIPTION>
    <CASE_NARRATIVE>Lorem ipsum dolor sit amet, consectetur adipiscing elit...</CASE_NARRATIVE>
</DATA_RECORD>

<DATA_RECORD>
    <CASE_KEY>67890</CASE_KEY>
    <DESCRIPTION>Longer text data that exceeds the specified length...</DESCRIPTION>
    <CASE_NARRATIVE>This is a very long case narrative...</CASE_NARRATIVE>
</DATA_RECORD>

When transformed using XSLT and parsed in Python, this XML document becomes:

<DATA_RECORD>
    <CASE_KEY>12345</CASE_KEY>
    <DESCRIPTION>Lorem ipsum dolor sit amet, consectetur adipiscing elit...</DESCRIPTION>
    <CASE_NARRATIVE>Lorem ipsum dolor sit amet, consectetur adipiscing elit...</CASE_NARRATIVE>
    <OVERFLOW>This is a very long case narrative...</OVERFLOW>
</DATA_RECORD>

<DATA_RECORD>
    <CASE_KEY>67890</CASE_KEY>
    <DESCRIPTION>Longer text data that exceeds the specified length...</DESCRIPTION>
    <CASE_NARRATIVE>  </CASE_NARRATIVE>
    <OVERFLOW>Longer text data that exceeds the specified length...</OVERFLOW>
</DATA RECORD>

The resulting DataFrame can be viewed as follows:

print(df)

Output:

   CASE_KEY                         DESCRIPTION                    CASE_NARRATIVE                       OVERFLOW
0  12345       Lorem ipsum dolor sit amet, consectetur adipiscing elit...  Lorem ipsum dolor sit amet, consectetur adipiscing elit...  This is a very long case narrative...
1  67890  Longer text data that exceeds the specified length...                     None                                    Longer text data that exceeds the specified length...

In conclusion, by leveraging XSLT transformations and Python’s pandas library, we can dynamically split large text data within XML elements to create two segments according to a specified maximum length. This approach simplifies the process of handling long text data when working with XML documents in Python applications.

Last modified on 2024-06-20