How to Parse XML with Python Pandas

In this blog, we’ll delve into the intricacies of parsing XML files using Python and Pandas, particularly for data scientists and software engineers who often encounter XML Extensible Markup Language files in their work. XML is a widely used format for data storage and exchange on the web. Parsing XML files can pose challenges, especially when the goal is to extract specific data from sizable XML files. Our focus in this article will be on demonstrating how to effectively parse XML with Python and Pandas, aiming to consolidate an entire block of tags into a single row.

As a data scientist or software engineer, you may come across XML files in your work. XML (Extensible Markup Language) is a popular format for storing and exchanging data on the web. However, parsing XML files can be a tricky task, especially when you need to extract specific data from a large XML file. In this article, we will explore how to parse XML with Python Pandas and get a complete block of tags in one row.

What is XML Parsing?

XML parsing is the process of analyzing an XML document and extracting information from it. The process involves identifying the structure of the XML document, locating the tags and attributes, and extracting the data associated with them. XML parsing is commonly used in web development, data extraction, and data analysis.

Why Use Python Pandas for XML Parsing?

Python Pandas is a powerful data analysis library that provides tools for reading, writing, and manipulating data in various formats, including XML. Pandas provides a simple and efficient way to parse XML files and extract data from them. Using Pandas for XML parsing allows you to perform complex data analysis tasks with ease.

How to Parse XML with Python Pandas

Let’s consider the following xml file:

<?xml version="1.0" encoding="utf-8"?>
   John Doe

Method 1: Using xml.etree.ElementTree

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

tree = ET.parse("sample.xml")
root = tree.getroot()

data = []
for person in root.findall("person"):
    name = person.find("name").text
    age = person.find("age").text
    data.append({"Name": name, "Age": age})

df = pd.DataFrame(data)

This method uses the xml.etree.ElementTree module to parse XML and convert it to a Pandas DataFrame. It involves manually iterating through the XML structure and extracting data.


       Name Age
0  John Doe  30

3. Method 2: Using xmltodict

Make sure that xmltodict has been installed. If not, you can install it by running:

pip install xmltodict
import xmltodict
import pandas as pd

with open("sample.xml") as xml_file:
    data_dict = xmltodict.parse(

data = []

# Check if there is more than one person in the XML
persons = data_dict.get("root", {}).get("person", [])

if not isinstance(persons, list):
    persons = [persons]  # If only one person, convert it to a list for consistency

for person in persons:
    name = person.get("name")
    age = person.get("age")
    data.append({"Name": name, "Age": age})

df = pd.DataFrame(data)

This method leverages the xmltodict library, which simplifies the XML parsing process by converting XML to a Python dictionary. It checks whether there is more than one person in the XML. If there is only one person, it converts it to a list for consistency. Additionally, it uses the get method to safely retrieve values from the dictionary.


       Name Age
0  John Doe  30

Common Errors and Solutions

Error 1: Malformed XML

Description: The XML file has syntax errors.

Solution: Validate XML syntax using online tools or an XML validator. Correct the errors in the XML file.

Error 2: Missing Tags

Description: Some XML tags or attributes are missing.

Solution: Check for missing tags or attributes in the XML file. Update the file to include the necessary elements.

Error 3: Namespace Issues

Description: XML file contains namespaces that are not handled properly.

Solution: Use namespace-aware parsing libraries or remove namespaces from the XML file if not needed.


Parsing XML with Python Pandas opens up opportunities for efficiently handling structured data. Whether you choose the simplicity of xml.etree.ElementTree or the convenience of xmltodict, understanding the methods, best practices, and potential pitfalls is crucial for successful XML parsing. Choose the method that best suits your data structure and complexity, keeping in mind the trade-offs between simplicity and flexibility. By following best practices and addressing common errors, you can streamline the XML parsing process and enhance the reliability of your data analysis workflows.

To parse XML with Python Pandas, you will need to install the lxml library. Lxml is a Python library that provides a fast and efficient way to parse XML files. You can install lxml using pip, the Python package manager.

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