How to Read XML Files into Python Pandas DataFrames (With Code)

In enterprise data engineering pipelines—especially within banking, financial services, healthcare, and legacy ERP systems—data is frequently delivered in XML (Extensible Markup Language) format.

Unlike flat CSV or JSON files, XML files contain nested hierarchies, namespaces, and node attributes that require specific parsing techniques. In this hands-on tutorial, we explore how to parse XML files into clean Pandas DataFrames using Python.

Method 1: Using Pandas `pd.read_xml()` (Fastest & Easiest)

Since Pandas version 1.3+, the built-in pd.read_xml() function parses flat XML structures directly into a DataFrame with a single line of code:

PYTHON

import pandas as pd

# Reading flat XML file into Pandas DataFrame
df = pd.read_xml("data.xml")

# Displaying first 5 rows
print(df.head())

Method 2: Using `xml.etree.ElementTree` for Complex Nested XML

For deeply nested XML schemas containing custom attributes, use Python's native xml.etree.ElementTree module to extract specific nodes:

PYTHON

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

# Parse XML file
tree = ET.parse('data.xml')
root = tree.getroot()

data = []
for item in root.findall('Record'):
    row = {
        'id': item.find('ID').text,
        'name': item.find('Name').text,
        'value': item.find('Value').text
    }
    data.append(row)

# Convert list of dictionaries to DataFrame
df = pd.DataFrame(data)
print(df.head())

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Learn how to parse, transform, and load complex XML, JSON, and Parquet data into cloud warehouses at JVM Institute Pune.

1. The Evolution of Data Engineering

Data engineering has undergone a monumental shift. In the early 2000s, traditional relational databases and monolith data warehouses handled nightly batch jobs. As data volume exploded, technologies like Apache Hadoop introduced distributed computing via HDFS and MapReduce.

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To excel as a data engineer, mastering a core set of technical competencies is essential: proficiency in programming languages (Python, PySpark, SQL), data modeling (Star & Snowflake schemas), automated ETL/ELT orchestration, and cloud infrastructure.

At JVM Institute, our hands-on training programs empower students to master these skills with live Databricks clusters and production Airflow DAGs. Over 200+ JVM alumni have successfully transitioned into high-paying engineering roles across Tier-1 tech MNCs.

Pro Tip: Combining Python XML element trees with PySpark DataFrames provides high performance when ingesting legacy enterprise files.

PYTHONUTF-8 SOURCE

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

# Parsing XML Data Files into Python DataFrames
tree = ET.parse("data_pipeline_export.xml")
root = tree.getroot()

records = []
for item in root.findall(".//record"):
    records.append({
        "id": item.find("id").text,
        "event": item.find("event").text,
        "value": float(item.find("value").text)
    })

df = pd.DataFrame(records)
print("Parsed Data Summary:\n", df.head())

3. Real-World Applications across Industries

Data engineering powers real-world innovations across every sector: in finance, stream processing detects fraudulent transactions in milliseconds; in healthcare, pipelines aggregate patient IoT telemetry; in e-commerce, automated pipelines fuel hyper-personalized recommendation engines.

Students at JVM Institute complete real-world capstone projects—such as building 100GB live streaming pipelines—demonstrating complete readiness for enterprise challenges.

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The impact of data engineering extends far beyond code—it is the foundation of AI, machine learning models, and executive decision-making. As enterprises migrate to cloud-native data platforms, demand for skilled Data Engineers continues to outpace supply, offering extraordinary career growth and compensation.

Conclusion: Embark on Your Data Journey

Data engineering is the driving force behind modern business transformation. By mastering data manipulation, cloud warehouses, and pipeline orchestration, you unlock endless career opportunities.

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How to Read XML Files into Python Pandas DataFrames (With Code)

JVM Technical Team

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