XML to Pandas DataFrame Converter

About XML to Pandas DataFrame Converter

Convert XML data to Python Pandas DataFrame code with automatic type detection, proper string escaping, and customizable options. Perfect for data analysis, machine learning, and scientific computing.

Key Features

• Automatic Type Detection: Detects int, float, bool, and str types automatically
• Column Name Sanitization: Converts XML tags to valid Python variable names
• String Escaping: Properly escapes special characters in strings
• None Handling: Converts empty/null values to Python None
• Type Comments: Optional type annotations for each column
• Import Statement: Optional pandas import statement
• Custom Variable Name: Specify your DataFrame variable name
• File Download: Save as .py file for Python scripts

How to Use

1. Input XML Data: Paste your XML data or upload an .xml file
2. Configure Options: Set DataFrame name and toggle options
3. Copy or Download: Use the Copy or Download button to save your Python code
4. Run in Python: Execute the code in Jupyter, Python scripts, or notebooks

Type Detection

The converter automatically determines the appropriate Python type for each column:

• int: Integer values (e.g., 1, 42, -10)
• float: Decimal values (e.g., 3.14, 99.99, -0.5)
• bool: Boolean values (true/false)
• str: Text values and mixed types
• None: Empty, null, or None values

Example Conversion

XML Input:

<?xml version="1.0" encoding="UTF-8"?>
<products>
  <product>
    <id>1</id>
    <name>Laptop</name>
    <price>999.99</price>
    <in_stock>true</in_stock>
  </product>
  <product>
    <id>2</id>
    <name>Mouse</name>
    <price>24.99</price>
    <in_stock>true</in_stock>
  </product>
</products>

Python Output:

import pandas as pd

# Column types:
# id: int
# name: str
# price: float
# in_stock: bool

df = pd.DataFrame({
    'id': [1, 2],
    'name': ['Laptop', 'Mouse'],
    'price': [999.99, 24.99],
    'in_stock': [True, True],
})

print(df)

Common Use Cases

• Data Analysis: Import XML data into Pandas for analysis
• Machine Learning: Prepare XML datasets for ML models
• Data Cleaning: Use Pandas to clean and transform XML data
• Scientific Computing: Process scientific data from XML sources
• Data Visualization: Create charts and plots from XML data
• ETL Pipelines: Extract data from XML for transformation
• Jupyter Notebooks: Quick data import for interactive analysis

Pandas Operations

Once you have the DataFrame, you can perform various operations:

• Filtering: df[df['price'] > 50]
• Sorting: df.sort_values('price')
• Grouping: df.groupby('category').mean()
• Statistics: df.describe(), df.mean(), df.sum()
• Visualization: df.plot(), df.hist()
• Export: df.to_csv(), df.to_excel(), df.to_json()

XML Structure Requirements

• Root Element: Single root element containing row elements
• Row Elements: Direct children of root represent DataFrame rows
• Column Elements: Children of row elements represent columns
• Consistent Structure: All rows should have the same column structure

Column Name Sanitization

XML tags are converted to valid Python variable names:

• Special Characters: Replaced with underscores (_)
• Numbers at Start: Prefixed with underscore (_1column)
• Multiple Underscores: Collapsed to single underscore
• Case Preserved: Original case is maintained

Python Environment

The generated code requires:

• Python 3.6+: Modern Python version
• Pandas Library: Install with: pip install pandas
• NumPy: Usually installed with Pandas

Privacy & Security

All conversions happen locally in your browser. Your XML data is never uploaded to any server, ensuring complete privacy and security.