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

FAQ

• How does the tool decide which Python type to use for each column?

  For every column, the converter inspects all values and classifies each as int, float, bool, or str. If all non-empty values are integers, the type becomes int. If the values are a mix of integers and floats, it upgrades to float. If all values are true/false (case-insensitive), it uses bool. Any column containing non-numeric or mixed content is treated as str. Empty, null, and none values are represented as None in the generated code.

• What if my XML contains nested structures or attributes?

  This converter focuses on simple table-like XML where each direct child of the root element represents a row and its children are scalar values. Deeply nested structures (for example, nested lists or sub-objects) are not flattened automatically. Attributes on the row elements are not included in this specific tool (unlike some other XML converters in the project). If you need more complex flattening, you can treat the generated code as a starting point and perform additional transformations in Python.

• Will the generated code handle very large XML files efficiently?

  The code this tool generates builds the entire DataFrame in memory by constructing Python lists in source code. This is ideal for small- to medium-sized datasets, but for very large XML files it may be more efficient to stream and parse the XML directly in Python using libraries like xml.etree.ElementTree or lxml. In those cases, you can still use the generated code as a template for column names and types, but adapt it to a streaming parser.

• Can I safely rename the DataFrame variable or columns after generation?

  Yes. The dataFrameName option lets you choose the initial variable name, and you can freely modify it later in your editor. Column names are sanitized from your XML tags to valid Python identifiers, but you can rename them using df.rename(columns={...}, inplace=True) or by editing the generated dictionary keys directly. Just keep the new names consistent with any downstream code that references them.

• Is it safe to use this tool with sensitive or proprietary XML data?

  Yes. All parsing and code generation happen entirely in your browser with client-side JavaScript. Your XML is not sent to any backend service as part of this tool, so you can safely convert internal or proprietary datasets and then run the resulting Python code in your own environment.

Privacy & Security

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