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HTML to Pandas DataFrame Converter

Transform HTML data into Python Pandas code

HTML Input

Python Output

About HTML to Pandas DataFrame Converter

Convert HTML tables and structured data to Python Pandas DataFrame code for data analysis, machine learning, and scientific computing. Perfect for importing web data into Python for processing.

Key Features

  • DataFrame Generation: Creates ready-to-run Pandas DataFrame code
  • Table Extraction: Automatically extracts data from HTML tables
  • Header Support: Preserves table headers as column names
  • Type Detection: Automatically detects numeric vs. text data
  • Series Support: Converts HTML lists to Pandas Series
  • Custom Variables: Specify variable names for DataFrames
  • Index Column: Option to set first column as DataFrame index
  • Import Statements: Optional import statements included

How to Use

  1. Input HTML: Paste HTML with tables or data or upload a file
  2. Configure Options: Set variable name and formatting options
  3. Review Output: The Python code updates automatically
  4. Copy or Download: Use the Copy or Download button to save your .py file
  5. Run in Python: Execute the code in Jupyter, Python script, or notebook

Example Output

import pandas as pd
import numpy as np

# HTML to Pandas DataFrame Conversion
# Generated: 20/11/2025

df = pd.DataFrame([
    ['Laptop', 1250, 1380, 1420, 1650],
    ['Desktop', 890, 920, 850, 980],
    ['Tablet', 2100, 2250, 2400, 2800]
],
    columns=['Product', 'Q1', 'Q2', 'Q3', 'Q4']
)

# Display DataFrame info
# print(df.head())
# print(df.info())
# print(df.describe())

Supported HTML Elements

  • Tables: Extracts data from <table> elements with headers
  • Lists: Converts <ul> and <ol> to Pandas Series
  • Paragraphs: Extracts text content as Series
  • Headings: Includes heading text in output

Common Use Cases

  • Web Scraping: Convert scraped HTML tables to DataFrames
  • Data Analysis: Import web data for analysis in Python
  • Machine Learning: Prepare web data for ML models
  • Data Science: Process HTML data in Jupyter notebooks
  • Report Generation: Import data for automated reports
  • Data Visualization: Prepare data for plotting with matplotlib/seaborn

Pandas Operations

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

# View data
print(df.head())
print(df.tail())

# Get info
print(df.info())
print(df.describe())

# Filter data
filtered = df[df['Q1'] > 1000]

# Sort data
sorted_df = df.sort_values('Product')

# Calculate statistics
mean_values = df.mean()
total = df.sum()

# Export to CSV
df.to_csv('output.csv', index=False)

Tips for Best Results

  • Use Tables: HTML tables convert most accurately
  • Include Headers: Table headers become column names
  • Numeric Data: Numeric values are automatically detected
  • Variable Names: Use valid Python variable names
  • Index Column: Set first column as index for time series or labeled data
  • Clean Data: Remove unnecessary HTML elements for cleaner output

Python Requirements

To run the generated code, you need:

  • Python 3.6 or later
  • Pandas library: pip install pandas
  • NumPy library: pip install numpy

Pandas Compatibility

Generated code is compatible with:

  • Pandas 1.0 and later
  • Jupyter Notebook / JupyterLab
  • Google Colab
  • Python scripts and applications

Privacy & Security

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