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SQL to Pandas DataFrame Converter

Transform SQL database dumps into Python Pandas DataFrame code for data analysis, machine learning, and scientific computing

SQL Input

Python Output

About SQL to Pandas DataFrame Converter

Convert SQL database dumps (CREATE TABLE and INSERT statements) to Python Pandas DataFrame code. Perfect for data analysis, machine learning, scientific computing, and data manipulation with Python's most powerful data analysis library.

Key Features

  • Automatic Type Detection: Detects integers, floats, booleans, strings, and None values
  • Import Styles: Choose between standard import or alias (pd) convention
  • Code Comments: Optional descriptive comments for better code readability
  • Display Methods: Includes print(), info(), and describe() for data exploration
  • Column Sanitization: Ensures valid Python variable names
  • File Download: Save as .py file for direct use in Python projects

How to Use

  1. Input SQL Data: Paste your SQL CREATE TABLE and INSERT statements or upload a .sql file
  2. Choose Import Style: Select standard import or alias (pd) convention
  3. Configure Options: Toggle comments and display code inclusion
  4. Copy or Download: Use the Copy or Download button to save your Python code
  5. Run in Python: Execute the code in Jupyter Notebook, Python script, or IDE

Example Conversion

SQL Input:

CREATE TABLE products (
  id INT,
  name VARCHAR(100),
  price DECIMAL(10,2)
);

INSERT INTO products VALUES (1, 'Laptop', 999.99);
INSERT INTO products VALUES (2, 'Mouse', 24.99);

Python Output:

# Import pandas library
import pandas as pd

# Create DataFrame from products table
products_df = pd.DataFrame({
    'id': [1, 2],
    'name': ['Laptop', 'Mouse'],
    'price': [999.99, 24.99]
})

# Display DataFrame
print(products_df)

# Display DataFrame info
print(products_df.info())

# Display basic statistics
print(products_df.describe())

Type Detection

The converter automatically detects and converts data types:

  • Integers: 1, 42, -10 → Python int
  • Floats: 3.14, 99.99, -0.5 → Python float
  • Booleans: true, false → True, False
  • Strings: 'text', "value" → 'text', 'value'
  • NULL: NULL, null, empty → None

DataFrame Methods

When display code is enabled, the following methods are included:

  • print(df): Display the entire DataFrame
  • df.info(): Show column types, non-null counts, and memory usage
  • df.describe(): Generate descriptive statistics (mean, std, min, max, etc.)

Common Use Cases

  • Data Analysis: Import SQL data for exploratory data analysis
  • Machine Learning: Prepare datasets for ML models
  • Data Visualization: Create charts and graphs with matplotlib/seaborn
  • Statistical Analysis: Perform statistical tests and calculations
  • Data Cleaning: Clean and transform data with Pandas methods
  • Report Generation: Generate automated reports from database data

Working with DataFrames

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

# Filter rows
expensive_products = products_df[products_df['price'] > 100]

# Sort by column
sorted_df = products_df.sort_values('price', ascending=False)

# Group by and aggregate
category_stats = products_df.groupby('category')['price'].mean()

# Add new column
products_df['discounted_price'] = products_df['price'] * 0.9

# Export to CSV
products_df.to_csv('products.csv', index=False)

# Export to Excel
products_df.to_excel('products.xlsx', index=False)

Import Styles

Alias (Recommended):

  • import pandas as pd
  • Standard convention in the Python community
  • Shorter syntax: pd.DataFrame()
  • Used in most tutorials and documentation

Standard:

  • import pandas
  • Explicit library name
  • Longer syntax: pandas.DataFrame()
  • Useful for learning or avoiding namespace conflicts

Column Name Sanitization

Column names are automatically sanitized to be valid Python identifiers:

  • Special characters: Replaced with underscores
  • Starting with numbers: Prefixed with underscore
  • Spaces: Replaced with underscores
  • Reserved keywords: Handled automatically by Pandas

Required Dependencies

Install Pandas before running the generated code:

# Using pip
pip install pandas

# Using conda
conda install pandas

# Using pip with specific version
pip install pandas==2.0.0

Integration Examples

Jupyter Notebook:

# Paste the generated code in a cell and run
import pandas as pd
products_df = pd.DataFrame({...})
products_df.head()  # Display first 5 rows

Python Script:

# Save as script.py and run
python script.py

# Or import in another script
from script import products_df

Privacy & Security

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