SQL to Pandas DataFrame Converter

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.

Frequently Asked Questions (FAQ)

Why are some values wrapped in quotes while others are not?

The converter inspects each value and emits integers, floats, and booleans without quotes so they become native Python types. Text values are quoted as Python strings, and NULL-like values become None.

Is the generated DataFrame code ready for production use?

The output is primarily optimized for analysis, exploration, and migration tasks. For production code, you may want to integrate the generated snippet into your existing data-loading pipeline or refactor it into functions.

What if my table has many rows?

Very large tables will produce long Python lists, which can be harder to maintain in source control. For big datasets, consider exporting to CSV or using a direct database connection in Pandas (for example, with read_sql and a database driver).

How are column names sanitized?

Column names are converted to valid Python identifiers by replacing non-alphanumeric characters with underscores and prefixing names that start with digits. This ensures they can be safely used as dictionary keys and DataFrame column names.

Can I easily modify the resulting DataFrame structure?

Yes. Once the DataFrame is created, you can rename columns, change dtypes, filter rows, or join with other DataFrames using standard Pandas operations.

Do I need a specific Pandas version?

The generated code uses core Pandas features that are stable across recent versions. Any modern 1.x or 2.x release of Pandas should work with the output.