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MySQL to MATLAB Converter

Transform MySQL database dumps into MATLAB cell arrays and matrices

MySQL Input

MATLAB Output

About MySQL to MATLAB Converter

Convert MySQL database dumps (CREATE TABLE and INSERT statements) to MATLAB cell array format. Perfect for data analysis, scientific computing, and importing database data into MATLAB for processing.

Key Features

  • Cell Array Format: Generates MATLAB cell arrays for mixed data types
  • Header Support: Optional header row for column names
  • String Escaping: Properly escapes single quotes in strings
  • Usage Examples: Includes comments showing how to access data
  • File Download: Save as .m file for direct use in MATLAB
  • Ready to Run: Generated code runs immediately in MATLAB

How to Use

  1. Input MySQL Data: Paste your MySQL CREATE TABLE and INSERT statements or upload a .sql file
  2. Configure Options: Toggle column headers on/off
  3. Review Output: The MATLAB code generates automatically
  4. Copy or Download: Use the Copy or Download button to save your MATLAB code
  5. Run in MATLAB: Paste or load the code in MATLAB/Octave

Example Conversion

MySQL Input:

CREATE TABLE employees (
  id INT PRIMARY KEY,
  name VARCHAR(100),
  age INT,
  salary DECIMAL(10,2)
);

INSERT INTO employees VALUES (1, 'John Doe', 28, 75000.00);
INSERT INTO employees VALUES (2, 'Jane Smith', 34, 82000.50);

MATLAB Output:

% MATLAB Cell Array from MySQL Data
% Generated by MySQL to MATLAB Converter

% Cell array with headers
data = {
  'id', 'name', 'age', 'salary'
  '1', 'John Doe', '28', '75000.00'
  '2', 'Jane Smith', '34', '82000.50'
};

% Access data:
% headers = data(1, :)
% firstRow = data(2, :)
% column1 = data(2:end, 1)

Common Use Cases

  • Data Analysis: Import MySQL data for statistical analysis in MATLAB
  • Scientific Computing: Process database data with MATLAB algorithms
  • Machine Learning: Prepare training data from MySQL databases
  • Visualization: Create plots and graphs from database data
  • Signal Processing: Analyze time-series data from MySQL
  • Research: Import experimental data for academic research

Working with Cell Arrays

MATLAB cell arrays can store mixed data types. Here's how to work with them:

% Access headers
headers = data(1, :);

% Access all data rows (excluding header)
dataRows = data(2:end, :);

% Access specific column
nameColumn = data(2:end, 2);

% Access specific cell
firstPersonName = data(2, 2);

% Convert to table (MATLAB R2013b+)
T = cell2table(data(2:end, :), 'VariableNames', data(1, :));

Converting to Numeric Arrays

If your data is purely numeric, convert cell arrays to matrices:

% Convert numeric columns to double
ages = str2double(data(2:end, 3));
salaries = str2double(data(2:end, 4));

% Perform calculations
avgAge = mean(ages);
totalSalary = sum(salaries);

MATLAB Table Format

For MATLAB R2013b and later, convert to table format for better functionality:

% Create table from cell array
T = cell2table(data(2:end, :), 'VariableNames', data(1, :));

% Access columns by name
T.name
T.age

% Filter rows
highEarners = T(T.salary > 80000, :);

Octave Compatibility

The generated code is compatible with GNU Octave, the open-source alternative to MATLAB:

  • Cell Arrays: Fully supported in Octave
  • String Handling: Works identically in Octave
  • Data Access: Same indexing syntax as MATLAB
  • Free Alternative: Use Octave if you don't have MATLAB license

Best Practices

  • Include Headers: Makes data more readable and easier to work with
  • Type Conversion: Convert strings to numbers when needed for calculations
  • Use Tables: Modern MATLAB tables provide better functionality than cell arrays
  • Variable Names: Use descriptive variable names instead of generic 'data'
  • Comments: Add comments to explain data structure and usage

Privacy & Security

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