Input NumPy Code
import numpy as np
# Calculate mean of array
def calculate_mean(data_array):
"""
This function calculates the mean of a NumPy array
"""
total_sum = np.sum(data_array)
array_length = len(data_array)
mean_value = total_sum / array_length
return mean_value
# Generate sample data
sample_data = np.random.randn(100, 50)
# Process the data
result = calculate_mean(sample_data)
# Display result
print(f"The mean value is: {result}")
# Additional operations
normalized_data = (sample_data - np.min(sample_data)) / (np.max(sample_data) - np.min(sample_data))
Minified Output
Your minified code will appear here...
Minification Features
Strip all single-line and multi-line comments from code
Remove function and module documentation strings
Rename variables to shorter names (a, b, c...)
Eliminate unnecessary spaces and blank lines
Inline simple functions to reduce overhead
Remove unused imports and consolidate imports
Convert simple functions to lambda expressions
Keep 'import numpy as np' for readability
Compression Statistics
Original Size
0 chars
Minified Size
0 chars
Reduction
0%
Lines Reduced
0
Compression Ratio
0.00:1
Quick Actions
How to Use the NumPy Minifier Tool
What is NumPy Code Minification?
NumPy code minification is the process of reducing the size of your Python scripts that use NumPy by removing unnecessary characters without changing functionality. This is particularly useful for optimizing code for production environments, reducing load times, and improving performance.
Step-by-Step Guide
- Paste your NumPy code in the input section. You can use the sample code provided or your own NumPy scripts.
- Configure minification options using the toggle switches in the Features panel. Each option targets specific aspects of your code for optimization.
- Click "Minify Now" to process your code in real-time. The minified version will appear instantly in the output section.
- Review the compression statistics to see how much you've reduced your code size and improved efficiency.
- Copy or download the minified code for use in your projects.
Key Features Explained
- Remove Comments: Strips all single-line (#) and multi-line (''' or """) comments that are not essential for code execution.
- Shorten Variable Names: Intelligently renames long variable names to shorter alternatives while maintaining code logic.
- Optimize Imports: Removes unused imports and consolidates multiple import statements where possible.
- Inline Short Functions: Replaces simple function calls with their actual code to reduce function call overhead.
- Preserve NumPy as 'np': Maintains the standard 'import numpy as np' convention for readability while optimizing other imports.
Best Practices
- Always test minified code before deploying to production to ensure functionality remains unchanged.
- Keep a backup of your original, unminified code for future modifications and debugging.
- Use the "Preserve NumPy as 'np'" option to maintain code readability for other developers.
- For collaborative projects, consider minifying only before deployment while keeping development code fully documented.
Pro Tip: The tool works in real-time, so you can see changes immediately as you adjust settings. Try different feature combinations to find the optimal balance between code size reduction and maintainability.