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📚 Comprehensive Examples

This document provides comprehensive examples of using the load module in various scenarios.

Table of Contents

Basic Usage

# Basic module loading
import load

# Load standard library modules
json = load('json')
data = json.dumps({"key": "value"})

# Load with aliases
pd = load('pandas', alias='pd')
df = pd.DataFrame({'A': [1, 2, 3]})

Working with External Packages

# Install and load external packages if not available
plt = load('matplotlib.pyplot', alias='plt')
import numpy as np

# Create a simple plot
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.title('Sine Wave')
plt.show()

Local Module Imports

# Load a local module
config = load('./config/settings.py')

# Or from a package
utils = load('../utils/helpers')

Using the Decorator

from load import load_decorator as load

@load('numpy', 'pandas', 'matplotlib')
def analyze_data():
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    
    # Your analysis code here
    data = np.random.rand(10, 3)
    df = pd.DataFrame(data, columns=['A', 'B', 'C'])
    df.plot()
    plt.show()

Advanced Caching

import load

# Force reload a module
fresh_module = load('module_name', force=True)

# Check cache info
cache_info = load.info()
print(f"Cached modules: {cache_info['cached_modules']}")

Error Handling

try:
    # Try to load a non-existent module
    missing = load('nonexistent_package')
except ImportError as e:
    print(f"Failed to load module: {e}")
    
    # Install the package if needed
    if input("Install package? (y/n): ").lower() == 'y':
        load.install('nonexistent_package')
        missing = load('nonexistent_package')

Performance Optimization

# Disable auto-print for better performance in scripts
load.disable_auto_print()

# Load multiple modules at once
modules = load.many(['numpy', 'pandas', 'matplotlib'])
np, pd, plt = modules

# Enable auto-print for interactive use
load.enable_auto_print()

Real-world Example: Data Analysis Pipeline

@load('pandas', 'numpy', 'sklearn', 'matplotlib')
def analyze_dataset(filepath):
    import pandas as pd
    import numpy as np
    from sklearn.preprocessing import StandardScaler
    import matplotlib.pyplot as plt
    
    # Load data
    data = pd.read_csv(filepath)
    
    # Preprocess
    scaler = StandardScaler()
    scaled_data = scaler.fit_transform(data.select_dtypes(include=[np.number]))
    
    # Plot
    plt.figure(figsize=(10, 6))
    plt.hist(scaled_data.flatten(), bins=50)
    plt.title('Distribution of Scaled Features')
    plt.show()
    
    return scaled_data

Next Steps

  • Explore more examples in the examples/ directory
  • Check out the API Reference for detailed documentation
  • Read about advanced features for more use cases