import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Set up the plotting style
plt.style.use('default')
sns.set_palette("husl")

print("Libraries imported successfully!")
print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")
print(f"Matplotlib version: {plt.matplotlib.__version__}")

# Create sample dataset
np.random.seed(42)
n_samples = 1000

# Generate synthetic data
data = {
    'x': np.random.randn(n_samples),
    'y': np.random.randn(n_samples),
    'category': np.random.choice(['A', 'B', 'C'], n_samples),
    'values': np.random.exponential(2, n_samples)
}

df = pd.DataFrame(data)
df['z'] = 0.5 * df['x'] + 0.3 * df['y'] + np.random.randn(n_samples) * 0.1

print(f"Dataset created with {len(df)} rows and {len(df.columns)} columns")
print("\nFirst few rows:")
df.head()

# Create a scatter plot with multiple subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
fig.suptitle('Multi-panel Data Visualization', fontsize=16, fontweight='bold')

# Scatter plot
axes[0, 0].scatter(df['x'], df['y'], c=df['values'], alpha=0.6, cmap='viridis')
axes[0, 0].set_xlabel('X values')
axes[0, 0].set_ylabel('Y values')
axes[0, 0].set_title('Scatter Plot with Color Mapping')

# Histogram
axes[0, 1].hist(df['values'], bins=30, alpha=0.7, color='skyblue', edgecolor='black')
axes[0, 1].set_xlabel('Values')
axes[0, 1].set_ylabel('Frequency')
axes[0, 1].set_title('Distribution of Values')

# Box plot by category
df.boxplot(column='values', by='category', ax=axes[1, 0])
axes[1, 0].set_title('Values by Category')
axes[1, 0].set_xlabel('Category')

# Correlation heatmap
correlation_matrix = df[['x', 'y', 'z', 'values']].corr()
im = axes[1, 1].imshow(correlation_matrix, cmap='coolwarm', aspect='auto')
axes[1, 1].set_xticks(range(len(correlation_matrix.columns)))
axes[1, 1].set_yticks(range(len(correlation_matrix.columns)))
axes[1, 1].set_xticklabels(correlation_matrix.columns)
axes[1, 1].set_yticklabels(correlation_matrix.columns)
axes[1, 1].set_title('Correlation Matrix')

plt.tight_layout()
plt.show()

# Statistical summary as JSON
summary_stats = {
    'dataset_info': {
        'n_samples': len(df),
        'n_features': len(df.columns),
        'memory_usage': f"{df.memory_usage(deep=True).sum() / 1024:.2f} KB"
    },
    'numerical_summary': {
        'x': {
            'mean': float(df['x'].mean()),
            'std': float(df['x'].std()),
            'min': float(df['x'].min()),
            'max': float(df['x'].max())
        },
        'y': {
            'mean': float(df['y'].mean()),
            'std': float(df['y'].std()),
            'min': float(df['y'].min()),
            'max': float(df['y'].max())
        },
        'values': {
            'mean': float(df['values'].mean()),
            'std': float(df['values'].std()),
            'min': float(df['values'].min()),
            'max': float(df['values'].max())
        }
    },
    'category_counts': df['category'].value_counts().to_dict()
}

# Display as JSON
import json
print(json.dumps(summary_stats, indent=2))

# Also return as object for JSON display
summary_stats

# Data Analysis Example

This notebook demonstrates basic data analysis using Python.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
np.random.seed(42)
data = np.random.randn(100)
print(f"Generated {len(data)} data points")
print(f"Mean: {np.mean(data):.2f}")
print(f"Std: {np.std(data):.2f}")

## Data Visualization

Let's create a simple histogram to visualize the data distribution.

plt.figure(figsize=(10, 6))
plt.hist(data, bins=20, alpha=0.7, color='skyblue', edgecolor='black')
plt.title('Distribution of Random Data')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.grid(True, alpha=0.3)
plt.show()

# Show first 5 data points
data[:5]