"""
AnalyticalPerformanceEnhancer
Generated by Eden via recursive self-improvement
2025-11-01 03:06:42.090451
"""

import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

def AnalyticalPerformanceEnhancer(data, n_components=2):
    """
    This function takes a dataset, normalizes it, applies PCA for dimensionality reduction,
    and returns the principal components along with summary statistics.

    :param data: A pandas DataFrame containing the dataset.
    :param n_components: The number of principal components to keep (default is 2).
    :return: Principal components and summary statistics.
    """
    # Ensure the input is a pandas DataFrame
    if not isinstance(data, pd.DataFrame):
        raise ValueError("Input data must be a pandas DataFrame.")

    # Normalize the data
    scaler = StandardScaler()
    normalized_data = scaler.fit_transform(data)

    # Apply PCA for dimensionality reduction
    pca = PCA(n_components=n_components)
    principal_components = pca.fit_transform(normalized_data)

    # Generate summary statistics
    mean_values = pd.DataFrame(principal_components).mean().to_dict()
    variance_explained = pca.explained_variance_ratio_.tolist()

    return (principal_components, mean_values, variance_explained)

# Example usage
if __name__ == "__main__":
    # Sample data for demonstration purposes
    sample_data = {
        'Feature1': [1, 2, 3, 4, 5],
        'Feature2': [5, 4, 3, 2, 1],
        'Feature3': [2.5, 2.5, 2.5, 2.5, 2.5]
    }
    df = pd.DataFrame(sample_data)

    # Enhance analytical performance with PCA
    principal_components, mean_values, variance_explained = AnalyticalPerformanceEnhancer(df)

    print("Principal Components:", principal_components)
    print("Mean Values:", mean_values)
    print("Variance Explained:", variance_explained)