# Advanced demo: Load CSV, clean with multiple methods, group/log, visualize with Pandas/Matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

def super_advanced_clean(file):
    df = pd.read_csv(file)
    # Clean missing with interpolation
    df['Value'].interpolate(method='linear', inplace=True)
    # Handle duplicates with custom logic
    df = df.groupby('ID').mean().reset_index()
    # Outliers with z-score
    df['z_score'] = np.abs((df['Value'] - df['Value'].mean()) / df['Value'].std())
    df = df[df['z_score'] < 3]
    df.drop('z_score', axis=1, inplace=True)
    # Group and agg with multiple functions
    grouped = df.groupby('Category').agg({'Value': ['mean', 'std', 'count']})
    grouped.columns = ['Mean Value', 'Std Value', 'Count']
    grouped['Log Mean'] = np.log(grouped['Mean Value'] + 1)
    return grouped

def super_visualize(grouped):
    fig, ax = plt.subplots(figsize=(8, 5))
    grouped['Log Mean'].plot(kind='bar', ax=ax, color='purple', label='Log Mean')
    ax.set_title('Advanced CSV Cleaning Analysis', fontsize=14)
    ax.set_ylabel('Log Mean Value', fontsize=12)
    ax.legend()
    high_index = grouped['Log Mean'].argmax()
    high_log = grouped['Log Mean'].iloc[high_index]
    ax.annotate('High Log Mean', xy=(high_index, high_log), xytext=(high_index + 0.5, high_log - 0.5), arrowprops=dict(facecolor='black', shrink=0.05))
    plt.show()

# Assume csv5.csv with ID, Value, Category columns (missing, duplicates, outliers)
grouped = super_advanced_clean("csv5.csv")
super_visualize(grouped)
print("Advanced Grouped Analysis:\n", grouped)