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**중복 코드 패턴**:
```bash

**매직 넘버**:
```bash

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**하드코딩된 시크릿**:
```bash

**위험한 함수 사용**:
```bash

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**TODO/FIXME 패턴**:
```bash

**에러 핸들링 패턴**:
```bash

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import re

patterns = {
    'email': r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',
    'phone': r'\d{3}[-.\s]?\d{4}[-.\s]?\d{4}',
    'ip_address': r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}',
    'credit_card': r'\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}',
    'ssn': r'\d{3}-\d{2}-\d{4}',
}

def detect_sensitive_data(text):
    found = {}
    for name, pattern in patterns.items():
        matches = re.findall(pattern, text)
        if matches:
            found[name] = len(matches)
    return found

import numpy as np
from scipy import stats

def detect_anomalies_zscore(data, threshold=3):
    """Z-score 기반 이상치 탐지"""
    z_scores = np.abs(stats.zscore(data))
    return np.where(z_scores > threshold)[0]

def detect_anomalies_iqr(data, k=1.5):
    """IQR 기반 이상치 탐지"""
    q1, q3 = np.percentile(data, [25, 75])
    iqr = q3 - q1
    lower = q1 - k * iqr
    upper = q3 + k * iqr
    return np.where((data < lower) | (data > upper))[0]

import pandas as pd

def analyze_trend(df, date_col, value_col):
    """시계열 트렌드 분석"""
    df[date_col] = pd.to_datetime(df[date_col])
    df = df.sort_values(date_col)

    # 이동 평균
    df['ma_7'] = df[value_col].rolling(window=7).mean()
    df['ma_30'] = df[value_col].rolling(window=30).mean()

    # 성장률
    df['growth'] = df[value_col].pct_change() * 100

    # 트렌드 방향
    recent_trend = df['ma_7'].iloc[-1] > df['ma_30'].iloc[-1]

    return {
        'trend_direction': 'up' if recent_trend else 'down',
        'avg_growth': df['growth'].mean(),
        'volatility': df[value_col].std()
    }

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