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"""
Data Analyzer - Analyze data patterns and statistics
"""
import re
from typing import List, Dict, Tuple
import statistics
class DataAnalyzer:
"""Analyze data patterns and statistics from text or datasets."""
def extract_numbers(self, text: str) -> List[float]:
"""Extract numeric values from text."""
pattern = r"-?\d+\.?\d*"
matches = re.findall(pattern, text)
return [float(m) for m in matches if m]
def analyze_numeric_data(self, data: List[float]) -> Dict:
"""Analyze numeric data."""
if not data:
return {}
return {
"count": len(data),
"sum": sum(data),
"mean": statistics.mean(data),
"median": statistics.median(data),
"mode": statistics.mode(data) if len(set(data)) < len(data) else None,
"stdev": statistics.stdev(data) if len(data) > 1 else 0,
"min": min(data),
"max": max(data),
"range": max(data) - min(data),
}
def detect_patterns(self, data: List[float]) -> Dict[str, str]:
"""Detect patterns in data."""
if len(data) < 2:
return {}
diffs = [data[i+1] - data[i] for i in range(len(data)-1)]
avg_diff = sum(diffs) / len(diffs)
patterns = {}
if abs(avg_diff) < 0.001:
patterns["trend"] = "flat"
elif avg_diff > 0:
patterns["trend"] = "increasing"
else:
patterns["trend"] = "decreasing"
return patterns
def find_outliers(self, data: List[float], std_threshold: float = 2) -> List[float]:
"""Find outliers using standard deviation."""
if len(data) < 2:
return []
mean = statistics.mean(data)
stdev = statistics.stdev(data)
return [x for x in data if abs((x - mean) / stdev) > std_threshold]
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