# Outlier Detective > Detect anomalies and outliers in datasets using statistical and ML methods. Use for data cleaning, fraud detection, or quality control analysis. - Skill: `majiayu000/outlier-detective` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add majiayu000/outlier-detective` - Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/outlier-detective/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: majiayu000 (https://skillmd.com/u/majiayu000) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/majiayu000/outlier-detective --- # Outlier Detective Detect anomalies and outliers in numeric data using multiple methods. ## Features - **Statistical Methods**: Z-score, IQR, Modified Z-score - **ML Methods**: Isolation Forest, LOF, DBSCAN - **Visualization**: Box plots, scatter plots - **Multi-Column**: Analyze multiple variables - **Reports**: Detailed outlier reports - **Flexible Thresholds**: Configurable sensitivity ## Quick Start ```python from outlier_detective import OutlierDetective detective = OutlierDetective() detective.load_csv("sales_data.csv") # Detect outliers in a column outliers = detective.detect("revenue", method="iqr") print(f"Found {len(outliers)} outliers") # Get full report report = detective.analyze("revenue") print(report) ``` ## CLI Usage ```bash # Detect outliers using IQR method python outlier_detective.py --input data.csv --column sales --method iqr # Use Z-score with custom threshold python outlier_detective.py --input data.csv --column price --method zscore --threshold 3 # Analyze all numeric columns python outlier_detective.py --input data.csv --all # Generate visualization python outlier_detective.py --input data.csv --column revenue --plot boxplot.png # Export outliers to CSV python outlier_detective.py --input data.csv --column value --output outliers.csv # Use Isolation Forest (ML) python outlier_detective.py --input data.csv --method isolation_forest ``` ## API Reference ### OutlierDetective Class ```python class OutlierDetective: def __init__(self) # Data loading def load_csv(self, filepath: str, **kwargs) -> 'OutlierDetective' def load_dataframe(self, df: pd.DataFrame) -> 'OutlierDetective' # Detection (single column) def detect(self, column: str, method: str = "iqr", **kwargs) -> pd.DataFrame def analyze(self, column: str) -> dict # Detection (multi-column) def detect_multivariate(self, columns: list = None, method: str = "isolation_forest") -> pd.DataFrame def analyze_all(self) -> dict # Visualization def plot_boxplot(self, column: str, output: str) -> str def plot_scatter(self, col1: str, col2: str, output: str) -> str def plot_distribution(self, column: str, output: str) -> str # Export def get_outliers(self, column: str, method: str = "iqr") -> pd.DataFrame def get_clean_data(self, column: str, method: str = "iqr") -> pd.DataFrame ``` ## Detection Methods ### Statistical Methods #### IQR (Interquartile Range) - Default and most robust method - Outliers: values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR - Multiplier configurable (default: 1.5) ```python outliers = detective.detect("price", method="iqr", multiplier=1.5) ``` #### Z-Score - Based on standard deviations from mean - Assumes normal distribution - Threshold configurable (default: 3) ```python outliers = detective.detect("price", method="zscore", threshold=3) ``` #### Modified Z-Score - Uses median instead of mean - More robust to existing outliers - Based on MAD (Median Absolute Deviation) ```python outliers = detective.detect("price", method="modified_zscore", threshold=3.5) ``` ### ML Methods #### Isolation Forest - Ensemble method, good for high-dimensional data - Contamination parameter sets expected outlier fraction ```python outliers = detective.detect_multivariate( method="isolation_forest", contamination=0.1 ) ``` #### Local Outlier Factor (LOF) - Density-based method - Compares local density to neighbors ```python outliers = detective.detect_multivariate( method="lof", n_neighbors=20 ) ``` ## Output Format ### detect() Result ```python # Returns DataFrame of outlier rows with additional columns: # - outlier_score: How extreme the value is # - outlier_reason: Description of why it's an outlier index value outlier_score outlier_reason 0 15 5000 4.2 Above Q3 + 1.5×IQR 1 42 -1000 -3.8 Below Q1 - 1.5×IQR ``` ### analyze() Result ```python { "column": "revenue", "total_rows": 1000, "outlier_count": 23, "outlier_percent": 2.3, "methods": { "iqr": {"count": 23, "indices": [...]}, "zscore": {"count": 18, "indices": [...]}, "modified_zscore": {"count": 20, "indices": [...]} }, "stats": { "mean": 5432.10, "median": 4890.00, "std": 1234.56, "min": -1000.00, "max": 15000.00, "q1": 3500.00, "q3": 6200.00, "iqr": 2700.00 }, "bounds": { "lower": -550.00, "upper": 10250.00 } } ``` ## Example Workflows ### Data Cleaning Pipeline ```python detective = OutlierDetective() detective.load_csv("raw_data.csv") # Analyze and visualize report = detective.analyze("price") print(f"Found {report['outlier_count']} outliers ({report['outlier_percent']:.1f}%)") # Get clean data clean_data = detective.get_clean_data("price", method="iqr") clean_data.to_csv("clean_data.csv") ``` ### Fraud Detection ```python detective = OutlierDetective() detective.load_csv("transactions.csv") # Use multiple methods for consensus iqr_outliers = set(detective.detect("amount", method="iqr").index) zscore_outliers = set(detective.detect("amount", method="zscore").index) # Transactions flagged by both methods high_confidence = iqr_outliers & zscore_outliers print(f"High-confidence anomalies: {len(high_confidence)}") ``` ### Multi-Variable Analysis ```python detective = OutlierDetective() detective.load_csv("sensors.csv") # Detect multivariate outliers outliers = detective.detect_multivariate( columns=["temp", "pressure", "humidity"], method="isolation_forest", contamination=0.05 ) print(f"Anomalous readings: {len(outliers)}") ``` ## Visualization Examples ```python # Box plot with outliers highlighted detective.plot_boxplot("revenue", "revenue_boxplot.png") # Distribution with bounds detective.plot_distribution("price", "price_dist.png") # Scatter plot (2D outliers) detective.plot_scatter("feature1", "feature2", "scatter.png") ``` ## Dependencies - pandas>=2.0.0 - numpy>=1.24.0 - scipy>=1.10.0 - scikit-learn>=1.3.0 - matplotlib>=3.7.0