---

name: outlier-detective description: Detect anomalies and outliers in datasets using statistical and ML methods. Use for data cleaning, fraud detection, or quality control analysis.

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

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

# 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

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)

outliers = detective.detect("price", method="iqr", multiplier=1.5)

Z-Score

• Based on standard deviations from mean
• Assumes normal distribution
• Threshold configurable (default: 3)

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)

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

outliers = detective.detect_multivariate(
    method="isolation_forest",
    contamination=0.1
)

Local Outlier Factor (LOF)

• Density-based method
• Compares local density to neighbors

outliers = detective.detect_multivariate(
    method="lof",
    n_neighbors=20
)

Output Format

detect() Result

# 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

{
    "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

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

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

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

# 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