# Python Data Analysis Server > A comprehensive MCP server providing advanced data analysis, statistical testing, visualization, and transformation capabilities. - Skill: `tools-only/python-data-analysis-server` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add tools-only/python-data-analysis-server` - Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/python-data-analysis-server/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tools-only (https://skillmd.com/u/tools-only) - Updated: 2026-09-29 - Page: https://skillmd.com/skills/tools-only/python-data-analysis-server --- # Python Data Analysis Server ## Overview A comprehensive MCP server providing advanced data analysis, statistical testing, visualization, and transformation capabilities. This server enables AI applications to perform sophisticated data science workflows through a standardized interface. **Key Features:** - 📊 Multi-format data loading (CSV, JSON, Parquet, SQL, Excel) - 📈 Statistical analysis and hypothesis testing - 📉 Rich visualization with matplotlib, seaborn, and plotly - ⏰ Time series analysis and forecasting - 🔄 Data transformation and feature engineering - 🔍 SQL-like querying with pandas - 💾 Smart dataset caching with automatic eviction ## Quick Start ### Installation ```bash # Navigate to the server directory cd mcp-servers/python/data_analysis_server # Install dependencies pip install -r requirements.txt # Or install in development mode pip install -e .[dev] ``` ### Running the Server ```bash # Run with default configuration python -m data_analysis_server.server # Run with custom configuration python -m data_analysis_server.server --config my_config.yaml ``` ### Integration with ContextForge ```bash # Register with ContextForge curl -X POST http://localhost:4444/servers \ -H "Content-Type: application/json" \ -d '{ "name": "data-analysis-server", "transport": "stdio", "command": "python -m data_analysis_server.server", "description": "Advanced data analysis and visualization server" }' ``` ## Available Tools ### Data Management #### load_dataset Load data from various sources and formats. ```json { "tool": "load_dataset", "arguments": { "source": "./data/sales.csv", "format": "csv", "dataset_id": "sales_data", "sample_size": 1000, "cache_data": true } } ``` Supported formats: - CSV, TSV - JSON, JSONL - Parquet - Excel (xlsx, xls) - SQL databases #### list_datasets List all currently loaded datasets. ```json { "tool": "list_datasets" } ``` ### Statistical Analysis #### analyze_dataset Perform comprehensive dataset analysis. ```json { "tool": "analyze_dataset", "arguments": { "dataset_id": "sales_data", "analysis_type": "exploratory", "include_distributions": true, "include_correlations": true } } ``` Analysis types: - `exploratory` - Full EDA with summary statistics - `descriptive` - Basic statistics only - `correlation` - Correlation analysis - `distribution` - Distribution analysis #### statistical_test Perform various statistical tests. ```json { "tool": "statistical_test", "arguments": { "dataset_id": "sales_data", "test_type": "t_test", "column": "revenue", "group_column": "region", "alpha": 0.05 } } ``` Supported tests: - **Parametric**: t-test, ANOVA, linear regression - **Non-parametric**: Mann-Whitney U, Wilcoxon, Kruskal-Wallis - **Correlation**: Pearson, Spearman, Chi-square ### Data Visualization #### create_visualization Generate various types of plots. ```json { "tool": "create_visualization", "arguments": { "dataset_id": "sales_data", "plot_type": "scatter", "x": "advertising_spend", "y": "revenue", "hue": "product_category", "title": "Revenue vs Advertising Spend", "save_path": "./plots/revenue_analysis.png" } } ``` Plot types: - **Distribution**: histogram, kde, box, violin - **Relationship**: scatter, line, regression - **Categorical**: bar, count, swarm, strip - **Matrix**: heatmap, pair plot - **Time Series**: line, area, seasonal decomposition #### create_interactive_plot Generate interactive Plotly visualizations. ```json { "tool": "create_interactive_plot", "arguments": { "dataset_id": "sales_data", "plot_type": "3d_scatter", "x": "price", "y": "quantity", "z": "revenue", "color": "region", "save_html": true } } ``` ### Time Series Analysis #### time_series_analysis Perform time series decomposition and analysis. ```json { "tool": "time_series_analysis", "arguments": { "dataset_id": "sales_data", "date_column": "date", "value_column": "daily_revenue", "frequency": "D", "decomposition_type": "additive", "include_forecast": true, "forecast_periods": 30 } } ``` Features: - Trend detection - Seasonality analysis - Stationarity testing - ARIMA forecasting - Seasonal decomposition ### Data Transformation #### transform_data Apply various transformations to datasets. ```json { "tool": "transform_data", "arguments": { "dataset_id": "sales_data", "transformations": [ {"type": "scale", "columns": ["price", "quantity"], "method": "standard"}, {"type": "encode", "columns": ["category"], "method": "onehot"}, {"type": "impute", "columns": ["rating"], "method": "mean"} ], "save_as": "sales_data_transformed" } } ``` Transformations: - **Scaling**: standard, minmax, robust, normalizer - **Encoding**: label, onehot, ordinal, target - **Imputation**: mean, median, mode, forward fill - **Feature Engineering**: polynomial, binning, interaction #### clean_data Automated data cleaning operations. ```json { "tool": "clean_data", "arguments": { "dataset_id": "sales_data", "remove_duplicates": true, "handle_missing": "drop", "remove_outliers": true, "outlier_method": "iqr", "save_as": "sales_data_clean" } } ``` ### Data Querying #### query_data Execute SQL-like queries on datasets using pandas. ```json { "tool": "query_data", "arguments": { "dataset_id": "sales_data", "query": "SELECT region, AVG(revenue) as avg_revenue FROM data WHERE date > '2024-01-01' GROUP BY region ORDER BY avg_revenue DESC", "limit": 100 } } ``` Supported SQL features: - SELECT with column aliases - WHERE clauses with complex conditions - GROUP BY with aggregations - ORDER BY (ASC/DESC) - JOINs between datasets - LIMIT and OFFSET #### filter_data Apply filters to create dataset subsets. ```json { "tool": "filter_data", "arguments": { "dataset_id": "sales_data", "filters": [ {"column": "revenue", "operator": ">", "value": 1000}, {"column": "region", "operator": "in", "value": ["North", "South"]} ], "save_as": "high_revenue_sales" } } ``` ## Configuration Create a `config.yaml` file: ```yaml server: max_datasets: 100 max_memory_mb: 1024 cache_ttl_seconds: 3600 visualization: plot_output_dir: "./plots" default_dpi: 100 default_figsize: [10, 6] style: "seaborn" analysis: max_query_results: 10000 default_sample_size: 5000 confidence_level: 0.95 logging: level: "INFO" format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" ``` ### Environment Variables - `DATA_ANALYSIS_CONFIG`: Path to configuration file - `DATA_ANALYSIS_CACHE_DIR`: Directory for cached datasets - `DATA_ANALYSIS_PLOT_DIR`: Directory for saved visualizations - `DATA_ANALYSIS_LOG_LEVEL`: Logging level (DEBUG, INFO, WARNING, ERROR) ## Advanced Usage ### Working with Large Datasets ```python # Use sampling for large datasets { "tool": "load_dataset", "arguments": { "source": "large_data.parquet", "format": "parquet", "dataset_id": "large_data", "sample_size": 10000, "sampling_method": "stratified", "stratify_column": "category" } } ``` ### Custom Statistical Models ```python # Linear regression with multiple features { "tool": "statistical_test", "arguments": { "dataset_id": "sales_data", "test_type": "linear_regression", "dependent": "revenue", "independent": ["price", "advertising", "season"], "include_intercept": true, "return_coefficients": true } } ``` ### Advanced Visualizations ```python # Complex multi-panel visualization { "tool": "create_visualization", "arguments": { "dataset_id": "sales_data", "plot_type": "pair_plot", "variables": ["revenue", "cost", "profit", "units"], "hue": "product_line", "diag_kind": "kde", "corner": true } } ``` ## Example Workflows ### Complete EDA Pipeline ```bash # 1. Load data { "tool": "load_dataset", "arguments": { "source": "sales_2024.csv", "dataset_id": "sales" } } # 2. Clean data { "tool": "clean_data", "arguments": { "dataset_id": "sales", "remove_duplicates": true, "handle_missing": "impute" } } # 3. Exploratory analysis { "tool": "analyze_dataset", "arguments": { "dataset_id": "sales", "analysis_type": "exploratory" } } # 4. Visualize distributions { "tool": "create_visualization", "arguments": { "dataset_id": "sales", "plot_type": "histogram", "column": "revenue", "bins": 30 } } # 5. Statistical testing { "tool": "statistical_test", "arguments": { "dataset_id": "sales", "test_type": "anova", "column": "revenue", "group_column": "region" } } ``` ### Time Series Forecasting ```bash # Load and prepare time series data { "tool": "load_dataset", "arguments": { "source": "daily_sales.csv", "dataset_id": "timeseries" } } # Perform time series analysis { "tool": "time_series_analysis", "arguments": { "dataset_id": "timeseries", "date_column": "date", "value_column": "sales", "frequency": "D", "include_forecast": true, "forecast_periods": 90 } } ``` ## Performance Considerations - **Dataset Caching**: Frequently accessed datasets are kept in memory - **Lazy Loading**: Large files are loaded on-demand - **Query Optimization**: SQL queries are converted to efficient pandas operations - **Memory Management**: Automatic eviction of least-recently-used datasets - **Parallel Processing**: Multi-core support for heavy computations ## Troubleshooting ### Common Issues **Out of Memory:** ```yaml # Reduce memory usage in config.yaml server: max_memory_mb: 512 max_datasets: 10 ``` **Slow Queries:** ```python # Use sampling for large datasets { "tool": "query_data", "arguments": { "dataset_id": "large_data", "query": "SELECT * FROM data TABLESAMPLE(10 PERCENT)" } } ``` **Missing Dependencies:** ```bash # Install optional dependencies pip install plotly # For interactive plots pip install xlrd # For old Excel files pip install sqlalchemy # For SQL databases ``` ## Related Resources - [Pandas Documentation](https://pandas.pydata.org/docs/) - [Seaborn Gallery](https://seaborn.pydata.org/examples/index.html) - [Plotly Documentation](https://plotly.com/python/) - [Statsmodels](https://www.statsmodels.org/)