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name: data-analysis description: Analyze datasets and create visualizations version: 1.0.0 author: Minion Team tags: [data, analysis, visualization, pandas] requirements:

• pandas>=2.0.0
• matplotlib>=3.7.0
• numpy>=1.24.0

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Data Analysis Skill

Description

This skill helps analyze datasets and create meaningful visualizations. It can handle CSV files, perform statistical analysis, and generate various types of plots.

Usage Instructions

When a user requests data analysis:

1. Load the dataset: Use pandas to read the data file
2. Inspect the data: Check shape, columns, data types, and basic statistics
3. Clean the data: Handle missing values and outliers if necessary
4. Perform analysis: Calculate relevant statistics based on user's question
5. Create visualizations: Generate appropriate plots (line, bar, scatter, etc.)
6. Save results: Export results and visualizations

Available Resources

Scripts

• scripts/analyze.py: Core analysis functions

  • load_dataset(filepath): Load data from various formats
  • basic_statistics(df): Calculate descriptive statistics
  • detect_outliers(df, column): Identify outliers
  • correlation_analysis(df): Compute correlations

• scripts/visualize.py: Visualization utilities

  • plot_distribution(df, column): Create distribution plots
  • plot_correlation_matrix(df): Visualize correlation heatmap
  • plot_time_series(df, date_col, value_col): Time series plots
  • save_plot(fig, filename): Save figure to file

References

• references/examples.md: Usage examples and common patterns
• references/best_practices.md: Data analysis best practices

Example Prompts

• "Analyze this CSV file and show me the trends"
• "Create a visualization of the sales data by month"
• "Find correlations in this dataset"
• "Identify outliers in the price column"
• "Generate a statistical summary of the data"

Output Format

Analysis results should include:

1. Data overview (shape, columns, types)
2. Statistical summary
3. Key insights and findings
4. Visualizations (saved as PNG files)
5. Recommendations or next steps

Notes

• Always inspect data before analysis
• Handle missing values appropriately
• Choose visualizations that match the data type
• Provide clear explanations of findings
• Save all outputs for user reference