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