Data Analysis Skill

Description

Load data files (CSV, XLSX, JSON, Parquet) into the AG Grid viewer, run pandas queries, save results, and generate visualizations.

Tools Used

Primary (Data Grid workflow)

• data_list - List available data files in /workspace/data/
• data_load - Load a data file into AG Grid (returns markdown preview for context)
• data_query - Execute pandas operations on loaded data (filter, aggregate, transform)
• data_save - Save the current DataFrame to a file

Secondary (Jupyter workflow for visualization)

• jupyter_execute - Execute Python code in Jupyter kernel (for plots and complex analysis)
• update_notebook - Add cells to Jupyter notebook
• update_gallery - Display generated plots in the gallery

Workflow

Recommended: Data Grid Workflow

For tabular data exploration, use the data tools which provide a spreadsheet-like experience:

1. List files: data_list to see what's in /workspace/data/
2. Load data: data_load to read a file and display in AG Grid
   • You'll receive a markdown preview to understand columns and types
3. Query/Filter: data_query to run pandas operations
   • The df variable contains the loaded data
   • Set result = ... to define output
4. Save results: data_save to export to CSV/XLSX

Alternative: Jupyter Workflow

For visualization, statistical analysis, or ML, use Jupyter tools:

1. Load data with jupyter_execute running pandas code
2. Create visualizations with matplotlib/seaborn
3. Display plots with update_gallery

Usage Patterns

Load and Explore Data

When user says: "Analyze this dataset" or "Show me the data"

1. data_list to find available files
2. data_load with the target file
3. Review the markdown preview to understand structure
4. data_query with result = df.describe() for statistics
5. Offer filtering, sorting, or visualization

Filter and Transform

When user says: "Show only rows where X > Y" or "Group by category"

1. data_query with pandas filter/groupby code
2. Grid updates automatically with filtered results
3. Inform user of result count and preview

Save Processed Data

When user says: "Export this" or "Save as Excel"

1. data_save with desired filename and format
2. Report file location and size

Visualize Data

When user says: "Create a chart" or "Plot the distribution"

1. Use jupyter_execute with matplotlib/seaborn code
2. Save plot and display via update_gallery

Code Snippets for data_query

Filter rows

result = df[df['score'] > 90]

Group and aggregate

result = df.groupby('category').agg({'value': ['mean', 'sum', 'count']}).reset_index()

Sort by column

result = df.sort_values('date', ascending=False)

Add computed column

df['ratio'] = df['value_a'] / df['value_b']
result = df

Summary statistics

result = df.describe()

Handle missing values

result = df.dropna(subset=['important_column'])

Best Practices

1. Start with data_list: Always check what files are available first
2. Use data_load first: Load data to get markdown preview before querying
3. Keep queries simple: One operation per data_query call for clarity
4. Save intermediate results: Use data_save for important filtered datasets
5. Switch to Jupyter for plots: AG Grid is for tabular data, use Jupyter for visualizations