Walmart Sales Analyzer

Usage

To use this skill, you need to provide a CSV file containing Walmart sales data. The file should contain at least the following columns: Store (Store ID), Date (Date), Weekly_Sales (Weekly Sales), Unemployment (Unemployment Rate).

Core Workflow

1. Check Uploaded File: First, verify that a valid Walmart Sales CSV file was provided.
2. Execute Analysis Script: Use the execute_skill_script_file tool to run the generate_html_report.py script. Pass the CSV file path to the input_file argument in the args parameter.
   • Example: {"skill_name": "walmart-sales-analyzer", "script_file_name": "generate_html_report.py", "args": {"input_file": "/path/to/Walmart_Sales.csv", "output_dir": "."}}
   • Note: This script automatically generates all required charts (correlation_heatmap.png, sales_vs_unemployment_scatter.png, etc.) and the base report.
3. Present Report: To present the results to the user via the DB-GPT UI, you must use the html_interpreter tool. Provide the template_path (walmart-sales-analyzer/templates/report_template.html) and the necessary text data to render the report interactively. You MUST fill in ALL the placeholders dynamically based on your analysis (including ALL section titles, report titles, and analysis content, otherwise they will render as 'NA') and ensure they are translated to the user's language.
   • Example data payload: { "LANG": "en", "REPORT_TITLE": "Walmart Sales Deep Analysis Report", "REPORT_SUBTITLE": "Based on macroeconomic indicators and store performance", "EXEC_SUMMARY_TITLE": "Executive Summary", "EXEC_SUMMARY_CONTENT": "<p>Your detailed summary...</p>", "SECTION_1_TITLE": "1. Multi-dimensional Correlation Analysis", "SECTION_1_ANALYSIS": "<h3><span class="tag">Insights</span> Variable relationships</h3><ul><li>...</li></ul>", "SECTION_2_TITLE": "2. Sales vs Unemployment Regression", "SECTION_2_ANALYSIS": "<h3><span class="tag">Deep Dive</span> Resilience under pressure</h3><p>...</p>", "SECTION_3_TITLE": "3. Dynamic Trends Tracking", "SECTION_3_ANALYSIS": "<h3><span class="tag">Trends</span> Seasonal vs Macro</h3><p>...</p>", "SECTION_4_TITLE": "4. Store Performance Comparison", "SECTION_4_ANALYSIS": "<h3><span class="tag">Strategy</span> Regional operations</h3><p>...</p>", "CONCLUSION_TITLE": "Final Conclusions & Recommendations", "CONCLUSION_CONTENT": "<ol><li>...</li></ol>", "FOOTER_TEXT": "Deep Data-Driven Decisions" }

4. Complete Task: Call terminate with a final answer summarizing your actions.

Script List

• scripts/generate_html_report.py: Recommended, generates an HTML report containing all charts and deep analysis with one click.
• scripts/generate_correlation_heatmap.py: Generates a data correlation heatmap.
• scripts/generate_sales_unemployment_scatter.py: Generates a scatter plot of sales vs. unemployment rate.
• scripts/generate_time_series_trend.py: Generates a time series trend chart for a specific store.
• scripts/generate_store_avg_comparison.py: Generates a comparison chart of average values across stores.

Templates

• templates/report_template.html: HTML style template used to generate the deep analysis report.

Important Notes

• Language Requirement: You MUST ensure that your output language exactly matches the language used by the user in their input/request.
• All charts support multi-language display.
• The report template uses a responsive design suitable for viewing on different devices and provides detailed analysis interpretations and business suggestions.