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Starting with release 2.0.0 B2, the legacy Pandas toolkit has been deprecated and replaced by the Data Analysis module. Existing Pandas toolkits are disabled and no longer functional.Action Required: Users must migrate to the Data Analysis module available in chat conversations and agents. See the Pandas Toolkit Migration Guide for step-by-step migration instructions.
Data Analysis module provides powerful Pandas-based data analysis capabilities directly within ELITEA chats. This module enables seamless data processing and analysis without separate configurations, making it easy to work with uploaded files using natural language queries. Key Features:
  • Direct Integration: Available in chats and agents
  • Natural Language Processing: Use plain English to request data analysis operations
  • File-Based Analysis: Works with files uploaded directly to conversations (CSV, Excel, and other tabular formats)
  • Automated Processing: Intelligent file format detection and data analysis
  • Chart Generation: Automatic creation of visualizations with downloadable results

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Prerequisites

  • Permission Level: User role with chat edit access
  • Chat: An active chat or agent configuration
The Data Analysis module must be explicitly enabled it can be used in a chat. It is hidden from the regular toolkit menu and exposed only through the Modules configuration.

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How to Enable Data Analysis

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In Chat

Enable the Data Analysis module for ad-hoc data analysis in conversations.
  1. Go to your chat.
  2. In the chat toolbar, select +.
  3. Select Modules → Data Analysis in the list.
  4. Toggle Data Analysis on to enable it.
Enable Data Analysis in chat
The module configuration persists for the duration of your chat session. You can toggle Data Analysis on/off at any time.

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For Agents

You can configure Data Analysis as part of an agent’s default configuration.
  1. Open the agent you want to configure (or create and opena new agent).
  2. Scroll down to TOOLS.
  3. In TOOLS, find MODULES.
  4. Find Data Analysis. If it is not immediately visible, select Show all to see the full list of modules.
  5. Toggle Data Analysis on.
  6. Select Save.
  7. New conversations created with this agent will have Data Analysis enabled by default. Enable Data Analysis for agents
  • Agent Configuration: Sets the default state for all new chats with that agent
  • Chat Configuration: Overrides the agent’s default for that specific chat session
  • Changes to agent configuration do not affect existing chats

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How to Use Data Analysis

Once enabled, the Data Analysis module allows you to perform comprehensive data analysis directly in chat using natural language commands. Simply upload your data files and request analysis operations in plain English.

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How It Works

  1. Upload Data: Upload CSV, Excel, or other data files to your chat
  2. Request Analysis: Ask the assistant to perform analysis using natural language
  3. Get Results: Receive summaries, transformations, charts, and downloadable files
Available Operations
  • Data Summaries: Descriptive statistics, data profiling, and overview reports
  • Data Filtering: Row and column filtering based on conditions
  • Transformations: Data cleaning, column operations, and restructuring
  • Aggregation: Grouping, totals, averages, and statistical calculations
  • Visualization: Automatic chart generation (bar charts, line graphs, histograms, etc.)
  • Export: Save transformed data as downloadable files
  • Always upload your data file to the chat before requesting analysis
  • For very large datasets (>100MB) or complex custom operations, consider using the Python Sandbox instead

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Example Workflows

These examples show typical workflows using the Data Analysis module. Example 1 — Natural language data summary
  1. Open the chat and click +.
  2. Check that the Data Analysis module is enabled.
  3. Upload a small sample file (CSV) and ask the assistant to return a summary.
  4. Ask the assistant:
What happens: the assistant indexes the uploaded data first, then performs the requested analysis on the indexed data. Example 1 Example 2 — Summarize bug reports and highlight hotspots
  1. Check that the Data Analysis module is enabled and upload bug_reports.xlsx.
  2. Ask the assistant:
What happens: the assistant indexes the uploaded data first, then performs the requested analysis. If a chart is helpful (for example a histogram of sales or a time series), the assistant will generate one or more charts and save each chart as an image file in chat. Example 2 Generated files Generated analysis files

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Best Practices

Upload moderately sized files for interactive analysis. Very large datasets may cause timeouts or higher latency. Consider using Python Sandbox for datasets larger than 100MB.
Use widely supported formats like CSV, Excel for best compatibility. These formats work reliably across different systems.
When you need a specific output (for example: a downloadable CSV of filtered rows, an aggregated table, or a PNG chart), state it clearly in your request: “Save filtered rows as CSV” or “Generate a time-series chart and export as PNG”.
Charts and exported files are saved to your chat. The assistant will provide links to view or download these files. Charts are saved as PNG files with auto-generated UUID filenames.
Break complex analyses into smaller steps (load → inspect → filter → aggregate → visualize). This reduces errors and makes results easier to validate.
Refer to column names exactly as they appear in your dataset and provide example values when helpful (for dates, currencies, or categories).
If you expect to re-run the same workflow, ask the assistant to “save transformed data” and include the intended filename. This makes it easy to re-open or share results later.
For advanced custom logic, very large datasets, or specialized operations, enable the Python sandbox and provide code snippets directly.
Requests that ask for extensive detailed outputs may be truncated. Prefer summaries or downloadable files for large results.
Avoid uploading highly sensitive data if you are unsure about retention policies. When in doubt, remove or anonymize personal information before uploading.
For CSV files with special characters, ensure UTF-8 encoding. UTF-8 files work most reliably.
Provide clear, specific analysis requests. Instead of “analyze this data”, try “Calculate monthly sales totals and create a bar chart showing top 5 products by revenue”.

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Troubleshooting

Possible causes:
  • Data Analysis module not enabled in chat
  • Chat configuration issue
Solution:
  1. Enable Data Analysis in Modules for the chat
  2. Refresh the chat and re-enable the tool if needed
Possible causes:
  • Unsupported file format uploaded
  • Corrupted or malformed data file
  • File encoding issues
  • Large file causing read timeouts
Solution:
  1. Ensure file is in a supported format (CSV, Excel, Parquet, or other common tabular formats)
  2. Check file integrity and try re-uploading a clean version
  3. Convert file to UTF-8 encoding if needed
  4. For large files, consider using Python Sandbox instead
Possible causes:
  • Insufficient user permissions
  • Modules are not accessible
  • Modules are hidden because no tools pass the availability filter
  • UI configuration issue
Solution:
  1. Confirm you have chat edit access permissions.
  2. Check that Modules are visible — the icon is hidden entirely when no modules are available for the project.
  3. Check that Data Analysis appears in the list.
  4. Refresh the page and try accessing the popup again.
  5. If the icon is missing, contact your project administrator to confirm modules are enabled for the project.
Possible causes:
  • Very large datasets causing processing delays
  • Complex analysis requests
  • Network connectivity issues
Solution:
  1. Reduce dataset size or use smaller sample files for testing
  2. Break complex requests into smaller iterative steps
  3. Prefer aggregated summaries over detailed outputs
  4. Check network connection and retry the request
  5. For very large data, consider enabling Python Sandbox instead
Possible causes:
  • Chart generation failed due to data issues
  • File saving permissions issue
  • Invalid filenames
Solution:
  1. Verify data is suitable for the requested chart type
  2. Check that file saving is working
  3. Use valid filenames without special characters
  4. Try simpler chart types first
  5. Request download links explicitly in your prompt
Possible causes:
  • Ambiguous or overly complex queries
  • Column names with special characters
  • Data type mismatches
Solution:
  1. Use clear, specific analysis requests with exact column names
  2. Break complex queries into simpler steps
  3. Provide example values for date formats, categories, etc.
  4. Try rephrasing the query if it fails
  5. Check for error messages and adjust accordingly
Possible causes:
  • Loading very large datasets
  • Complex operations on big datasets
  • Multiple concurrent requests
  • Insufficient system resources
Solution:
  1. Use smaller sample datasets for initial testing
  2. Consider using Python Sandbox for memory-intensive operations
  3. Close other conversations to free up resources
  4. Optimize queries to use less memory
  5. Monitor system resource usage