ChartGen Data Analysis
Data analysis skill based on ChartGen API, supporting natural language-based data analysis, interpretation, and visualization.
Overview
This skill enables codeless data analysis through natural language interaction. It supports Text2SQL, Text2Data, and Text2Code analysis. Simply provide Excel/CSV files or JSON data to automatically execute data queries, data interpretation, and data visualization (ChatBI).
The skill will intelligently parse time, metrics, and analytical dimensions through conversational queries, then generate SQL queries for data, create interactive BI charts, structured analysis reports. Optimized for standardized vertical datasets, powered by enterprise-grade analytics engine for reliable results.
API Service: This skill uses the ChartGen API service hosted at chartgen.ai. All data is sent to https://chartgen.ai/api/platform_api/ for processing.
Quick Start
1. Apply for an API Key
You can easily create and manage your API Key at chartgen.ai. To begin with, you need to register for an account.
Steps:
- Visit chartgen.ai and sign up for an account
- Access the API management dashboard
- Create a new API and set the credit consumption limit
- Copy the API Key for use
2. Configure Environment Variable
export CHARTGEN_API_KEY="your-api-key-here"
3. Run Scripts
# Data Analysis
python scripts/data_analysis.py --query "Calculate total sales by region" --file sales.xlsx
# Data Interpretation
python scripts/data_interpretation.py --query "Analyze sales trends" --file sales.xlsx
# Data Visualization
python scripts/data_visualization.py --query "Draw a bar chart of sales by region" --file sales.xlsx
Credit Rules
- Calling a single tool consumes 20 credits
- You get 200 free credits per month for free accounts
- When credits run out, you can purchase more or upgrade your account on the chartgen.ai Billing page
Scripts Reference
| Script | Function | Use Case |
|---|
data_analysis.py | Data Analysis | Statistics, filtering, aggregation, calculation |
data_interpretation.py | Data Interpretation | Trend analysis, pattern discovery, report generation |
data_visualization.py | Data Visualization | Chart generation, data display |
Parameters
Common Parameters
| Parameter | Required | Description |
|---|
--query | ✅ | Natural language query statement |
--file | ❌ | Local file path (.xlsx/.xls/.csv), mutually exclusive with --json |
--json | ❌ | JSON data (string or file path), mutually exclusive with --file |
Visualization Specific Parameters
| Parameter | Description |
|---|
--output, -o | Output HTML file path (defaults to /tmp/openclaw/charts/) |
Data Format
File Format
Supports .xlsx, .xls, .csv Excel and CSV files.
Note: Only one of --file or --json is needed. If both are provided, --file takes precedence. File types support both row-metric-column data files and column-metric-row data files.
JSON Format
JSON data should be an array format, where each element is a row of data:
[
{"name": "Product A", "sales": 1000, "region": "East"},
{"name": "Product B", "sales": 1500, "region": "North"},
{"name": "Product C", "sales": 800, "region": "South"}
]
Or pass via file:
python scripts/data_analysis.py --query "Analyze the data" --json data.json
Usage Examples
Data Analysis
# Statistical calculation
python scripts/data_analysis.py --query "Calculate total and average sales by region" --file sales.xlsx
# Data filtering
python scripts/data_analysis.py --query "Filter products with sales greater than 1000" --file sales.xlsx
# Sorting
python scripts/data_analysis.py --query "Sort by sales in descending order" --file sales.xlsx
Data Interpretation
# Trend analysis
python scripts/data_interpretation.py --query "Analyze monthly sales trends" --file monthly_sales.xlsx
# Anomaly detection
python scripts/data_interpretation.py --query "Find and explain anomalies in the data" --file data.xlsx
# Comprehensive interpretation
python scripts/data_interpretation.py --query "Provide a comprehensive analysis of this data with key insights" --file report.xlsx
Data Visualization
# Bar chart
python scripts/data_visualization.py --query "Draw a bar chart of sales by product" --file sales.xlsx
# Line chart
python scripts/data_visualization.py --query "Draw a line chart of sales trends" --file trends.xlsx
# Pie chart
python scripts/data_visualization.py --query "Draw a pie chart of sales by region" --file sales.xlsx
# Save to specific path
python scripts/data_visualization.py --query "Draw a scatter plot" --file data.xlsx -o /path/to/chart.html
Output Description
Data Analysis & Data Interpretation
Returns Markdown format text results, including analysis conclusions, data tables, etc.
Data Visualization
- Console Output: ECharts configuration JSON
- HTML File: Can be opened in browser to view the chart
Error Handling
Common errors and solutions:
| Error Message | Cause | Solution |
|---|
CHARTGEN_API_KEY not set | Environment variable not set | export CHARTGEN_API_KEY="your-key" |
API request timeout | Request timeout | Check network connection and retry |
File not found | File does not exist | Check if file path is correct |
credits are insufficient | Insufficient credits | Recharge or contact administrator |
Technical Details
- API Base URL:
https://chartgen.ai/api/platform_api/
- Authentication: Header
Authorization: <api-key>
- Request Format: JSON
- Timeout: 60 seconds
- Required Environment Variable:
CHARTGEN_API_KEY
See scripts/chartgen_api.py for implementation details.
Privacy Notice
Data sent to remote API: This skill reads your provided data files (CSV/XLSX/JSON), base64-encodes them, and sends them to the ChartGen API at https://chartgen.ai/api/platform_api/ for analysis and chart generation. Your data will leave your machine.
Recommendations:
- Do not upload sensitive or regulated data
- Use a dedicated API key with limited scope/credits
- Review the privacy practices at chartgen.ai before use
