slm_data/README.md

SLM Data Analyst

A local CPU-optimized Data Analyst agent designed to profile CSV files, auto-generate pandas summary scripts, and print descriptive statistics of local datasets.

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Features

• Pandas Script Gen: Auto-generates clean pandas DDL summaries.
• Descriptive Statistics: Analyzes column metrics, shape, and datatypes.
• Sandboxed Operations: Interfaces with local sandbox components to test generated pandas execution plans.

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Installation

pip install -e ./slm_data

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API Reference

SLMDataAnalyst

from slm_data import SLMDataAnalyst

analyst = SLMDataAnalyst()

analyze_file(file_path: str, query: str) -> dict

Runs profiling statistics on the target file.

• Arguments:
  • file_path (str): Path to local CSV file.
  • query (str): Task request query.
• Returns:
  • dict:

    {
        "success": True/False,
        "file": str,                # Target file path
        "columns": list,            # Detected columns (if pandas environment ok)
        "script": str,              # Generated pandas evaluation script
        "summary": str              # Natural language summary description of metrics
    }
    

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Usage Example

from slm_data import SLMDataAnalyst

analyst = SLMDataAnalyst()
csv_file = "sales.csv"

result = analyst.analyze_file(csv_file, "Summarize total sales")

print(f"Summary:\n{result['summary']}")
print(f"Python Script:\n{result['script']}")

Input & Output Example

Input (CSV File + Query):

• File: sales.csv
• Query: "summarize sales"

Output:

{
  "success": true,
  "file": "sales.csv",
  "columns": [],
  "script": "import pandas as pd\ndf = pd.read_csv('sales.csv')\nprint('Data summary:')\nprint(df.describe(include='all'))\n",
  "summary": "Calculated total revenue by region: East ($15,000), West ($22,000)."
}