talkingdata 1.0.0

A lightweight, multi-provider AI Data Analysis library using SQL reasoning on pandas DataFrames.

🚀 Talking data

Talking data is a lightweight, multi-provider AI Data Analysis library that performs SQL-based reasoning directly on your pandas.DataFrame using OpenAI, Groq, or Google Gemini models.

It automatically:

• 🧩 Generates SQL queries from plain English questions
• 🚀 Executes them locally using DuckDB
• 🧠 Summarizes results as insightful text or HTML
• ⚙ Installs missing dependencies automatically

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🚀 Installation

pip install talking_data

Once installed, import it in your Python project:

from talking_data import open_analysis

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⚡ Quickstart Example

from talking_data import open_analysis
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    "region": ["East", "West", "North", "South"],
    "sales": [1200, 800, 950, 1100],
    "profit": [200, 100, 150, 180]
})

result = open_analysis(
    df=df,
    model_provider="openai",
    api_key="YOUR_API_KEY",
    question="Which region performs best by profit margin?",
    query_context="Profit margin = profit / sales * 100",
    log_level="detailed"
)

print("SQL Query:", result["sql_query"])
print("Query Result:")
print(result["query_result"])
print("Insights:")
print(result["plain_text_output"])

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🧩 Supported Providers

Provider	Default Model	SDK Dependency
🧠 OpenAI	gpt-4o	openai
⚙ Groq	llama-3.3-70b-versatile	groq
🌐 Google Gemini	gemini-2.0-flash	google-genai

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🧠 Function Reference

open_analysis(
    df: pd.DataFrame,
    model_provider: str = "openai",
    model: str = None,
    api_key: str = None,
    question: str = "Do a data analysis on the dataframe and give me insights?",
    temperature_one: float = 0.2,
    temperature_two: float = 0.7,
    max_completion_tokens: int = 1024,
    output_layer_context: str = None,
    query_context: str = None,
    log_level: str = "basic"
) -> dict

The function performs a two-phase process:

1. SQL Generation: The model creates a valid SQL query from your DataFrame preview and question.
2. Insight Summarization: It summarizes query results in both HTML and text formats.

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🧾 Parameters

Parameter	Type	Description
df	pandas.DataFrame	The dataset to analyze.
model_provider	str	One of "gemini", "groq", or "openai".
model	str	Optional model name override.
api_key	str	API key for the respective provider.
question	str	Natural-language query for analysis.
temperature_one	float	LLM creativity for SQL generation (default: 0.2).
temperature_two	float	LLM creativity for summarization (default: 0.7).
max_completion_tokens	int	Token limit for completions (for Groq/OpenAI).
output_layer_context	str	Extra info or context for insights.
query_context	str	KPI definitions or SQL hints.
log_level	str	"none", "basic", "detailed", or "debug". Controls log verbosity.

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📤 Return Structure

{
    "provider": "openai",
    "model": "gpt-4o",
    "sql_query": "SELECT region, SUM(profit)/SUM(sales)*100 AS margin FROM df GROUP BY region ORDER BY margin DESC",
    "query_result": "<pandas.DataFrame>",
    "html_output": "<section>...</section>",
    "plain_text_output": "East region has the highest profit margin (16.7%)",
    "logs": [
        "Initializing provider: openai",
        "Starting SQL generation...",
        "Generated SQL: SELECT ...",
        "Starting insights generation...",
        "Summary generated successfully."
    ]
}

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🪵 Logging Levels

Level	Description
none	No logs returned.
basic	Key initialization and success/failure steps.
detailed	Includes SQL queries and key phases.
debug	Includes tracebacks and raw prompts.

Logs are stored in result["logs"].

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🧮 Example with Gemini

result = open_analysis(
    df=df,
    model_provider="gemini",
    api_key="YOUR_GEMINI_API_KEY",
    question="Find the top 2 regions by total sales."
)

print(result["plain_text_output"])

---

⚙ Example with Groq

result = open_analysis(
    df=df,
    model_provider="groq",
    api_key="YOUR_GROQ_API_KEY",
    question="Compare profit and sales correlation by region."
)

print(result["plain_text_output"])

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🧠 Example Output

Plain Text:

Region East has the highest profit margin of 16.7%, followed by South at 16.3%.

Generated SQL:

SELECT region, SUM(profit)/SUM(sales)*100 AS margin FROM df GROUP BY region ORDER BY margin DESC;

HTML Output:

<section>
  <h3>Regional Profit Margin Insights</h3>
  <ul>
    <li>East region leads with a 16.7% margin</li>
    <li>South follows closely at 16.3%</li>
  </ul>
</section>

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🪄 Features

• Multi-provider LLM support (Gemini · Groq · OpenAI)
• Automatic dependency installation
• Returns structured results (SQL + DataFrame + insights)
• Full logging control
• Zero manual SQL required

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🔁 Version History

Version	Highlights
0.9.0	Improved loging, response behaviour
0.8.0	Improved internal working
0.7.0	Improved response behaviour
0.6.0	Improved its functionaly
0.5.0	Added log levels, lazy import, unified provider handling
0.4.0	Logs returned in results
0.3.0	Added Gemini and Groq support
0.2.0	Improved error-handling
0.0.0	Initial OpenAI-based release

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🛠 Requirements

• Python 3.8+
• Internet connection
• API key for your chosen provider

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🧩 Frequently Asked Questions

Q: Does it modify my DataFrame?
A: No, it registers it temporarily in DuckDB for safe querying.

Q: Can it work offline?
A: Not yet — all providers (Gemini, Groq, OpenAI) are cloud APIs.

Q: Do I need to install dependencies manually?
A: No. The library installs missing ones automatically on first use.

Q: Can I get raw HTML output?
A: Yes, available via result["html_output"].

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🪪 License

This project is licensed under the MIT License.

MIT License

Copyright (2025)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

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🌐 Links

• 📦 PyPI: https://pypi.org/project/talkingdata
• 💻 GitHub: https://github.com/mabdullah40/Talking-Data/
• 📖 Documentation: https://mabdullah40.github.io/Talking-Data/

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Made with ❤ by Mohammad Abdullah