dataanalysiscompare

dataanalysiscompare is a lightweight Python package that helps you quickly compare four popular data‑analysis tools—Excel, Power BI, SQL, and Python—based on your specific needs, project requirements, or skill level. By leveraging a language model (LLM) under the hood, the package returns a clear, standardized comparison that includes key differentiators, best‑use cases, learning curves, and integration capabilities.

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✨ Features

• Instant, structured comparison of Excel, Power BI, SQL, and Python.
• Works with the default ChatLLM7 model (no extra setup required) or any other LangChain‑compatible LLM you prefer.
• Simple API: just pass a natural‑language description of your use case.
• Returns a list of strings that can be easily displayed, logged, or further processed.

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📦 Installation

pip install dataanalysiscompare

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🚀 Quick Start

fromdataanalysiscompareimportdataanalysiscompare# Simple call using the default LLM (ChatLLM7)user_query="""I have a medium‑sized sales dataset in CSV format.I need to clean the data, create visual dashboards, and share insights with my team.I have basic Excel skills but want something more powerful."""result=dataanalysiscompare(user_input=user_query)
forlineinresult:
print(line)

Output (example)

- Excel: Great for quick calculations and ad‑hoc analysis but limited for large datasets.
- Power BI: Excellent for interactive dashboards and sharing reports; steeper learning curve.
- SQL: Ideal for querying large relational datasets; requires knowledge of SQL syntax.
- Python: Most flexible; powerful libraries (pandas, matplotlib, seaborn) but higher learning curve.
...

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🛠️ Advanced Usage

Providing Your Own LLM

If you prefer to use a different LangChain LLM (e.g., OpenAI, Anthropic, Google Gemini), simply pass the instantiated model via the llm argument.

OpenAI Example

fromlangchain_openaiimportChatOpenAIfromdataanalysiscompareimportdataanalysiscomparellm=ChatOpenAI(model="gpt-4o-mini")
response=dataanalysiscompare(
user_input="I need to automate monthly reporting from a PostgreSQL database.",
llm=llm
)
print(response)

Anthropic Example

fromlangchain_anthropicimportChatAnthropicfromdataanalysiscompareimportdataanalysiscomparellm=ChatAnthropic(model_name="claude-3-haiku-20240307")
response=dataanalysiscompare(
user_input="My team wants a low‑code solution for building interactive charts.",
llm=llm
)
print(response)

Google Gemini Example

fromlangchain_google_genaiimportChatGoogleGenerativeAIfromdataanalysiscompareimportdataanalysiscomparellm=ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response=dataanalysiscompare(
user_input="I need to integrate data from Excel and a MySQL database into a single dashboard.",
llm=llm
)
print(response)

Supplying a Custom API Key for LLM7

The default LLM7 free‑tier limits are sufficient for most usage. If you need higher limits, provide your own API key:

fromdataanalysiscompareimportdataanalysiscompareresponse=dataanalysiscompare(
user_input="Describe the best data‑analysis tool for a beginner who wants to learn data science.",
api_key="YOUR_LLM7_API_KEY"
)
print(response)

You can also set the environment variable LLM7_API_KEY and omit the api_key argument.

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📋 Function Signature

defdataanalysiscompare(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]:
""" Compare Excel, Power BI, SQL, and Python based on the provided user description. Parameters ---------- user_input: str Natural‑language description of the data‑analysis needs, project, or skill level. llm: Optional[BaseChatModel] A LangChain LLM instance to use. If omitted, the default ChatLLM7 is used. api_key: Optional[str] API key for LLM7. If omitted, the function looks for the LLM7_API_KEY environment variable or falls back to the free tier. Returns ------- List[str] A list of strings containing the comparative insights. """

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🧩 Dependencies

• langchain-core
• langchain-llm7
• llmatch-messages
• re, os, typing (standard library)

All dependencies are installed automatically with the package.

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📖 Documentation & Support

• Source code / Issues:https://github....
• LLM7 documentation:https://pypi.org/project/langchain-llm7/
• LangChain docs:https://docs.langchain.com/

If you encounter any problems or have feature requests, please open an issue on GitHub.

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👤 Author

Eugene Evstafev
📧 Email: hi@euegne.plus
🐙 GitHub: chigwell

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.