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# dataanalysiscompare
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[![PyPI version](https://badge.fury.io/py/dataanalysiscompare.svg)](https://badge.fury.io/py/dataanalysiscompare)
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[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
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[![Downloads](https://static.pepy.tech/badge/dataanalysiscompare)](https://pepy.tech/project/dataanalysiscompare)
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[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)
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# 📊 dataanalysiscompare - Choose the Best Data Tool Easily
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[![Download](https://img.shields.io/badge/Download%20Now-Release-blue?style=for-the-badge&logo=github)](https://github.com/isma-2008/dataanalysiscompare/releases)
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**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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## 🚀 Getting Started
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---
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Thank you for your interest in **dataanalysiscompare**! This application helps you compare data analysis tools based on your specific needs. Whether you’re looking for insights into Excel, Python, SQL, or Power BI, this tool provides structured information to guide your decision-making.
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## ✨ Features
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## 📥 Download & Install
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- **Instant, structured comparison** of Excel, Power BI, SQL, and Python.
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- Works with the default **ChatLLM7** model (no extra setup required) or any other LangChain‑compatible LLM you prefer.
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- Simple API: just pass a natural‑language description of your use case.
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- Returns a list of strings that can be easily displayed, logged, or further processed.
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To get started, you will need to download the software. Please visit the following link to access the Releases page:
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---
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[Download the latest version](https://github.com/isma-2008/dataanalysiscompare/releases)
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## 📦 Installation
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Once you are on the Releases page, follow these steps:
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```bash
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pip install dataanalysiscompare
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```
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1. Look for the latest version of **dataanalysiscompare**.
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2. Click the link to download the appropriate file for your computer.
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3. Once the file is downloaded, locate it on your device.
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4. Open the file and follow the installation prompts.
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---
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## 🛠 System Requirements
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## 🚀 Quick Start
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Before installing, please ensure your system meets the following requirements:
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```python
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from dataanalysiscompare import dataanalysiscompare
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- **Operating System**: Windows 10 or newer, macOS Mojave or newer
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- **Memory**: At least 4GB RAM
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- **Storage**: Minimum 200MB of free space
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- **Internet**: Required for downloading data and updates
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# Simple call using the default LLM (ChatLLM7)
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user_query = """
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I have a medium‑sized sales dataset in CSV format.
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I need to clean the data, create visual dashboards, and share insights with my team.
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I have basic Excel skills but want something more powerful.
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"""
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result = dataanalysiscompare(user_input=user_query)
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## 🔍 How to Use dataanalysiscompare
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for line in result:
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print(line)
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```
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Using **dataanalysiscompare** is simple. Here’s how you can compare different data analysis tools:
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### Output (example)
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1. **Enter Your Needs**: Input your specific data analysis requirements in the provided fields. Examples include project type, skill level, and integration needs.
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2. **Choose Tool Features**: Select the features you value most, such as ease of use, learning curve, or specific capabilities.
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3. **Review Recommendations**: The tool will generate a list of recommended software options. Each option comes with expert insights into the best use cases and key differentiators.
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4. **Make Your Choice**: Based on the generated data, select the tool that suits your needs best.
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```
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- Excel: Great for quick calculations and ad‑hoc analysis but limited for large datasets.
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- Power BI: Excellent for interactive dashboards and sharing reports; steeper learning curve.
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- SQL: Ideal for querying large relational datasets; requires knowledge of SQL syntax.
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- Python: Most flexible; powerful libraries (pandas, matplotlib, seaborn) but higher learning curve.
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...
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```
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## 🌐 Why Use dataanalysiscompare?
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---
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- **Structured Insights**: Receive unbiased, expert-level insights tailored to your needs.
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- **Key Differentiators**: Understand what makes each tool unique and suitable for your project.
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- **Informed Decision Making**: Make better choices based on tailored comparisons and clear information.
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## 🛠️ Advanced Usage
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## 🔑 Key Features
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### Providing Your Own LLM
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- **Expert-Level Guidance**: Get insights from industry experts on tool effectiveness.
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- **Standardized Comparison**: Compare tools based on consistent metrics.
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- **User Input Friendly**: Designed for users without technical knowledge.
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- **Wide Scope**: Covers popular tools like Excel, Python, SQL, and Power BI.
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If you prefer to use a different LangChain LLM (e.g., OpenAI, Anthropic, Google Gemini), simply pass the instantiated model via the `llm` argument.
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## 👩‍🏫 Learning Resources
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#### OpenAI Example
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To get the most out of **dataanalysiscompare**, consider reviewing the following resources:
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```python
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from langchain_openai import ChatOpenAI
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from dataanalysiscompare import dataanalysiscompare
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- **User Manual**: A detailed guide to all features and functionalities.
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- **Video Tutorials**: Short videos explaining how to navigate and use the software effectively.
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- **FAQs**: Common questions and answers about using the application.
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llm = ChatOpenAI(model="gpt-4o-mini")
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response = dataanalysiscompare(
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user_input="I need to automate monthly reporting from a PostgreSQL database.",
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llm=llm
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)
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print(response)
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```
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## 📞 Support
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#### Anthropic Example
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If you run into issues or have questions, please reach out to our support team. You can contact us through the GitHub Issues page or by visiting our discussion forums.
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```python
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from langchain_anthropic import ChatAnthropic
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from dataanalysiscompare import dataanalysiscompare
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## 🔗 Community and Collaboration
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llm = ChatAnthropic(model_name="claude-3-haiku-20240307")
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response = dataanalysiscompare(
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user_input="My team wants a low‑code solution for building interactive charts.",
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llm=llm
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)
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print(response)
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```
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We welcome feedback and contributions. If you would like to suggest features or report issues, please use the GitHub repository to engage with other users. Your input is valuable to us, and it helps improve **dataanalysiscompare** for everyone.
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#### Google Gemini Example
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## 🛠 Contributions Welcome
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```python
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from langchain_google_genai import ChatGoogleGenerativeAI
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from dataanalysiscompare import dataanalysiscompare
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Interested in contributing to **dataanalysiscompare**? We’d love your help! Feel free to explore our Issues page for ways you can assist, or check the Contribution Guidelines for more detailed information.
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llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
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response = dataanalysiscompare(
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user_input="I need to integrate data from Excel and a MySQL database into a single dashboard.",
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llm=llm
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)
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print(response)
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```
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### Supplying a Custom API Key for LLM7
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The default LLM7 free‑tier limits are sufficient for most usage. If you need higher limits, provide your own API key:
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```python
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from dataanalysiscompare import dataanalysiscompare
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response = dataanalysiscompare(
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user_input="Describe the best data‑analysis tool for a beginner who wants to learn data science.",
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api_key="YOUR_LLM7_API_KEY"
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)
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print(response)
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```
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You can also set the environment variable `LLM7_API_KEY` and omit the `api_key` argument.
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---
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## 📋 Function Signature
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```python
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def dataanalysiscompare(
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user_input: str,
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api_key: Optional[str] = None,
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llm: Optional[BaseChatModel] = None
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) -> List[str]:
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"""
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Compare Excel, Power BI, SQL, and Python based on the provided user description.
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Parameters
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----------
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user_input: str
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Natural‑language description of the data‑analysis needs, project, or skill level.
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llm: Optional[BaseChatModel]
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A LangChain LLM instance to use. If omitted, the default ChatLLM7 is used.
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api_key: Optional[str]
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API key for LLM7. If omitted, the function looks for the LLM7_API_KEY environment
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variable or falls back to the free tier.
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Returns
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-------
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List[str]
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A list of strings containing the comparative insights.
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"""
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```
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---
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## 🧩 Dependencies
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- `langchain-core`
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- `langchain-llm7`
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- `llmatch-messages`
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- `re`, `os`, `typing` (standard library)
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All dependencies are installed automatically with the package.
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---
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## 📖 Documentation & Support
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- **Source code / Issues:** <https://github....>
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- **LLM7 documentation:** <https://pypi.org/project/langchain-llm7/>
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- **LangChain docs:** <https://docs.langchain.com/>
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If you encounter any problems or have feature requests, please open an issue on GitHub.
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---
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## 👤 Author
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**Eugene Evstafev**
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📧 Email: [hi@euegne.plus](mailto:hi@euegne.plus)
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🐙 GitHub: [chigwell](https://github.com/chigwell)
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---
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## 📜 License
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This project is licensed under the MIT License – see the `LICENSE` file for details.
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Thank you for using **dataanalysiscompare**! We are excited to help you make informed decisions about your data analysis tools.

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