> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> The official documentation for DataAnalyzr – your go-to tool for seamless and comprehensive data analysis

DataAnalyzr is an innovative conversational analytics framework that provides access to LLM-integrated advanced data analysis through a conversational interface to derive actionable insights and intuitive visualizations.

With DataAnalyzr you can streamline the complexity of data analytics into a powerful, intuitive, and conversational interface that lets you command data with ease.
Whether you're an experienced data scientist or a business analyst, DataAnalyzr opens up a world of possibilities by transforming raw data into actionable insights and engaging visualizations.

DataAnalyzr supports both [Pythonic](/pre-built-agents/data-analyzr/pythonic-analysis-agent) and [SQL-based analysis](/pre-built-agents/data-analyzr/text-sql-agent), allowing you to choose the best approach for your data analysis needs.

<AccordionGroup>
  <Accordion title="Pythonic Data Analysis">
    DataAnalyzr's pythonic analysis provides users with a comprehensive toolkit for conducting machine learning-based analysis using Python's extensive array of libraries such as NumPy, pandas, scikit-learn, and statsmodels.
    Users can tap into Python's vast ecosystem of machine learning tools and techniques to derive actionable insights from their data and solve complex analytical problems.
    <Info>For documentation on the pythonic analysis, please refer to the [Pythonic Analysis Agent](/pre-built-agents/data-analyzr/pythonic-analysis-agent) documentation.</Info>
  </Accordion>

  <Accordion title="Text to SQL Analysis">
    DataAnalyzr’s SQL analysis type enables users to seamlessly integrate SQL-based analysis into their workflow and harness the versatility and efficiency of SQL for extracting actionable insights from their data.
    With comprehensive support for SQL-based analysis, DataAnalyzr facilitates a seamless and intuitive user experience, enabling users to unleash the full potential of SQL for driving data-driven decision-making and achieving business objectives.
    <Info>For documentation on the SQL-based analysis, please refer to the [SQL Analysis Agent](/pre-built-agents/data-analyzr/text-sql-agent) documentation.</Info>
  </Accordion>
</AccordionGroup>

**DataAnalyzr comes in two distinct editions**

| Open Source Edition (OSE)      | Enterprise Edition (EE)                       |
| ------------------------------ | --------------------------------------------- |
| Free to Use                    | Paid Subscription Required                    |
| Limited Features               | Full Suite of Features                        |
| Handles limited data sources   | Handles a wide range of data sources          |
| Does preprocessing of data     | Handles data preprocessing                    |
| Only Handles Simple Analysis   | Handles Complex Analysis                      |
| Only Matplotlib Visualizations | Plotly, Matplotlib and Seaborn Visualizations |
| Only PNG as output format      | PNG, SVG, PDF, HTML and JSON output format    |

## **Key Features**

<CardGroup cols={2}>
  <Card title="Versatile Analysis" icon="square-1">
    Choose from SQL-based analysis, or pythonic analysis, or opt to skip analysis altogether and generate insights from the raw data, depending on your project requirements.
  </Card>

  <Card title="Powerful Integration" icon="square-2">
    Seamlessly integrate DataAnalyzr into your existing workflows and applications, leveraging its APIs and versatile capabilities.
  </Card>

  <Card title="Insightful Outputs" icon="square-3">
    Gain valuable insights, generate queries, and receive actionable recommendations, all powered by advanced language models and external services.
  </Card>

  <Card title="Flexible Configuration" icon="square-4">
    Customize analysis parameters, logging settings, and more to suit your specific use cases and preferences.
  </Card>
</CardGroup>