# Query

Zerve simplifies data access by removing the need for matching driver packages in Python, streamlining workflows for data manipulation and analysis. It supports native SQL query execution, GraphQL for vector database, and database connectivity through Query Blocks, allowing users to execute queries and save the results in a dataframe for use in subsequent operations.

<figure><img src="/files/z77HxQMPpoup0G2ZyIeQ" alt=""><figcaption></figcaption></figure>

**SQL On Pandas Dataframe:**

Zerve supports utilizing any pandas dataframe as a database source, enabling the execution of SQL queries directly on the dataframe. To leverage this feature, connect a Python block containing a dataframe to an SQL block.&#x20;

This integration allows you to perform SQL queries on the dataframe as if it were a traditional database, enhancing data manipulation and analysis capabilities within Zerve.

<figure><img src="/files/0H0A2QMgtfnir4RDg7gZ" alt=""><figcaption></figcaption></figure>

<figure><img src="/files/ywecmTC01pQNhmfuOHNl" alt=""><figcaption></figcaption></figure>


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.zerve.ai/guide/canvas-view/blocks-and-connections/block-types/query.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.