semvec/pypi-packages huggingface dataset:
orange-mcp-server-vegalite 0.0.1
A simple Data Visualization MCP server using Vega-Lite
Requires Python: >=3.10

# Data Visualization MCP Server

## Overview
A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.

## Components

### Tools
The server offers two core tools:

- `save_data`
   - Save a table of data agregations to the server for later visualization
   - Input:
     - `name` (string): Name of the data table to be saved
     - `data` (array): Array of objects representing the data table
   - Returns: success message
- `visualize_data`
   - Visualize a table of data using Vega-Lite syntax
   - Input:
     - `data_name` (string): Name of the data table to be visualized
     - `vegalite_specification` (string): JSON string representing the Vega-Lite specification
   - Returns: If the `--output_type` is set to `text`, returns a success message with an additional `artifact` key containing the complete Vega-Lite specification with data. If the `--output_type` is set to `png`, returns a base64 encoded PNG image of the visualization using the MPC `ImageContent` container.

## Usage with Claude Desktop

```python
# Add the server to your claude_desktop_config.json
{
  "mcpServers": {
    "datavis": {
        "command": "uv",
        "args": [
            "--directory",
            "/absolute/path/to/mcp-datavis-server",
            "run",
            "mcp_server_datavis",
            "--output_type",
            "png" # or "text"
        ]
    }
  }
}
```
pydtc 0.8.4
tools collection for data engineer
Requires Python: >=3.5
Keywords: pandas, multiprocessing, database, restapi, requests

This pacakge provides various tools to perform task on data, in easy and efficient manner; more
modules could be added into the tools collection with development.

1. universal way to connect most database softwares via JDBC (include kerberos auth for Hive), using Fast/Batch load
technology to speed up the temporary table creation and query; as well as functions to convert clob 
into string or save the blob into specified file. 

2. add multiprocessing capablity to pandas dataframe when dealing with cpu intensive
operation on large volume data.

3. form based authentication module for requests package.

4. restapi client using aiohttp package with retry function.

sample usage:

    ## connect to mysql
        import pydtc

        conn = pydtc.connect('mysql', '127.0.0.1', 'user', 'pass')
        pydtc.read_sql('select * from demo.sample', conn)
        conn.close()
    
    ### or use with clause for auto close
        with pydtc.connect('mysql', '127.0.0.1', 'user', 'pass') as conn:
            conn.read_sql('select * from demo.sample')
            # pydtc.read_sql('select * from demo.sample', conn)

        ## DBAPI 2.0    
        with pydtc.connect_dbapi('mysql', '127.0.0.1', 'user', 'pass') as conn:
            pd.read_sql('select * from demo.sample', conn)

    ## pandas multiprocessing groupby then apply
        def func(df, key, value):
            dd = {key : value}
            dd['some_key'] = [len(df.other_key)]

            return pd.DataFrame(dd)

        new_df = pydtc.p_groupby_apply(func, df, 'group_key')

    ## access web page in website with form based authenticaion
        from pydtc import HttpFormAuth
        import requests

        r = requests.get('http://www.example.com/private_webpage.html', auth=HttpFormAuth('user', 'password'))

    ## restapi get and update
    # Fake Online REST API for Testing and Prototyping
    # https://jsonplaceholder.typicode.com/
        from pydtc import api_get, api_update

        api_get('https://jsonplaceholder.typicode.com/todos/1')
        # or
        api_update('https://jsonplaceholder.typicode.com/todos/1', data={'title': 'foo'}, method='patch')