semvec/pypi-packages huggingface dataset:
wstd2daisy 0.5.3
A utility for generating Daisy board support files from JSON descriptions
License: MIT
Requires Python: >=3.8

# json2daisy

Utility for converting JSON board definitions into valid, [libDaisy](https://github.com/electro-smith/libDaisy) compatible C++ board support files for the [Daisy platform](https://www.electro-smith.com/daisy).

## Install

Note that this module is not intended as a standalone application at this time. You should not need to install it manually for most use cases.

You can install this package with pip:
~~~
python -m pip install json2daisy
~~~

If you'd like to build the package and install locally, you'll need the `build` package:
~~~
python -m pip install build
~~~
Once that's set up, navigate to the root directory of json2daisy and run:
~~~
python3 -m build
python3 -m pip install dist/json2daisy-0.2.0.tar.gz
~~~
If pip complains about a missing file, make sure you've entered the correct version number in the archive path.

## Usage

This package can be used as a standalone program on the command line or as an imported module.

On the command line:
~~~
python -m json2daisy path/to/board_description.json
~~~

As a module:
~~~python
import json2daisy

# If you want to generate from an Electrosmith board:
header_string, board_name, component_info, component_aliases = json2daisy.generate_header_from_board('field')
# If you're supplying custom JSON
header_string, board_name, component_info, component_aliases = json2daisy.generate_header_from_file('path/to/board_description.json')
~~~

As you can see, the two functions above will return a number of supplementary objects in addition to the header string. These provide detailed information for how to interact with the generated header. The `component_info` dictionary provides getters and setters for I/O, and the `component_aliases` dictionary provides valid aliases for everything in `component_info`. 

For examples of how to structure the board description, take a look at the JSON files in [src/json2daisy/resources](https://github.com/electro-smith/json2daisy/tree/main/src/json2daisy/resources).
snowflake-pandas 0.1.2
Snowflake Connector Package for Python and Pandas by hiteshlabs[dot]com.
License: MIT
Requires Python: >=3.8

# SNOWFLAKE PANDAS

## Installation

1. Install the package
    ```bash
    pip install snowflake-pandas
    ```

## Requirements

Currently, the pandas-oriented API methods in the Python connector API work with:
- Snowflake Connector 2.1.2 (or higher) for Python.
- PyArrow library version 3.0.x.
- Pandas 0.25.2 (or higher). Earlier versions might work, but have not been tested.

For more information, see the [Snowflake Connector for Python documentation](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-install).

## Caution

If you already have any version of the PyArrow library other than the recommended version listed above, please uninstall PyArrow before installing the Snowflake Connector for Python. Do not re-install a different version of PyArrow after installing the Snowflake Connector for Python.

For more information, see the [PyArrow library documentation](https://arrow.apache.org/docs/).


# Snowflake Connector for Python Pandas

This package provides a simple and efficient interface for connecting to Snowflake and interacting with it using Pandas. It supports common operations such as querying data, writing DataFrames to Snowflake, and managing table schemas.

## Benefits of Using This Package

1. **Seamless Integration with Pandas**:
    - Effortlessly write and read data using familiar Pandas DataFrames, simplifying data manipulation.

2. **Connection Pooling**:
    - Utilizes a connection pool to manage multiple connections efficiently, improving performance for concurrent operations.

3. **Custom Exception Handling**:
    - Implements a custom exception class for better error management, allowing for clearer debugging and error reporting.

4. **Automatic Table Creation**:
    - Supports automatic creation of tables when writing DataFrames, reducing the need for manual schema management.

5. **Flexible Schema Management**:
    - Easily add new columns to existing tables, making schema evolution straightforward.

6. **Comprehensive Query Support**:
    - Execute any SQL query and retrieve results as a DataFrame, providing flexibility for various data operations.

7. **Compatibility**:
    - Built to work with Snowflake Connector for Python (v2.1.2 or higher) and Pandas (v0.25.2 or higher), ensuring that you have access to the latest features and improvements.