semvec/pypi-packages huggingface dataset:
markdown-table-repair 0.1.0
A package to repair and clean malformed markdown tables.

# 🛠️ Markdown Table Repair
```markdown-table-repair``` is a python package designed to repair and clean malformed markdown tables and convert them into structured pandas.

# ✨ Supported Broken Markdown Table Format
- Inconsistent column and header count
```
Header1 | Header2 | 
|---|---|
Value1  | Value2 | Value3
```
- Improperly formatted header 
```
| Header1 | Header2 | Header3  
|---|---|---  
Value1 | Value2 | Value3 |
```
- Missing separator
```
| Header1 | Header2 | Header3
Value1 | Value3
```  
- Unclosed table blocks
```
| Header1 | Header2 | Header3  
|---|---|--- 
Value1 | Value2 | Value3
```
- No separator in header row
```
 Header1 | Header2 | 
 |---|---|
  Value1  | Value2 | Value3
```
- Unnecessary whitespaces
- etc.

# 🔧 Usage
To use the markdown-table-repair package, follow these steps:
1. **Install the Package**
Before using markdown-table-repair, ensure that ```pandas``` is installed.
```
pip install markdown-table-repair  
```
2. **Repair Malformed Tables**
```
import markdown_table_repair as mtr

malformed_table = """| Header1 | Header2 |  Header3 \n|---|---|\n Value1  | Value2 | Value3"""
repaired_table = mtr.repair(malformed_table)
print(repaired_table)
```
3. **Optional - Convert to DataFrame**
```
# If you want to convert the repaired table into a pandas DataFrame

dataframe = repaired_table.to_df()
```
# 🤝 Contributing
Your contributions to this project are welcome! Feel free to fork the repository, create pull requests, and help improve the markdown-table-repair package.

# 📝 License
This project is licensed under the MIT License. See the LICENSE file for details.
dataExploreNew 0.5
Dataset visualization package
Requires Python: >=3.6

This package provides data visualiazation on dataset
To install press pip install dataExplore
To run please call
from dataExplore.builder import Builder
import seaborn as sns
tips = sns.load_dataset("tips")
print(tips.head())
b=Builder(tips)
d=b.getExplorer()
fireflies-cli 0.1.0
Command line interface for Fireflies.ai
Requires Python: >=3.6

# Fireflies CLI

Command line interface for Fireflies.ai. This package provides a convenient way to interact with Fireflies.ai from the command line.

## Installation

```bash
pip install fireflies-cli
```

## Usage

```bash
# Set your API key (only needed once)
ff config set-key YOUR_API_KEY

# Join a meeting
ff join --url https://meet.google.com/xxx-yyyy-zzz

# Get video URL for a transcript
ff video TRANSCRIPT_ID

# Download video
ff video -d TRANSCRIPT_ID

# List recent transcripts
ff list

# Delete a transcript
ff delete TRANSCRIPT_ID
```

## Features

- Join meetings with optional duration and name
- Get video URLs for transcripts
- Download meeting recordings
- List transcripts
- Delete transcripts

## Note

This CLI tool uses the `fireflies-sdk` package. If you need programmatic access to Fireflies.ai, consider using the SDK directly.
fireflies-sdk 0.1.0
SDK for interacting with Fireflies.ai API
Requires Python: >=3.6

# Fireflies SDK

Python SDK for interacting with the Fireflies.ai API. This package provides a simple interface for accessing Fireflies.ai functionality programmatically.

## Installation

```bash
pip install fireflies-sdk
```

## Usage

```python
from fireflies_sdk import FirefliesAPI

# Initialize with your API key
api = FirefliesAPI("your-api-key")

# Join a meeting
result = api.join_meeting(url="https://meet.google.com/xxx-yyyy-zzz")

# Get video URL for a transcript
video_info = api.get_video_url("transcript-id")
print(video_info["title"])
print(video_info["video_url"])
```

## Features

- Join meetings
- Get video URLs
- Download meeting recordings
- List transcripts
- Delete transcripts

## For AWS Lambda

This SDK is designed to be Lambda-friendly. Here's an example Lambda function:

```python
from fireflies_sdk import FirefliesAPI
import os

def lambda_handler(event, context):
    api = FirefliesAPI(os.environ["FIREFLIES_API_KEY"])
    
    transcript_id = event.get("transcript_id")
    try:
        result = api.get_video_url(transcript_id)
        return {
            "statusCode": 200,
            "body": result
        }
    except Exception as e:
        return {
            "statusCode": 500,
            "body": str(e)
        }
```
treefy 0.1
A dynamic tree library for algorithmic problem solving
Requires Python: >=3.6

# Treefy - A Binary Tree Library

Treefy is a simple yet flexible Python library designed to help you easily work with binary trees. It allows you to define custom functions and perform a variety of tree operations like insertion, traversal, height calculation, and more.

## Features

- **BinaryTree** class for working with binary trees.
- **TreeNode** class to represent individual nodes.
- **Custom function registration** allowing users to define and use custom functions.
- **Tree Operations**:
  - Preorder, Inorder, and Postorder traversals.
  - Level order traversal (breadth-first search).
  - Height calculation of the tree.
  - Drawing a textual representation of the tree.
- **Attach Subtree**: Attach a whole tree/subtree as a left or right child.
- **Support for DSA Problems**: You can register custom DSA functions like "maximum path sum" or "LCA" (Lowest Common Ancestor) and solve problems dynamically.

## Basic Usage

With this project, there is one test.py, which includes the usage of BinaryTree class, how to add nodes, traversals etc
In addition to that, there are two custom functions provided: Max Path Sum and LCA for your reference

## Installation

Clone the repository or download the source files and place them in your project directory. Alternatively, you can install it via pip (if the package is uploaded to PyPI):

```bash
pip install treefy
whist-backend 0.2.3
Play rounds of whist (with bots or real players).
Requires Python: >=3.9.0

# Whist Backend

[![Lint](https://github.com/Danie-1/whist_backend/actions/workflows/lint.yml/badge.svg)](https://github.com/Danie-1/whist_backend/actions/workflows/lint.yml)
[![Tests](https://github.com/Danie-1/whist_backend/actions/workflows/tests.yml/badge.svg)](https://github.com/Danie-1/whist_backend/actions/workflows/tests.yml)
[![Documentation Status](https://readthedocs.org/projects/whist-backend/badge/?version=latest)](https://whist-backend.readthedocs.io/en/latest/?badge=latest)
[![PyPI Version](https://img.shields.io/pypi/v/whist_backend.svg)](https://pypi.python.org/pypi/whist_backend)

To install, run
```
pip install whist-backend
```

# Examples

See the `examples` folder of this repository for example usage, or view the
[Example page in the documentation](https://whist-backend.readthedocs.io/en/latest/example.html).
xmrig 1.1.2
A python package for easy in line interfacing with XMRig Miner HTTP API
Requires Python: <4.0,>=3.9

# xmrig-python
[![PyPi](https://img.shields.io/badge/PyPi-1.1.2-green?labelColor=026ab5&style=flat-square&logo=pypi&logoColor=ffffff&link=https://pypi.org/project/xmrig/)](https://pypi.org/project/xmrig/)
[![Python](https://img.shields.io/badge/Python-%203.9,%203.10,%203.11,%203.12-green?labelColor=026ab5&style=flat-square&logo=pypi&logoColor=ffffff&link=https://pypi.org/project/xmrig/)](https://pypi.org/project/xmrig/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/xmrig?label=PyPI%20Downloads)
![License](https://img.shields.io/github/license/CoulterStutz/python-xmrig?label=License&color=brightgreen)

A wrapper for the XMRig HTTP API and client manager

## Installing xmrig-python

### From Source

```shell
git clone https://github.com/CoulterStutz/python-xmrig.git && cd python-xmrig
poetry install # or use `pip install .`, dont forget the period to set the source location to the current directory.
```

### Using PyPi

```shell
poetry install xmrig # or use `pip install xmrig`.
```

After that the package will be available to use!

## Example Usage

Here is a basic implementation of the API Wrapper now dubbed XMRigAPI.

```python
import xmrig, logging
x = xmrig.XMRigAPI(ip="127.0.0.1", port="5555", access_token="example")
logging.basicConfig()
logging.getLogger("XMRigAPI").setLevel(logging.INFO)            # Change to DEBUG to print out all responses when their methods are called
log = logging.getLogger("MyLOG")

# Control the miner using JSON RPC.
x.pause_miner()
x.resume_miner()
x.stop_miner()
x.start_miner()

# Update cached data from endpoints individually or all at once
x.update_summary()
x.update_all_responses()

# Edit and update the miners `config.json` via the HTTP API.
x.update_config()                                               # This updates the cached data
config = x.config()                                             # Use the `config` property to access the data
config["pools"]["USER"] = "NEW_WALLET_ADDRESS"
x.post_config(config)

# Summary and Backends API data is available as properties in either full or individual format.
log.info(x.summary)                                             # Prints the entire `summary` endpoint response
log.info(x.sum_hashrates)                                       # Prints out the current hashrates
log.info(x.sum_pool_accepted_jobs)                              # Prints out the accepted_jobs counter
log.info(x.sum_pool_rejected_jobs)                              # Prints out the rejected_jobs counter
log.info(x.sum_current_difficulty)                              # Prints out the current difficulty
```