semvec/pypi-packages huggingface dataset:
DizzyBlog 0.1.0
A simple blog app
Requires Python: >=3.6

# Blog Post Manager

## Overview
The Dizzy Blog is a blog Post Manager,  Python-based application designed to help you manage your blog posts efficiently. It provides features to create, edit, delete, and organize your blog content.

## Features
- **Create Posts**: Easily create new blog posts with a simple interface.
- **Edit Posts**: Update your existing posts with new content or corrections.
- **Delete Posts**: Remove posts that are no longer needed.
- **Organize Posts**: Categorize and tag your posts for better organization.
- **Comments Manage**: You can add , update , delete and view you comments.

## Installation
To install the Blog Post Manager, follow these steps:
1. Clone the repository:
    ```bash
    git clone https://github.com/yourusername/blogpostmanager.git
    ```
2. Navigate to the project directory:
    ```bash
    cd blogpostmanager
    ```
3. Install the required dependencies:
    ```bash
    pip install -r requirements.txt
    ```

## Usage
To start the Blog Post Manager, run the following command:
```bash
python main.py
```
pymc-bart-rs 0.1.2
Rust implementation of Bayesian Additive Regression Trees for Probabilistic programming with PyMC
Requires Python: <3.13,>=3.8

# PyMC-BART-rs

Rust implementation of [PyMC-BART](https://github.com/pymc-devs/pymc-bart). PyMC-BART extends the [PyMC](https://github.com/pymc-devs/pymc) probabilistic programming framework to be able to define and solve models including a Bayesian Additive Regression Tree (BART) random variable. PyMC-BART also includes a few helpers function to aid with the interpretation of those models and perform variable selection.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Modifications](#modifications)

## Installation

PyMC-BART is available on PyPI with pre-built wheels for Linux (x86_64, aarch64), Windows (x64), and macOS (x86_64, aarch64). To install using `pip`

```bash
pip install pymc-bart-rs
```

## Usage

Get started by using PyMC-BART to set up a BART model

```python
import pymc as pm
import pymc_bart_rs as pmb

X, y = ... # Your data replaces "..."
with pm.Model() as model:
    bart = pmb.BART('bart', X, y)
    ...
    idata = pm.sample()
```

## Modifications

The core Particle Gibbs (PG) sampling algorithm for BART remains the same in this Rust implementation as the original Python implementation. What differs is the choice of data structure to represent the Binary Decision Tree.

A `DecisionTree` structure is implemented as a number of parallel arrays. The i-th element of each array holds information about node `i`. The zero'th node is the tree's root. Some of the arrays only apply to either leaves or split nodes. In this case, the values of the nodes of the other arrays are arbitrary. For example, `feature` and `threshold` arrays only apply to split nodes. The values for leaf nodes in these arrays are therefore arbitrary.
little-data-preprocessor 1.0.4
A pandas dataframe preprocessing python package
Keywords: python, pandas, preprocessing

# Little Data Preprocessor

Welcome to Little Data Preprocessor! This Python package provides utility functions to aid in the preprocessing stages of machine learning and deep learning implementation. With a focus on ease of use and efficiency, it offers functions for handling missing values, removing infinity values, checking data integrity, and more.

## Features

- **Drop Columns Based on Mean:** Easily drop columns with a mean value of zero from your DataFrame.
- **Drop Infinity Columns:** Replace infinity values with NaN and drop rows containing these values.
- **Write Column Datatypes to Text File:** Export the datatypes of each column in your DataFrame to a text file.
- **DataFrame Integrity Check:** Perform comprehensive checks on your DataFrame for NaN values, infinity values, duplicates, and more.

## Installation

You can install Little Data Preprocessor using pip:

```bash
pip install little_data_preprocessor
```