semvec/pypi-packages huggingface dataset:
qafunnypet 0.1.1
Testing a package that can be inherit to classes that use it
Requires Python: >=3.6

# qafunnypet

### Install qafunnypet from PyPi.
```bash
pip install qafunnypet
```

#### Example
```python
  from qafunnypet import Qafunnypet

  class Dog(Qafunnypet):
      def __init__(self, name, age, color=None):
          super().__init__(name, age)
          self.color = color

      def present(self):
          if self.color is None:
              pass
          else:
              print(f"I am {self.name} and I am {self.age} years old and I am {self.color}")

      def speak(self):
          print("Bark")

>>> doggy = Dog("Tim", 15, "Bronw")
>>> doggy.present()
>>> doggy.speak()

>>> some = Qafunnypet("Cuasi", 10)
>>> some.present()
>>> some.speak()
```
akgoodreads 0.0.3
Goodreads python wrapper
Requires Python: >=3.5

<!--- Heading --->
<div align="center">
  <img src="assets/banner.png" alt="banner" width="auto" height="auto" />
  <h1>Goodreads Scraper</h1>
  <p>
    A python wrapper for <a href="https://www.goodreads.com/api/index">goodreads API</a>
  </p>
<h4>
    <a href="https://github.com/rpakishore/goodreads/">View Demo</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/goodreads">Documentation</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/goodreads/issues/">Report Bug</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/goodreads/issues/">Request Feature</a>
  </h4>
</div>
<br />


<!-- Table of Contents -->
<h2>Table of Contents</h2>

- [1. Getting Started](#1-getting-started)
  - [1.1. Dependencies](#11-dependencies)
  - [1.2. Installation](#12-installation)
- [2. Usage](#2-usage)
  - [2.1. Initialize the client](#21-initialize-the-client)
  - [2.2. Search for books](#22-search-for-books)
  - [2.3. Search for books](#23-search-for-books)
- [3. License](#3-license)
- [4. Contact](#4-contact)
- [5. Acknowledgements](#5-acknowledgements)

<!-- Getting Started -->
## 1. Getting Started
You will need a Goodreads API key for this to work. Unfortunately, Goodreads is not giving out new keys at this time, so unless you have signed up from a while ago - this wont work for you. 

On the launch of the script, you will be prompted for the API key. If you are using windows, you will be prompted to save this key locally for ease (using `keypass` library). 

### 1.1. Dependencies
All the dependencies should automatically be installed when installing the script. This project heavily relies on the `requests` library to make the api calls.

<!-- Installation -->
### 1.2. Installation

Install with pip

```bash
  pip install akgoodreads
```
<!-- Usage -->
## 2. Usage

### 2.1. Initialize the client

```python
import akgoodreads
client = goodreads.Goodreads("<your email>")
```

### 2.2. Search for books
With title
```python
client.book("Ender's Game", limit = 5)
```

or with goodreads ID
```python
client._book_from_id(50)
```

### 2.3. Search for books
With title
```python
client.author("Rowling")
```

or with goodreads ID
```python
client._author_from_id(7995)
```

<!-- License -->
## 3. License
See LICENSE.txt.

<!-- Contact -->
## 4. Contact

Arun Kishore - [@rpakishore](mailto:goodreads@rpakishore.co.in)

Github Link: [https://github.com/rpakishore/](https://github.com/rpakishore/)


<!-- Acknowledgments -->
## 5. Acknowledgements
 - [Awesome README Template](https://github.com/Louis3797/awesome-readme-template/blob/main/README-WITHOUT-EMOJI.md)
 - [Banner Maker](https://banner.godori.dev/)
 - [Shields.io](https://shields.io/)
 - [Carbon](https://carbon.now.sh/)
mixmod 0.2.0
An implementation of mixture models with robust fitting methods for a fixed set of components
Requires Python: >=3.8

# MixMod
MixMod is package for fitting mixture models with an arbitrary number of components to data. While it can store parameters and calculate probabilities for mixture models containing components from arbitrary distributions, it can currently only fit model parameters for a relatively small subset of the named distributions defined in SciPy's stats module. The EM equations are explicitly solved for these distributions, which makes fitting the parameters fast and robust, however, at the cost of limiting the types of distributions which are supported as possible components.

The MixtureModel class is fully documented. However, the [tutorial](https://github.com/marcsingleton/mixmod/blob/main/tutorial.ipynb) is the recommended introduction to this package.

## Dependencies
MixMod is designed to be lightweight and only requires NumPy for array calculations and SciPy for optimization and some special functions. The minimum versions were set to the most recent at the time of initial release (1.17 and 1.8, respectively), but no functionality specific to these releases is needed to my knowledge. There is also a "soft" dependency on the SciPy's implementation of the supported distributions since MixMod expects the random variables to implement pdf and cdf methods with the parametrizations used by SciPy.

## Installation
To install MixMod, run the following command:

```
pip install mixmod
```