semvec/pypi-packages huggingface dataset:
coh-piggs-extractor 1.0.1
A simple module to extract files from the COH piggs file format
Requires Python: >=3.8

# coh-piggs-extrator

A module that allows the extracting of files from a City Of Heroes. The pigg file is used to create "virtual drives" to
load data from during the running of the game.

Basically it is similar to a zip file and therefore can be processed by parsing the files and extracting the information

## Usage
The design of this library is simple in use. The library makes use of a simple implementation of the
[strategy](https://en.wikipedia.org/wiki/Strategy_pattern) pattern to process each of the entries of the pigg file. 

With this in mind there are two objects of interest:
```python
    coh_piggs_extractor.coh_piggs_extractor.PiggFile(pigg_file_path, strategy)
```

This is used to load the pigg file and process the contents to get the metadata and data for each of the contained files.
The constructor takes two parameters (as shown above):
* pigg_file_path: The path to the pigg file
* The strategy implementation used to process each of the file

```
    coh_piggs_extractor.coh_piggs_extractor.PiggFileEntryProcessingStrategy
```
This is a simple no op class that implements the expected strategy interface. It has one function 
```process_pigg_file_entry``` that is called for each of the files in the pigg file. The function takes two parameters 
as follows:
* meta: This is the metadata of the file and is a simple value object DirEntry (defined in 
  [coh_piggs_extractor](src/coh_piggs_extractor/coh_piggs_extractor.py)). The main entry of use is the name field.
* data: This is the binary data that has been extracted for the file.

A simple file extracting strategy has been provided as well
```python
    coh_piggs_extractor.coh_piggs_extractor.SimpleFileOutputEntryProcessingStrategy(out_dir)
```
This will output each of the contained files into the specified ```out_dir``` provided during construction.
processdat 0.0.1
Data preprocessing for ML, returns X_train, X_test, y_train, y_test

# **Data Preprocessing Module**

### *This python module preprocesses a csv dataset, which has a categorical data at the last column. This module makes use of scikit-learn, pandas and numpy.*

The following steps of preprocessing be done in order: 
1. Importing Dataset
1. Missing Value treatement
1. Encoding Categorical Data
1. Splitting Dataset into training and testing set
1. Feature Scaling using Standard Scaler

****
## Inut and output

- The input value to the sole function preprocess is a csv dataset

- The return value is a tuple of 4 values in the fashing of train_test_split of scikit-learn, i.e X_train, X_test, y_train, y_test** 

****
## Module Installation from PyPI

```bash
$ pip install processdat
```
****

## Usage

```python
> import processdat as pro

...
X_train, X_test, y_train, y_test = pro.preprocess('Data.csv')
...

```
****
## Developing *processdat*

To install *processdat*, along with the tools you need to develop and run tests, run the following in the terminal/environment:

```bash
$ pip install -e .[dev]
```
****

### Created by: Shayan Banerjee (shayanbanerhee96@gmail.com)
SToolBox 0.0.1
for backend tasks in python.
Requires Python: >=3.7

# Aesthetic ASCII

Generate **A E S T H E T I C** ASCII art.

## Instructions

1. Install:

```
pip install aesthetic-ascii
```

2. Generate an aesthetic ASCII visual:

```python
from aesthetic_ascii import synthesize

# initialize drive object (to generate visuals)
drive = synthesize.Drive()
# generate a ASCII visual (dark_mode optional)
drive.generate(dark_mode=True)
# save to png
drive.to_png('aesthetic.png')
```

3. Ride the space skyway home to 80s Miami