semvec/pypi-packages huggingface dataset:
unipose 1.0.8
2D Pose in Unity with Python backend
Requires Python: >=3.7

# UniPose - 2D Pose in Unity, using Pytorch PoseNet
Written by Julie Ganeshan

*This codebase is adapted from https://github.com/rwightman/posenet-pytorch* Please see `original_docs`

All rights for PoseNet in Pytorch to Ross Wightman. My modifications 
are entirely contained in `pose_server.py`

*The model weights are from Google*


For the full docs, see the github repo 

https://github.com/Sanjay-Ganeshan/UniPose


*This package relies on Pytorch, and is only compatible with Windows 10*

Please install Pytorch, as per https://pytorch.org/get-started/locally/ - this is NOT auto installed.

To install this package, just use

`pip install unipose`
dataliner 1.3.1
A dataprocessing package for data preprocess and feature engineering.
Requires Python: >=3.5
Keywords: data processing machine learning preprocess feature engineering

## DataLiner - Data processing package for Python 
A dataprocessing package for data preprocess and feature engineering.<br>
Please feel free to send pull requests for bug fix, improvements or new preprocessing methods!

## Installation
```
! pip install dataliner
```

## Documentation
https://shallowdf20.github.io/dataliner/preprocessing.html

## Quick Start
Train data from Kaggle Titanic is used in this example. https://www.kaggle.com/c/titanic/data

```python
import pandas as pd
from sklearn.pipeline import make_pipeline
import dataliner as dl

df = pd.read_csv('train.csv')
target_col = 'Survived'
X = df.drop(target_col, axis=1)
y = df[target_col]

process = make_pipeline(
    dl.DropNoVariance(),
    dl.DropHighCardinality(),
    dl.BinarizeNaN(),
    dl.ImputeNaN(),
    dl.TargetMeanEncoding(),
    dl.DropHighCorrelation(),
    dl.StandardScaling(),
    dl.DropLowAUC(),
)

process.fit_transform(X, y)

```